A welcoming environment is important to promote learning activities in a tertiary institution. Students’ violence has become a major concern in developing countries like Ghana. There is therefore the need to provide a system that will offer assistance to ensure or minimize this violence from students in other to provide tranquil learning environment. With the use of technology like Radio-frequency identification (RFID) the security of Kumasi Technical University (KsTU) can be up graded. Considering the enrollment of staff and the number of students, physical checks for security will demand time and cost. In this paper, a system which does not consume time, very efficient and cost effective will be utilized to oversee the physical security of KsTU. RFID and Local temporary capacity administration can also be used to manage student’s attendance database server. Two algorithms will be presented for efficient communication between the database server and nearby gadgets which will be raspberry pi. A discussion will be done to assess the time and cost involved in student verification authentication process and attendance. This paper is a theoretical analysis of the proposed system.

We examined a similarity measure between text documents clustering. Data mining is a challenging field with more research and application areas. Text document clustering, which is a subset of data mining helps groups and organizes a large quantity of unstructured text documents into a small number of meaningful clusters. An algorithm which works better by calculating the degree of closeness of documents using their document matrix was used to query the terms/words in each document. We also determined whether a given set of text documents are similar/different to the other when these terms are queried. We found that, the ability to rank and approximate documents using matrix allows the use of Singular Value Decomposition (SVD) as an enhanced text data mining algorithm. Also, applying SVD to a matrix of a high dimension results in matrix of a lower dimension, to expose the relationships in the original matrix by ordering it from the most variant to the lowest.