Alexandros Emvoliadis, Nikolaos Vryzas, Marina-Eirini Stamatiadou, Lazaros Vrysis, Charalampos Dimoulas
Multimodal Environmental Sensing Using AI & IoT Solutions: A Cognitive Sound Analysis Perspective
Sensors (Basel, Switzerland)
research paper focuses effective infectious municipal waste management urban settings highlighting dearth dedicated research domain unlike general specific waste types infectious waste poses distinct health environmental risks leveraging advanced artificial intelligence techniques prioritize infectious waste categorization optimization integrating metaheuristics optimization methods create robust dualensemble framework model enhanced artificial intelligence infectious municipal waste classification system combines ensemble image segmentation methods diverse convolutional neural network models innovative geometric image augmentation enhances model robustness diversifies training data improves accuracy across waste types pivotal aspect integration reinforcement learningdifferential evolution algorithm decision fusion strategy optimizing classification harmonizing outputs ensemble methods convolutional neural network models computational results using newly collected dataset demonstrate models accuracy exceeding 9654 using existing solid waste dataset achieve accuracy 9781 outperforming advanced approaches margins ranging 202 880 research significantly advances sustainable waste management showcasing artificial intelligences transformative potential addressing intricate waste challenges establishes foundational framework prioritizing efficiency effectiveness sustainability future waste management solutions acknowledging importance diverse datasets customization urban contexts practical integration existing infrastructures work contributes broader discourse role artificial intelligence evolving waste management practices