Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The exact MP MRI scans attributes included for each patient are [34]: (FLAIR), T1-weighted pre-contrast (T1w), T1-weighted post-contrast (T1Gd) and T2-weighted (T2). The proposed model focuses on two tasks: segmenting tumor subregions and Radiogenomic classification of MGMT promoter methylation. Figure 1 displays mpMRIs images from the BraTS-2021 dataset. 4 The proposed methodThe proposed method will apply mining techniques like improved k-means algorithm and (CNN) Convolutional neural network for Radiogenomic Classification. predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans to train and test the proposed model to detect for the presence of MGMT promoter methylation as shown in Fig. 2. In glioblastoma patients, methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. By achieving the research results, this will help brain cancer patients receive less invasive diagnoses and treatments. The introduction of new and customized treatment strategies before surgery has the potential to improve the management, survival, and prospects of patients with brain cancer. 4. 1 Preprocessing steps: Data preprocessing step is a vital stage in segmentation that can improve the network model's efficiency. The BraTS-2021 dataset uses standardized preprocessing processes for all magnetic resonance imaging (MRI scans). Pre-processing steps included converting the NIfTI file format, co-registering using a template of normal adult human brain anatomy using an MRI-based atlas (SRI2459) and resizing to 1mm3. isotropic resolution followed by skull stripping to isolate the cerebral cortex and cerebellum from the remaining part of the brain. Preprocessing was done u001 00sing freely available tools like (CaPTk) and Segmentation (FeTS). The preprocessing channel began with data normalization the conversion of the DICOM file format to NIfTI files. The BraTS-2021 dataset has two tasks: Radiogenomic segmentation and classification. classification, the files are not co-registered. By using k-means clustering algorithm the MRI scans divided into three clusters (grade ) as shown in Fig. 3.

Output: pass


Input: Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: India takes the top spot amongst 148 countries in terms of affordability of ICT technology17. Hence, the implementation of ICT in HSM should be an easy choice. Since most companies are using cloud-based platforms, '[this helps] to reduce infrastructure cost. Scaling up becomes easier on a cloud environment'18. The HSM is also efficacious in disseminating information for health awareness, immunizations or precautions to be taken during epidemics (such as Ebola). Companies implementing the HSM have helped create and sustain thousands of rural jobs, both direct and indirect. Lessons learnedHSM is an effective mechanism that allows for greater distribution while minimizing costs when paired with ICT. There are many areas in emerging nations in Africa and where healthcare accessibility is limited. This also holds true for remote areas in advanced nations such as Sweden, Finland, Russia, Australia, Canada and the USA where there is limited access due to weather conditions and other factors. HSM paired with ICT will effectively service such regions, reducing unnecessary travel and other related costs. 'You don't need a PhD in economics to figure out this model is a lot cheaper and probably a lot more convenient for patients'10. healthcare companies such as iKure, Vaatsalya Healthcare, (Dental), are demonstrating that using HSM paired with ICT not only allows for treatment of medical conditions but also lowers costs and improves availability, making health care accessible and affordable to the nation. AcknowledgementsThe author would like to express special thanks to the management team at iKure (www. ikure. in and www. ikuretechsoft. com) (- Founder & CEO, and ), who walked him through the implementation of iKure's hub and spoke model and for sharing their company's information. 1. Govindarajan V, Ramamurti R. Delivering world-class health care, affordably. Review (Internet) 2013; 2. Available: (Accessed 26 February 2014). 2. Museum. History - in the Air, 1955. (Internet). Available: (Accessed 7 August 2015). 3. Rivett G. of the System. 1823-2015 (Internet) 2015. Available: (Accessed 2 August 2015). 4. Dawson B. Interim report on the future provision of medical and allied services. Great Britain, Ministry of Health. (Internet) 1920.

Output: corroborate


Input: Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: Our rule, on the contrary, depends on an economic principle, i. e. , the cost structure of the mining that is real economic activity. 6. 5. 5 of Bitcoin as CurrencyWe have analyzed the Bitcoin system in general and the role of mining as the proof of work. We’ve proposed an alternative to Bitcoin, (IBC), that is supposed to overcome the inherent instability of Bitcoin. But can IBC compete with major currencies issued by major central banks? In this section, we note one of many possible problems with such cryptocurrencies. Cryptocurrencies are more expensive to produce while they cannot be absorbed once produced. The production costs are hard to retrieve. Bank notes issued by the central banks require some printing and material costs. These costs are negligible compared with the face (nominal) value. Also, bank notes are reversible between new issues and absorption because the central bank basically buys and sells securities with bank notes. These points are fundamental shortfalls of cryptocurrency. As currently described, cryptocurrency values are based on associated production costs. This mechanism is similar to commodity money, notably gold and silver coins. Historically gold and silver coins have been replaced by credit (or fiat) money basically because of the above-mentioned points. Shall we choose bank notes or a cryptocurrency? There is no unconditional answer. Bitcoin-type cryptocurrencies, with some amendments, can be reasonably competitive with central bank notes in terms of value/price stability. Currency competition in the sense of θ Friedrich A. Hayek (1976) is desirable. Such competition must be encouraged, not only between central bank notes and a cryptocurrency, but also between central bank notes and among different crypto-currencies. The key differentiation of Bitcoin from central bank notes and existing digital cash-type electronic money is a framework in which all vintage information of each segment of Bitcoin is recorded. Footnote 28 Not many people are aware of this useful feature of Bitcoin. If this feature is introduced in bank note-like electronic money, each atom of bank note-like electronic money with its vintage information can reflect time value, i. e. , each note is priced differently according to the time passed since its issuance. In other words, we can provide interest with each note. This system implies that owners of bank note-like electronic money can receive interest or pay some penalty, depending on economic conditions. In the current central banking system, these benefits are transferred to the government as seigniorage.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The k-means clustering algorithm, which is an unsupervised learning algorithm, clusters each pixel in MRI intensity in which in-homogeneities can be modeled, resulting in a segmented image of an MRI with the same intensity regions and segments all three brain matters, namely grey matter, white matter, and dark matter. 4. 2 The rejection sampling algorithm: The rejection algorithm, shown in Fig. 4, was executed for each class [35]. The goal is to remove instances that do not add discrimination to Radiogenomic classification processes. To load the whole picture set for either MGMT− (Class "0") or MGMT+ (Class "1"), follow the "Datapath" of the binary class. The algorithm stops after fetching a certain number of photos. The rejection sampling removes redundant/irrelevant images from specific classes using a threshold based on the sum of pixel values (Th) [36]. After comprehensive examination, Th was discovered to be equal to zero for best results. After RA, the result includes details for each rejected file, as well as better class details from changed Datapath loading. The difference in a class's first instance count should match the rejected files count plus the final class size (after RA). The RA reduced the number of cases by 26. 75% and 27. 51% respectively. In the first stage, a 3D convolutional neural network (CNN) architecture is constructed to extract brain tumors, and the extracted tumors are fed into a pretrained CNN model for feature extraction which is shown in Fig. 5. The obtained features are sent into a correlation-based selection process, which provides the best features. For the final segmentation, the selected characteristics are validated using a feed-forward neural network. 4. 3 The second stage of the proposed model: For classification, the dataset is registered to a standard template provided by NIfTI. files. The NIfTI format cuts all related metadata, including Information (PHI), from DICOM files. Skull-stripping reduces the amount of facial reconstruction that may be used for patient face recognition. This method is based on representation learning and is independent of MRI input. To classify radio genes, mpMRIs images are preprocessed to create skull-stripped volumes. These images are then translated from NIfTI to DICOM format.

Output: pass


Input: Claim: AI technology in healthcare is universally accessible and affordable. [SEP] Text: Quantitative data shows how the program significantly reduced participants’ sense of career anxiety, improved their sense of hope for the future, and built their self-efficacy while moderately impacting their ability to set goals. This study provides insight on how to implement an institution-wide initiative centred on design thinking. Keywords: life design; design your life; coaching; design thinking; learning; empowerment; hopefulness; self-efficacy; goal setting; study. DOI: 10. 1504/IJTCS. 2025. 10066480 The thief within: a case of broken trust and employee theft by Wendy S. Becker Abstract: Small businesses often lack the resources to provide adequate human resource management systems. Management may lack experience in human resources and neglect to implement adequate control mechanisms. The case is based on a real-life small business collective in which a lack of internal policy and procedures led to insider theft. Students analyse the case using research reviewed from five disciplines clinical psychology, industrial/organisational psychology, criminology, workplace security and organisational science. The objective of the case is to improve student awareness of the importance of basic human resource management functions. Students are asked to put themselves in the managers position and respond to how to prevent future employee theft. Keywords: employee theft; human resource management; clinical psychology; industrial/organisational psychology; criminology; workplace security; organisational science; retail employer; pilferage; petty theft; grand theft. DOI: 10. 1504/IJTCS. 2025. 10066513 Larkai healthcare: enabling AI in diagnostics and making healthcare accessible and affordable with disruptive technology by Jordi Diaz, Abstract: This case study delves into the entrepreneurial careers of the founder and co-founder of Larkai Healthcare. The company’s flagship product, WREN, is a smart multiparameter portable device that seamlessly integrates measures of respiratory rate, body temperature, NIBP (Non-invasive blood pressure), and ECG (electrocardiogram) to revolutionize patient monitoring. The benefits of WREN are seen in better patient care, more accessibility to healthcare, and more efficiency. The case explores the origins of Larkai, Pritam’s product developmental history, Abhilash’s business and financial heft, and the difficulties encountered in early business when artificial intelligence was still in the realm of theory.

Output: corroborate


Input: Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: The minimum unit of BTC is not 1 BTC, but it can be divided into 1/108 units of BTC. 3. In fact, settlement is made over (multiple) part of lots that can only be identified as quantities. But we believe that this metaphor by a real estate register record captures the essence of BTC trading. 4. We will discuss this problem in detail in Sect. 6. 2. 5. Four years after January 2009 and November 2012 must be January 2013 and November 2016, respectively. The actual events seem to happen quicker than the original statement. This is due to the program that sets a reward to be halved in every 210 thousand BTC block extensions, i. e. , a mining reward is halved not by calendar date, but by the block extension numbers. In Sect. 6. 2, the meaning of block extension is fully explained. 6. According to Nakamoto (2008), the system is supposed to authenticate the longest blockchain. In practice, however, the chain whose “total difficulty”, which is the sum of difficulties to win the mathematical lottery associated with each block in the chain, is the greatest (therefore usually the longest chain) and prevails. 7. Eyal and Sirer (2013) illustrates that Bitcoin’s mining algorithm is not incentive-compatible, and that the Bitcoin ecosystem is open to manipulation, and potential takeover, by miners seeking to maximize their rewards. It points out that collective miners having more than as little as 33% of the total computational power (instead of widely believed 50%) can cheat the system with selfish mining and earn more than their fair share. 8. For example, in case of ten-trillion-yen deposits by 1,000 million people, although it is possible to keep the ownership records of each yen, it may require very large computational and maintenance costs. Design of such a system is far more complex than a bank account-type of record keeping. 9. A hash value is the value returned by a hash function that maps data of arbitrary size to data of fixed size. A cryptographic hash function is a one-way hash function, so that it is practically impossible to recreate the input data from its hash value. The same hash value will always result from the same data, but modifying the data by even one bit will completely change the hash value. In this 6. 2. 10.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Finally, a two-step data deidentification process is performed, which includes the use of the (Processor) anonymizer and the authorization of these result DICOM files. This removes superfluous tags, including PHI entries, from DICOM headers. Radiogenomic images are classified as "0" or "1" based on the presence of MGMT promoter methylation status. T1W, T12W and segmentation result may be combined in either class based on annotation by expert radiologists and pathologists. To speed up training on a large dataset, images are transformed from DICOM (12 bits) to jpg (8 bits) format and scaled from 512×512 to 64×64. This also addresses memory difficulties with an average-price GPU-based portable system. The rejection algorithm has been run for each class. The goal is to remove instances that do not add discrimination to Radiogenomic classification processes. To load the whole picture set for either MGMT− (Class "0") or MGMT+ (Class "1"), follow the "Datapath" of the binary class. The algorithm stops after retrieving a certain number of photos. The rejection algorithm eliminates redundant or irrelevant photos from a specific class using a threshold based on the sum of pixel values (Th). After comprehensive examination, Th was discovered to be equivalent to zero for optimal results. After RA, the result includes details for each rejected file, as well as better class details from changed data path loading. The difference in a class's initial instance count should equal the rejected files count plus the final class size (after RA). Data volume augmentation is employed to increase model resilience and prevent overfitting. tumor segmentation tasks often involve data augmentation methods such as flip, rotations, random noise, scaling, elastic deformations, and the mixed data (mix-up) method (46,47). The SoftMax function adjusts the outputs of a convolutional neural network (CNN) so that they sum to one. This means that the model output values are scaled to represent relative "probabilities" that the network predicted a specific category. The SoftMax activation produces one value for each node in the output layer. The output values will be probabilities that sum to 1. 0. When modeling a multi-class classification problem, the data must first be prepared.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: On the other hand, since Bitcoin is a decentralized technology, it was warmly adopted by anarchist organizations worldwide [1, 2, 3], but for similar reasons, it was attacked by many others [4, 5, 6, 7, 8, 9]. Some attacked the Bitcoin from the fear of shaking the current centers of powers, but most attacked it out of ignorance. Strangely enough, even in the libertarian community, which in general embraced the new currency, there are some that used allegedly Austrian economics arguments to debunk the foundation of the Bitcoin economy [10, 11, 12, 13, 14]. In general, we encounter two strategies to attack Bitcoin: (1) presenting multiple minor or even clearly erroneous arguments. Consequently, the arguments keep reappearing, despite the fact that they are constantly refuted. And (2) using fundamental economic laws to allegedly demonstrate that Bitcoin does not possess the essential properties of money. The object of the 2. a Ponzi scheme? The most common criticism against Bitcoin is that Bitcoin is actually a Ponzi scheme [7, 10]. Clearly, there is no resemblance between the two. A Ponzi scheme is a fraudulent investment operation. The investors are made to believe that they gain from the investment operation, while in fact, the money comes from new investment, that is, a Ponzi scheme is a pyramid fraud. While some cryptocurrencies or tokens do seem to be a Ponzi scheme, Bitcoin is definitely not. Everything about Bitcoin: its algorithm, its network, and its development projects are completely transparent. The network, the mining process, and the entire project are all decentralized. There are no managers, no organizers, and no control. Therefore, there could be no fraud. Bitcoin does not even have a pyramid structure. An investor in Bitcoin can make a profit by selling it at a higher price, just like in any other trade. Unlike pyramid structures, the owner of Bitcoins does not have to convince multiple people to invest in order to make a profit. It is true that in both cases, the first investors gain more than the last ones, and that their profit rises with the number of traders. However, by the law of demand, it is clear that the price rises with the increase in demand, that is, the price increases with the number of buyers. This is valid for any commodity, and Bitcoin is no exception. This is definitely not a pyramid structure’s fingerprint.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Initially, address the constant value ( ∝) of the model as, (1) Fa0=Argmin∑k=1KL(bk,∝) The number of n iterations belongs to [1−N]. Then computes the gradient direction of residuals as follows, (2) Rnk=−[∂L(bk,F(ak))∂(ak)]Fa=Fan−1 The essential learner h is utilized to fit sample data and get the initial model. According to the least square approach, the parameter βin of the model is obtained as, (3) βin=Argmin∑k=1K(b∗k−∝) The loss function is minimized. According to Eq. (4), a new step size of the model, namely the current model weight, is calculated. (4) ∝in=∑k=1KL(bk,Fakn−1+∝) The updated model is as follows: (5) Fa0=Fakn−1+V∑i=1In∝inl(a∈rin) When the raw data has limited dimension and size as input, the feature branch points information must be computed multiple times. Using an excellent learning rate will make optimization easier (Sprangers, Schelter & de Rijke, 2021). Still, it is usually supposed to be a smaller value, such as 0. 1. This shows how the framework of gradient boost predicts complex targets, such as finding the disease or syndrome of the patient with the help of the sensors with IoT (Velthoen et al. , 2021). Blockchain for healthcare As the digitalization process is disseminated speedaily, the attacks of cyber security and data breaches have increased; thus, information about patients becomes more complicated than before, so the highest standard of privacy for data is more critical in the healthcare system. In case security is breached, the individuals whose health information was inappropriately accessed face several potential harms (Fu et al. , 2023). Thus, to increase the system’s safety, the authentication of data using blockchain technology is incorporated into this system.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The target variable containing the class labels is first label encoded, which means that each class label is assigned an integer ranging from zero to N-1, where N represents the number of class labels. The labeled (or integer-encoded) target variables are then one-hot encoded. This is a probabilistic representation of the class label, like the SoftMax output. A vector is generated with a position for each class label and position. All values are denoted as 0 (unmethylated MGMT), with a 1 (methylated MGMT) indicating the position of the class label. 4. 4 : After building the proposed deep learning model, the performance of this model should be measured by different accuracy measures to evaluate it [37]. In this paper, there are two models(clustering model and classification model), so evaluation of their performance was measured by using these measures shown in the following equations (4, 5, 6, 7) and MCC (Matthew’s correlation coefficient)equation [38].

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Bitcoin is a chimera, in that regard, since it is both digital, that is, intangible, and scarce. In fact, the essence of the blockchain technology is the creation of scarce digital assets. That is, the blockchain creates artificial scarcity. Therefore, despite the complex philosophical issue, I believe there is a consensus that Bitcoins can be owned, and property rights can and should be applied to it. There is a claim, however, that Bitcoin advocates ignore traditional property rights, contracts, and traditional legal systems, which were developed and refined for millennia [12]. It is true that currently, it is easier to evade law enforcement using cryptocurrencies than using most bank transactions. The facts that there is nothing tangible in the Bitcoin network and that digital transactions can take place without the regulated banking system complicate law enforcement. However, on the one hand, it can be regarded as a technical difficulty, which may be solved using artificial intelligence technologies, and on the other hand, one may argue that these properties of the Bitcoin network only strengthen property rights, because they prevent government confiscation of private property (private money). Not only aren’t crypto networks against contracts, but also smart contracts are an integral part of these networks. The most well-known is the Ethereum network, but there are many more cryptocurrencies with this property (like Neo, EOS, Cardano, etc. ), and even the Bitcoin network can support smart contracts (Mastercoin, Rootstock, Particl, etc. ). Therefore, the blockchain technology eliminates the need for (external) law enforcement in some cases, because the contracts are enforced within the blockchain itself. In general, cryptocurrencies strengthen private property and property rights. They do not weaken them. 17. Bitcoin and crime Some politicians and economists believe that Bitcoin and other cryptocurrencies have no value to society since most of their users are outlaws and criminals [28]. For one thing, one has to make a distinction between the different cryptocurrencies. Most of the criminal activity was transferred from the Bitcoin network to the more private cryptos (such as Monero, Zcash, Verge, etc. ) since transactions in the Bitcoin network can be traced. Moreover, most of the illegal trade that takes place on the Internet belongs to the category of victimless crimes (like drugs, gambling, and tax evasion), whose prohibition is disputed.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Some features for authentication using blockchain, namely the decryption keys and signing keys on the device, encryption and verification keys, are stored on a blockchain, and it protects data against cyber-attacks (Le et al. , 2021). For an authorised individual to participate in the review process, the user’s identity is necessary. Usually, the authentication is based on the user id and password. Still, blockchain uses an infrastructure of public keys and smart contracts as authentication processes based on certificates, as illustrated in Fig. 3. This removes human factors from the authentication process. These certificates were formulated using a key size higher than a size of a password, as 2,048 bits. These certificates contain the expiry date to reduce the risk of long-drawn-out exposure. Blockchain technology can leverage smart contracts to validate user certificates and effectively mitigate the risk of a single point of failure (Zou et al. , 2021). Figure 3: Proposed authentication process using blockchain technology. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-3 Thus, to restrict access to a particular function, a method of authorization is considered to mitigate the risk of information disclosure for unauthorized users. The restriction of help to users is based on the distributed list of points: action, object, contextual, and subject. The action attribute consists of activities that allow users to perform, like retrieving data and updating records. The object attribute is used to implement which type of object can be accessed by a user or doctor that is medical data related to a particular department (Diao, Wang & Gong, 2022). At last, the subject attributes denote a user’s request for data access. These attribute types are encoded in a smart contract and shared across the blockchain network. For recording each action, a user identity is used on the network of the blockchain method. A transaction is used to add, update, or retrieve data from a database. This is also used for sharing information between authorized users. An administrator or doctor creates the flow of information in the authentication-based blockchain method and system-based health records and revokes digital certificates (Zhou et al. , 2022). A CRL gets generated while revoking a user’s digital certificates. The peers in the blockchain network receive a CRL update command to update the CRL stored on the peers. The users who require access can authenticate with healthcare records by giving certificates to the system.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: All the experiments have been conducted using open-source libraries, and the standard programming tools used to tune the proposed system using Google Co-lab environment (CPU : Intel(R) Xeon(R) CPU @ 2. 20GHz,GPU : T4 16GB, RAM : 16 GB. In most deep learning projects, the training and validation losses are shown simultaneously on a graph. The goal of this is to diagnose the model's performance and determine which aspects require adjustment. In Fig. 6 below, the training loss and validation loss both drop and stabilize at a certain point. This represents an optimal fit, that is, a model that does not overfit or underfit. A confusion matrix is essential for evaluating the performance of a classification model. It provides a comprehensive examination of true positive, true negative, false positive, and false negative predictions, allowing for a more in-depth understanding of a model's recall, accuracy, precision, and overall effectiveness in class differentiation. When there is an uneven class distribution in a dataset, this matrix is extremely useful for assessing a model's performance beyond simple accuracy measurements. As shown in Fig. 7 the confusion matrix for the experiment. Matrix was built with four distinct quadrants: (Quadrant), (Quadrant), (Quadrant), (Quadrant). True shows that the numbers were accurately anticipated, whereas False indicates that there was a mistake or incorrect forecast. Now that we have created a Confusion Matrix, we may use various methods to assess the model's quality. First, let us look at accuracy. After using the rejection algorithm, the accuracy results of the experiment are improved with different metrics. The following figures show the improvement in the results. First, Fig. 8 shows the improvement in the accuracy result. Another typical statistic is AUC, or area under the receiver operating characteristic (ROC) curve. The receiver operating characteristic curve compares the true positive (TP) and false positive (FP) rates at various classification levels. Because AUC is a metric that uses the probabilities of class predictions, we can be more confident in a model with a higher AUC score than one with a lower score, even if their accuracies are comparable. In Fig. 9, it shows the enhancement in area under the curve result before and after applying the rejection algorithm which increases from 0. 94 to 0. 99. The F1 score is a machine learning evaluation metric that finds a model's accuracy.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Bitcoin is not the only thing that can be sent via the blockchain, in fact, almost any kind of information can be sent via the transactions, and this is an irreversible process, since any such information will remain in the blockchain for eternity, and everyone in the network will have an access to this information. Bitcoins are the cost of perpetuating the information. In other words, Bitcoins can be regarded as the “real estate” of the blockchain. The more Bitcoins a person has, the more information he can send on the blockchain. There are all kinds of data encapsulated on the blockchain: poems, prays, political statements, commercials, and even photos [36]. In fact, it is amazing how many kinds of information can be found there. It is not difficult to find many applications for this kind of information retention. One of the special applications is the announcement of contracts. Such contracts will be confirmed by the blockchain. Or another important application is a decentralized registrar of real estate (or any other type of property). With the Bitcoin network, one can announce ownership of objects, the information will be available instantaneously, and since it is not centralized, then there is no fear of losing this information. The important thing is to realize that there is no direct connection between this information and the value of Bitcoin. The relation is indirect. The value of these announcements is directly related to the number of nodes in the network. The more people are connected to the Bitcoin network, the more valuable this information is. Therefore, if the network is large enough, then people will like to use it by sending and announcing information. However, it is clear that when the network grows, sending information becomes more valuable, and therefore the value of the network’s coin, which controls it, increases as well. The more coins a person owns, the more he controls the network. Again we see that owning Bitcoin is like owning real estate on the blockchain. Clearly, at its inception, when the Bitcoin network was small, the value of publishing data over it was low, but then the Bitcoin’s value was accordingly small. In fact, in its genesis, the Bitcoin’s value could have been only the subjective value of the first few geeks, who mined it (including Satoshi himself). When the network grows, the Bitcoin value increases exponentially, just like the laser power.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Figure 4 shows the centralized approval architecture with distributed blockchain method for revokes digital certificates from the distributed blockchain method. These certificates are then shared with users and received by peers in the network; they are established by running a smart contract that validates the credentials and returns a response. When the above-stated conditions are valid, the system allows access to the user; otherwise, it denies access to users (Aljuhani et al. , 2022). The privacy preservation of hospital data uses blockchain technology that secures and maintains the hospital data as a ledger. This is referred to as a distributed database; a group of individual people updates that. In this technology, each user or device is named a node. The user is a patient, the wearable devices send the data to the peer network such as the hospital node, and these electronic health records of a patient data are immutable. When updating the record initially, it needs a user for the process. Figure 4: Centralized approval architecture with distributed blockchain method. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-4 Data structures created by blockchain technology include built-in security features. It eliminates duplication, streamlines procedures, eases the load of audits, boosts security, and guarantees data integrity. It keeps a secure, decentralised log of cryptocurrency transactions. It has a distinctive and secure digital identification reference that enables the distribution and recording of digital information without its editing. From above figure a new distributed blockchain aspect diagram concerning the healthcare system and respective public keys smart contracts with 2,048 bits for revokes digital certificates. It will maintain the adequate security verification of the hospital data using privacy preservation of hospital data based on blockchain technology. This will be illustrated in the following and algorithms. Pseudocode for the security verification of the hospital data using privacy preservation of hospital data based on blockchain Initially, the sensor collects data from a patient Data. Store→. Store→blockchain () For each user Create digital certificate revokes. Certificate→(CA) certificate authority generate URL end for for each user authentication validate certificate user→peer authenticates if (CA sign present in the certificate) && (user present in CRL) allow user access else refuse user access end if end for Output: allow or refuse the user access DOI: 10. 7717/peerj-cs. 1308/table-5 The permissioned blockchain technology needs authentication access to verify certificates and intelligent contracts.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: It combines a model's precision and recall scores. The F1 score uses the harmonic mean to combine accuracy and memory, and maximizing the F1 score means maximizing both precision and recall at the same time. As a result, researchers have adopted the F1 score as their preferred method for evaluating their models alongside accuracy. Figure 10 shows us the f1 score result before and after using the rejection algorithm in the proposed model. The result is increased from 0. 95 to 0. 99 after using the rejection algorithm. In machine learning, precision score refers to the fraction of positively predicted labels that are correct. Precision is often referred to as the positive predictive value. Precision is mostly used for predicting the positive class, and false positives have a higher cost than false negatives. in Fig. 11, it shows the change in the precision result for the proposed model before and after applying the rejection algorithm. correlation coefficient (MCC) is a statistic that can be used to evaluate the effectiveness of classification models. This statistic is especially useful when the two classes are unbalanced, meaning that one class appears significantly more than the other. Figure 12 shows the comparison between the result of MCC before and after using the rejection algorithm. Table 1 below shows the comparison results by different accuracy metrics before and after applying the rejection algorithm. Table 1 comparison results before and after applied rejection algorithm Accuracy metric Result before rejection algorithm Result after rejection algorithm Accuracy 0. 95 0. 9913 F1 score 0. 95 0. 99 Precision 0. 95 0. 99 MCC 0. 90 0. 99 AUC 0. 94 0. 99 2 below discuss the results for the experiment (precision, recall, f1 score and the test accuracy) Table 2 The final experiment result: f-1 score 1. 00 0. 99 0. 99 19314 methylated MGMT 0. 99 1. 00 0. 99 20988 Accuracy 0. 9913 40302 Macro avg 0. 99 0. 99 0. 99 40302 Weighted avg 0. 99 0. 99 0. 99 40302 Test accuracy = 99. 13 After ending the experiment on python, Table 3 below discusses the Deep learning parameters used in proposed deep learning module. Table 3 Deep learning parameters used in proposed module. DL parameters Optimal values 0. 0001 Momentum 0. 99 0.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Dori decides to transfer $100 to Aris. In the physical world, it’s a straightforward exchange: Dori now has no money, and Aris has $100. Simple, right? ! But let us imagine the following situation:In the digital scenario, Dori’s transfer of a photo representing $100 to Aris introduces a unique challenge. Unlike the physical transfer of money, the digital photo is duplicable, leading to questions about its ownership and potential misuse. retain a copy of the photo, share it with additional friends, or even post it publicly on social media where it could be freely downloaded? The inherent replicability of digital assets like these challenges the traditional notion of ownership and transfer, highlighting the need for a secure and trustworthy system to manage digital transactions. Blockchain technology, with its decentralized and tamper-resistant characteristics, aims to address such issues in the digital realm. Digital LedgerTo address the questions raised earlier, we rely on the concept of a digital ledger. The key is to have a record of the quantity of $100 digital photos owned by Dori and how she distributes them among her friends. However, it’s not Dori’s responsibility to maintain these records. In the contemporary landscape of digital banking transactions, this record-keeping role is assumed by the bank. The bank, subject to regulatory oversight, ensures transparency about the funds Dori possesses and how she utilizes them. In this context, the bank functions as a third-party entity, independent and responsible for maintaining accurate records. Nonetheless, it’s crucial to remember that digital banking transactions are underpinned by actual, physical banknotes. The quantity and issuance of these banknotes are regulated by the respective central bank. Here, we return to the initial point: the bank resolved the issue of digitally distributing physical banknotes. However, in the second scenario, Dori possesses a digital photo of a banknote. Questions arise: Who captured that photo? How did Dori obtain it? What are her intentions for spending it? Furthermore, uncertainties linger regarding the total number of such digital photos and whether they hold genuine value. Blockchain digital ledger conceptBlockchain provides a straightforward solution to the scenario involving the digital photo on Dori’s phone and the question of record-keeping.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Systems can identify user access and restrict unauthorized access to system functions (Abyaneh, Zorba & Hamdaoui, 2022). Generally, this research system uses password authentication, but this research system is replacing this authentication with certificate-based authentication. This certificate-based authentication removes human interference during the process of authentication. These certificates are created with a critical size of 2,048-bit, which is larger than the size of an average password and includes the expiration date, reducing the risk of prolonged data exposure (Beniiche, Rostami & Maier, 2022). Blockchain technology has the advantage of smart contracts that validate the user certificates and can efficaciously mitigate the risk of a single point of failure. The administration creates digital certificates to maintain the electronic health record (EHR) system, and the authority revokes issues in the certificates. The certificate revocation list (CRL) is then generated among all network peers (Park et al. , 2023). Before that, the system needs to authenticate by digital certificates (Khan et al. , 2022). The certificate is passed to the client, which is then sent to a peer in the network. The peer authenticates the user by running the smart contract, which contains information about the user/doctors, action attribute, object attributes, and contextual and subject attributes (Xu et al. , 2021). That validates the certificate and returns a response. This response influences the authentication condition of the user’s access as allowing or refusing. Algorithm for improving the data transmission method for wireless transmission Input: Triangles (G), L-link, R-result Output: Predict the path Require: G = (V, E) Load(G) Triangles = (G) return null initialize: N = size (Triangles), k= 1 While (k ≤ N) New_L= Predict (Triangles) [i] For each_L ∈ New_L If (R_contains L) R [L]++ = R + L increment k R = : 10. 7717/peerj-cs. 1308/table-6 Tracking the location of a threatening person Medical records can be misused unless coded to hide patients’ identifying information. If a patient has been treated for a particular disease and their medical records are not held in confidence might be insecure.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 0001 1 Channels 64 Channels 128 Channels 256 3 × 3 Stride 1 Padding Conv1 & Conv2: 1, Conv3: 0 (Note: Adjusted in forward pass) 2 × 2 2 After Conv-2 (128 Output Channels) (%) 20 7 × 7 × 256 512 512 64 512 + 64 2 ReLU (Unit) Comparison results of the literature survey research: In this part, we discuss and analyze the last results of the literature survey papers which used the same dataset with the results of the proposed model as shown in Table 4. Table 4 Comparison results of the literature survey research: Name of researcher Date of publishing Accuracy rate Used technique et al. [33] June 2022 accuracy = 0. 91 residual network (ResNet) et al. [32] September 2022 Accuracy = 0. 93 Deep learning-based U-Net framework et al. [31] June 2022 Accuracy = 0. 993 machine-learning (ML) models Do et al. [30] august 2022 Accuracy = 0. 925 an Boosting (XGBoost) Qureshi1 et al. [29] 2023 Accuracy = 97. 44% Extraction (DLRFE) module et al. [28] April 2023 mean AUC = 0. 69 Deep learning algorithms et al. [27] November 2023 F1 score = 96. 1%. using automated segmentation algorithms et al. [26] December 2023 Dice value = 86. 6 the Model (SAM) and a voting network Anton François et al. [25] January 2024 Accuracy = 0. 94 Machine learning approach et al. [24] January 2024 Accuracy = 0. 6174 Deep learning approach (the Exception model) Emad dawood et al. 2024 Accuracy = 99. 13% deep convolutional neural network (DCNN) 6 ConclusionOur research proposes MGMT-PMP, a novel classification approach that combines imaging and genomics to predict Radiogenomic classes based on genetic variation and radiation sensitivity. This paper proposes a fine-tuned CNN architecture, the DLRFE module, for extracting latent features from mpMRIs scans. The module captures quantitative knowledge about the spatial distribution and size of tumors. Finally, a dynamic feature vector is created and used for Radiogenomic classification. The rejection algorithm effectively selects and outlines instances from the main dataset.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Rather than relying on a third party like a bank, trusted friend, or notary, blockchain proposes that the record should be decentralized and shared with everyone interested in sharing digital photos of banknotes through their mobile phones or computers. In contrast to a centralized model, blockchain introduces a distributed approach. Rather than having a centralized ledger tracking the number of digital photos of $100 on Dori’s phone or computer, this information is dispersed among the phones and computers of all participants involved. This includes not only Dori but also Aris and others engaged in exchanging digital photos for $100 via mobile phones or computers. The blockchain diligently records and disseminates this information to everyone. shares her unique digital photo of $100 with Aris, the blockchain accurately registers an “outflow” from Dori and an “inflow” to Aris, ensuring verification and distribution across the network. Attempting to send the same digital photo to 100 more friends becomes impractical since the blockchain records of all others are not synchronized. In their records, Dori no longer owns a digital photo of $100, leading to the rejection of her attempted transaction. Consequently, Dori no longer possesses the digital photo, while Aris does. 2. 1 Distributed and traditional databaseBlockchain operates as a decentralized database, recording a comprehensive audit trail of all transactions. Unlike traditional databases, it is managed by a network of computers, with no single computer holding sole responsibility for storage or administration. This ensures that any computer can join or leave the network without compromising data integrity or availability. The database can be restored from scratch by any computer, simply by downloading it from the network and processing the audit trail. In contrast, traditional databases are typically controlled by a single organization with complete authority, including the ability to manipulate data. While this may not pose issues in certain contexts, the sensitive nature of financial transactions creates a risk of data manipulation and falsification. To counter this, banks undergo continuous audit and regulatory scrutiny, facing persistent threats from external malicious hackers. Blockchain addresses these challenges by allowing the database to be managed by a distributed network, making it publicly accessible to everyone. This open model enables anyone to create a redundant copy of the database and verify it against other copies. However, this approach is best suited for static data that does not change over time. For dynamic data, a consensus problem arises when changes are needed after the database has been distributed.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Therefore, these records are secured using a piece of code that is difficult to access by unauthorized people. Although the information is confirmed, there might be a chance to steal medical data with personal information by a trusted person who knows a private key or by a hacker who can break the security code and access the data illegally to make money or for some other reasons by threatening patients (Akter et al. , 2022). They protect data whenever information is accessed illegally or when a patient is intimate about any threatening call, message, or another. There is a chance of doctors getting threatened as the details of doctors are also stored in the medical record based on their treatments and so on (Kumar et al. , 2022). When a threat is reported, management tracks a person’s location using hybrid microwave transmission, which combines millimeter-wave and microwave. The millimeter wave is a spectrum band with a wavelength between 10 to 1 mm with a highly high-frequency band. Thus, millimeter waves can hardly be used for long-distance applications. A microwave uses a beam of radio waves in the microwave frequency range to transmit data between two locations which can be located within a few feet or meters or miles or kilometers. It also has higher bandwidth and a limited line of sight mode (Hsiao & Sung, 2022). Both are used in communication, but millimeter wave has a higher frequency range than microwaves (Wan et al. , 2022). Thus by combining these wave transmission methods, the distance covered, line of sight, amount of data carried from sender to receiver, and speed of data transmission can be effectively increased; this is called hybrid microwave transmission. In this system, hybrid microwave transmission tracks a person at the time of the call. If a person is not disturbed through the ring, it might be through a message or any other method. Considering it, a person can be tracked faster to a considerable distance (Althubiti et al. , 2022). To find the location, this research uses a MapQuest open-source mapping service connected to a tracking system to view the exact location of a threatening person. It provides street details and directions for different countries (Deng et al. , 2022). The user is provided access to a map of any place in the world.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: A hybrid feature collection (HFS) is created by fusing latent and radiomic data. by applying the proposed model on mpMRIs scans yielded 99. 13% test classification. Using the BraTS-2021 dataset, we compared the proposed approach to other brain tumor classifiers and found that rejection algorithm significantly improved Radiogenomic classification performance. Results show that combining dynamic and static features with HFS improves classification performance compared to using separate features. This research study has wide-ranging ramifications. This study connects genetic variation with radiomic-based data characterization techniques, bridging the gap between two distinct areas of research. The second idea is to use it in real-time brain tumor surgery to remove leftover tumor cells by chemotherapy instead of radiotherapy. Third, the proposed technology offers a portable and cost-effective alternative to dedicated machines for brain surgery. This study has significant implications for the clinical management and survival of GB patients. Declarationswork was conducted in partial fulfillment of the requirements for a Ph. D. in information systems and was supported by the University of Zagazig at Egypt. Funding is subject to STDF/EKB’s review of eligibility and approval. Under the agreement between with the Science, Technology and Authority and the Al-Publishing (OA - Access Open) which aims to support the scientific research system through funding (OA) publishing (for researchers in the of Egypt). Zagazig university is one of the shared institutions in this agreement. Author ContributionAll authors reviewed the manuscript, wrote the main manuscript text and prepared all figures and tables10. 15379/ijmst. v10i5. 2506 Lodzinski, Paulina, Marek G. Beby, Jacek Furtak, Janusz Kowalewski, and Marzena A. Lewandowska. 2021. "Prognostic and in Gliomas" of 22, no. 19: 10373. surgery for glioblastoma: Recent advances and modern challenges, Neuro-Oncology Practice, Issue 5, October 2022, Pages 364–379, Hanif, F. , Muzaffar, K. , Perveen, K. , Malhi, S. M. , & Sime, S. hU. (2017). Glioblastoma Multiforme: A Review of its Epidemiology and Pathogenesis through and Treatment.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Determining the validity, permission, and order of changes becomes crucial. Allowing any entity with a copy of the database to make changes could lead to desynchronization, and a consensus must be reached on which database reflects the true state. Introducing an entity with the privilege to make changes first, followed by others copying the database, attempts to address this issue. However, this raises concerns of potential data manipulation and censorship by the chosen entity for personal gain. A solution could involve a rotational system for managing entities and copying the “correct” state of the database. Yet, determining the order and process of changing controlling entities remains a question [4]. 2. 2 Blockchain: the final solutionBlockchain technology addresses these challenges by establishing a network of computers, known as nodes, each of which possesses a copy of the database and adheres to a set of rules defined as a consensus protocol. The consensus protocol outlines the sequence in which nodes can introduce changes to the database, ensuring unanimous agreement among all network nodes regarding the database’s state. This structure prevents any single entity from having the authority to manipulate or censor transactions. Moreover, the blockchain includes an immutable audit trail documenting all modifications to the database. This feature enables all controlling entities to scrutinize and validate the accuracy of the database. The audit trail comprises individual changes referred to as transactions. A set of transactions added to the database by a single node constitutes a “block. ” Each block contains a reference to the preceding block in the blockchain, establishing a chronological order [4]. The blockchain, essentially a chain of blocks, interconnects each block with the previous one and includes a list of new transactions occurring after the preceding block. When a new node joins the network, it initiates an empty database and retrieves all previous blocks from the network. This process ensures alignment with the databases of all other nodes. Fundamentally, blockchain orchestrates the sequence of transactions within the database, enabling anyone to verify its accuracy by reconstructing it from the beginning. 2. 3 Blockchain in actionBlockchain technology finds widespread application in Bitcoin, a prominent example of its utilization. Bitcoin operates as a digital cryptocurrency, employing blockchain for monitoring transactions and issuing digital currency units. When an individual intends to spend Bitcoins, they generate a transaction specifying the amount and recipient. This transaction is digitally signed and disseminated to nodes within the Bitcoin network.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Through a gas price feature, the user can find information about a particular location because the person who threatens a patient might be in any of the locations, and it also allows one to find a person’s current location. The link prediction method improves a data transmission’s efficiency (Li et al. , 2022). It is used to route the link prediction based on the behavior of a mobile node. During the person’s movement, various nodes might meet neighboring nodes. When the node moves, the path’s weight increases from the source node to the information interaction node. The weight increases are directly proportional to the quality and amount of data obtained. The weight depicts the coordination between nodes in the social process that enables other nodes to find the optimal path to get information faster and to track the transmission target node as quickly as possible to complete the community structure reconfiguration (Yang et al. , 2022). In the social graph, the probability of a mobile node i from the source node j to interaction node k, as shown below path prediction (Pr) equation, (6) Prijk=WβjmCαjm∑mϵMji(WβjmCαjm) It indicates where the Wβjm denotes the weight of the path and Cαjm The cost of moving from node j to k corresponds to the coefficient of basic parameters. Mji denotes the critical points between nodes i to j. This model assumes that all edges in the social network graph have initial weights. The optimal solution is that the target node must obtain information (Zhou et al. , 2022). The link where people receive information will increase the weight, as shown in the below equations (7) ΔWijk=1Ciwhere,(j,k∈Li) (8) ΔWijk=0,otherwise (9) Wjk=Wjk+∑i=1tΔdenotes the path traversed by the mobile device that from the information source, the length represented as Ci. Where t is the total count of mobile nodes, which indicates the search for the shortest path from the starting point to the information source. Assuming that each edge’s weight will gradually decrease, the more the path is traversed, the greater the weight. Then analyze the reduction mechanism of path weights, as shown in the below equation.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: journal of cancer prevention: APJCP, 18(1), 3–9. Louis, D. N. , Perry, A. , Wesseling, P. , Brat, D. J. , Cree, I. A. , Figarella-Branger, D. , Hawkins, C. ,. , Soffits, R. , von Deimling, A. , & Ellison, D. W. (2021). The 2021 WHO Classification of Tumors of the System: a summary. Neuro-oncology, 23(8), 1231–1251. Ribeiro, I. P. , Melo, J. B. , & Carreira, I. M. (2019). Cytogenetics and in Cancer. International journal of molecular sciences, 20(19), 4711. Yu, W. , Zhang, L. , Wei, Q. , & Shao, A. (2020). O6-Methylguanine-DNA Methyltransferase (MGMT): Challenges and in Glioma Chemotherapy. Frontiers in oncology, 9, 1547. Yu, W. , Zhang, L. , Wei, Q. , & Shao, A. (2020). O6-Methylguanine-DNA Methyltransferase (MGMT): Challenges and in Glioma Chemotherapy. Frontiers in oncology, 9, 1547. Qureshi, S. A. , Hussain, L. , Ibrar, U. , Alabdulkreem, E. ,. , Mohammed, G. P. , & Duong, T. Q. (2023). Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. Scientific reports, 13(1), 3291. Yadav, S. S. , Jadhav, S. M. Deep convolutional neural network based medical image classification for disease diagnosis. J 6, 113 (2019). Taye, M. M. Understanding of with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers2023, 12, 91. Sarvamangala, D. R. , & Kulkarni, R. V. (2022).

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Upon the creation of the next block in the transaction chain by one of the nodes, it verifies the validity of the new transaction. If deemed valid, the transaction is included in the block, which, as part of the replicated chain, is distributed across all nodes in the network. This process effectively records an outflow from the sender and an inflow to the recipient [5]. 2. 4 Blockchain consensus protocolThe primary function of a consensus protocol is to establish rules governing the addition of blocks to the chain in terms of order and timing. This is crucial for a blockchain to maintain a consistent and unalterable sequence of events, universally agreed upon by all nodes, representing the current state of the database. Furthermore, this event sequence should be immune to censorship, ensuring that no single node possesses unilateral control over the admission of information. Currently, there are two main types of consensus protocols in use [6]:2. 4. 1 Proof of work (PoW)Proof of Work (PoW) serves as the original consensus protocol within the Bitcoin network. This protocol relies on solving intricate logic puzzles that are challenging to solve but easy to verify once completed. It can be likened to assembling a complex jigsaw puzzle with a multitude of tiny pieces — a formidable task to piece together, but a quick glance can confirm if the picture is complete and accurate. In the PoW protocol, the exertion involved in solving the puzzle is termed “Work,” and the puzzle solution is the “Proof of Work. ” Essentially, the ability to verify the correctness of the solution confirms that someone has performed the work accurately. A blockchain utilizing a PoW consensus protocol requires proof for each block added to the chain. As a reward for their efforts, the node receives a specific number of digital tokens, characteristic of the network. In the case of the Bitcoin network, this reward is in the form of Bitcoins, constituting the initial generation of these digital tokens. All circulating Bitcoins and those to be released in the future are generated as a reward for the work executed by the nodes in the block generation process, commonly known as “mining. ” The PoW protocol inherently values the chain with the most blocks as the current “correct” chain, given the greater cumulative work invested in it. Considered highly secure, the PoW protocol presents a significant challenge for any attempt to overwrite or forge a block.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: (10) Wjk=(1−p)Eq. (1), the mobile devices are moving from node j to node k from the equation (11) Prijk=(1Wjk)β∑mϵMji(1Wjk)βwhere wjk is the weight of the corresponding path, and the coefficient β is the primary parameter of the model, which is set to 1. Comparing Eq. (6) can see that α = 0, which is inversely proportional to the weight value. If the current memory module is filled, it will be covered one by one according to the order of the arrow’s direction on the way. Additionally, the accuracy of each node is confirmed each time this research acquires three additional nodes. Let us say that one of the memory modules already has the written data. It is assumed that a triangle relationship has been discovered in that situation (Dhieb et al. , 2020). All pathways start with a weight of one. The weight of each direction is raised by one each time a new triangular connection is discovered. The edge’s weight is increased by one if the present pathway is a part of an existing triangle connection. Each path’s importance will not diminish with time. The patient consults a doctor for any biological changes found in the body or due to any disease symptoms. Then, the doctors gather all the information from patients through a lab test, MRI, sensor data, and so on, providing treatment accordingly. All these details, like test reports, sensor data, scan reports, personal information, treatment information, etc. , are stored in a database as electronic records. These records can be accessed by both the patients and doctors and then managed by hospital management. When a patient receives a threatening call or message, they intimate control and track the location of a person through Hybrid microwave transmission for location tracking and MapQuest for providing an exact location in map format to the management, as illustrated in Fig. 4. Results examining resemblances of performance The IoT devices continuously sense and collect data on a user’s health that helps the user’s health status. In this work, the personal health data of a user is collected by wearable devices.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, 15(1), 1–22. image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals, Journal, Issue 1178, December 2023, Pages 1287–1294, Krichen, M. Networks: A Survey. Computers 2023, 12, 151. Kayadibi, I. , Güraksın, G. E. , Ergün, U. et al. : AlexNet-Network. Syst 15, 49 (2022). Arkin, E. , Yadikar, N. , Xu, X. et al. A survey: object detection methods from CNN to transformer. Appl 82, 21353–21383 (2023). Yu, F. ; Zhang, Q. ; Xiao, J. ; Ma, Y. ; Wang, M. ; Luan, R. ; Liu, X. ; Ping, Y. ; Nie, Y. ; Tao, Z. ; et al. Progress in the Application of CNN-Classification and Recognition in Growth Cycles. Remote Sens. 2023, 15, 2988. Manimegalai, P. , Suresh Kumar, R. , Valsalan, P. , Dhanagopal, R. , Vasanth Raj, P. T. , & Christhudass, J. (2022). 3D with for Nuclear Medicine. Scanning, 2022, 9640177. (Retraction published Scanning. 2023 Dec 13;2023:9765894) Kerkhof, M. , Wu, L. , Perin, G. et al. No (good) loss no gain: systematic evaluation of loss functions in deep learning-based side-channel analysis. J 13, 311–324 (2023). . , Narayan, V. , Paprzycki, M. , Jaworska, T. , & Ganzha, M. (2023). A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analytics, 100216. Tian, Y. ; Zhang, Y. ; Zhang, H. in Descent in Deep Learning. Mathematics 2023, 11, 682. Kaur, M.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Such an alteration would invalidate previous blocks, requiring the reworking of all prior blocks before a new block could be added. For a successful alteration, a node would need a processing speed exceeding 51% of the combined speed of all other nodes — an event known as a “51% attack. ” While configuring such a system is currently nearly impossible, the potential rise of quantum computers may necessitate a reevaluation of this blockchain protocol in the future. 2. 4. 2 Proof of stake (PoS)Proof of Stake (PoS) represents a more recent consensus protocol model, presently adopted by networks such as Peercoin and BitShares. In contrast to Proof of Work (PoW), the PoS consensus protocol operates on distinct principles unrelated to the high processing power of verification nodes. Instead, preference is granted to nodes with a larger share of the digital (crypto) currency underpinning the process. Advocates of the PoS protocol argue that nodes possessing a substantial stake in the background cryptocurrency exhibit a heightened interest in preserving its market value. Safeguarding their investment becomes paramount, as any network attack or data falsification would erode trust and, consequently, diminish value. Consequently, it is not in their interest to engage in censorship or the falsification of transactions. While PoW necessitates substantial processing power, with technologically advanced nodes operating complex puzzle-solving processes in specialized facilities (often termed computer farms), PoS imposes minimal requirements on processing power. This means that even an ordinary smartphone can effectively contribute to the creation of complete blocks. This characteristic significantly enhances the decentralized nature of the network, as a broader range of entities can afford to participate in the validation process. 2. 5 Types of blockchainBlockchain technology is characterized by its openness and accessibility, allowing for diverse implementations based on specific needs. There are essentially three types of blockchain implementations [7]:Public blockchain: This type constitutes a fully distributed network that is openly accessible to anyone with internet connectivity. Participants can engage at any level, the program code is open and community-maintained, and the validation process can be undertaken by any participant with the requisite technical equipment. Participants typically remain anonymous. Examples include Bitcoin and Ethereum. Controlled blockchain (permissioned or hybrid blockchain): In this model, participants assume strictly controlled roles within the network. The validation process is not open to just anyone; instead, it is managed by pre-selected entities.

Output: corroborate


Input: Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: The user needs privacy for their health data and access by the doctor and denies access from third parties. Figure 9: Result analysis of accuracy score. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-9 As illustrated in Fig. 10, the number of data and average response time for data submission and the number of data and average response time for data validation, respectively. When the data is submitted to the blockchain ledger, this takes time for the process; this processing time must be less or higher. This is only based on the amount of data. If the data count is the lesser time taken for the process is minor; similarly, if the data is large, then the time taken for the operation or response time might increase. Besides, the validating portion of the blockchain follows this condition. The data submission analysis and validation for the existing decision tree took an average response time of 3,212 ms (on data submission). It took an average response time of 17. 640 ms (on data validation). This average response time is 10% higher than the existing approach. Figure 10: Comparison of various classification methods with execution time. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-10 blockchain method and proposed hybrid gradient boost that was suggested concentrated on data transfer to compare several new technologies employing wireless communication. The use of decision trees and blockchain-based decision-making were used to optimize wireless communication. The recommended system produces the following results in terms of prediction accuracy, execution time, execution accuracy, and average response time of data submission and data validation. The suggested hybrid gradient boost methodology offers the best precision value of 99% compared to the prior classification method. The decision tree and the naïve Bayes algorithm have 87 and 84% accuracy rates, respectively. Over the same datasets, the hybrid gradient boost classifier has the fastest execution time, clocking in at 12 ms. Naïve Bayes and the decision tree method have the most prolonged execution durations compared to the other classifiers. However, naïve Bayes and the decision-making strategy had the lowest execution accuracy when using the gradient boost classifier. Performance for the gradient boost classifier, which offers the highest level of precision, is 97. 9%. The research on data submission and validation for an existing decision tree found that average reaction times were 3,212 and 17. 640 ms, respectively.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: , Kaur, N. , & Singh, H. (2014). Adaptive K-means clustering techniques for data clustering. of in Science, Engineering and Technology, 3(9), 1-6. Dawood, E. A. E. , Elfakhrany, E. , & Maghraby, F. A. (2019). Improve profiling bank customer’s behavior using machine learning. IEEE Access, 7, 109320-109327. DOI: 10. 1109/. 2019. 2934644 Mohamed, A. , Rabea, M. , Sameh, A. , & Kamal, E. (2024). Radiogenomic Classification. arXiv preprint arXiv:2401. 09471. DOI:10. 48550/arXiv. 2401. 09471. François, A. , & Tinarrage, R. (2024). Train-in MRI with Homology. arXiv preprint arXiv:2401. 01160. Barakat, M. , Magdy, N. , William, J. G. , Phiri, E. , Confidence, R. , Zhang, D. , & Anazodo, U. C. (2023). : Model for Segmentation in Sub-Populations. arXiv preprint arXiv:2312. 11775. Cheng, D. , Gao, X. , Mao, Y. , Xiao, B. , You, P. , Gai, J. ,. . . & Mao, N. (2023). Brain tumor feature extraction and edge enhancement algorithm based on U-Net network. Heliyon, 9(11). Saeed, N. , Ridzuan, M. , Alasmawi, H. , Sobirov, I. , & Yaqub, M. (2023). MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. arXiv preprint arXiv:2304. 00774. Qureshi, S. A. , Hussain, L. , Ibrar, U. et al. Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. 13, 3291 (2023).

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: The program code may or may not be available to participants. Ripple is an example of a controlled blockchain. Private blockchain: Access to private blockchain networks is meticulously controlled and centrally managed. These networks are closed, permitting entry only to pre-approved participants. The program code is closed and not publicly accessible. Private blockchains find utility in consortia and larger corporations that necessitate sharing information and documents among approved entities. In the financial sector, developing a private blockchain is meant to facilitate back-office operations related to clearing and settlement. Advertisement 3. The application of blockchain in worldwide financial systems: its advantages and implications! The intersection of financial systems with information technologies has witnessed a revolutionary shift with the advent of blockchain technology, offering the potential to fundamentally reshape financial landscapes. Several key advantages of blockchain contribute to its transformative impact [8]:Publicly available ledger system: Blockchain operates as a publicly accessible ledger, where all transactions are recorded and verified. This inherent transparency ensures a secure and reliable system, instilling trust among participants. Transaction immutability: Every transaction within the blockchain is authorized and validated by controllers known as “miners. ” Once recorded, transactions become immutable, meaning they cannot be altered or tampered with. This feature enhances security, providing robust protection against malicious intrusions and hacks—common concerns in contemporary financial IT systems. Elimination of intermediaries: Blockchain technology eliminates the need for intermediaries, enabling direct peer-to-peer transactions. Traditional financial systems often involve intermediaries such as banks or clearinghouses, introducing delays and costs. Blockchain streamlines the process, facilitating more efficient and cost-effective transactions. Decentralization: One of the core tenets of blockchain is decentralization. This technology is practically available to everyone, fostering a decentralized network where no single entity holds exclusive control. This decentralization not only enhances accessibility but also contributes to the resilience and security of the system. As blockchain continues to evolve, these advantages pave the way for more inclusive, efficient, and secure financial systems. The elimination of intermediaries, coupled with enhanced transparency and immutability, opens doors to innovative financial applications and services, driving a shift towards more direct and peer-to-peer interactions in the digital economy. The above-mentioned advantages of blockchain technology are manifested in various ways, particularly within financial systems, bringing about transformative possibilities:Cross-border payments: Enable fast, direct one-to-one payments, reducing costs for international transactions.

Output: corroborate


Input: Claim: No personal data is ever shared by smart home devices. [SEP] Text: Prof. --2020, No 3 Full text (pdf) Aims and on the Issue: to the Protection of Employees' the Associated with Recommendations for e-books & Tools for News and about in the Issue: CounterCraft (UK) Aims and current online newsletter is focused on the topic challenges related to the protection of employees’ home networks and how smart devices changing the threat pattern associated with data sharing. Due the COVID-19 crisis, many security and IT teams have to support and protect employees who must work remotely. The practical recommendations from security experts toward the mitigating risks to enterprise security from home networks and smart-home devices are presented. Eight practical recommendations for remote workers’ protection are given from the security experts. In this respect, a strong policy for all home employees should involve the usage of an antivirus tool on the machines that access the organization’s resources. Consideration should be given to implementing alternative cloud-based workstation monitoring tools. An overview of supported console tools and required licenses to access non-domain computers should be done. A quick decision is to set up tools such as Splashtop SOS or LogmeinRescue to allow IT support team to remotely access employees’ home machines to assist in setting up remote access. In the current issue the CounterCraft company that is a pioneering provider of cyber deception and counterintelligence products to detect targeted attacks is presented. CounterCraft’s solution deploys deception-based campaigns and offers deep monitoring and complex response actions. is used by government's law enforcement agencies. Some useful information about the problems of secure remote work is given by providing free e-books and free cybersecurity toolkits that will help to protect and manage remote workers. Remote workforce security tips and best practices from 8 security pros can be found too. Finally, some additional news and security tips about remote working at home is given. A list of links to Bulgarian, UK and international cyber-related institutions are also presented. Issue Editors: Prof. Daniela Borissova, DSc and on This Issue: Challenges related to the protection of employees' home networks and how smart devices changing the threat pattern associated with data sharing Remote work is not a new challenge. Many companies were already embracing remote work to give employees a better balance between work and life, along this contributes to reduce the carbon footprint and drive corporate efficiencies.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Do, D. T. , Yang, MR. , Lam, L. H. T. et al. Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. 12, 13412 (2022). Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences. Research Square; 2022. U-Segmentation and Characterization of Gliomas. Cancers. 2022; 14(18):4457. Chen, S. , Xu, Y. , Ye, M. , Li, Y. , Sun, Y. , Liang, J. , Lu, J. , Wang, Z. , Zhu, Z. , Zhang, X. , & Zhang, B. (2022). in Learning with Radiomics. Journal of clinical medicine, 11(12), 3445. inversion, sprint, Spyridon Bakas, Ujjwal. (2021). RSNA-. Kaggle. Martino, L. , Míguez, J. A generalization of the adaptive rejection sampling algorithm. 21, 633–647 (2011). Villa-Blanco, C. , Bielza, C. & Larrañaga, P. Feature subset selection for data and feature streams: a review. Rev 56 (Suppl 1), 1011–1062 (2023). Singh, S. (2022, November 23). Top methods to evaluate the performance of deep learning models. Labellerr. D. Chicco, V. Starovoitov and G. Jurman, "of the Coefficient (MCC) Over the Ratio (DOR) in Assessment," in IEEE Access, vol. 9, 2021. DOI: 10. 1109/. 2021. 3068614 Additional DeclarationsNo competing interests reported. :PostedVersion 1posted You are reading this latest preprint versionlets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community.

Output: pass


Input: Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Companies should adopt a proactive approach to risk management and stay abreast of developments to ensure the successful and secure implementation of blockchain solutions. Advertisement 4. ConclusionsBlockchain, at its core, operates as a decentralized and tamper-resistant digital ledger, offering a transparent and secure way to record and verify transactions. While it initially gained prominence through its association with cryptocurrencies like Bitcoin, the technology’s potential extends far beyond the realm of digital money. In the modern financial industry, blockchain’s distributed ledger concept is particularly promising. Traditional financial systems often rely on centralized databases maintained by trusted intermediaries, introducing vulnerabilities to fraud, errors, and data manipulation. Blockchain addresses these challenges by distributing the ledger across a network of nodes, ensuring consensus and transparency. This decentralized nature reduces the dependence on a central authority, fostering trust and integrity in financial transactions. However, blockchain technology is recognized as a foundational element in the financial sector, offering promising prospects for economic development, financial innovation, and internet advancement. As the financial industry undergoes a digital transformation, successful adoption of blockchain technology becomes imperative. Current research delves into the mediating role of blockchain adoption in the relationship between digital business strategy and process innovation, as well as financial performance. Furthermore, new findings investigate how information technology alignment plays a moderating role in this scenario. The investigation seeks to unveil the nuanced dynamics and interconnections among these crucial components, with the overarching goal of utilizing blockchain for strategic and operational improvements within the financial sector [9]. Moreover, Blockchain technology significantly impacts the financial sector by enhancing security through decentralization and cryptography, leading to efficient processes and reduced costs. Its transparency and traceability improve risk management, support cross-border transactions, and facilitate innovations such as decentralized finance (DeFi) and tokenization. Blockchain ensures regulatory compliance, strengthens data security, and prevents fraud, fostering financial inclusion and revolutionizing traditional financial products and services. Beyond finance, various industries are exploring blockchain applications. Sectors such as supply chain management, healthcare, real estate, and even governance are considering how blockchain can enhance efficiency, security, and transparency. For instance, in supply chain management, blockchain can enable real-time tracking of products from manufacturing to delivery, reducing fraud and ensuring the authenticity of goods. However, the adoption of blockchain technology comes with its share of challenges and risks.

Output: corroborate


Input: Claim: Urban agriculture can support biodiversity by creating green spaces in urban areas. [SEP] Text: Possible effects of rainfall in urban areasFloodsTemporary surface cover with water related to climatic events (extreme rainfalls)represents a flow that exceeds the system’s maximum capacity and is discharged in the receiving waters. The overflow occurs at pump stations or in devices before the WWTP (Plant)Stormwater runoffRunoff produced by rainfall in impervious areas, collected in separate sewer systems and discharged in receiving watersTable 1. Results of rainfall events in urban areas. Reducing runoff in urban areas contributes to reduce the impacts of stormwater runoff on receiving water quality, and minimize the occurrence of the overflow of combined or separate systems. 1. 6 Roofs landscape in urban areasWe based our evaluation of the residential building stock in São Miguel island, namely in and Lagoa urban areas, on the (RBSS) developed by the University of the Azores in the framework of the Project, a project which associated the of Technology and several Universities in Portugal [14]. This RBSS was applied to a sample of 500 households in São Miguel, the largest and most populated island of the Azores. That database shows that 77. 3% of residential buildings in municipality and Lagoa municipality (corresponding to 295 buildings) were located in urban and periurban areas. From that sample of residential building stock, some statistical results are presented at Table 2. ParameterPercentage of buildings (%)Horizontal rooftops3. 1Elevated wind exposure24. 5Hight between 3 to 9 m78. 5Location at less than 50 m altitude46. 5Distance to ocean less than 1000 m56. 6Table 2. Characterization of residential buildings in and Lagoa urban and periurban areas [14]. Lately, with air space liberalization, there has been a major increase in tourism [15], enhancing the rehabilitation/transformation of the existing building stock and a boom of new constructions. This growth is confirmed by the registrations of new building constructions [1], between 2011 and 2021 in peri-urban areas of Ponta Delgada, like Fajã de Baixo (4. 42%) and São Roque (3. 86%), and much higher in Lagoa, where the increase of building stock varied between 11. 93% (Cabouco) and 4. 31% (Santa Cruz). Advertisement 2. Design of the experimental green rooftop pilot study2.

Output: pass


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Abstract Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain. claim, responsible artificial intelligence, machine learning IntroductionA sizeable amount of healthcare spending is allocated to processing insurance claims, making the healthcare sector in the USA one of the world’s largest and most complex enterprises. National health expenditure (NHE) grew 4. 6% to $3. 6 trillion in 2018, accounting for 17. 7% of the gross domestic product (GDP) [1]. Medicare spending grew 6. 4% to $750. 2 billion, Medicaid expenditure grew 3. 0% to $597. 4 billion, private health insurance spending grew 5. 8% to $1. 243 trillion, and out-of-pocket spending grew 2. 8% to $375.

Output: pass


Input: Claim: Bitcoin has a fixed supply cap of 21 million coins. [SEP] Text: The predicted translational start sites for the antidote (ATG in exon 1) and poison (ATG in exon 2) proteins are indicated, as is the FLEX transcriptional regulatory motif (Supplementary file 1b-d). (B) Long-read RNA sequencing of mRNAs from meiotic S. octosporus cells revealed two main transcript isoforms of the wtf25 gene, presumably encoding an antidote and a poison protein, respectively. cDNA reads obtained using the Technologies (ONT) platform are shown in pink. Blue lines indicate sequences missing in the reads due to splicing. The diagram at the top depicts the two main transcript isoforms. The 3' transcript ends shown in the diagram correspond to the major transcript end revealed by cDNA reads. Figure 2—source data 1 wtf genes share similar exon and intron sizes. Table of the average size (in base pairs) of the exons and introns of intact wtf genes in all Schizosaccharomyces species. For S. chizosaccharomyces pombe, only the genes containing 5 exons were considered. Download elife-81149-fig2-data1-v1. xlsx We next looked for similarities between promoters controlling the transcription of the S. pombe wtf genes and the potential promoters of the candidate wtf genes in other species. The promoters of the S. pombe wtf4 gene are representative of the promoters of wtf drivers in S. pombe (Nuckolls et al. , 2021). The Wtf4antidote protein is encoded on exons 1–6, with the promoter found upstream of exon 1. We found no shared homology between the S. pombe wtfantidote promoter sequences and sequences upstream of exon 1 in the candidate wtf genes found in the other species. The Wtf4poison protein is encoded on exons 2–6, and the promoter is found within what is intron 1 of the wtf4antidote transcript. The S. pombe wtf4 poison promoter contains a cis-regulatory FLEX motif that is bound by the Mei4 master meiotic transcription factor and is essential for expression of the Wtf4poison protein (Nuckolls et al. , 2021).

Output: pass


Input: Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: KeywordsInformal food systemsUrban agricultureFood securityFood resilience IntroductionSince the beginning of the twenty-first century, urban agriculture (UA) in sub-cities has been an expanding feature of the urban landscape and economy, mainly because of increasing food insecurity, poverty, and unemployment in many parts of the continent. Informal food enterprises, including street food, have evolved to be important sources of urban employment and food. Previous food studies in cities that dominate their national urban systems, such as Harare (Drakakis-Smith et al. , 1995; Mbiba, 1995), Lusaka (Hampwaye et al. , 2007), Kampala (Maxwell et al. , 1998), Maputo (Paganini et al. , 2018), and (Battersby & Marshak, 2013), have provided interesting insights into the drivers of UA and the spatial extent of the activity in several national cities across sub-Saharan Africa. Studies that have been done in the past decade have shed light on food production issues in secondary cities and towns. However, most of the studies have focused on single cities and have not generated comparative cross-country discussions on UA in different size and function urban centres. In recent years, secondary cities in Africa have been receiving increased attention, which is largely attributable to their vast numbers (about 7,500 across the continent) and their rapid growth since the new millennium (Tacoli & Agergaard, 2017). Secondary cities and towns are closely connected to the apex and usually primate cities (their main source of consumer and capital goods) and to their rural hinterlands (their primary source of cultural foods and various environmental resources, such as wild fruits and edible insects). of the (FAO) defines UA as any agricultural activity that entails the raising, growing, or processing (and distribution) of agricultural produce in cities and towns (FAO, 2001). In sub-Saharan Africa, UA takes place across a whole range of spaces, including backyard home gardens (especially in middle-income suburbs), community gardens (at sites such as school grounds, church premises, and community centres) and open space cultivation on vacant lots and road verges. UA in African cities has generated considerable debate regarding the impact of its contribution to household food and nutrition security (Battersby & Haysom, 2016; Binns & Lynch, 1998; Crush et al.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 6 billion (these are 21%, 16%, 34%, and 10% of total NHE) in 2018 [1]. According to the 2021 national health statistics report, 28. 1 million individuals, or 8. 6% of the total population of the USA, lack health insurance coverage, 65. 4% have private health insurance, 56. 9% are employer-based, and 7. 2% have directly purchased coverage for the population under age 65 [2]. Among this age group, nearly two of every five children and one of every five adults relied on public health coverage through Medicaid and children’s health insurance programs. These estimates are characterized based on specific sociodemographic attributes, such as age, gender, race, Hispanic origin, family income, education, employment situation, and marital status [2]. A study explored that healthcare access is a fundamental right for every American citizen [3]. Unfortunately, the USA is confronted with a substantial and expanding segment of its population lacking insurance coverage. After hitting a low point of 28. 7 million individuals in 2016, the projected trajectory indicates a rise in the number of uninsured individuals to 37. 2 million by 2028 [3]. This concerning trend coincides with a period in which an increasing wealth of research establishes a strong connection between having insurance coverage and notable enhancements in financial stability, overall well-being, and lifespan [3]. Health insurance claims processing is crucial in this rapidly changing environment, as it holds the potential to revolutionize the healthcare landscape by providing prompt and accurate reimbursements and optimizing insurance provider cost management and risk assessment. The processing of health insurance claims entails looking over and verifying medical records, billing data, and payment authorization. The insurance market in the USA provides a vital safety net for both individuals and corporations. Insurance companies employ data and predictive modeling to measure risk and establish premiums. However, the insurance sector’s adoption of responsible artificial intelligence (AI) ensures that forecasting models are impartial, fair, and open. In the insurance sector, predicting insurance claims is essential because it aids insurers in calculating the likelihood of an event occurring and the potential cost. The employment of AI in this process has prompted questions about bias and discrimination, particularly against specific demographic groups. Developing and deploying prediction models can be guided by responsible AI principles to alleviate all the concerns. This includes ensuring the model is transparent, understandable, constantly monitored, and updated to prevent bias and discrimination.

Output: pass


Input: Claim: Bitcoin has a fixed supply cap of 21 million coins. [SEP] Text: AES is part of a consortium of solar companies trying to incentivize a more resilient supply chain in the US. The consortium is promising to spend more than $6 billion on solar panels, as long as suppliers set up US manufacturing operations. “That’s what gives them certainty,” Moreno says. “They can build, and the demand will be there. ” To encourage production outside China and avoid supply chain risks, IEA analyst Bahar says, governments also need to offer solar manufacturers support. He says they should use tax policies or incentive schemes to reduce the start-up costs for polysilicon manufacturers and ensure that they have access to cheap electricity, just as China has done. In August, US president signed the Act into law. It offers a direct tax credit for the production of solar-grade polysilicon and also offers incentives for other parts of the supply chain, like ingot and wafer manufacturing. Related: U. S. In an investor call the day after the signing, REC Silicon’s May said the tax credits were a big part of the company’s decision to restart operations at its plant in Washington. The company expects to receive $48 million in credits annually once the facility is operating at full capacity. Korean company Hanwha Solutions, which made a major investment in REC Silicon earlier this year, is scouting out sites in Texas, South Carolina, and Georgia for a facility to produce ingots, wafers, and solar panels, according to documents filed with the Texas comptroller’s office. If the project moves forward, the company says, it would begin operating in 2025. SPI Energy hopes to set up wafer production in the US by 2023, and hopes to be producing wafers in upstate by 2024. Credit: produces polysilicon using the fluidized-bed reactor process, which consumes less energy than the more typical Siemens process. Building wafer manufacturing in the US would have a domino effect, Bernreuter says, and likely complete a solar supply chain outside China. “If you solve that step of the value chain, I think a lot of the other steps could follow relatively easily,” he says. The US could also avoid silicon-based solar panels altogether by using another technology: thin-film cadmium telluride, which makes up about 5% of the global solar market.

Output: pass