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.

Bounding of 3 partition algorithms decrease the time period of applying the algorithm for NK set depending on k value and required summation which generate the bounds of the algorithm. The flow chart of partitioning algorithm is constructed and simulated through Matlab. Matlab results are compared between bounded and un-bounded algorithm.

The research looked at librarians' knowledge about and attitudes toward using cloud computing technology in university libraries in South-South, Nigeria. For this investigation, a descriptive survey approach was used. There are 374 participants in the research. The goal of this study was to determine how well-informed librarians were about cloud computing, the types of cloud computing that is been used by libraries, what they used it for, how they felt about adopting it, and what difficulties they encountered when using it in university libraries. 374 librarians from universities in Nigeria's South-South area make up the study's population. 214 of the 374 administered surveys were properly completed, returned, and deemed useful. Frequencies and percentages were used to examine the data. The study's conclusions showed that South-South, Nigerian librarians had a high degree of understanding of cloud-based technologies. OCLC, Dropbox and WorldCat were some of the cloud computing technologies used by the libraries. Their usage was intended for digital preservation of information resource as well as bibliographic checking and bibliographic compilation. The majority of participants were enthusiastic with their use. Some of the obstacles preventing the use of cloud computing technologies in the examined libraries were epileptic power supplies, sluggish internet connections, and a lack of technical expertise. The research suggested, among other things, that appropriate training in cloud computing be done regularly. Attending workshops and conferences may help you accomplish this. This should be made mandatory for all librarians, and enhanced backup generators and an adequate power supply should also be offered to reduce the risk of frequent power outages.