http://repository.unizik.edu.ng/handle/123456789/84 Department of Computer Science 2026-04-04T04:22:12Z http://repository.unizik.edu.ng/handle/123456789/1044 Title: Survey of Cryptography models for Security of Computer-based Integrated School Information Management System Authors: Chinenye P., Ikeagwu; Virginia E., Ejiofor; Everistus Eze, Orji; Godspower, Akawuku Abstract: This survey examines cryptography models for securing computer-based integrated school information management systems. The study reviews symmetric and asymmetric key cryptography, hash functions, and digital signatures. Cryptography techniques, including encryption, decryption, key exchange, and digital certificates, are also discussed. The application of these models and techniques to school information management systems is explored, highlighting benefits such as confidentiality, integrity, and authenticity. Thus, the work adopted systematic literature review (SLR) methodology: extensively on books, e-books, internet, compendium, journals and research papers published in symposia, seminars and conferences organized in and beyond Nigeria were exploded. Nevertheless, challenges abound in critical areas, including key management, performance, user education, and regulatory compliance, are also identified. This survey provides a comprehensive overview of cryptography models and their potential to enhance the security of school information management systems. Description: Scholarly work 2025-01-01T00:00:00Z http://repository.unizik.edu.ng/handle/123456789/1043 Title: Design and Implementation of Persuasive Technology for Maternal health care: Nigeria as a Case study Authors: Charity E., Mbanusi; Virginia E, Ejiofor; Irabor Chuks, Clarence; Godspower I., Akawuku Abstract: Persuasive technologies are Human-Computer Interaction (HCI) systems integrated with strategies designed to encourage users to perform specific target behaviors. Maternal healthcare services in Nigeria are structured to promote information-seeking behaviors and enhance the health and wellness of pregnant and nursing mothers through social and motivational approaches. However, these strategies have been largely ineffective, resulting in a persistently high rate of maternal deaths due to poor health practices and the lack of engagement with smart e-health solutions. This research investigates the immediate and remote factors contributing to inappropriate maternal healthcare behaviors, aiming to design and implement a responsive persuasive technology that encourages responsible maternal health-seeking behaviors. To achieve this goal, a mixed-method approach was adopted, incorporating user studies (survey), research design, and the Persuasive System Design (PSD) framework. The findings from the user-based research were carefully mapped to five critical factors affecting the adoption of appropriate maternal health-seeking behaviors in rural communities. These factors were then aligned with corresponding persuasive strategies, which informed the development of innovative and cost-effective design guidelines. These guidelines are intended to motivate attitudinal and behavioral changes in the health and information-seeking practices of expectant and nursing mothers in rural African communities. Following the research phase, a persuasive technology for maternal healthcare was designed and implemented using an object oriented methodology. This technology was developed in PHP and it addresses negative maternal healthcare attitudes by encouraging positive behavioral changes among users. The system incorporates relevant persuasive strategies and is built from the ground up to ensure privacy, while providing personalized and tailored views for users. Description: Scholarly work 2025-01-01T00:00:00Z http://repository.unizik.edu.ng/handle/123456789/1042 Title: Design and Implementation of an enhanced Cryptography model using the combination of Blowfish and RSA for the Security of Computer-based Integrated School Information Management System Authors: Chinenye P, Ikeagwu; Virginia E., Ejiofor;; Orji Everistus, Eze; Godspower, Akawuku Abstract: Data transmission threats have become a critical concern for organizations and individuals globally. Information’s most valuable assets, alongside human resources, and the compromise of organizational data can lead to significant damage or even destruction. Consequently, organizations invest substantial resources to ensure the security of their data. The primary focus of the research is the development of a secure cryptographic model for a computer-based integrated school information management system (SIMS). The study addresses the critical need for secure data transmission between users and employs advanced cryptographic techniques to protect against intruders. To achieve this, the research integrates two cryptographic algorithms: RSA, an asymmetric encryption model, and Blowfish, a 64-bit symmetric system encryption model. This hybrid cryptographic framework referred to as Paschaline, is designed to enhance overall message security while improving system performance. The developed system incorporates biometric, and digital monitoring tools to ensure accurate supervision and inspection of school activities. Features include a surveillance camera for classroom monitoring biometric systems for teacher presence verification and teacher image capture for easy identification. The study utilized object-oriented Analysis and Design Methodology (OOADM) for system development and deployed technologies such as python, Django, PHP, and MYSQL for implementation, testing in selected schools in one of the zones in Ebonyi state demonstrated the efficiency of the online monitoring system and the reliability of the hybrid cryptographic model. The results confirm that the system ensures the availability, reliability and authenticity of secured data, thereby addressing the challenges of data transmission threats in educational institution. Description: Scholarly work 2025-01-01T00:00:00Z http://repository.unizik.edu.ng/handle/123456789/718 Title: Review of Some Machine Learning Techniques for Big Data, Their Uses, Strengths and Weaknesses Authors: Asogwa, D.C; Anigbogu, S.O; Onyesolu, M.O; Chukwuneke, C.I Abstract: Machine learning is a field of computer science which gives computers an ability to learn without being explicitly programmed. Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy.Big data are now rapidly expanding in all science and engineering domains. The potentials of these increased volumes of data are obviously very significant to every aspect of our lives. To aid us in decision making and future predictions requires new ways of thinking and new learning techniques to address the various challenges.Traditional analytical approaches are insufficient to analyze big data because they are highly scalable and unstructured data captured in real time. Machine learning (ML), addresses this challenge, by enabling a system to automatically learn patterns from data that can be leveraged in future predictions.This paper reviews some machine learning techniques for big data highlighting their uses/applications, strengths and weaknesses in learning data patterns. The techniques reviewed include Bayesian network, association rules, naïve bayes, decision trees, nearest neighbor and super vector machines (SVM) 2019-10-01T00:00:00Z