A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

This chapter explores the integration of artificial intelligence of things (AIoT) with tele-health monitoring, presenting an intelligent remote patient activity tracking system. Using interconnected sensors and machine learning models, the system monitors various patient activities and vital signs, providing real-time insights. The proposed AIoT-enabled health monitoring device tracks activities such as running, sleeping, walking, and exercising, while observing vital signs like heart rate and breathing patterns. The chapter delves into the system's architecture, functionalities, and technical intricacies, emphasizing its potential to transform healthcare delivery. It highlights the role of AIoT-driven solutions in enhancing patient care and fostering personalized treatment approaches, offering a glimpse into the promising future of intelligent healthcare delivery.

In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short- Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.

This paper proposes an electrical array reconfiguration system depending on the Coronavirus optimization algorithm (CVOA) for reducing the partial shading conditions and increasing its power. The increasing use of fossil fuels to generate energy is causing environmental issues. Therefore, human society moves toward renewable energies, such as wind energy, solar energy, and geothermal energy. Nowadays, solar energy plays a significant role in the energy crisis. In recent years, the photovoltaic (PV) system is used to gain solar energy. But, partial shading is the most significant and prominent one in changing the behavior of PVs. The impact of partial shading reduces the photovoltaic output power and it is overcome by the electrical array reconfiguration. The proposed approach generates a reconfigured array pattern, thus shading property spread over the array and growing the power output. In the reconfiguration method, the physical position of the modules remains constant, but their electric connections are modified depending on the irradiance level. The proposed CVOA approach has been experimented with under dissimilar shading conditions on 6 x 6 sizes of the array and analyzed with TCT and SuDoKu patterns. The efficiency of the CVOA algorithm is higher compared to those of the reconfiguration methods.