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This study introduces an IoT and deep learning-based framework designed to enhance biomedical waste management through real-time monitoring and efficient waste classification. By utilising IoT sensors in conjunction with advanced machine learning models and autoencoders, the system automates waste tracking, timely collection, and the categorisation of hazardous materials. IoT sensors enable continuous data collection on waste generation, supporting pattern analysis to optimise waste management processes. This approach addresses critical challenges in mismanagement, including the prevention of spills, control of odours, and the handling of hazardous waste types. By leveraging real-time data, healthcare facilities can improve the segregation and disposal of BMW, minimising risks to human health and the environment. The integration of these technologies contributes to a cleaner, more sustainable waste management ecosystem, ensuring regulatory compliance and enhancing operational efficiency. Ultimately, the proposed system demonstrates how IoT and AI-driven solutions can revolutionise waste management in healthcare settings, mitigating environmental impacts and improving overall public health outcomes.

This study presents the development of a Feed-Forward Neural Network (FFNN)-based model for security of Internet of Things (IoT) network protocols. The proposed method applied in the execution of the study involves data collection, preprocessing, feature selection, and model training using the CIC-IoT 2022 dataset, which includes normal and attack traffic from various IoT devices. In the study, Synthetic Minority Oversampling Technique (SMOTE) was used for data balancing, Principal Component Analysis (PCA) technique was used for feature selection and hyperparameter optimization were employed to enhance the performance of the model during training. The system was simulated in the NS-3 environment to replicate real-world IoT network conditions, and its effectiveness was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results demonstrated that the FFNN-based model achieves an average validation accuracy of 89.7%, precision of 88.9%, recall of 86.9%, and an F1-score of 87.9%. The system results showcased robustness in detecting various attacks, including DoS, brute force and RTSP attacks in mixed traffic scenarios, meanwhile this study serves as a strong foundation for leveraging deep learning techniques to enhance IoT network security protocols.