Transactions on Engineering and Computer Science

The challenge of indecision among humans cannot be completely eliminated. This leads to the necessity of addressing questions such as what, which, where, who, and how as issues. Therefore, when situations take a negative turn, the problem of identifying the right person to communicate with becomes pivotal. Many individuals are burdened by concerns related to security, social risks, and other similar factors. As a result, they tend to isolate themselves and make decisions that might not be rational. Academic advising stands as a crucial endeavor within educational institutions. It assists students in exploring potential career paths, academic fields, and opportunities within the college setting. This project has the objective of addressing the widespread issue of student failure and university dropouts. It involves the creation of an AI-driven student portal recommender system designed to tackle well-known human challenges autonomously, without requiring direct human intervention. Recommender systems offer users' individualized needs by leveraging Artificial Intelligent (AI) techniques, computational intelligence and machine learning algorithms which recommend to users what materials to learn by on psychometric assessment result and possible other individual characteristics then suggest relevant items to user in term of its preferences. Consequently, the integration of AI-based systems has emerged as a solution, allowing machines to emulate human thinking and tackle real-world problems. Addressing the questions of what, which, where, who, and how is crucial because the inherent complexity of human desires cannot be easily resolved in determining the next action. A diligent and growth-oriented student must make informed decisions regarding which courses to pursue, what materials to study, when to engage with them, and how to approach the learning process. By reflecting on past actions and considering current preferences, students strive to achieve these ultimate objectives. To aid users in their decision-making, recommender systems filter out unnecessary information, provide guidance and recommend to user based on the specific context. Personalization method was adopted as a technological tool for critical human complex reasoning, which provides students with proactive and intelligent access to information, taking into account past performance and current preferences. This is made possible through the use of MySQL, an open-source database tool, for the application's backend, and high-level programming languages like PHP, AJAX, and JavaScript for the frontend. These technologies enhance prediction accuracy and address challenges such as sparse data and the cold start problem. The application is employed to create a personalized and intelligent recommender service for students. A scheme based on machine learning is proposed, which involves traditional statistical analysis and collaborative filtering techniques to discover personalized explicit interests from user data. Furthermore, machine learning methods are utilized to identify users' current demands, analyze demand features, and analyze demand trends. It is important to acknowledge that recommender system may occasionally be incorrect, leading to disappointment. Privacy concerns related to profiling can also contribute to such dissatisfaction. To ensure students find the most suitable options for their needs, we propose a method that allows them to connect with a human representative.

The substantive increase in the use of internet-based technologies has subjected organizations to malicious attacks. Cybersecurity is the practice of protecting systems, networks, and programs from digital attacks. A blockchain is a datastructure, which stores transactional records in the form a block chained and stored in several databases, and is fundamentally a digital ledger of transactions (DLT) that upon duplication is distributed on the blockchain throughout the entire computer network. To improve on Cybersecurity, Machine Learning is considered for data mining among the data. This research is purposed to determine a cybsersecurity risk management plan, develop a Blockchain Technology Application Model that manages cyber risks and vulnerabilities, ascertain how the Framework of Blockchain Technology and Cryptocurrency addresses cyber risks and creates value to the financial services sector. The research used the Pragmatism paradigm, which is a philosophy that is closely linked to the Mixed Method Research (MMR). The Focus Group comprised a total of 100 participants from the Masters students attending the Applied Business Informatics module taught by the author at the University of Zimbabwe in 2020. The National Institute of Standards and Technology (NIST) Cybersecurity Framework and the General deterrence theory (GDT) were used to formulate the Cybersecurity strategy. Blockchain cyber vulnerabilities were determined. A model structure for block chain technology and cryptocurrency was developed and an explanation provided on how the framework of Blockchain Technology and Cryptocurrency addresses cyber risks and creates value to financial services sector.