The potential for an intelligent transportation system (ITS) has been made possible by the growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration of IoT and ITS—known as the Internet of vehicles (IoV). To achieve the goal of automatic driving and efficient mobility, IoV is now combined with modern communication technologies (such as 5G) to achieve intelligent connected vehicles (ICVs). However, IoV is challenged with security risks in the following five (5) domains: ICV security, intelligent device security, service platform security, V2X communication security, and data security. Numerous AI models have been developed to mitigate the impact of intrusion threats on ICVs. On the other hand, the rise in explainable AI (XAI) results from the requirement to inject confidence, transparency, and repeatability into the development of AI for the security of ICV and to provide a safe ITS. As a result, the scope of this review covered the XAI models used in ICV intrusion detection systems (IDSs), their taxonomies, and outstanding research problems. The results of the study show that XAI though in its infancy of application to ICV, is a promising research direction in the quest for improving the network efficiency of ICVs. The paper further reveals that XAI increased transparency will foster its acceptability in the automobile industry.

This paper conducts a comprehensive analysis of the impact of Artificial Intelligence (AI)-driven Internet of Things (IoT) systems on air quality management in Nigeria. The study examines the current state of air quality in Nigeria, highlighting prevailing challenges, health implications, and the environmental significance of escalating pollution levels. It delves into the existing conventional methods and technologies utilized for air quality management, emphasizing monitoring systems, regulatory frameworks, and pollution control measures. In addition, this review elucidates the integration of AI and IoT technologies globally in addressing environmental concerns, particularly in the realm of air quality management. It specifically zooms into the emergence and adoption of AI-driven IoT systems within Nige- ria’s environmental landscape, spotlighting ongoing initiatives or projects. This review seeks to investigate the implementation and consequences of AI-powered Internet of Things (IoT) systems in air quality management within Nigeria context. It bridges existing knowledge gaps by scrutinizing the effectiveness of these advanced systems in mitigating air pollution and assesses the challenges encountered in practical implementation, encompassing technical, regulatory, and socio-economic barrier.

Soot pollution is one of the most complex environmental problems. The enormous amount of soot in Rivers State, Nigeria, has turned into an issue for both the economy and public health. To put it simply, soot is a by-product of burning fossil fuels, including smoke from cars and oil refineries. Soot particles are released into the air because they are microscopic particles, whereas gas particles can travel thousands of miles from their source. This paper explores that soot is a form of air pollution (an environmental problem) that has negative consequences on economic growth, human health, and the environment. A qualitative systematic literature review was used as the research methodology. This was done by reviewing peer-reviewed journals. However, qualitative content analysis coupled with quantitative analysis was used for data analysis. The content analysis was done by carefully creating codes from the research question, developing clusters, categorizing the clusters, and then drawing insight from the code. The results show the impact of soot pollution on the environment, health, social, and economic growth in Rivers State together with some sustainable measures to mitigate this impact. The results, however, show that soot pollution has contributed negatively to the environmental, health, social, and economic growth of Rivers State, Nigeria. Furthermore, in this study, the environmental, social, health, and economic impacts of soot pollution in Rivers State, Nigeria was thoroughly discussed with the inclusion of valid sustainable measures.

A menstrual cycle prediction system was developed in this paper. To train the dataset obtained from Kaggle, twenty-two machine-learning algorithms were used. Twelve features were used to obtain the predicting variable. The model accuracy and performance were obtained using six standard evaluation metrics as shown in the result section. 0.9864 was obtained as the R2 score for the Decision Tree algorithm, which was the best amongst other models. This indicates that with the Decision Tree, the ovulation day of women can be predicted with higher accuracy. After saving the model with the highest accuracy, it was deployed on a web application using Python, Django, HTML, CSS. Also, VS Code and Jupyter Noteboook were the IDEs that were used in developing this system.

The integration of Internet of Things (IoT) technology into healthcare systems has revolutionized patient monitoring and Information management, leading to enhanced efficiency and improved patient outcomes. This research developed and implemented an IoT-enabled Patient Information and Heartbeat Monitoring System designed to improve Health Information System (HIS). The system employs a network of interconnected sensors and devices to continuously monitor vital signs, especially focusing on heartbeat data. These sensors transmit real-time data to a centralized cloud platform, where it is processed and analyzed. Healthcare professionals can access this data through a user-friendly interface, enabling timely and informed decision-making. The system not only ensures accurate and continuous monitoring but also provides critical alerts in case of abnormal heart rate variations, thereby facilitating prompt medical intervention. The deployment of this IoT-based solution demonstrates significant potential in improving patient care, reducing hospital readmissions, and optimizing resources utilization within healthcare facilities. Results from pilot implementations indicate a promising future for IoT-enabled patient monitoring in transforming modern healthcare practices.