Covenant Journal of Informatics and Communication Technology

Development of Academic Warehouse Inventory Management System for Educational Institutions

Abraham Ayegba Alfa — 2024-05-27

Inventory control and management are a core in assessing operational efficiencies of organisations. Reasons being that, understocking, overstocking, out-of-stock, material theft, and delivery delays hampers operations of organizations like educational institutions. In developing economies, there is still slow and little adoption of advanced technologies in the services rendered by warehouses of organization including quantity of items held, monitored, recorded and tracked, which are performed manually causing large delays in processing inventory, inaccuracies and inconsistencies of stock records, duplication of stock requests, slow or delays in processing inventories. These renewed the calls among researchers as to the need to properly and securely maintain and control inflows and outflows of items, materials and equipment necessary for effective learning and experiences of the learners. To this end, this paper developed an academic warehouse inventory management system for effective services delivery and prompt responses to needs of the educational institutions. The prototype system was developed using HTML, JavaScript, PHP, and MySQL database web programming tools. This offers better speed, accuracy, and security of inventory control and management when compared to manual approach currently in use.

Performance Evaluation of Covolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for Intrusion Detection

Nureni Ayofe Azeez — 2024-06-03

In the context of cybersecurity, effective intrusion detection plays a crucial role in safeguarding computer networks and systems from malicious activities. The motivation for this project stems from the increasing complexity and sophistication of cyberattacks, which necessitates the development of advanced and accurate intrusion detection models. The aim of this work is to perform a comprehensive evaluation of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for intrusion detection. CNN and RNN are two popular deep learning architectures known for their ability to extract meaningful patterns and temporal dependencies, respectively, making them suitable candidates for intrusion detection tasks. Two benchmark datasets: NSL-KDD and CICIDS2017 containing labeled network traffic data with various types of intrusions were employed and compared through multiple evaluation metrics. The results obtained from the experiments demonstrate the effectiveness of both CNN and RNN models in detecting intrusions. The CNN model achieved an accuracy of 86.40% on the NSL-KDD dataset and 95.20% on the CICIDS2017 dataset, while the RNN model achieved higher accuracy values of 96.20% and 94.10% on the respective datasets. Additionally, precision, recall, F1-score, error rate and other metrics were calculated and compared for both models. The results highlight the superiority of RNN in the NSL-KDD dataset and CNN in the CICIDS2017 datasets in terms of accuracy on the evaluated datasets. These findings contribute to the body of knowledge in the field of intrusion detection and can guide the selection and deployment of appropriate models for real-world applications, ultimately enhancing the security of computer networks and systems.

Development of a Virtual Assistant Maintenance System for Some Computer System Issues

Mutiat A. Ogunrinde — 2024-06-03

Virtual assistants constitute a rich history dating back to the early 1960s; however, they did not gain popularity until the 21st century. The manual maintenance of computer systems can be time-consuming, complex, and error-prone, leading to system downtime and decreased productivity. This study developed a Virtual Assistant system for some Computer System Maintenance issues. The analysis of existing applications and the design was done using Customtkinter. The design was implemented using Python. The Applications were tested for all four major functionalities (Network Test, Sort Files, Clear Bin, and Find) of the developed system. The Evaluation metrics used were CPU Utilisation, Memory Utilisation, and Response Time. The result of the evaluation shows that the Application's CPU usage had an average of 40%, memory space takes about 22.7MB, and the system takes around 1 minute to respond to users' requests. The application was deployed to the Microsoft Store. System maintenance through VAMS will pave the way for a new era of efficiency, productivity, and user satisfaction, propelling organisations toward success in the digital era.

Queue Management Application for Healthcare Providers

Oluwasogo Adekunle Okunade — 2024-06-05

Increase in healthcare services demands, coupled with the traditional methods of attending to medical services that demand physical attention of the medical practitioners and everyone seeking medical attention, to physically appear in a particular location at a point in time. As a result of the high demand for medical attention congestion, long patient queues, inpatient attention due to long queues, and discomfort experienced by those physically waiting in line for medical attention. This coupled with the danger and risk faced by medical practitioners and patients, especially during the Covid-19 outbreak, calls for other efficient means of booking an appointment with a medical practitioner without facing the usual abnormal rigours. These calls for the introduction of a Virtual Queue Management Application (VQMA, a mobile APP developed using scheduling and priority algorithms to regulate the flow of people seeking medical services. This allows patients to schedule appointments online through a mobile APP, and arrive at the health centre at their appointed time. Patient appointments are categorised as critical and non-critical. The Critical category handles emergencies or life-threatening cases requiring urgent attention, while the non-critical category is divided into Regular and Delayed. It enables individuals seeking essential services to wait in line virtually. VQMA was tested and observations were made on the patient arrival rate within a 60-minute interval. The report derived from the experiments, reveals a consistent decrease or gradual reduction in average waiting time for non-critical appointments. This improvement demonstrated a 67% better performance over the existing model.

Proof-of-Congruity Consensus Scheme: A Blockchain Consensus Mechanism for Mining Potential Data for Forensic Analysis

Omoniyi Wale Salami — 2024-06-07

Consensus mechanisms are crucial to achieving the desired security for blockchain data. They are used to coordinate the agreement process and endorse the value mined in blockchain. Forensic analysis requires data with sound integrity and fidelity to establish the truth. Existing blockchain data endorsement mechanisms are based on either the concept of effort throttling or risk-taking as used in Proof-of-Work and Proof-of-Stake, respectively. Effort throttling could make forensic analysis take longer time than necessary, while risk-taking may divert miners’ focus from ensuring the accuracy of data to protecting their stakes. This paper proposed a new data endorsement solution, called the Proof-of-Congruity Consensus Scheme, for ensuring miners’ trust and data authenticity in blockchain. Gestalt psychology principles, the principle of pattern completion and the law of perceptual constancy, were used for implementing partial hash collision in the proposed solution to enhance data accuracy and validation speed. It also attaches to endorsers’ responses the trend of compliance of a miner in the past consensus processes it participated. The proposed solution took an average of 207.44 milliseconds to mine data when it was tested. It ensures that the miner’s level of trustworthiness is accessible from its responses for authenticating the response and provides faster data mining that is suitable for forensic investigation.