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Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press
The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
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A deep learning approach to building a framework for Urdu POS and NER
Authors: Kazi, Samreen | Rahim, Maria | Khoja, Shakeel
Abstract: The study examines various studies on Named Entity Recognition (NER) and Part of Speech (POS) tagging for the Urdu language conducted by academics and researchers. POS and NER tagging for Urdu still faces obstacles in terms of increasing accuracy while lowering false-positive rates and labelling unknown terms, despite the efforts of numerous researchers. In addition, ambiguity exists when tagging terms with different contextual meanings within a sentence. Due to the fact that Urdu is an inflectional, derivational, morphologically rich, and context-sensitive language, the existing models, such as Linguistic rule application, N-gram Markov model, Tree Tagger, random forest (RF) tagger, etc., …were unable to produce accurate experimental results on Urdu language data. The significance of this study is that it fills a gap in the literature concerning the lack of POS and NER tagging for the Urdu language. For Urdu POS and NER tagging, we propose a deep learning model with a well-balanced set of language-independent features as well as a survey of important Urdu POS/NER techniques. In addition, this is the first study to use residual biDirectional residual Long short-term memory (residual biLSTM) architecture trained on the Urmono dataset in conjunction with the randomly initialised word2vec, fastText and mBERT embeddings are utilised to generate word or character vectors.For each experiment, the paper also employs the evaluation methods of Macro-F1, precision, precision, and recall. The proposed method with mbert embedding as word vectors provides best results of F1 score for POS and NER at 91.11% and 99.11% respectively. Also, the accuracy, precision and recall for POS are reported at 94.85%, 91.79% and 90.77% . Similarly, the accuracy, precision and recall for NER of the proposed model are reported at 99.77%, 98.78% and 99.45% respectively, which are higher than baseline models. Show more
Keywords: POS, NER, Urdu language, tagger, natural language, linguistic, deep learning, machine learning
DOI: 10.3233/JIFS-211275
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2022
Enhanced regression model using cluster based sampling techniques
Authors: Dhamodharavadhani, S. | Rathipriya, R.
Abstract: This paper aims to develop the methodology for enhancing the regression models using Cluster based sampling techniques (CST) to achieve high predictive accuracy and can also be used to handle large datasets. Hard clustering (KMeans Clustering) or Soft clustering (Fuzzy C-Means) to generate samples called clusters, which in turn is used to generate the Local Regression Models (LRM) for the given dataset. These LRMs are used to create a Global Regression Model. This methodology is known as Enhanced Regression Model (ERM). The performance of the proposed approach is tested with 5 different datasets. The experimental results revealed that the proposed …methodology yielded better predictive accuracy than the non-hybrid MLR model; also, fuzzy C-Means performs better than the KMeans clustering algorithm for sample selection. Thus, ERM has potential to handle data with uncertainty and complex pattern and produced a high prediction accuracy rate. Show more
Keywords: Clustering, KMeans, fuzzy c-means, multiple linear regression, regression, sampling methods
Improved self-attention generative adversarial adaptation network-based melanoma classification
Authors: Gowthami, S. | Harikumar, R.
Abstract: Melanoma is one of the widespread skin cancers that has affected millions in past decades. Detection of skin cancer at preliminary stages may become a source of reducing mortality rates. Hence, it is required to develop an autonomous system of reliable type for the detection of melanoma via image processing. This paper develops an independent medical imaging technique using Self-Attention Adaptation Generative Adversarial Network (SAAGAN). The entire processing model involves the process of pre-processing, feature extraction using Scale Invariant Feature Transform (SIFT), and finally, classification using SAAGAN. The simulation is conducted on ISIC 2016/PH2 datasets, where 10-fold cross-validation is undertaken …on a high-end computing platform. The simulation is performed to test the model efficacy against various images on several performance metrics that include accuracy, precision, recall, f-measure, percentage error, Matthews Correlation Coefficient, and Jaccard Index. The simulation shows that the proposed SAAGAN is more effective in detecting the test images than the existing GAN protocols. Show more
Keywords: Autonomous, melanoma, generative adversarial network, scale invariant feature transform, synthetic datasets
Novel multi objective evolutionary framework for solving next release problem
Authors: Ghasemi, Mohsen | Bagherifard, Karamollah | Parvin, Hamid | Nejatian, Samad
Abstract: Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of …same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works' results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published. Show more
Keywords: Next release problem, multi-objective evolutionary algorithm, search-based software engineering, teaching-learning based optimization, non-repetitive population
Fuzzy theory and method for evaluating the importance of college course teaching for future education
Authors: Liu, Yuyang | Ma, Tinghuai | Huang, Xuejian | Li, Ting
Abstract: As the latest and most popular concept in the world, metaverse as well as its application and technology integration has attracted the attention of all walks of life including information, economics, management, design and education, etc. However, the definition of metaverse as a technology or an intelligent scene still has no unified consensus in the academic and scientific fields. We believe that the metaverse should be a key concept and emerging theory in the future field of wisdom. This research focuses on the evaluation of the importance of college teaching courses for future education in the context of the metaverse, …and discusses which courses may be greatly affected by the concept of the metaverse. First, on the basis of analyzing the scholars' understanding of the concept of the metaverse and related application research literature, we give the specific framework of this paper and the definition of the edu-metaverse, and propose a future intelligent teaching environment construction model based on the metaverse. It should be noted that our research is under the framework of the metaverse intelligent teaching construction model, and mainly focuses on the in-depth analysis of the teaching evaluation problem in colleges, which is a multi-attribute decision-making problem in the field of systems science. We propose an improved Pythagorean fuzzy multi-attribute decision-making method based on cumulative prospect theory, including improved scoring function, improved distance measure method, improved combination weighting method, etc., and construct a cumulative prospect value function. The proposed theory and method were applied to teaching courses of 10 majors in Chinese colleges to construct an importance evaluation indicator system. The importance of the courses was ranked, verifying the applicability and scientificity of the proposed method. The research content of this paper can provide a reference for the decision-making of Chinese education authorities. More importantly, the method proposed in this research is also universal, and can also provide theoretical support and experience reference for multi disciplines and fields, such as financial investment, engineering construction evaluation, enterprise management decision-making, and emergency management, etc. Show more
Keywords: Metaverse, intelligent teaching environment, teaching importance evaluation, cumulative prospect theory, Pythagorean fuzzy set theory
Lorentz chaotic trigonometric function pedigree based arithmetic optimization algorithm
Authors: Li, Xu-Dong | Wang, Jie-Sheng | Hao, Wen-Kuo | Song, Hao-Ming | Zhao, Xiao-Rui
Abstract: With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. The arithmetic optimization algorithm (AOA) design is inspired by the distribution behavior of the main arithmetic operators in mathematics, including multiplication (M), division (D), subtraction (S) and addition (A). In order to improve the global search ability and local development ability of the AOA, the Lorentz triangle search variable step coefficient was proposed based on the broad-spectrum trigonometric functions combined with the Lorentz chaotic mapping strategy, which include a total of 24 search functions …in four categories, such as regular trigonometric functions, inverse trigonometric functions, hyperbolic trigonometric functions, and inverse hyperbolic trigonometric functions. The position update was used to improve the convergence speed and accuracy of the algorithm. Through test experiments on benchmark functions and comparison with other well-known meta-heuristic algorithms, the superiority of the proposed improved AOA was proved. Show more
Keywords: Arithmetic optimization algorithm, trigonometric function, function optimization
Cloud computing based deduplication using high-performance grade byte check and fuzzy search technique
Authors: Andavan, Mohanaprakash Thottipalayam | Vairaperumal, Nirmalrani
Abstract: Background: Data redundancy (DR) and data privacy (DP) is a critical issue that increases storage and security problems in cloud environments. Data de-duplication (DD) is one of the efficient backup storage techniques to reduce DR. The main problem with using cloud computing (CC) is more storage, the cost of deployment and maintenance. Objective: To minimize this problem, High-performance Grade Byte Check and Fuzzy search Techniques (HP-GBC-FST) based DD is proposed in this paper. Methods: The HP-GBC-FST is based on the pre-process of data by comparing their first byte and categorizing the byte based on the first …byte. After DD, encryption has been processed on data to improve the data security in the cloud environment and then encrypted data is stored in the cloud. This HP-GBC-FST recognizes DR at the block level, reducing the redundancy of data more effectively. Then, HP-GBC-FST is created to detect and eliminate duplicates, improve security and storage efficiency (SE), reduce DD time and computation cost (CPC) in the DD verification and auditing phase. Result: The experiment has been conducted in an Intel I5 system and 500GB, 1Tb memory space and implemented in the Java programming environment. The results of the experiment reveal that the HP-GBC-FST improved the DD ratio and security by 3.7 and 97%, respectively, and reduced the DD time and CPC by 87% and 84.4%, respectively, over the existing technique. Conclusion: It concluded that the HP-GBC-FST has greater improvement over DD data in the cloud. Finally, the performance analysis of the HP-GBC-FST achieves higher storage, both privacy and security attributes, and incurs minimal CPC, DD time compared with the state he art research. Show more
Keywords: Fuzzy search (FS), cloud computing (CC), data deduplication (DD), encryption, grade byte check (GBC)
Study on the spatial spillover effects of capital enrichment on industrial green technology innovation in China
Authors: Lina, Ma | Hao, Ma | Yang, Zhang | Iqbal, Najaf
Abstract: In the context of the strategic target of carbon emission peaking and carbon neutrality, industrial green technology innovation (GTI) has become the focus of discussion in academia these days. Based on the panel data of 30 provinces in China from 2011 to 2019, we construct Spatial Durbin Models to explore the spatial effects of capital enrichment (CE) on GTI by using the geographical distance matrix, the economic distance matrix and the adjacency matrix. The results reveal that: (1) The regional differences in the development of GTI are prominent, showing a higher level in the east and lower in the west. …(2) GTI exhibits the spatial characteristic of polarization. Its spatiotemporal evolutionary pattern reveals a phased feature of first strengthened and then weakened. (3) The CE has a significant inhibitory effect on GTI, which may be caused by the "rebound effect", dominated by short-term economic interests and the ineffective capital allocation. This effect is more prominent in regions with unbalanced economies. (4) The spatial spillover effect of CE is significantly negative, indicating a "siphon effect". Based on these findings, the suggestions for promoting GTI are put forward. Show more
Keywords: Capital enrichment, green technology innovation, spatio-temporal evolution, spatial spillover effect, low-carbon economy
Performance evaluation of image fusion techniques and implementation of new fusion technique for remote sensing satellite data
Authors: Lawrance, N.A. | Angel, T.S. Shiny
Abstract: The technique of integrating images from two or more sensors that were taken from the same place or the same object is known as image fusion. The goal is to get more spectral and spatial information from the combined image as a whole than from the individual images. It is required to fuse the images in order to improve the spatial and spectral quality of both panchromatic and multispectral images. This study introduces a novel method for fusing remote sensing images that combines L0 smoothing, NSCT (Non-subsampled Contourlet Transform), SR (Sparse Representation), and MAR (Max absolute rule). The multispectral and …panchromatic images are initially divided into lower and higher frequency components using the L0 smoothing filter as the method of fusion. The fusion process is then carried out, utilising a technique that combines NSCT and SR to fuse low-frequency components. Similar to this, the Max-absolute fusion rule is used to fuse high-frequency components. In conclusion, the disintegration of fused low-frequency and high-frequency data yields the final image. Our method yields an enhanced outcome in terms of the correlation coefficient, Entropy, spatial frequency, and fusion of mutual information for both the term of picture quality enhancement and visual evaluation. This suggested approach produces superior outcomes after execution. This study makes use of the Landsat-7ETM+, IKONOS, and Quick Bird datasets. Different satellites are used to take each image. There have been two examples of each image used. In comparison to previous Traditional Methods, the proposed image fusion techniques' output has a quality that is more than 20% higher. Show more
Keywords: Remote sensing, multispectral image, pan chromatic image, L0 smoothening filter, NSCT, SR
Possibilistic c-means clustering based on the nearest-neighbour isolation similarity
Authors: Zhang, Yong | Chen, Tianzhen | Jiang, Yuqing | Wang, Jianying
Abstract: Clustering is widely used in data mining and machine learning. The possibilistic c-means clustering (PCM) method loosens the constraint of the fuzzy c-means clustering (FCM) method to solve the problem of noise sensitivity of FCM. But there is also a new problem: overlapping cluster centers are not suitable for clustering non-cluster distribution data. We propose a novel possibilistic c-means clustering method based on the nearest-neighbour isolation similarity in this paper. All samples are taken as the initial cluster centers in the proposed approach to obtain k sub-clusters iteratively. Then the first b samples farthest from the center of …each sub-cluster are chosen to represent the sub-cluster. Afterward, sub-clusters are mapped to the distinguishable space by using these selected samples to calculate the nearest-neighbour isolation similarity of the sub-clusters. Then, adjacent sub-clusters can be merged according to the presented connecting strategy, and finally, C clusters are obtained. Our method proposed in this paper has been tested on 15 UCI benchmark datasets and a synthetic dataset. Experimental results show that our proposed method is suitable for clustering non-cluster distribution data, and the clustering results are better than those of the comparison methods with solid robustness. Show more
Keywords: Clustering, nearest-neighbour isolation similarity, possibilistic c-means, K-means, merging strategy
State monadic residuated lattices and their corresponding filters
Authors: Wang, Juntao | Kang, Mengna | Fu, Xuesong | Li, Fei
Abstract: In this paper, we introduce the notion of state monadic residuated lattices and study some of their related properties. Then we prove that the relationship between state monadic algebras of substructural fuzzy logics completely maintains the relationship between corresponding monadic algebras. Moreover, we introduce state monadic filters of state monadic residuated lattice, giving a state monadic filter generated by a nonempty subset of a residuated lattice, and obtain some characterizations of maximal and prime state monadic filters. Finally, we give some characterization of special kinds of state monadic residuated lattices, including simple, semisimple and local state monadic residuated lattices by …state monadic filters. Show more
Keywords: Mathematical fuzzy logic, mondaic residuated lattice, state monadic residuated lattice, state monadic filter
Prediction poverty levels of needy college students using RF-PCA model
Authors: Wang, Sheng | Shi, Yumei | Hu, Chengxiang | Yu, Chunyan | Chen, Shiping
Abstract: Nowadays, poverty-stricken college students have become a special group among college students and occupied a higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this study, a novel model, which incorporated Random Forest with Principle Components Analysis (RF-PCA), is proposed to predict poverty levels of college students. To establish this model, we collect some useful information is to construct the datasets which include 4 classes of poverty levels and 21 features of poverty-stricken college students. Furthermore, the feature dimension reduction consists of two steps: the first …step is to select the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF) model; the second step is to extract 11 dimensions by means of Principle Components Analysis (PCA). Subsequently, confusion metrics and receiver operating characteristic (ROC) curves are utilized to evaluate the promising performance of the proposed model. Especially the accuracy of the model achieves 78.61% . Finally, compared with seven states of the art classification algorithms, the proposed model achieves a higher prediction accuracy, which indicates that the results provide great potential to identify the poverty levels of college students. Show more
Keywords: RF-PCA, poverty levels, feature selection, feature extraction
On neutrosophic multi-level multi-objective linear programming problem with application in transportation problem
Authors: Fathy, E. | Ammar, E.
Abstract: In this research, we use the harmonic mean technique to present an interactive strategy for addressing neutrosophic multi-level multi-objective linear programming (NMMLP) problems. The coefficients of the objective functions of level decision makers and constraints are represented by neutrosophic numbers. By using the interval programming technique, the NMMLP problem is transformed into two crisp MMLP problems, one of these problems is an MMLP problem with all of its coefficients being upper approximations of neutrosophic numbers, while the other is an MMLP problem with all of its coefficients being lower approximations of neutrosophic numbers. The harmonic mean method is then used …to combine the many objectives of each crisp problem into a single objective. Then, a preferred solution for NMMLP problems is obtained by solving the single-objective linear programming problem. An application of our research problem is how to determine the optimality the cost of multi-objective transportation problem with neutrosophic environment. To demonstrate the proposed strategies, numerical examples are solved. Show more
Keywords: Neutrosophic number, multi-level linear programming, multi-objective programming, harmonic mean technique, transportation problem
Hybrid connected attentional lightweight network for gangue intelligent segmentation in top-coal caving face
Authors: Fu, Chengcai | Lu, Fengli | Wu, Fan | Zhang, Guoying
Abstract: The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can …provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset. Show more
Keywords: Gangue intelligent segmentation, the top-coal caving face, depthwise separable dilation convolution, attention mechanism
Performance evaluation of family business strategic transition based on the 2-tuple linguistic neutrosophic number multiple attribute group decision making
Authors: Liu, Hui
Abstract: Since 2010, China's traditional industry has entered a critical stage of development and enterprise reform and development is imminent. Product homogenization is serious in this market, so that the competition among enterprises is fierce. At the same time, international brands continue to enter the Chinese consumption market, which intensifies the competition and seriously squeezes the market share of Chinese local brands. However, the popularization and development of the Internet and the change of people's consumption concept and level make the market put forward higher requirements for the development of business operation and many traditional family enterprises have embarked on the …road of transformation. It is of great significance and value to clarify the influence of internal factors of family enterprises on strategic transformation. The performance evaluation of family business strategic transition is really a multiple attribute group decision making (MAGDM) problems. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is proposed along with on the traditional grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Firstly, the 2TLNNSs is introduced. Then, combine the traditional fuzzy GRA model with 2TLNNSs information, the 2TLNN-GRA method is established and the computing steps for MAGDM are built. Finally, a numerical example for performance evaluation of family business strategic transition has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method. Show more
Keywords: Multiple attribute group decision making (MAGDM) problems, 2-tuple linguistic neutrosophic sets (2TLNSs), GRA method, family business strategic transition
Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction
Authors: Amanullah, M. | Ramya, S.Thanga | Sudha, M. | Pushparathi, V.P. Gladis | Haldorai, Anandakumar | Pant, Bhaskar
Abstract: On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address …the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist. Show more
Keywords: Index Terms: Software defect prediction, improved random-SMOTE oversampling technique, linear pearson correlation, heuristic learning vector quantization (LVQ), training and test datasets
On the least amount of training data for a machine learning model
Authors: Zhao, Dazhi | Hao, Yunquan | Li, Weibin | Tu, Zhe
Abstract: Whether the exact amount of training data is enough for a specific task is an important question in machine learning, since it is always very expensive to label many data while insufficient data lead to underfitting. In this paper, the topic that what is the least amount of training data for a model is discussed from the perspective of sampling theorem. If the target function of supervised learning is taken as a multi-dimensional signal and the labeled data as samples, the training process can be regarded as the process of signal recovery. The main result is that the least amount …of training data for a bandlimited task signal corresponds to a sampling rate which is larger than the Nyquist rate. Some numerical experiments are carried out to show the comparison between the learning process and the signal recovery, which demonstrates our result. Based on the equivalence between supervised learning and signal recovery, some spectral methods can be used to reveal underlying mechanisms of various supervised learning models, especially those "black-box" neural networks. Show more
Keywords: Machine learning, sampling theorem, frequency principle, signal recovery, neural network, Gaussian process regression
A novel prediction and control method for solar energy dispatch based on the battery energy storage system using an experimental dataset
Authors: Wang, Yongguo | Bi, Xuewen | Zhang, Xinxin
Abstract: The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day …ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE % =1.68 %. Show more
Keywords: Photovoltaic systems, battery energy storage system, control method, prediction method, RBF neural network, experimental dataset
Social media analytical CRM: a case study in a bank
Authors: Duman, Ekrem
Abstract: The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and …develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models. Show more
Keywords: Social media, banking, CRM, NLP, sentiment analysis
IoT-enabled smart healthcare data and health monitoring based machine learning algorithms
Authors: Deepa, S. | Sridhar, K.P. | Baskar, S. | Mythili, K.B. | Reethika, A. | Hariharan, P.R.
Abstract: A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA ) …have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual's health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance. Show more
Keywords: Health monitoring, machine learning algorithms, IoT, smart healthcare
A dynamic optimal solution approach for solving neutrosophic transportation problem
Authors: Joseph Robinson, M. | Veeramani, C. | Vasanthi, S.
Abstract: Neutrosophic Set (NS) allows us to handle uncertainty and indeterminacy of the data. Several researchers have investigated the Transportation Problems (TP) with various forms of input data. This paper emphasizes a dynamic optimal solution framework for TPs in a neutrosophic setting. This paper investigates a Neutrosophic Transportation Problem (NTP) in which supply, demand, and transportation cost are considered as Single-Valued Neutrosophic Trapezoidal Numbers (SVNTrNs). The weighted possibilistic mean value of their truth, indeterminacy, and facility membership function are calculated. Then, NTP is modelled as a parametric Linear Programming Problem (LPP) and solved. Further, the drawbacks of the existing approaches and …advantages of the developed method are discussed. Finally, the real-time problem and numerical illustrations are presented and compared to existing solutions. This study helps the Decision-Makers (DMs) in budgeting their transportation expenses through strategic distribution. Show more
Keywords: Single valued neutrosophic trapezoidal number, transportation problem, linear programming problem, weighted possibilistic mean
Global layout optimization of star-tree gas gathering pipeline network via an improved genetic optimization algorithm
Authors: Peng, Jinghong | Zhou, Jun | Liang, Guangchuan | Qin, Can | Peng, Cao | Chen, YuLin | Hu, Chengqiang
Abstract: Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is …used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network. Show more
Keywords: Natural gas, pipeline network, layout design, global optimization, genetic algorithm
Forecasting digital economy of China using an Adaptive Lasso and grey model optimized by particle swarm optimization algorithm
Authors: Yu, Wenmei | Xia, Lina | Cao, Qiang
Abstract: With the development of big data, Internet finance, the digital economy is developing rapidly and has become an important force to drive the continuous transformation of the global economy and society. China has put forward plans for the development of digital economy from 2021 to 2025, requiring the number of core industries of digital economy to reach 10% of GDP by 2025, while continuously improving China's digital economy to achieve high-quality development of China's digital economy. Aiming at China's digital economy, we use the adaptive lasso method and select feature variables based on quantitative and qualitative perspectives, so as to …predict the development trend of China's digital economy from 2021 to 2025 based on the TDGM (1, 1, r) grey model optimized by the particle swarm algorithm. Meanwhile, we have added the comparative analyses with TDGM(1,1), Grey Verhulst, GM(1,1) models and evaluate the prediction results both Ex-ante and Ex-post, demonstrating the feasibility of the proposed model and the accuracy. Finally, we find that the future of China's digital economy will meet the planned objectives in terms of quantity and quality, but the trend of digital economy development in quantity is faster, thanks to the development of digital technology application industry. Show more
Keywords: Digital economy development, adaptive lasso grey model, TDGM(1, 1, r) model, quantity and quality
Performance improvement and complexity reduction in the classification of EMG signals with mRMR-based CNN-KNN combined model
Authors: Flower, X. Little | Poonguzhali, S.
Abstract: For real-time applications, the performance in classifying the movement should be as high as possible, and the computational complexity should be low. This paper focuses on the classification of five upper arm movements which can be provided as a control for human-machine interface (HMI) based applications. The conventional machine learning algorithms are used for classification with both time and frequency domain features, and k-nearest neighbor (KNN) outplay others. To further improve the classification accuracy, pretrained CNN architectures are employed which leads to computational complexity and memory requirements. To overcome this, the deep convolutional neural network (CNN) model is introduced with …three convolutional layers. To further improve the performance which is the key idea behind real-time applications, a hybrid CNN-KNN model is proposed. Even though the performance is high, the computation costs of the hybrid method are more. Minimum redundancy maximum relevance (mRMR), a feature selection method makes an effort to reduce feature dimensions. As a result, better performance is achieved by our proposed method CNN-KNN with mRMR which reduces computational complexity and memory requirement with a mean prediction accuracy of about 99.05±0.25% with 100 features. Show more
Keywords: Empirical mode decomposition, minimum redundancy maximum relevance, spectrogram representation, k-nearest neighbor, deep learning
Rumor detection model fused with static spatiotemporal information
Authors: Wang, Biao | Wei, Hongquan | Li, Ran | Liu, Shuxin | Wang, Kai
Abstract: Spotting rumors from social media and intervening early has always been a daunting challenge. In recent years, Deep neural networks have begun to discover rumors by exploring the way of rumor propagation. The existing static graph models either only focus on the spatial structure information of rumor propagation or on time series propagation information but do not effectively combine them. This paper proposes the Static Spatiotemporal Model (SSM), which first extracts the textual semantic information and constructs undirected and directed propagation trees. Then obtains spatial structure information of rumor propagation through Graph Convolutional Network and extracts time series propagation information …through the Recurrent Neural Network. The extracted spatiotemporal information is enhanced using different source node information hopping. Finally, SSM uses a weighted connection ensemble to rumor classification. Experimentally validated on datasets such as Weibo and Twitter, the results show that the proposed method outperforms several state-of-the-art static graph models. To better apply SSM in early detection and characterize early concepts, this paper presents a new data collection index for early detection, which can detect events that spread faster and have more significant influence in a targeted manner. The experimental results on the new indicators further verify the superiority of SSM as it can extract sufficient information in early detection or events with fewer participants. Show more
Keywords: Rumor detection, deep learning, SSM, spatiotemporal information, early detection, data collection index
Improved rank discrepancy-aware sampling based on bidirectional distillation for top- K recommender system
Authors: Du, Weidong
Abstract: Nowadays, the model compression method of knowledge distillation has drawn great attentions in Recommender systems (RS). The strategy of bidirectional distillation performs the bidirectional learning for both the teacher and the student models such that these two models can collaboratively improve with each other. However, this strategy cannot effectively exploit representation capabilities of each item and lack of the interpretability for the importance of items. Thus, how to develop an effective sampling scheme is still valuable for us to further study and explore. In this paper, we propose an improved rank discrepancy-aware item sampling strategy to enhance the performance of …bidirectional distillation learning. Specifically, by employing the distillation loss, we train the teacher and student models to reflect the fact that a user has partiality for the unobserved items. Then, we propose the improved rank discrepancy-aware sampling strategy based on feedback learning mechanism to transfer just the useful information which can effectively enhance each other. The key part of the multiple distillation training aims to select valuable items which can be re-distilled in the network for training. The proposed technique can effectively solve the problem of high ambiguity in nature for recommender system. Experimental results on several real-world recommender system datasets well demonstrate that the improved bidirectional distillation strategy shows better performance. Show more
Keywords: Bidirectional distillation, student-teacher learning, rank discrepancy aware items selection, recommender system
On L-valued tree automata: The topological characterization
Authors: Abolpour, Kh. | Zahedi, M.M. | Shamsizadeh, M.
Abstract: The current study aims to investigate the L-valued tree automata theory based on t-norm/t-conorm and it further examines their algebraic and L-valued topological properties. Specifically, the concept of L-valued operators with t-norm/t-conorm is introduced and the existing relationships between them are also studied. Interestingly, we associate L-valued co-topologies/topologies for a given L-valued tree automaton, using them to characterize some algebraic concepts. Further, we introduce the concepts of Alexandroff L-graded co-topologies and Alexandroff L-graded topologies which correspond to the L-valued operators with t-norm and L-valued operators with t-conorm/implicator, respectively. In addition, we aim to specify the relationship between the L-graded co-topologies/topologies, …showing that the introduced L-graded co-topologies/topologies have some interesting consequences under homomorphism. Show more
Keywords: L-valued tree automaton, operator, L-valued topology, homomorphism
Perturbation analysis of fully fuzzy linear systems 1
Authors: Guo, Xiaobin | Zhuo, Quanxiu
Abstract: [[[This paper considers the perturbation analysis of a class of fully fuzzy linear systems in which the coefficient matrix is a positive fuzzy matrix. The original fuzzy linear systems is extended into a brand new and simple crisp matrix equation using an embedding method. By discussing the perturbation of the extended crisp linear equation, the paper completes the perturbation analysis of the original fuzzy linear system. There are three cases of perturbation are analysed and the respective relative error bounds for solutions of fuzzy linear system are derived. Some numerical examples are given to illustrated our obtained results.]]]
Keywords: Fuzzy linear system, fuzzy solutions, matrix norm, perturbation analysis
Customer-oriented multi-objective optimization on a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows
Authors: Zhou, Tong | Zhang, Shuai | Zhang, Dongping | Chan, Verner | Yang, Sihan | Chen, Mengjiao
Abstract: With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multi-depot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to …ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-II is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model. Show more
Keywords: Electric vehicle routing problem, multi-objective optimization, collaborative multi-heterogeneous-depot, mixed time windows, nondominated sorting genetic algorithm-II
Design of chaotic black hole based feature selection with classification for Chronic Kidney Disease diagnosis
Authors: Gokiladevi, M. | Santhosh kumar, Sundar
Abstract: Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The …proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures. Show more
Keywords: Chronic kidney disease, data classification, feature selection, machine learning, metaheuristics, disease diagnosis
Comparative analysis on recent deep learning techniques for identifying chatter in milling process
Authors: Sharmila Devi, J. | Balasubramanian, P.
Abstract: Milling seems to be the most extensively utilized production technology in modern manufacturing industries, and it plays a significant role. Chatter is a type of disturbance in the form of vibration that has a negative impact on machining operation. Chatter recognition utilizing sensor outputs is a hot topic in academia. Although some progress has indeed been documented utilizing various featurization techniques and ml techniques, conventional approaches have a number of limitations, including manual preparation and a huge dataset need. Although, these are widely being used to evaluate milling operations in terms of production efficiency & work piece surface quality,.they are …not suited for real applications due to their computing duration and require large data for training process. Therefore, in this study, three well-performing deep learning approaches such as LSTM, DTW, and Bi-LSTM are used to provide an effective way for monitoring and managing chatter in the milling processes with the Duplex 2205 material. Here, some of the parameters like acceleration is measured while the milling operation is taking place, and the measured acceleration value is processed using selected three DL techniques for identifying the presence of chatter and are tested to see which one performs the best. The Bi-LSTM outperformed other approaches in detecting chatter present, according to the data. Show more
Keywords: Bi-directional long short term memory, long short term memory, dynamic time warping, deep learning, acceleration, milling chatter detection
Detection and counting of overlapped apples based on convolutional neural networks
Authors: Gao, Mengyuan | Ma, Shunagbao | Zhang, Yapeng | Xue, Yong
Abstract: Automatic identification picking robot is an important research content of agricultural modernization development. In order to overcome the difficulty of picking robots for accurate visual inspection and positioning of apples in a complex orchard, a detection method based on an instance segmentation model is proposed. To reduce the number of model parameters and improve the detection speed, the backbone feature extraction network is replaced from the Resnet101 network to the lightweight GhostNet network. Spatial Pyramid Pooling (SPP) module is used to increase the receptive field to enhance the semantics of the output network. Compared with Resnet101, the parameter quantity of …the model is reduced by 90.90%, the detection speed is increased from 5 frames/s to 10 frames/s, and the detection speed is increased by 100% . The detection result is that the accuracy rate is 91.67%, the recall rate is 97.82%, and the mAP value is 91.68% . To solve the repeated detection of fruits due to the movement of the camera, the Deepsort algorithms was used to solve the multi-tracking problems. Experiments show that the algorithm can effectively detect the edge position information and categories of apples in different scenes. It can be an automated apple-picking robot. The vision system provides strong technical support. Show more
Keywords: Instance segmentation, apple detection, GhostNet, Spatial Pyramid Pooling
Methodology for teaching quality evaluation of college volleyball training with probabilistic double hierarchy linguistic information
Authors: Huang, Bogang | Chen, Fu
Abstract: The physical education teaching quality evaluation is a very important part of the current physical education teaching reform in colleges and universities, and many experts and scholars have achieved fruitful results in this area, which has played a role in promoting the development of physical education teaching evaluation theory and practice. But at the same time, it should be soberly recognized that, with the deepening reform of physical education teaching in colleges and universities, the current teaching quality evaluation system can no longer meet the needs of the current education situation, and there are still many problems that need to …be further studied and improved. The teaching quality decision evaluation of college volleyball training is looked as the MAGDM. Thus, a useful MAGDM process is needed to cope with it. The information entropy is used for determination of target weight. Based on the grey relational analysis (GRA) and probabilistic double hierarchy linguistic term sets (PDHLTSs), this paper constructs the PDHLTS-GRA for MAGDM issues. Finally, an example for teaching quality evaluation of college volleyball training is used to illustrate the designed method. Show more
Keywords: Multiple attribute group decision making (MAGDM), probabilistic double hierarchy linguistic term sets (PDHLTSs), GRA method, teaching quality evaluation
Soft isomorphism for digital images and computational topological applications
Authors: Öztunç, Simge | İhtiyar, Sultan
Abstract: In this paper the concept of soft continuity is focused on for digital images by using soft sets which is defined on κ - adjacent digital images. Also the definitions of digital soft isomorphism and digital soft retraction are given. Some theorems are obtained deal with soft isomorphism and soft retraction for digital images and some numerical examples are presented in dimension 2. Finally digital soft retraction is obtained as a soft topological invariant.
Keywords: Digital image, soft set, soft continuous function, soft retraction
Improved seagull optimization algorithm combined with random division method to solve dynamic optimization problems
Authors: Xu, Le | Mo, Yuanbin | Lu, Yanyue
Abstract: The numerical solution of dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Therefore, we propose improved seagull optimization algorithm (ISOA) combined with random division method to solve dynamic optimization problems. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull's attack behavior to make the group approach the best position. Secondly, this paper uses the 14 benchmark test functions to verify ISOA. Finally, the improved seagull optimization algorithm is combined with the random division method to solve …two chemical dynamic optimization problems. The experimental results show that ISOA algorithm has better performance in function optimization. Show more
Keywords: Dynamic optimization, cognitive part, random division method, chemical processes, function optimization
Outlier detection for incomplete real-valued data based on inner boundary
Authors: Zhao, Zhengwei | Yang, Genteng | Li, Zhaowen
Abstract: Outlier detection is a process to find out the objects that have the abnormal behavior. It can be applied in many aspects, such as public security, finance and medical care. An information system (IS) as a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. A RVIS with missing values is an incomplete real-valued information system (IRVIS). The notion of inner boundary comes from the boundary region in rough set theory (RST). This paper conducts experiments directly in an IRVIS and investigates outlier detection in an IRVIS …based on inner boundary. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relations on the object set are defined according to the distance, by the way, the tolerance classes, the λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the inner boundary under each conditional attribute in an IRVIS is presented. The more inner boundaries an object belongs to, the more likely it is to be an outlier. Finally, an outlier detection method in an IRVIS based on inner boundary is proposed, and the corresponding algorithm (DE) is designed, where DE means degree of exceptionality. Through the experiments base on UCI Machine Learning Repository data sets, the DE algorithm is compared with other five algorithms. Experimental results show that DE algorithm has the better outlier detection effect in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the DE algorithm. Show more
Keywords: RST, IRVIS, Outlier detection, Inner boundary
Induced aggregation operators for interval basic uncertain information
Authors: Jin, Xu | Jin, LeSheng | Chen, Zhen-Song | Mesiar, Radko | Yager, Ronald
Abstract: Interval basic uncertain information is a generalization of basic uncertain information. Due to their special structures, the induced aggregation and induced OWA operators have diversified inducing aggregation modes for them. In order to provide both normative paradigms and special ways to perform reasonable induced aggregation with vectors of interval basic uncertain information, this work systematically analyzes some substantial ways of performing induced aggregation by special means of non-induced aggregation. Numerous inducing posets are suggested to use which can help automatically generate weight vectors. Some special weights generation methods based on complex inducing information with numerical examples are also proposed and …presented. Show more
Keywords: Aggregation operators, basic uncertain information, decision making, induced aggregation operators, interval basic uncertain information, preference aggregation
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2022
A model proposal for measuring service quality of eduaction with fuzzy rule-based approach and fuzzy ranking and an application
Authors: Altinsoy, Ufuk | Aktepe, Adnan | Ersoz, Suleyman
Abstract: In today's understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In …this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores. Show more
Keywords: Service quality, fuzzy logic, artificial intelligence, higher education, generation-z
An intrusion detection system based on hybrid machine learning classifier
Authors: Reji, M. | Joseph, Christeena | Nancy, P. | Lourdes Mary, A.
Abstract: Intrusion detection systems (IDS) can be used to detect irregularities in network traffic to improve network security and protect data and systems. From 2.4 times in 2018 to three times in 2023, the number of devices linked to IP networks is predicted to outnumber the total population of the world. In 2020, approximately 1.5 billion cyber-attacks on Internet of Things (IoT) devices have been reported. Classification of these attacks in the IoT network is the major objective of this research. This research proposes a hybrid machine learning model using Seagull Optimization Algorithm (SOA) and Extreme Learning Machine (ELM) classifier to …classify and detect attacks in IoT networks. The CIC-IDS-2018 dataset is used in this work to evaluate the proposed model. The SOA is implemented for feature selection from the dataset, and the ELM is used to classify attacks from the selected features. The dataset has 80 features, in the proposed model used only 22 features with higher scores than the original dataset. The dataset is divided into 80% for training and 20% for testing. The proposed SOA-ELM model obtained 94.22% accuracy, 92.95% precision, 93.45% detection rate, and 91.26% f1-score. Show more
Keywords: Intrusion detection, IoT, SOA, ELM, feature selection, attack classification, machine learning
Modeling and evolution analysis of patent competition strategy system of AI enterprises
Authors: Li, Shugang | Ji, Xiaoru | Zhang, Beiyan | Liu, Ying | Lu, Hanyu | Yu, Zhaoxu
Abstract: The maintenance and strategy operation after patent licensing can bring great market competitiveness and benefits to enterprises. But the large time span from patent licensing to market application makes it challenging to discern the benefits of patent competition strategy. Besides, artificial intelligence (AI) is an emerging industry without ready-to-use experience to formulate patent competition strategy, and particularly current researches have not designed patent competition strategy from the micro patent management perspective of AI enterprises to solve the uncertainty caused by the lag of market application relative to patent licensing. This research builds an expert group discriminant system based on the …system dynamics method to address this problem. It integrates expert tacit knowledge to determine the fuzzy variable value and the fuzzy relationship. The patent competition strategy subsystem in national dimension, industry dimension and enterprise dimension for capturing the market from the perspective of enterprise technology competitiveness are constructed. By combining the three subsystems, the enterprise patent competition strategy system dynamics model with evolution analysis is established. Finally, taking typical Chinese AI enterprise iFLYTEK as an example, the multi-scenario simulation is carried out and the results under four different scenarios can provide effective decision supports for managers to formulate reasonable patent competition strategy and gain high market share. This research sheds light on modeling and evolution analysis of the patent competition strategy which comprehensively and systematically considers the operation mechanism of patent management and contributes to dealing with the uncertainty and ambiguity in the system dynamics model effectively. Show more
Keywords: Patent competition strategy, system dynamics method, expert group discrimination, artificial intelligence
A hybrid discriminant fuzzy DNN with enhanced modularity bat algorithm for speech recognition
Authors: Venkata Lakshmi, S. | Sujatha, K. | Janet, J.
Abstract: In recent years, speech processing resides a major application in the domain of signal processing. Due to the audibility loss of some speech signals, people with hearing impairment have difficulty in understanding speech, which reintroduces a crucial role in speech recognition. Automatic Speech Recognition (ASR) development is a major challenge in research in the case of noise, domain, vocabulary size, and language and speaker variability. Speech recognition system design needs careful attention to challenges or issues like performance and database evaluation, feature extraction methods, speech representations and speech classes. In this paper, HDF-DNN model has been proposed with the hybridization …of discriminant fuzzy function and deep neural network for speech recognition. Initially, the speech signals are pre-processed to eliminate the unwanted noise and the features are extracted using Mel Frequency Cepstral Coefficient (MFCC). A hybrid Deep Neural Network and Discriminant Fuzzy Logic is used for assisting hearing-impaired listeners with enhanced speech intelligibility. Both DNN and DF have some problems with parameters to address this problem, Enhanced Modularity function-based Bat Algorithm (EMBA) is used as a powerful optimization tool. The experimental results show that the proposed automatic speech recognition-based hybrid deep learning model is effectively-identifies speech recognition more than the MFCC-CNN, CSVM and Deep auto encoder techniques. The proposed method improves the overall accuracy of 8.31%, 9.71% and 10.25% better than, MFCC-CNN, CSVM and Deep auto encoder respectively. Show more
Keywords: Speech recognition, adaptive filter, feature extraction, deep learning, discriminant fuzzy function, deep neural networks, Mel-frequency
Cooperative spectrum sensing based hybrid machine learning technique for prediction of secondary user in cognitive radio networks
Authors: Paramasivam Thuraipandi, Sivagurunathan | Nagarajan, Sathish Kumar
Abstract: The spectrum scarcity problem in today's wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels …(R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models. Show more
Keywords: Energy detector, primary user, cognitive radio network, secondary user, cooperative spectrum sensing
Multi-modal approach for COVID-19 detection using coughs and self-reported symptoms
Authors: Nguyen-Trong, Khanh | Nguyen-Hoang, Khoi
Abstract: COVID-19 (Coronavirus Disease of 2019) is one of the most challenging healthcare crises of the twenty-first century. The pandemic causes many negative impacts on all aspects of life and livelihoods. Although recent developments of relevant vaccines, such as Pfizer/BioNTech mRNA, AstraZeneca, or Moderna, the emergence of new virus mutations and their fast infection rate yet pose significant threats to public health. In this context, early detection of the disease is an important factor to reduce its effect and quickly control the spread of pandemic. Nevertheless, many countries still rely on methods that are either expensive and time-consuming (i.e., Reverse-transcription polymerase …chain reaction) or uncomfortable and difficult for self-testing (i.e., Rapid Antigen Test Nasal). Recently, deep learning methods have been proposed as a potential solution for COVID-19 analysis. However, previous works usually focus on a single symptom, which can omit critical information for disease diagnosis. Therefore, in this study, we propose a multi-modal method to detect COVID-19 using cough sounds and self-reported symptoms. The proposed method consists of five neural networks to deal with different input features, including CNN-biLSTM for MFCC features, EfficientNetV2 for Mel spectrogram images, MLP for self-reported symptoms, C-YAMNet for cough detection, and RNNoise for noise-canceling. Experimental results demonstrated that our method outperformed the other state-of-the-art methods with a high AUC, accuracy, and F1-score of 98.6%, 96.9%, and 96.9% on the testing set. Show more
Keywords: COVID-19 diagnostics, multi-modal classification, Convolutional neural network (CNN), bidirectional-LSTM, Cough classification
Multi-granularity decision-theoretic rough sets based on the fuzzy T-equivalence relation with new strategies
Authors: Qian, Jin | Han, Xing | Yu, Ying | Liu, Caihui
Abstract: Fuzzy rough sets and multi-granularity rough sets are essential extensions of Pawlak rough sets, which have become artificial intelligence research hotspots. Previous studies of the rough sets based on the fuzzy T-equivalence relation did not take the multi-granularity into account. The multi-granularity data is typically the multi-view cognition obtained by different granularity of the data, and its distinctive feature is that the data can be presented in different granularity spaces. In this paper, we integrate the idea of multi-granularity and propose four new models of "optimistic," "pessimistic," "optimistic-pessimistic," and "pessimistic-optimistic" decision-theoretic rough sets based on the fuzzy T-equivalence relation for …the first time, followed by a preliminary analysis of the intrinsic relations and properties of these new decision-theoretic rough set models by a concrete example. At last, we use experiments to show the effectiveness of suggested models, proving that they are both rational and practical. Show more
Keywords: Three-way decision, fuzzy similarity relationship, multi-granularity, decision-theoretic rough set, rough set
DOI: 10.3233/IFS-222910
Design and simulation of Advanced intelligent deep learning MPPT approach to enhance power extraction of 1000 W grid connected Photovoltaic System
Authors: Sreenivasulu, A. | Subramanian, S. | Raju, P. Sangameswara
Abstract: The world's energy offer has been beneath an incredible pressure because of the speedy depletion of fossil resources, energy security, environmental issues and therefore the ever-increasing fashionable living sophistication. The problem of persistent hikes in oil costs, climate threats and soaring energy demand has pleased the worldwide interest to exploiting and investment in renewable sorts of energy (RE), alternative energy specially. A electrical phenomenon, PV system is simple to put in, has no moving components, is sort of freed from maintenance, reduced vulnerability to power loss and is expandable. Despite these benefits, PV energy prices significantly on top of fossil …fuels. This can be because of its lower effectiveness and better prices. In PV systems tracking MPPT in effective manner is still the problem. In this paper, the 1000 W grid connected PV system has been taken for analysis of various MPPT techniques. Grid connected PV system modeled, tested under totally different irradiation conditions and conjointly for partial shading conditions. additional it's enforced under partial shading condition for early MPPT ways, improvement methodology,at finally adopted deep learning methodology for the system and therefore the obtained results were compared with different methods. Show more
Keywords: Maximum power point tracking, deep learning, partial shading conditions, efficiency, power
Rat swarm optimizer adjusted by fuzzy inference system
Authors: Ferrari, Allan Christian Krainski | Leandro, Gideon Villar | Coelho, Leandro dos Santos | Delgado, Myriam Regattieri De Biase Silva
Abstract: The rat swarm optimizer is one of the most recent metaheuristics focused on global optimization. This work proposes a fuzzy mechanism that aims to improve the convergence of this algorithm, adjusting the amplitude of the parameter that directly affects the chasing mechanism of the behavior of rats. The proposed fuzzy model uses the normalized fitness of each individual and the population diversity as input information. For evaluation criteria, the fuzzy mechanism proposed, was implemented in the optimization of third-three single objective problems. For comparison criteria, the proposed fuzzy variant is compared with other algorithms, such as GWO (Grey Wolf Optimizer), …SSA (Salp Swarm Algorithm), WOA (Whale Optimization Algorithm), and also with two proposed alternative fuzzy variants. One of the simpler fuzzy variants uses only population diversity as input information, while the other uses only the normalized fitness value of each rat. The results show that the proposed fuzzy system improves the convergence of the conventional version of the rat algorithm and is also competitive with other metaheuristics. The Friedman test shows statistically the results obtained. Show more
Keywords: Rat swarm optimizer, metaheuristics, fuzzy system, optimization, friedman test
Grey relational analysis method for building material suppliers selection based on the 2-tuple linguistic neutrosophic number
Authors: Lin, Jiaoqing | Yu, Rui | Xu, Xinrui
Abstract: The construction of real estate projects is a large and complex system project, and the completion of the construction goals on time and with quality is the key to the sustainable development of construction enterprises. In the process of real estate project construction, the management performance of building decoration material suppliers will directly affect the efficiency of real estate enterprises. How to correctly evaluate the building material suppliers (BMSs) of real estate enterprises and establish a good partnership affects the economic benefits of the enterprise and the possibility of subsequent cooperation between the two sides, which has become one of …the issues of importance to real estate enterprises. The selection and application of BMSs is the MAGDM. In this defined paper, the defined 2-tuple linguistic neutrosophic number (2TLNN) grey relational analysis (2TLNN-GRA) decision method is generated based on GRA and 2-tuple linguistic neutrosophic sets (2TLNSs). The 2TLNN-GRA method is generated for MAGDM. Finally, the decision example for BMSs selection is generated and some comparisons is generated. Show more
Keywords: Multiple attribute group decision making (MAGDM), 2TLNSs, GRA method, building material suppliers (BMSs)
Method for forecasting the level of software quality based on quality attributes
Authors: Hovorushchenko, Tetiana | Medzatyi, Dmytro | Voichur, Yurii | Lebiga, Mykyta
Abstract: The paper develops the method for forecasting the level of software quality based on quality attributes. This method differs from the known ones in that it provides forecasting the quality level of future software based on the processing the software quality attributes' values, which are available in the software requirements specification (SRS). So, the proposed method makes it possible to compare the SRSs, to immediately refuse the realization of a software based on unsuccessful SRS (saving money and time, reducing the probability of failed and challenged projects), and to make a reasonable choice of the specification for the further implementation …of a software with the highest quality (of course, if errors will not be introduced at subsequent stages of the software life cycle). During the experiments, 4 SRS were analyzed, which were fulfilled by different IT firms of Khmelnytskyi (Ukraine) for the solution of the same task. Taking into account the forecasted quality level of the future software, which will have developed according to each of the analyzed SRS, a comparison of the 4 analyzed SRS was made, and a reasoned choice of the specification was made for the further realization of the highest quality software. Show more
Keywords: Software quality, software quality attributes, software quality characteristics, software quality level, artificial neural network (ANN)
A novel twin-support vector machines method for binary classification to imbalanced data
Authors: Li, Jingyi | Chao, Shiwei
Abstract: Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have …more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel. Show more
Keywords: Binary classification, imbalanced data, support vector machine
MFRCNN: Marshalled FRCNN with optimized reading order in XY tree for document layout analysis in scientific research articles
Authors: Lovelyn Rose, S. | Ravitha Rajalakshmi, N. | Sabari Nathan, M. | Suraj Subramanian, A. | Harishkumar, R.
Abstract: Recently computer vision and NLP based techniques have been employed for document layout analysis where different types of elements in the document and their relative position are identified. This process is trickier as there are blocks which are structurally similar but semantically different such as title, text etc. This works attempts to use region-based CNN architecture (F-RCNN) for determining five different sections in the scientific articles. To improve the performance of detection algorithm, reading order is used as an additional feature and this model is known as MF-RCNN. First, an algorithm is formulated to find the reading order in documents …which adopts Manhattan-layout using a color-coding scheme. Secondly, this information is fused with the input image without changing its shape. Experimental results show that MF-RCNN which uses the reading order performs better when compared with F-RCNN when tested on Publaynet dataset. Show more
Keywords: FRCNN, reading order, XY tree, multiple channels, manhattan layout
An energy efficient deep learning model for intrusion detection in smart healthcare with optimal feature selection mechanism
Authors: Rajalakshmi, R. | Sivakumar, P. | Prathiba, T. | Chatrapathy, K.
Abstract: In healthcare (HC), Internet of Things (IoT) integrated cloud computing provides various features and real-time applications. However, owing to the nature of IoT architecture, their types, various modes of communication and the density of data transformed in the network, security is currently a critical issue in the IoT healthcare (IoT-HC) field. This paper proposes a deep learning (DL) model, namely Adaptive Swish-based Deep Multi-Layer Perceptron (ASDMLP) that identifies the intrusions or attacks in the IoT healthcare (IoT-HC) platform. The proposed model starts by clustering the patients' sensor devices in the network using the Probability-based Fuzzy C-Means (PFCM) model. After clustering …the devices, the cluster heads (CHs) among the cluster members are selected based on the energy, distance and degree of the sensor devices for aggregating the data sensed by the medical sensor devices. The base station (BS) sends the patient's data collected by the CHs to the cloud server (CS). At the cloud end, the proposed model implements an IDS by applying training of the DL model in publicly available databases. The DL approach first performs preprocessing of the data and then selects optimal features from the dataset using the Opposition and Greedy Levy mutation-based Coyotes Optimization Algorithm (OGCOA). The ASDMLP trains these optimal features for the detection of HC data intrusions. The outcomes confirm that the proposed approach works well on real-time IoT datasets for intrusion detection (ID) without compromising the energy consumption (EC) and lifespan of the network. Show more
Keywords: Smart healthcare, Internet of Things (IoT), intrusion detection system, deep learning, healthcare security
Early diagnosis of autism using indian autism grading tool
Authors: Kanimozhi Selvi, C.S. | Jayaprakash, D. | Poonguzhali, S.
Abstract: Autism spectrum disorder is a neuro-developmental disorder that affects communication and social skills in individuals. Screening and diagnosis of autism using conventional methods, such as interviews with parents or caregivers and observational assessments takes a long time. The accurate diagnosis of autism by physicians and healthcare professionals seems to be challenging. By analyzing data on autistic children, medical professionals can learn about autism screening assessment decision making. The present study aims to develop a parental autism screening tool termed the Indian Autism Grading Tool (IAGT) for early screening of autism. Data are collected using the Indian Autism Parental Questionnaire and …assigned with grades. This dataset is employed to test five supervised machine learning models, which compare classification performance based on accuracy, precision and recall. The most effective model should be used to implement the autism screening application. MLR is known to be more robust and to support fewer data sets, so it can be employed for the implementation of ML-powered mobile applications. MLR achieves the overall accuracy of 97.85%, which equates to 0.72%, 2.37%, 0.84% and 1.54% better than SVM, DT, KNN and GNB respectively. The proposed tool is developed in both Tamil and English. The pilot study is conducted with 30 children and the predictability of the tool is compared with the clinician. Therefore, the tool consistently achieves the same level of accuracy as clinicians. Show more
Keywords: Artificial intelligence, machine learning techniques, autism spectrum disorder, Indian autism parental questionnaire, mobile application
Hybrid DC-AC microgrid energy management system using grey wolf optimized neural network
Authors: Sahaya Elsi, S. | Michael Raj, F. | Prince Mary, S.
Abstract: Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system's charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf …optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures. Show more
Research on the optimization strategy of lithium battery electrode correction system based on algorithm fusion
Authors: Xiao, Yanjun | Zhao, Churui | Qi, Hao | Liu, Weiling | Meng, Zhaozong | Peng, Kai
Abstract: In the control system of a lithium battery rolling mill, the correction system was crucial. This was because the correction system had a significant impact on the performance of the lithium battery rolling mill, including high precision and efficient rolling quality. However, the non-linearity of the correction system and the uncertainty of the correction system made it a challenging problem to achieve a high precision correction control. The contribution and innovation of this paper was a genetic fuzzy PID control strategy based on Kalman filter, which was proposed and applied to the control of lithium battery rolling mill correction technology. …In order to achieve intelligent control of a high-precision electrode rolling mill correction system, an algorithm fusion control scheme was proposed. Firstly, a novel and detailed correction system model was presented. Next, the initial PID parameters of the correction were optimized by means of a genetic algorithm so that the PID parameters could be adapted to the correction control process and then optimized again by adding an extended Kalman filter. Finally, the lithium battery rolling mill correction control system was validated, tested and commissioned in the field. The results showed that the designed algorithm could meet the working requirements of the lithium battery rolling mill and that it improved the accuracy of the correction system. In the actual lithium battery rolling mill production process, the algorithm was compared with a conventional PID. Compared with the common single algorithm, the fusion algorithm proposed in this paper was a complete set of high precision correction control system algorithm to solve the high precision problem faced by the correction system in the actual lithium battery rolling mill correction system. Show more
Keywords: Pole piece rolling mill, deviation correction system, fuzzy PID, genetic algorithm, algorithm fusion, extended kalman filter
Comprehensive performance evaluation of high embankments in heavy-haul railways using an improved extension model with attribute reduction algorithm
Authors: Zhang, Qi | Su, Qian | Liu, Baosen | Pei, Yanfei | Zhang, Zongyu | Chen, De
Abstract: Effectively evaluating high-embankment deformation and stability is important for heavy-haul railway safety. An improved extension model with an attribute reduction algorithm was proposed for the comprehensive evaluation method. First, a hierarchical evaluation system for high embankments in heavy-haul railways was established using the attribute reduction algorithm, which includes the principal component analysis, maximum information coefficient, coefficient of variation, and improved Dempster-Shafer evidence theory. Furthermore, the improved extension model was used to evaluate high-embankment performance in heavy-haul railways. In this improved extension model, the combination weighting method, an asymmetric proximity function, and the maximum membership principle effectiveness verification were used. Finally, …three high embankments in a Chinese heavy-haul railway were studied. The results illustrate that the main influencing factors for high-embankment performance in a heavy-haul railway are annual rainfall, annual temperature, and 21 other indicators. The performance of the three embankments is level III (ordinary), level II (fine), and level III (ordinary), respectively, indicating that these embankments have generally unfavourable performance. The three embankments' performance matches field measurements, and the proposed method outperforms the Fuzzy-AHP method, cloud model, and gray relational analysis. This study demonstrates the feasibility of the proposed method in assessing the high-embankment performance under heavy axle loads. Show more
Keywords: Heavy-haul railway, high embankment, comprehensive evaluation, improved extension model, attribute reduction
BOSF-SVM: A thermal image-based fault diagnosis method of circuit boards
Authors: Song, Xudong | Wan, Xiaohui | Yi, Weiguo | Cui, Yunxian | Li, Changxian
Abstract: In recent years, the lack of thermal images and the difficulty of thermal feature extraction have led to low accuracy and efficiency in the fault diagnosis of circuit boards using thermal images. To address the problem, this paper presents a simple and efficient intelligent fault diagnosis method combined with computer vision, namely the bag-of-SURF-features support vector machine (BOSF-SVM). Firstly, an improved BOF feature extraction based on SURF is proposed. The preliminary fault features of the abnormally hot components are extracted by the speeded-up robust features algorithm (SURF). In order to extract the ultimate fault features, the preliminary fault features are …clustered into K clusters by K-means and substituted into the bag-of-features model (BOF) to generate a bag-of-SURF-feature vector (BOSF) for each image. Then, all of the BOSF vectors are fed into SVM to train the fault classification model. Finally, extensive experiments are conducted on two homemade thermal image datasets of circuit board faults. Experimental results show that the proposed method is effective in extracting the thermal fault features of components and reducing misdiagnosis and underdiagnosis. Also, it is economical and fast, facilitating savings in labour costs and computing resources in industrial production. Show more
Keywords: Thermal images, circuit boards, fault diagnosis, bag-of-features, support vector machine
An enhancement of transformer-based architecture with randomized regularization for wind speed prediction
Authors: Vo, Tham
Abstract: The wind power is considered as a potential renewable energy resource which requires less management cost and effort than the others like as tidal, geothermal, etc. However, the natural randomization and volatility aspects of wind in different regions have brought several challenges for efficiently as well as reliably operating the wind-based power supply grid. Thus, it is necessary to have centralized monitoring centers for managing as well as optimizing the performance of wind power farms. Among different management task, wind speed prediction is considered as an important task which directly support for further wind-based power supply resource planning/optimization, hence towards …power shortage risk and operating cost reductions. Normally, considering as traditional time-series based prediction problem, most of previous deep learning-based models have demonstrated significant improvement in accuracy performance of wind speed prediction problem. However, most of recurrent neural network (RNN) as well as sequential auto-encoding (AE) based architectures still suffered several limitations related to the capability of sufficient preserving the spatiotemporal and long-range time dependent information of complex time-series based wind datasets. Moreover, previous RNN-based wind speed predictive models also perform poor prediction results within high-complex/noised time-series based wind speed datasets. Thus, in order to overcome these limitations, in this paper we proposed a novel integrated convolutional neural network (CNN)-based spatiotemporal randomization mechanism with transformer-based architecture for wind speed prediction problem, called as: RTrans-WP. Within our RTrans-WP model, we integrated the deep neural encoding component with a randomized CNN learning mechanism to softy align temporal feature within the long-range time-dependent learning context. The utilization of randomized CNN component at the data encoding part also enables to reduce noises and time-series based observation uncertainties which are occurred during the data representation learning and wind speed prediction-driven fine-tuning processes. Show more
Keywords: Wind speed prediction, deep learning, transformer, randomization, nomenclatures
BIM and ANN-based rapid prediction approach for natural daylighting inside library spaces
Authors: Ni, Ting | Wang, Bo | Jiang, Jiaxin | Wang, Meng | Lei, Qing | Deng, Xinman | Feng, Cuiying
Abstract: The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal …daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk ® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis. Show more
Keywords: Daylight factor, rapid prediction, building information modelling, artificial neural network, library, app
Multimodal prediction of breast cancer using radiogenomics and clinical trials with decision fusion
Authors: Ramkumar, N. | Sadasivam, G. Sudha | Renuka, D. Karthika
Abstract: Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in …the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system's effectiveness is shown by experimental findings. Show more
Keywords: Convolution neural networks, multimodal analysis, gray level co-occurrence matrix, histopathological, mammogram, sonogram and integrated stacking classifier
Generalized cross weighted Heronian mean operators and their application to binary classification
Authors: Xu, Juan | Ma, Zhen Ming | Xu, Zeshui
Abstract: Heronian mean (HM) operators, which can capture the interrelationship between input arguments with the same importance, have been a hot research topic as a useful aggregation technique. In this paper, we propose the generalized normalized cross weighted HM operators on the unit interval which can not only capture the interrelationships between input arguments but also aggregate them with different weights, some desirable properties are derived. Then, generalized cross weighted HM operators are extended to real number set and applied to binary classification. We list the detailed steps of binary classification with the developed aggregation operators, and give a comparison of …the proposed method with the existing ones using the Iris dataset with 5-fold cross-validation (5-f cv), the accuracy of the proposed method for the training sets and the testing sets are both 100%. Show more
Keywords: Generalized cross weighted HM operator, cross weight vector, binary classification
Stance detection of user reviews on social network with integrated structural information
Authors: Zhou, Lixin | Zhou, Kexin | Liu, Chen
Abstract: Stance detection is the task of classifying user reviews towards a given topic as either supporting, denying, querying, or commenting (SDQC) . Most approaches for solving this problem use only the textual features, including the linguistic features and users' vocabulary choice. A few approaches have shown that information from the network structure like graph model can add value, in addition to the textual features, by providing social connections and interactions that may be vital for the stance detection task. In this paper, we present a novel model that combines the text features with the network structure by (1) creating a …graph-structure model based on conversational structure towards specific topics and (2) constructing a tree-gated neural network model (TreeGGNN) to capture structure information among reviews. We evaluate our model on four baseline models, which shows that the combination of text and network can achieve an improvement of 2–6% over the state-of-the-art baselines. Show more
Keywords: Stance detection, gated graph neural network, deep learning, structure of conversation thread
Multiple vehicles tracking and detection using weight high order singular value decomposition dimensionality reduction and double classifiers
Authors: Ranjeeth Kumar, C. | Kalaiarasu, M.
Abstract: Controlling and managing city traffic is one of them. In order to use image processing to prevent accidents on the road, vehicle tracking and detection are essential. By following moving objects, surveillance video monitoring and human activity recording are carried out. By taking this into account, a useful technique for image processing that detects automobiles from the image is suggested. For numerous vehicle tracking and detection systems, the ECNN-SVM (Enhanced Convolution Neural Network with Support Vector Machine) has just been introduced. However, the larger dimensional data space and inaccurate edge recognition make this system's performance difficult. The WHOSVD (Weight High …Order Singular Value Decomposition) approach, which reduces the dimension and breaks up the positive and negative training picture samples, is established to improve training speed and visual vehicle recognition. To effectively identify the edges at corners, improved canny edge detection is used for edge detection. Mean Kernel Fuzzy C Means (MKFCM) clustering algorithm-based three-dimensional bounding box estimation is used to identify the vehicle items. By merging the feature value of samples with their class labels, the Speed Factor Based Cuckoo Search Algorithm (SFCSA) is introduced for feature selection. The WHOSVD algorithm was used as the input for the enhanced convolutional neural network (ECNN), which is introduced for low-dimensional space and is used for vehicle detection and tracking. Occlusion problems are resolved and target features are further identified using a machine learning classifier. For common algorithms like CNN+SVM, Support Vector Machine (SVM), and the proposed technique, experimentation is done in regards to the metrics of accuracy, f-measure, precision, and recall for performance evaluation. Show more
Keywords: Edge detection, dimensionality reduction, Weight High Order Singular Value Decomposition (WHOSVD), Speed Factor Based Cuckoo Search Algorithm (SFCSA), Mean Kernel Fuzzy C Means (MKFCM) clustering, CNN (Convolutional Neural Network), SVM (Support Vector Machine), multiple vehicles tracking and multiple vehicle detection
A review of rumor detection techniques in social networks
Authors: Liu, Yao | Shen, Hao | Shi, Lei
Abstract: Social networks have accelerated the speed and scope of information dissemination. However, the lack of regulation and freedom of speech on social platforms has resulted in the widespread dissemination of the unverified message. Therefore, rapid and effective detection of social network rumors is essential to purify the network environment and maintain public security. Currently, the defects of rumor detection technology are that the detection time is too long and the timeliness is poor. In addition, the differences based on specific regions or specific fields will lead to deviations in the training dataset. In this paper, firstly, the definition of rumor …is described, and the current problems and detection process of rumor detection are described; Secondly, introduce different data acquisition methods and analyze their advantages and disadvantages; Thirdly, according to the development of rumor detection technology, the existing rumor detection methods of artificial, machine learning and deep learning are analyzed and compared; Finally, the challenges of social network rumor detection technology are summarized. Show more
Keywords: Rumor, rumor detection, machine learning, deep learning, social networks
Investment decision making in the fuzzy context: An integrated model approach
Authors: Zhang, Shasha | Liu, Xiaodi | Garg, Harish | Zhang, Shitao
Abstract: With the implementation and steady progress of the Belt and Road (B&R) initiative, China's investment in countries along the B&R has maintained a high growth trend. Generally speaking, investment problems are often accompanied by high risk and uncertainty, and how to make the suitable investment decision is a difficult issue. This paper investigates an investment decision approach under the probabilistic hesitant fuzzy environment. Firstly, a new probabilistic hesitant fuzzy distance and correlation coefficient are defined to overcome the defects of the existing probabilistic hesitant fuzzy information measures. Secondly, an attribute weight integrated model is constructed by combining the maximum deviation …method, the CRITIC method and the maximum entropy principle, which is able to take into account the correlation between attributes and make full use of the decision information. In addition, a disappointment theory-based probabilistic hesitant fuzzy multi-attribute decision making (PHFMADM) method is proposed to solve the investment decision problem, which can integrate the psychological behavior of decision makers into the decision making process and make the decision results more authentic and reliable. Finally, the rationality and validity of the method are verified by comparing with the existing methods. Show more
Keywords: Investment decision making, Distance, Correlation coefficient, Disappointment theory, Probabilistic hesitant fuzzy sets
Image classification using gaussian curvature based local tetra pattern descriptor with enhanced feature selection technique
Authors: Lakshmi Narayanan, S. | Ignatia, K. Majella Jenvi | Alfurhood, Badria Sulaiman | Bhat, Nagaraj
Abstract: A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method's effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to …other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques. Show more
Keywords: Feature extraction, feature selection, classification, texture analysis
A novel transfer learning model on complex fuzzy inference system
Authors: Huong, Trieu Thu | Lan, Luong Thi Hong | Giang, Nguyen Long | Binh, NguyenThi My | Vo, Bay | Son, Le Hoang
Abstract: Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment …from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. Show more
Keywords: Complex fuzzy set, mamdani complex fuzzy inference system, transfer learning, fuzzy transfer learning, complex fuzzy transfer learning
NeutroMAS4SCRM : a combined multi-agent system with neutrosophic data analytic hierarchy process framework for supply chain risk management
Authors: Meziani, Ahlem | Bourouis, Abdelhabib | Chebout, Mohamed Sedik
Abstract: Effective risk management reaction improves the absorption of critical impacts on supply chains. Supply chain risk (SCR) sources, like control, process, demand, and supply, need to be identified, assessed, and mitigated to make rational decisions immediately. Late detection of a disruptive event can cause delays in handling risk. Since SCRs consist of complex, uncertain, and incomplete information, most of the provided risk assessment mechanisms cannot handle it in real-time. Hence, in this paper, we introduce NeutroMAS4SCRM, a framework incorporating real-time Multi-Agent Systems (MAS) with Neutrosophic Data Analytic Hierarchy Processes to best deal with the complexity, uncertainty, and vagueness of SCR …management-related issues and which can hence help decision-makers adopt less risky decisions. In addition, the MAS technology contribution to SCR management is outlined through a comparative study among the most recent studies. In contrast, the proposed MAS for the supply chain is implemented under the JADE agent platform, where the FIPA-ACL-based message content is specified using a dedicated ontology. A simulation-based decision support system is used to assess the cost risk and its harmful effects and determine how well the proposed framework can help companies manage risks efficiently. The simulation has proven to reduce risk costs by about 85% . Show more
Keywords: Supply chain risk management, single-valued neutrosophic set, neutrosophic DAHP, multi agent system, simulation
A new framework for APT attack detection based on network traffic
Authors: Nguyen, Hoa Cuong | Xuan, Cho Do | Nguyen, Long Thanh | Nguyen, Hoa Dinh
Abstract: Advanced Persistent Threat (APT) attack detection and monitoring has attracted a lot of attention recently when this type of cyber-attacks is growing in both number and dangerous levels. In this paper, a new APT attack model, which is the combination of three different neural network layers including: Multi-layer Perceptron (MLP), Inference (I), and Graph Convolutional Networks (GCN) is proposed. The new model is named MIG for short. In this model, the MLP layer is in charge of aggregating and extracting properties of the IPs based on flow network in Network traffic, while the Inference layer is responsible for building IP …information profiles by grouping and concatenating flow networks generated from the same IP. Finally, the GCN layer is used for analyzing and reconstructing IP features based on the behavior extraction process from IP information records. The APT attacks detection method based on network traffic using this MIG model is new, and has yet been proposed and applied anywhere. The novelty and uniqueness of this method is the combination of many different data mining techniques in order to calculate, extract and represent the relationship and the correlation between APT attack behaviors based on Network traffic. In MIG model, many meaningful anomalous properties and behaviors of APT attacks are synthesized and extracted, which help improve the performance of APT attack detection. The experimental results showed that the proposed method is meaningful in both theory and practice since the MIG model not only improves the ability to correctly detect APT attacks in network traffic but also minimizes false alarms. Show more
Keywords: APT attacks, behavior profile, inference, graph convolutional neural network, graph analysis
Design of optimal power supply parameters for industrial expansion
Authors: Wu, Guanghua | Li, Hongsheng | Li, Hongyu | Guo, Shiping | Ma, Wenjian | Dong, Jing
Abstract: The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation …is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value. Show more
Keywords: Grey correlation degree, fuzzy C-means clustering algorithm, business expansion newspaper installation, Optimal power supply parameters
A multi-criteria decision-making with regret theory-based MULTIMOORA method under interval neutrosophic environment
Authors: Peng, Lijuan | Xu, Dongsheng
Abstract: The MULTIMOORA (multiple multi-objective optimization by ratio analysis) method is useful for multiple criteria decision-making method. It is based on expected utility theory and assumes that decision makers are completely rational. However, some studies show that human beings are usually bounded rational, and their regret aversion behaviors play an important role in the decision-making process. Interval neutrosophic sets can more flexibly depict uncertain, incomplete and inconsistent information than single-valued neutrosophic sets. Therefore, this paper improves the traditional MULTIMOORA method by combining the regret theory under interval neutrosophic sets. Firstly, the regret theory is used to calculate the utility value and …regret-rejoice value of each alternatives. Secondly, the criteria weights optimization model based on the maximizing deviation is constructed to obtain the weight vector. Then, the MULTIMOORA method is used to determine the order of the alternatives. Finally, an illustrative example about school selection is provided to demonstrate the feasibility of the proposed method. Sensitivity analysis shows the validity of the regret theory in the proposed method, and the ranking order change with different regret avoidance parameter. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers' psychological preference. Show more
Keywords: Interval neutrosophic set, regret theory, multiple criteria decision making, MULTIMOORA
Pseudo almost periodic solutions for Clifford-valued neutral-type fuzzy neural networks with multi-proportional delay and D operator 1
Authors: Xu, Huili | Li, Bing
Abstract: In this paper, a class of Clifford-valued neutral fuzzy neural-type networks with proportional delay and D operator and whose self feedback coefficients are also Clifford numbers are considered. By using the Banach fixed point theorem and some differential inequality techniques, we directly study the existence and global asymptotic stability of pseudo almost periodic solutions by not decomposing the considered Clifford-valued systems into real-valued systems. Finally, two examples are given to illustrate our main results. Our results of this paper are new.
Keywords: Clifford-valued neural network, fuzzy neural network, proportional delay, D operator, pseudo almost periodic solution, global asymptotic stability.
Representations of ordered semigroups and their interconnection
Authors: Lekkoksung, Somsak | Iampan, Aiyared | Julatha, Pongpun | Lekkoksung, Nareupanat
Abstract: It is known that any ordered semigroup embeds into the structure consisting of the set of all fuzzy sets together with an associative binary operation and a partial order with compatibility. In this study, we provide two classes of ordered semigroups in which any model in these classes is a representation of any ordered semigroup. Moreover, we give an interconnection of a class we constructed.
Keywords: ordered semigroup, fuzzy ordered semigroup, representation
Myoelectric human computer interaction using CNN-LSTM neural network for dynamic hand gesture recognition
Authors: Li, Qiyu | Langari, Reza
Abstract: Human-computer interaction(HCI) has broad range of applications. One particular application domain is rehabilitation devices. Several bioelectric signals can potentially be used in HCI systems in general and rehabilitation devices in particular. Surface ElectroMyoGraphic(sEMG) signal is one of the more important bioelectric signals in this context. The sEMG signal is formed by muscle activation although the details are rather complex. Applications of sEMG are referred is commonly referred to as myoelectric control since the dominant use of this signal is to activate a device even if (as the term control may imply) feedback is not always used in the process. With …the development of deep neural networks, various deep learning architectures are used for sEMG-based gesture recognition with many researchers having reported good performance. Nevertheless, challenges remain in accurately recognizing sEMG patterns generated by gestures produced by hand or the upper arm. For instance one of the difficulties in hand gesture recognition is the influence of limb positions. Several papers have shown that the accuracy of gesture classification decreases when the limb position changes even if the gesture remains the same. Prior work by our team has shown that dynamic gesture recognition is in principle more reliable in detecting human intent, which is often the underlying idea of gesture recognition. In this paper, a Convolutional Neural Network (CNN) with Long Short-Term Memory or LSTM (CNN-LSTM) is proposed to classify five common dynamic gestures. Each dynamic gesture would be performed in five different limb positions as well. The trained neural network model is then used to enable a human subject to control a 6 DoF (Degree of Freedom) robotic arm with 1 DoF gripper. The results show a high level of accurate performance achieved with the proposed approach. In particular, the overall accuracy of the dynamic gesture recognition is 84.2%. The accuracies vary across subjects but remain at approximately 90%for some subjects. Show more
Keywords: Human-computer interaction, sEMG signal, neural network, gesture recognition
Soft semi-linear uniform spaces and their perceptual application
Authors: Rawshdeh, Amani A. | Al-jarrah, Heyam H. | Tiwari, Surabhi | Tallafha, Abdalla A.
Abstract: In this paper, we use the soft set theory and the concept of semi-linear uniform spaces to introduce the notion of soft semi-linear uniform spaces with its generalization, briefly soft-GSL US . We investigate some properties of soft topology that induced by soft-GSL US . Also, we use the members of soft-GSL US to define a soft proximity space and a soft filter then we establish the relationships between them. Finally, we give the perceptual application of soft semi-linear uniform structures by employing the natural transformation of …a soft semi-linear uniform space to a soft proximity. Show more
Keywords: Soft set, soft point, soft topology, soft semi-linear uniform spaces, soft proximity
Another view on tolerance based multigranulation q-rung orthopair fuzzy rough sets with applications
Authors: Park, Choonkil | Rehman, Noor | Ali, Abbas
Abstract: The q -rung orthopair fuzzy sets accommodate more uncertainties than the Pythagorean fuzzy sets and hence their applications are much extensive. Under the q -rung orthopair fuzzy set, the objective of this paper is to develop new types of q -rung orthopair fuzzy lower and upper approximations by applying the tolerance degree on the similarity between two objects. After employing tolerance degree based q -rung orthopair fuzzy rough set approach to it any times, we can get only the six different sets at most. That is to say, every rough set in a universe can be approximated by only six …sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Furthermore, we propose tolerance degree based multi granulation optimistic/pessimistic q -rung orthopair fuzzy rough sets and investigate some of their properties. Another main contribution of this paper is to disclose the ideas of different kinds of approximations called approximate precision, rough degree, approximate quality and their mutual relationship. Finally a technique is devloped to rank the alternatives in a q -rung orthopair fuzzy information system based on similarity relation. We find that the proposed method/technique is more efficient when compared with other existing techniques. Show more
Keywords: q-rung orthopair fuzzy set, fuzzy rough set, similarity relation. tolerance classes, multigranulation q-rung orthopair fuzzy rough sets
Scrutinizing student dropout issues in MOOCs using an intuitionistic fuzzy decision support system
Authors: Pandey, Mamta | Litoriya, Ratnesh | Pandey, Prateek
Abstract: Massive open online courses (MOOCs) are a recent e-learning programme that has received widespread acceptance among several colleges. Student dropout from MOOCs is a big worry in higher education and policy-making circles, as it occurs frequently in colleges that offer these types of courses. The majority of student dropouts are caused by causes beyond the institution's control. Using an IF-DEMATEL (Intuitive Fuzzy Decision-making Trial and Evaluation Laboratory) approach, the primary factors and potential causal relationships for the high dropout rate were identified. The most effective aspects of massive open online courses (MOOCs) are identified using IF-DEMATEL and CIFCS. Moreover, it …explains the interconnectedness of the various MOOC components. As an added measure, a number of DEMATEL techniques are used to conduct a side-by-side comparison of the results. Decisions made by the educational organisation could benefit from the findings. According to the research, there are a total of twelve indicators across four dimensions that are related to online course withdrawal amongst students. Then, experienced MOOC instructors from various higher education institutions were invited to assess the level of influence of these characteristics on each other. Academic skills and talents, prior experience, course design, feedback, social presence, and social support were identified as six primary characteristics that directly influenced student dropout in MOOCs. Interaction, course difficulty and length, dedication, motivation, and family/work circumstances have all been found to play a secondary part in student dropout in massive open online courses (MOOCs). The causal connections between the major and secondary factors were traced and discussed. The results of this study can help college professors and administrators come up with and implement effective ways to reduce the high number of students who drop out of massive open online courses (MOOCs). Show more
Keywords: Massive open online courses, lifelong learning, intuinistic fuzzy DEMATEL, online learning environments
Two stages cascades neural network for multi-class brain lesion classification system in MRI images
Authors: Perumal, T. Suderson Rama | Jegatheesan, A. | Jayachandran, A.
Abstract: Brain tumor is one of the deadliest cancerous diseases and their severity has turned them into the leading cause of cancer-related mortality. Automatic detection and classification of severity-level for a brain tumor using MRI is a complex process in multilevel classification and needs an improved learning method without computational complexity. In this research article, we propose an innovative Multi-Dimensional Cascades Neural Network work (MDCNet) that takes full advantage of two networks with different dimensions, which can balance the complete semantic information and high-resolution detail information of a large-volume MRI image. In stage 1, a shallow-layer-enhanced 3D location net obtains the …location and rough segmentation of brain lesions. In stage 2, a high-resolution attention map is used to obtain the 2D high-resolution image slice sets from the original image and the output of stage 1. The high-resolution images pick up the lost detailed information, refining the boundaries further. Moreover, a multi-view 2.5D net composed of three 2D refinement sub-networks is applied to deeply explore the morphological characteristics of all brain lesions from different perspectives, which compensates for the mistakes and missing spatial information of a single view, increasing the stability of the whole algorithm. The robustness of the proposed model is analyzed using several performance metrics of three different data sets. Through the prominent performance, the proposed model can outperform other existing models attaining an average accuracy of 99.13%. Here, the individual accuracy for Dataset 1, Dataset 2, and Dataset 3 is 99.67%, 98.16%, and 99.76% respectively. Show more
Keywords: Keywords Brain tumor, classification, convolutional neural network, two stage ensemble, magnetic resonance imaging
Combining BERT with TCN-BiGRU for enhancing Arabic aspect category detection
Authors: Bensoltane, Rajae | Zaki, Taher
Abstract: Aspect-based sentiment analysis (ABSA) is a challenging task of sentiment analysis that aims at extracting the discussed aspects and identifying the sentiment corresponding to each aspect. We can distinguish three main ABSA tasks: aspect term extraction, aspect category detection (ACD), and aspect sentiment classification. Most Arabic ABSA research has relied on rule-based or machine learning-based methods, with little attention to deep learning techniques. Moreover, most existing Arabic deep learning models are initialized using context-free word embedding models, which cannot handle polysemy. Therefore, this paper aims at overcoming the limitations mentioned above by exploiting the contextualized embeddings from pre-trained language models, …specifically the BERT model. Besides, we combine BERT with a temporal convolutional network and a bidirectional gated recurrent unit network in order to enhance the extracted semantic and contextual features. The evaluation results show that the proposed method has outperformed the baseline and other models by achieving an F1-score of 84.58% for the Arabic ACD task. Furthermore, a set of methods are examined to handle the class imbalance in the used dataset. Data augmentation based on back-translation has shown its effectiveness through enhancing the first results by an overall improvement of more than 3% in terms of F1-score. Show more
Keywords: Aspect-based sentiment analysis, aspect category detection, deep learning, BERT, data augmentation, arabic language
A Filter-APOSD approach for feature selection and linguistic knowledge discovery
Authors: Yu, Jianping | Yuan, Laidi | Zhang, Tao | Fu, Jilin | Cao, Yuyang, | Li, Shaoxiong | Xu, Xueping
Abstract: The development of natural language processing promotes the progress of general linguistic studies. Based on the selected features and the extracted rules for word sense disambiguation (WSD), some valuable knowledge of the relations between linguistic features and word sense classes may be discovered, which may provide theoretical and practical evidence and references for lexical semantic study and natural language processing. However, many available approaches of feature selection for WSD are in the end to end operation, they can only select the optimal features for WSD, but not provide the rules for WSD, which makes knowledge discovery impossible. Therefore, a new …Filter-Attribute partial ordered structure diagram (Filter-APOSD) approach is proposed in this article to fulfill both feature selection and knowledge discovery. The new approach is a combination of a Filter approach and an Attribute Partial Ordered Structure Diagram (APOSD) approach. The Filter approach is designed and used for filtering the simplest rules for WSD, and the APOSD approach is used to provide the complementary rules for WSD and visualize the structure of the datasets for knowledge discovery. The features occurring in the final rule set are selected as the optimal features. The proposed approach is verified by the benchmark data set from the SemEval-2007 preposition sense disambiguation corpus with around as the target word for WSD. The test result shows that the accuracy of WSD of around is greatly improved comparing with the one by the state of the art, and 17 out of 22 features are finally selected and ranked according to their contribution to the WSD, and some knowledge on the relations between the word senses and the selected features is discovered. Show more
Keywords: Filter-APOSD approach, feature selection, word sense disambiguation, knowledge discovery, English preposition
New optimization technique for group decision analysis with complex pythagorean fuzzy sets
Authors: Akram, Muhammad | Zahid, Kiran | Kahraman, Cengiz
Abstract: The striking theory of ELECTRE III approach, being a marvelous strategy to deal with pseudo criterion, prevails over the traditional variants of ELECTRE method and other decision-making approaches for veracious decision-making. The noticeable efficiency and broader space of complex Pythagorean fuzzy model make it more significant and dominant for modeling two dimensional imprecise knowledge. The remarkable contribution of this study is to present a high aptitude variant of ELECTRE method by taking the advantage of the flexible structure of complex Pythagorean fuzzy sets closely following the outranking principles of ELECTRE III method. The proposed complex Pythagorean fuzzy ELECTRE III method …is accredited to employ the theory of ELECTRE III technique to excellently deal with pseudo criterion as well as the two dimensional imprecise data for authentic decision-making. The proposed methodology uses three different threshold values, including preference, indifference and veto threshold values, to check the preference relation between alternatives. The presented strategy is applied to a case study for material selection to get the befitting decision. The comparative study with Pythagorean fuzzy ELECTRE III method is also included in this article to verify its decision-making aptitude. Show more
Keywords: Group decision-making, ELECTRE III method, Pseudo criteria, Complex Pythagorean fuzzy set, Threshold values
SCDAP –secured cluster based data aggregation protocol for energy efficient communication in wireless sensor networks
Authors: Lavanya, G. | Velammal, B.L. | Kulothungan, K.
Abstract: A network of real time devices that can sense and transmit the information from the deployed environment by using multi hop communication is called as Wireless Sensor Network (WSNs). Despite the rapid advancement of WSN, where an increasing number of physical devices so called as sensors nodes are connected with each other, providing the improved security with optimized energy consumption during data transmission, communication and computation remains huge challenge. In wireless sensor networks, numerous sensor nodes are deployed in the physical environment to sense and collect the required information from the given environment. The sensed information is needed to be …transmitted from the nodes to the control station in an energy efficient manner. Data aggregation is one kind of techniques which will optimize the energy usage in wireless sensor networks during the data transmission. In data aggregation, the unnecessary data is removed which will significantly reduce energy of the nodes during data transmission. However, collected data during the data aggregation should be completely protected and there are various threats that can be launched by the intruders to carry out unauthorised data access and can cause threat to the integrity of the network. Therefore, ensuring data security during the data aggregation process is very important and essential for the security of the network. In this paper, a Secure Cluster based Data Aggregation Protocol (SCDAP) have been proposed to provide better security through secure authentication and verification process, and to reduce overall energy consumption of the network by implementing secure clustering process to eliminate the redundant data in the network. Moreover, the proposed system is more efficient in generating public and private keys for effective and secure data transmission and verification process. The proposed system is experimentally tested in NS-3 tool and proves that the proposed system reduces high energy consumption, computational and communicational cost, end-to-end delay and improves the packet delivery ratio. Moreover, the proposed system provides better security in the network when compared to other existing systems during the data aggregation. Show more
Keywords: Wireless networks, data aggregation, energy optimization, efficient authentication, key generation
An offline English optical character recognition and NER using LSTM and adaptive neuro-fuzzy inference system
Authors: Suganthi, M. | Arun Prakash, R. | Anbalagan, G.
Abstract: Everything becomes smart in the modern era, for everything we need a better plan or arrangements. In the olden days, essential information was noted as a document with the help of paper and pen or printed texts. But the intelligent world needs a paperless environment by converting handwritten or printed text documents into software copies. This can be achieved by the electronic data conversion concept called Optical Character Recognition (OCR). OCR of some documents is complex because of different writing styles and quality of scanned image issues, which can be solved by adopting a deep learning technique for better accuracy. …We employed Long Short Term Memory (LSTM) for English Optical Character Recognition for paperless and effortless data storage and fast access in this work. Still, the records may contain the entities like names, contact details, drug details, diseases, educational qualifications, dates, etc. These entities cannot be separated by employing OCR alone; we need an entity recognition framework for deeper and faster data analysis. For efficient Named Entity Recognition, we utilize the Adaptive Fuzzy Inference System (ANFIS) powered by the algorithms CRF and BERT to automatically labels each entity by training the vast amount of unlabeled text data. The ANFIS model is equipped with both linguistic and numerical knowledge. It is more accurate than the ANN when it comes to identifying patterns and classification data. Also, it is more transparent to the user. Our proposed framework aims to improve the performance of the character recognition system by using a feed-forward network. One of the main issues that have been identified in the development of this system is noise. Through this network, we can provide a single input and one output layer. The main components of the system are the training and recognition sections. These two sections are mainly focused on image acquisition and feature extraction. Besides these, they also include training and simulation of the classifier. The first step in the process of image recognition is to extract the features from the normalized image matrix. We then train the network using a proposed training algorithm. Experimentation on medical records attains a higher accuracy value of 0.9637, recall value of 0.9627, and f1 score of 0.9627, respectively. Show more
Keywords: Paperless environment, Optical Character Recognition, Named Entity Recognition, faster data analysis, accuracy
Vehicle health monitoring and accident avoidance system based on IoT model
Authors: Muthumanickam, Arunkumar | Balasubramanian, Gomathy | Chakrapani, Venkatesh
Abstract: The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable …weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems. Show more
Keywords: Accidents-free driving, autonomous vehicles, deep learning, fleet management, internet of things, microcontroller, sensors
Estimation of properties of ultra great workability concrete by optimized ANFIS models
Authors: Wang, Yaqin | Xu, Jing | Luo, Chen
Abstract: The mechanical properties of the ultra-great workability concrete (UGWC ) are deeply related to the weights of components, curing period and condition, and occasionally property of admixtures. This study aimed to appraise the usefulness of the adaptive neuro-fuzzy inference system (ANFIS) technique for forecasting the compressive strength of UGWC and enhancing the accuracy of the literature. To outline the forecasting process, two improved ANFIS were suggested, in which determinative variables of them were determined by metaheuristic algorithms named imperialist competitive algorithm (ICA) and multi-verse optimizer (MVO) algorithms. For this purpose, 170 data samples were collected from published literature separated …accidentally for the train and test phase. The calculated performance criteria for proposed ANFIS models demonstrate that both ICA-ANFIS and MVO-ANFIS models can result in justifiable workability for f c of the UGWC prediction procedure. The MVO-ANFIS model could outperform ICA-ANFIS regarding all criteria. For instance, the value of R 2 and VAF for the ICA-ANFIS model are roughly smaller than the MVO-ANFIS model, at 0.9012 and 90% in the training dataset and 0.8973 and 89% in the testing stage, respectively. While the best values of criteria have belonged to the MVO-ANFIS model, with R 2 at 0.937 and 0.944 for the train and test phases, respectively. Overall, the hybrid MVO-ANFIS model can obtain higher workability than ICA-ANFIS and literature (R2 at 0.801), where causes are recognized as the proposed model. Show more
Keywords: Terms— Ultra great workability concrete, compressive strength prediction, adaptive neuro-fuzzy inference system, Hybrid ANFIS
Distributed immune time-delay SEIR-S model for new power system information network virus propagation
Authors: Shao, Sijie | Li, Zhiyong
Abstract: The new power system information network has the security problem of computer virus attack, and the study of its transmission mechanism is helpful to discover the law and influence of virus transmission. In this paper, the research method of epidemic theory is introduced, and a new Susceptible-Exposed-Infectious-Recovered-Susceptible(SEIR-S) virus model is proposed. The immune time-delay parameter is introduced to simulate the evolution and mutation of the virus so that nodes immune to the virus can still be re-infected after a certain time interval. At the same time, the immune time of different nodes is different, and the distributed immune time delay …is used to enhance the authenticity of the simulated virus transmission; and considering the influence of the scale-free characteristics of the information network, this paper establishes a continuous Markov chain based on time. The transmission process of the virus, and then deduce the theoretical analysis results of the virus infection rate threshold. Based on theoretical analysis, the propagation process of the SEIR-S virus model with distributed immune time delay was simulated by using the Monte Carlo method, and the accuracy of the threshold formula of virus infection rate was verified. The influence rule of the hysteresis parameter, that is, increasing the average immune time of nodes to viruses can reduce the infection density of the network in a steady, and at the same time, making the immune time of network nodes obey a normal distribution can effectively reduce the oscillation effect of viruses on the network. Show more
Keywords: New power system, information network, computer virus, SEIR-S model, distributed immune time-delay
An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT
Authors: Sathish, S. | Kavitha, K. | Poongodi, J.
Abstract: The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing …algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. Show more
Keywords: Software defined networking, Dantzig Wolfe Decomposition algorithm, industrial internet of things networks, multi-constrained shortest path problem, centralized route optimization
A feature map aggregation network for unconstrained video face recognition
Authors: Zhang, Luyang | Wang, Huaibin | Wang, Haitao
Abstract: Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single …image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art. Show more
Keywords: Video face recognition, Aggregation, Deep convolutional neural network, Feature map
Traffic flow state prediction based on space-time correlation of vehicle trajectory using the deep hybrid model
Authors: Jun-Fang, Song | Yan, Chen
Abstract: In order to alleviate the increasingly serious traffic congestion problem in China, realize intelligent traffic control, and provide accurate and real-time traffic flow prediction data for traffic flow guidance and traffic travel, this paper designs a GPS-based vehicle trajectory fusion optimization deep model BN-LSTM-CNN which makes full use of the temporal and spatial correlation characteristics of dynamic traffic flow to improve the accuracy of short-term traffic flow prediction. The parameters of the historical GPS dynamic trajectory of the traffic network link are converted into a two-dimensional matrix image of time and space relationship. First, the spatial features are input to …the CNN network, and the spatial dependence relationship between the links is mined, then the traffic flow time series modeling is performed with a four-layer ConvLSTM network, and the BN normalization layer is added to normalize the activation value of the previous layer on each batch, so that the model can obtain higher training accuracy and quickly complete the prediction of the traffic flow state in a certain period of time in the future. The experimental results show that the prediction model is fast to optimize, the prediction error is the smallest compared with other methods, and it can meet the real-time requirements of urban traffic control. Show more
Keywords: Traffic flow state prediction, vehicle trajectory, GPS, matrix image, CNN, ConvLSTM
The fast image segmentation algorithms using dynamic programming for modals of image histograms
Authors: Jindaluang, Wattana
Abstract: A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster …than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation. Show more
Keywords: Image segmentation, histogram thresholding, dynamic programming, optimization problem, time-optimal algorithm
Role-oriented network embedding via anonymous walks
Authors: Qiu, Yutan | Zhou, Qing
Abstract: Role-oriented network embedding aims to preserve the structural similarity of nodes so that nodes with the same role stay close to each other in the embedding space. Role-oriented network embeddings have wide applications such as electronic business and scientific discovery. Anonymous walk (AW) has a powerful ability to capture structural information of nodes, but at present, there are few role-oriented network embedding methods based on AW. Our main contribution is the proposal of a new framework named REAW, which can generate the role-oriented embeddings of nodes based on anonymous walks. We first partition a number of anonymous walks starting from …a node into the representative set and the non-representative set. Then, we leverage contrastive learning techniques to learn AW embeddings. We integrate the learned AW embeddings with AW's empirical distribution to obtain the structural feature of the node, and finally we generate the node's embedding through message passing operations. Extensive experiments on real network datasets demonstrate the effectiveness of our framework in capturing the role of nodes. Show more
Keywords: Network embedding, network structure, role-oriented, anonymous walk
Towards enhancing emotion recognition via multimodal framework
Authors: Akalya devi, C. | Karthika Renuka, D. | Pooventhiran, G. | Harish, D. | Yadav, Shweta | Thirunarayan, Krishnaprasad
Abstract: Emotional AI is the next era of AI to play a major role in various fields such as entertainment, health care, self-paced online education, etc., considering clues from multiple sources. In this work, we propose a multimodal emotion recognition system extracting information from speech, motion capture, and text data. The main aim of this research is to improve the unimodal architectures to outperform the state-of-the-arts and combine them together to build a robust multi-modal fusion architecture. We developed 1D and 2D CNN-LSTM time-distributed models for speech, a hybrid CNN-LSTM model for motion capture data, and a BERT-based model for text …data to achieve state-of-the-art results, and attempted both concatenation-based decision-level fusion and Deep CCA-based feature-level fusion schemes. The proposed speech and mocap models achieve emotion recognition accuracies of 65.08% and 67.51%, respectively, and the BERT-based text model achieves an accuracy of 72.60% . The decision-level fusion approach significantly improves the accuracy of detecting emotions on the IEMOCAP and MELD datasets. This approach achieves 80.20% accuracy on IEMOCAP which is 8.61% higher than the state-of-the-art methods, and 63.52% and 61.65% in 5-class and 7-class classification on the MELD dataset which are higher than the state-of-the-arts. Show more
Keywords: Emotion recognition, time-distributed models, CNN-LSTM, BERT, DCCA
A survey of multi-class imbalanced data classification methods
Authors: Han, Meng | Li, Ang | Gao, Zhihui | Mu, Dongliang | Liu, Shujuan
Abstract: In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the …performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given. Show more
Keywords: Classification, multi-class imbalance data, data preprocessing method, algorithm-level classification method
Upper rough approximation operators of quantale-valued similarities related to fuzzy orderings
Authors: Zhang, Bei | Zhou, Chang-Jie | Yao, Wei
Abstract: Let L be a commutative unital quantale. For every L -fuzzy relation E on a nonempty set X , we define an upper rough approximation operator on L X , which is a fuzzy extension of the classical Pawlak upper rough approximation operator. We show that this operator has close relation with the subsethood operator on X . Conversely, by an L -fuzzy closure operator on X , we can easily get an L -fuzzy relation. We show that this relation can be characterized by more smooth ways. Without the help of the lower …approximation operator, L -fuzzy rough sets can still be studied by means of constructive and axiomatic approaches, and L -fuzzy similarities and L -fuzzy closure operators are one-to-one corresponding. We also show that, the L -topology induced by the upper rough approximation operator is stratified and Alexandrov. Show more
Keywords: L-fuzzy rough set, commutative unital quantale, L-fuzzy similarity, upper rough approximation operator, L-fuzzy closure operator, stratified Alexandrov L-topology
Efficient feature based video retrieval and indexing using pattern change with invariance algorithm
Authors: Namala, Vasu | Karuppusamy, S. Anbu
Abstract: The amount of audio visual content kept in networked repositories has increased dramatically in recent years. Many video hosting websites exist, such as YouTube, Metacafe, and Google Video. Currently, indexing and categorising these videos is a time-consuming task. The system either asks the user to provide tags for the videos they submit, or manual labelling is used. The aim of this research is to develop a classifier that can accurately identify videos. Every video has content that is either visual, audio, or text. Researchers categorised the videos based on any of these three variables. With the Pattern Change with Size …Invariance (PCSI) algorithm, this study provides a hybrid model that takes into account all three components of the video: audio, visual, and textual content. This study tries to classify videos into broad categories such as education, sports, movies, and amateur videos. Key feature extraction and pattern matching would be used to accomplish this. A fuzzy logic and ranking system would be used to assign the tag to the video. The proposed system is tested only on a virtual device in addition a legitimate distributed cluster for the aim of reviewing real-time performance, especially once the amount and duration of films are considerable. The efficiency of video retrieval is measured with metrics like accuracy, precision, and recall is over 99% success. Show more
Keywords: Video indexing, video retrieval, key feature extraction, pattern change with size invariance (PCSI) algorithm
A shuffled frog-leaping algorithm with Q-learning for unrelated parallel machine scheduling with additional resource and learning effect
Authors: Yi, Tian | Li, Mingbo | Lei, Deming
Abstract: Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention; however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined …as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP. Show more
Keywords: parallel machine scheduling, additional resource, learning effect, shuffled frog-leaping algorithm, reinforcement learning
FBCNN-TSA: An optimal deep learning model for banana ripening stages classification
Authors: Saranya, N. | Srinivasan, K. | Pravin Kumar, S.K.
Abstract: Ripeness of the fruit is significant in agriculture since it affects the fruit's quality and sales. Manually determining the fruit's ripeness has various drawbacks, including the fact that it consumes time, needs a lot of work, and occasionally results in errors. One of the crucial areas of the economies of nations is the agricultural sector. However, the manual approach is still occasionally used to assess the maturity of fruit. Fruit ripeness could be automatically categorized by the advancement of computer vision and machine learning technology. The Convolutional Neural Network (CNN) is used in this work is to classify the different …ripeness stages of banana fruit. The four stages of banana ripeness are unripe, mid-ripe, ripe, and overripe. Proposed method uses a fuzzy-based convolutional neural network with tunicate swarm algorithm. The proposed model outperforms cutting-edge computer vision-based algorithms in both coarse and perfectly acceptable classification of maturation phases. The experimental results using images of bananas at various stages of ripening, achieves overall accuracy of 96.9%. Show more
Keywords: Banana, ripening stages, convolutional neural network, fuzzy logic, and tunicate swarm algorithm
An IoT and Fuzzy aware e-Healthcare system using feature optimization tuned T-CNN with high dimensional data
Authors: Sudhagar, D. | Arokia Renjit, J.
Abstract: Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The …proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy. Show more
Keywords: Feature optimization, clustering, e-healthcare system, high dimensional data, internet of things
VGGNet-Cnn based classification of white blood cell leukemia with efficient salp swarm optimization algorithm
Authors: Amutha, S.
Abstract: White blood cell (WBC) leukemia is caused by an excess of leukocytes in the bone marrow, and image-based identification of malignant WBCs is important for its detection. This research describes a new hybrid technique for accurate classification of WBC leukemia. To increase the image quality, the preprocessing is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). The images are then segmented using Hidden Markov Random Fields (HMRF). To extract features from WBC images, Visual Geometry Group Network (VGGNet), a powerful Convolutional Neural Network (CNN) architecture, is used After that, an Efficient Salp Swarm Algorithm (ESSA) is used to optimize the …extracted features. The proposed method is tested on two Acute Lymphoblastic Leukemia Image Databases, yielding good accuracy of 98.1% for dataset 1 and 98.8% for dataset 2. While enhancing accuracy, the ESSA optimization picked just 1K out of 25K features retrieved with VGGNet. The combination of CNN feature extraction with ESSA feature optimization could be effective for a variety of additional image classification tasks. Show more
Keywords: WBC leukemia, VGGNet-CNN, ALLIDB, efficient scalp swarm algorithm
Preemptive multi-skill resource-constrained project scheduling of marine power equipment maintenance tasks 1
Authors: Wang, Peng | Lu, Shaojun | Cheng, Hao | Liu, Lin | Pei, Feng
Abstract: The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. …Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm. Show more
Keywords: Project scheduling, multi-skill, preemption, moth-flame optimization algorithm, ship maintenance
Fuzzy logic based nodes distributed clustering for energy efficient fault tolerance in IoT-enabled WSN
Authors: Sebastin Suresh, S. | Prabhu, V. | Parthasarathy, V.
Abstract: The Internet of Things (IoT) enabled wireless sensor network (WSN) is now widely employed in various sectors like smart city and vehicle transportation for their expanded capabilities such as data storage, access, and monitoring. The use of smart sensors that continuously collect data from the smart environment makes these possible. Furthermore, these facilitate the easy access of stored data over a secure IoT-gateway for mobile users. This device mobility that allows shifting to multiple locations, makes it challenging to route data across many access points. In this regard, it induces packet loss and improper node selection, which could result in …connection failure and network unreliability. This study proposes a new data routing protocol called as Fuzzy Logic Nodes Distributed Clustering for Energy-Efficient Fault Tolerance (F-NDC-EEFT). It can be deployed on any network platform, including mobile and non-mobile nodes. It considers performance metrics such as delivery rate, withstand node aliveness, communication delay, and energy efficiency to find an optimized path for the better performance of IoT enabled WSNs. The clustering approach is applied to the instant data load, which divides it into the distinct node groups. When proposed algorithm is tested alongside existing routing protocols for performance, it is found to save energy, minimize the number of connection failures, boost the throughput, and increase the network's lifetime. Show more
Keywords: CH eligibility, energy efficiency, fuzzy modules, energy aware routing protocol, IoT enabled WSN
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redpajama_set_name: RedPajamaCommonCrawl