R.O. OSASERI

The management of personnel records in many organizations remains largely manual, leading to inefficiencies, data redundancy, and delays in decision-making. This study focuses on the design and implementation of a computerized Personnel Management Information System (PMIS) to address these challenges and improve human resource management processes. The system was developed using Python as the programming language, MySQL as the database management system, and a web-based framework to ensure ease of access and usability. The proposed system automates key human resource functions, including employee registration, attendance tracking, leave management, payroll computation, and report generation. A modular approach was adopted during implementation, ensuring each system component operates independently while maintaining seamless interaction with the central database. Testing was conducted using unit testing, integration testing, system testing, and user acceptance testing, demonstrating that the system is reliable, accurate, and efficient. The results show that the developed system significantly improves personnel data management by reducing errors, enhancing data security, and providing timely reports for informed decision making. This system offers a scalable solution that can be adopted by organizations seeking to modernize their human resource management operations, thereby demonstrating the importance of information technology in enhancing organizational efficiency.

Phishing attacks are one of the most common and dangerous cybersecurity threat today, with attackers using techniques that are getting sophisticated daily to deceive users and get access to their sensitive information. This study shows the development and implementation of a machine learning based phishing URL detection tool designed to identify malicious URLs and do so with high accuracy. This research addresses the growing challenge of detecting phishing websites by analyzing URL characteristics and patterns that distinguish legitimate sites from fraudulent ones. Utilizing a comprehensive dataset of over 10,000 URLs (comprising both phishing and legitimate websites), this study implements multiple machine learning algorithms including Random Forest, Support Vector Machines (SVM), and Gradient Boosting to classify URLs. The system extracts 30 distinct features from URLs, including lexical properties, domain-based characteristics, and third-party service indicators. Feature engineering techniques were applied to optimize model performance, with priority given to handling imbalanced datasets through Synthetic Minority Over-sampling Technique(SMOTE ). The results shows that the Random Forest classifier achieved the highest accuracy of 96.8%, with precision and recall scores of 95.2% and 97.1% respectively. The Gradient Boosting model closely followed with 95.9% accuracy, while the SVM model achieved 92.4% accuracy. Cross-validation techniques were used to make sure the model is robust and prevent overfitting. Feature importance analysis revealed that URL length, presence of suspicious keywords, domain age, and SSL certificate status were among the most significant predictors of phishing attempts. To validate practical applicability, a web-based detection tool was developed using Flask framework, enabling real-time URL scanning and classification. The system incorporates a user-friendly interface that provides instant feedback on URL legitimacy, along with detailed risk analysis and security recommendations. Performance testing also verified an average response time below 200 milliseconds per analysis for URL, making the tool practical for real-world deployment. This research contributes to the study of cybersecurity with the presentation of an efficient, automated phishing detection system that can be employed with web browsers, email clients, or independently

Fuzzy inference systems (FIS) are particularly suited for aggregating multiple data to feed multi-variables decision support systems. Moreover, wine quality is a complex concept that refers to the simultaneous achievement of optimal levels in many parameters, thus single wine attributes spatial data are not adequate to define wine suitability for a specific end use. The aim of this study was to design and implement a fuzzy inference system on wine quality prediction using physiochemical parameters from wine dataset. The proposed system adopted the conventional fuzzy inference system which consists of four major components which are: knowledge acquisition, knowledge base, fuzzy inference engine and a user interface. The dataset is fuzzified into variables that were used to develop rule for the classification of wine quality. The fuzzy inference system followed three transformation stages; fuzzification, rule based and defuzzification processes. The model was implemented using C#, programming language and MYSQL as the relational data base management. The model was developed on window Microsoft system