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.

A software product is expected to be subjected to critical evaluation on its quality attributes in order to ascertain that target quality requirements are met, and that those quality attributes responsible for revealing software quality are not omitted in the software development process. Software metrics are essential to accomplish the task. This paper has carried out an exploratory study of software measurement and software metrics in tandem. The study took into cognizance the interwoven nature of the duo in measuring and revealing software quality. The study formulated a model that expressed the mutual bonding that propels both measurement and metrics to describing software quality in numeric quantities of software attributes. The study identified six software attributes whose values are considered enough quantities to reveal the quality of a software product. The identification enabled the study to create a model equation aimed at giving a numeric value for the complete evaluation of a software system. The result of the implementation of the six software attributes into the model equation showed that two software products employed in the study are of high-quality, having quality values of 0.93 and 0.86 respectively. The attributes produced values that confirmed the maintainability (25 seconds & 20 seconds respectively) and reliability (0.78 & 0.80 respectively) of both software products among other differing features that characterize them.

Artificial Intelligence (AI) is rapidly transforming numerous sectors, and the legal industry is no exception. This paper explores the significant impact AI has had on legal research and case analysis, highlighting how it enhances efficiency, accuracy, and decision-making processes within the legal domain. Traditional legal research often involves manually sifting through vast databases of legal texts, case laws, and statutes—a time-consuming and sometimes error-prone task. However, AI technologies such as natural language processing (NLP), machine learning algorithms, and predictive analytics are now being employed to automate and streamline these processes. This paper examines the role of AI-powered legal tools like ROSS Intelligence, LexisNexis, and CaseText, which are capable of analyzing case law, identifying relevant precedents, and even predicting case outcomes based on historical data. Moreover, the study investigates how AI assists legal professionals in uncovering patterns, drafting legal documents, and conducting due diligence with increased speed and precision. While the adoption of AI offers several benefits, the paper also considers its limitations, such as concerns about data privacy, the potential for algorithmic bias, and the ethical implications of relying on machine-driven insights in judicial contexts. The research draws upon existing literature, case studies, and expert opinions to present a balanced analysis. It also explores the current trends and future potential of AI in reshaping legal workflows. Ultimately, the findings suggest that while AI will not replace legal professionals, it will significantly augment their capabilities, paving the way for a more data-driven and accessible legal system.