A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

Natural disasters, particularly earthquakes and floods, can generate substantial waste, presenting challenges for waste management and energy recovery. This study explores the application of machine learning, specifically Artificial Neural Networks (ANN), to optimize Waste-to-Energy (WTE) conversion processes for disaster-generated Municipal Solid Waste (MSW). Using data from Sundernagar City, India, we constructed and evaluated multiple ANN models (collectively named ANNSNAGAR) to estimate High Heating Value, HHV, based on ultimate/elemental analysis of MSW. The models were rigorously assessed using statistical error metrics. Our best-performing network, with a 9-50-1 architecture (ANNMS), achieved a mean absolute percentage error (MAPE) of 1.47%, demonstrating high predictive accuracy.

Recently, all have witnessed a rapid growth of COVID-19 coronavirus worldwide. The calculation of COVID-19 time-series prediction is done using many techniques like compartment models, machine learning models (ML), and deep learning models. Therefore, in this paper, the authors have proposed an ensemble-based neural network model. Neural networks (NN), along with non-linear autoregressive (NAR) functions, fitting neural networks (FITNET), or fuzzy Systems, are widely used in time-series forecasting. The responses of NAR, FITNET predictor modules are aggregated using fuzzy logic, which improves the final prediction by intelligently integrating outputs of different modules. The whole model was put to the test in terms of forecasting the coronavirus time series in India, at 13 States. In the validation data set, results of ensemble NN models with fuzzy response integration demonstrate extremely better-predicted values. Overall, the results reveal that a modular neural network with fuzzy (MNNF) beats all other approaches in performance metrics, like Root Mean Squared Error (RMSE). Prediction errors of ensemble NN i.e., MNNF are much smaller than those of classic monolithic neural networks, showing advantages of the method proposed. The model provides the prediction for the upcoming 8 days.

Natural Disaster provide serious problems for communities all over the world, with severe effects on the environment, infrastructure, and human lives. Effective disaster management solutions are increasingly important as the frequency and severity of these events rise to limit losses and ensure quick responses. A creative and promising approach to improving disaster management skills is the introduction of the Internet of Things (IoT). A brief description of the use of IoT in managing natural disasters is provided in this abstract. With the help of several networked sensors and devices, the Internet of Things (IoT) enables real-time data gathering, processing, and transmission. Utilizing this technology can result in preemptive and more efficient disaster response plans. Although there is great promise for IoT integration in disaster management, there are also issues with privacy of data, protection, and interoperability. For Internet of Things (IoT) technologies in disaster management to be successfully implemented and accepted, these problems must be addressed. This offers a brief glimpse of the revolutionary potential of IoT in tackling the intricate problems of catastrophe management.

People have been using social media as a category for exchanging content for decades. It has revolutionized communication and enhanced the sharing of information during emergency situations. The key features of social media are collective action, connectivity, comprehensiveness, and clarity. Consequently, it performed a significant function in natural disaster management by keeping track of and reporting disaster-related incidents. The volume and diversity of the data acquired from social media during a natural disaster pose the greatest challenge. For natural disaster management it is extremely difficult to derive actionable information from the data collected from social platforms. Various strategies have been presented in the literature to address the difficulties posed by social media for natural disaster management. The proposed work is a semi-supervised machine learning model for detecting and classifying tweets. The proposed work centre’s on preparing the data by performing cleansing and various transformations, generating word embedding vectors with DistillBERT, using the vision transformer, the image model was constructed. The attention mechanism is utilized for text and image model integration. In the proposed work the data pertaining to seven distinct natural disasters, such as cyclones, floods, and earthquakes are analyzed. A novel decision diffusion technique is proposed for classifying them into informative and non-informative groups and evaluating the accuracy of the results. The MAM model increases the accuracy to 97% for crisisMMD dataset.

Natural disasters cause immense hardship for communities around the world, necessitating effective and coordinated responses to mitigate the consequences on infrastructure and human life. Cloud computing is a breakthrough technology that has enormous promise for improving disaster management tactics. This study investigates the critical role of cloud computing in disaster management, showing its multiple benefits during the phases of planning, response, and recovery. The study investigates how cloud computing improves disaster preparedness by allowing stakeholders to simulate and prepare for various disaster scenarios using data-gathering capabilities, collaborative tools, and simulation models. The paper discusses the role of cloud computing in preparation of disaster management and how an organization can use the latest technology to minimize the impact of disaster on it.