| 1 |
Author(s):
Anthony Obogo Otiko, Kolawole Olamide Micheal, Sylvia A. Akpotuzor, Nasiru Abdulsalam.
Page No : 1-14
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IoT-Enabled Energy Management Monitoring System for Sustainable Resource Optimization.
Abstract
This research, "IoT-Enabled Energy Management Monitoring System for Sustainable Resource Optimization" introduces an innovative IoT-enabled energy management monitoring system designed to address the escalating global energy demands and promote sustainable practices. By integrating smart controllers, advanced sensors, and a user-centric interface, the system empowers users with real-time insights into their energy consumption patterns, enabling informed decisions for energy optimization. Our experimental results demonstrate the system's ability to provide detailed energy consumption data with a high degree of accuracy, achieving a minimum percentage error of 0.03% in load measurements. This precision not only facilitates effective load management and energy conservation but also contributes to a greener environment by minimizing energy waste. The research aligns with the United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) and Goal 13 (Climate Action) by promoting responsible energy consumption and contributing to the reduction of greenhouse gas emissions. The developed system offers a practical and cost-effective solution for residential settings, with potential for broader applications across various sectors, fostering a more sustainable energy future.
| 2 |
Author(s):
Jesufemi Olanrewaju, Matthias Oluloni Togunde, Oyebola Akande .
Page No : 14-29
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Risk Mitigation Approach to Cyber Threat using AI-Driven Models for the Evolving Threat Landscape.
Abstract
This systematic review examines the effectiveness of AI-driven models in mitigating evolving cyber threats, using the PRISMA framework to analyze studies published between 2019 and 2024. The review focuses on machine learning techniques, including supervised, unsupervised, and deep learning. Findings show that deep learning excels in detecting complex threats like Advance Persistent Threats (APTs) and zero-day vulnerabilities, while supervised learning is effective for known threats but struggles with new attack types. Unsupervised learning adapts well to dynamic environments but has higher false positive rates. The review proposes a multi-layered framework combining AI models with traditional security measures for enhanced threat detection and response. A hybrid approach is recommended as the most effective strategy, though challenges like data quality and algorithmic bias must be addressed for optimal implementation.
| 3 |
Author(s):
Eweoya I., Awoniyi A., Adeniyi O., Okesola K., Udosen A., Adigun T., Fatade O., Amusa A..
Page No : 40-41
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Development of Web-Based Hostel Management System.
Abstract
The Hostel Management System (HMS) is a web-based software application designed to streamline the management of hostels in educational institutions and other accommodation facilities. The system provides a platform for managing various aspects of hostel operations, including student registration, room allocation, maintenance and reporting. The project involved extensive research and analysis of existing systems, identification of user requirements, and questionnaires to obtain a clear picture of what the system should entail and what problems should be solved, as well as the design and development the implementation of the HMS offers several benefits, including increased efficiency, transparency, and accountability, and enhanced student experience. The system also provides real-time access to critical information, enabling quick decision-making by hostel administrators. The project outcomes demonstrate the effectiveness of the HMS in managing hostel operations and improving service delivery, thereby enhancing the student experience.
| 4 |
Author(s):
Ibukun O. Eweoya, Taiwo O. Adigun, Oluwabamise Adeniyi, Amos Awoniyi, Alfred Udosen, Felix Idepefo, Moradeke Adewumi.
Page No : 42-54
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Financial Fraud Prediction in Credit Administration: An Ensemble Approach.
Abstract
The rate at which banks lose funds to loan beneficiaries due to loan default is alarming. As a result of this, subsequent applications to get loans are declined for paucity of funds while job loss is also a resultant effect. Due to the volatility, volume, and variety of data, the way human beings judge credit history has proven inefficient; including statistical approaches but the big data involved cannot be efficiently dealt with. This research uses past loan records based on employment of ensemble learning for fraud prediction in bank credit transactions in order to avoid credit. It evolves an ensemble learning approach to predict fraud in credit administration. AdaBoost ensemble approach was used for the work; MATLAB was employed for training, testing, validation, and to make fraud predictions. The result obtained was benchmarked with Naïve Bayes, Sequential Minimal Optimization (SMO), and decision tree; based on accuracy. The adopted approach attained an accuracy of 80.9% in 2.09 seconds being the highest accuracy compared to all learners used for the evaluation.
