| 1 |
Author(s):
Kupolusi Joseph Ayodele, Balogun Oluwatobiloba Oluwajuwon, Akomolafe Abayomi Ayodele.
Page No : 1-35
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Credit Card Fraud Detection Using Machine Learning Algorithms.
Abstract
The increasing use of credit cards in various transactions has resulted in an upsurge in fraudulent activities. This has caused significant financial losses for both individuals and businesses. This research attempted to focus on developing an efficient credit card fraud detection system using machine learning algorithms. Specifically, the Random Forest, Logistic Regression, K-nearest neighbours, Decision Trees, and naive Bayes algorithms were used to analyze the dataset and predict fraudulent activities. The dataset was preprocessed, and feature engineering techniques were applied to improve the performance of the models. Experimental results show that the Random Forest algorithm outperformed other models with an accuracy rate of 99.95%, precision of 0.85%, and recall of 0.85%. These findings indicate the potential of using machine learning algorithms in detecting credit card fraud, and the proposed system could be implemented in financial institutions and payment processing companies to improve their fraud detection systems
| 2 |
Author(s):
Okafor P. C., James G. G., Ituma C..
Page No : 36-57
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Design of an Intelligent Radio Frequency Identification (RFID) Based Cashless Vending Machine for Sales of Drinks.
Abstract
This study examines the evolution of vending machines, emphasizing their integration with RFID technology for cashless transactions. Vending machines have transformed human-machine interaction, offering convenient access to products and services. Transitioning from coin-operated to RFID-enabled systems has revolutionized the industry, enhancing security, reducing costs, and improving user experience. Through exploration of technical specifications and design objectives, the research highlights RFID vending machines' potential to reshape consumer behavior and optimize operations. Traditional cash-based vending machines face challenges such as limited storage, recognition issues, and security concerns. To address these, the paper proposes an intelligent RFID-based cashless vending machine for drink sales. The system incorporates RFID technology for payment, allowing users to swipe cards and select drinks without cash involvement. Prototype development involved software design, utilizing C-language for multiproduct vending. Comparatively, the RFID-based system outperforms cash-based counterparts in efficiency, security, and sales tracking, presenting a superior solution for drink sales.
| 3 |
Author(s):
Samson Obaloluwa Ojo, Ibitayo Johnson Adelaja, Timothy Oladotun Adio, Adebayo Ola Afolaranmi (Ph.D.).
Page No : 58-72
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Assessing the Impact of Technology on Church Services and Youth Engagement.
Abstract
The advent of technology has revolutionized many aspects of human life, including the operation and engagement methods of religious institutions. Historically, church services were confined to physical spaces, limiting congregational engagement to in-person attendance. Traditional worship, involving sermons, hymns, and communal activities, fostered a sense of community and spiritual enrichment. However, declining attendance in traditional settings necessitates the integration of modern digital tools to revitalize worship experiences and engage tech-savvy youth. This study explores the transformative impact of technology on church services, particularly its effectiveness in engaging younger congregants, through a quantitative survey of church leaders, technology experts, and congregants across various denominations. The research examines the adoption and impact of digital innovations such as live-streaming, social media outreach, mobile apps, and multimedia worship formats. Findings highlight enhanced accessibility, participation, and connection, making faith more relevant to younger generations. Challenges such as the potential dilution of spiritual experiences and the digital divide are also identified. Applying the Diffusion of Innovations theory, the study underscores the need for strategic, thoughtful integration of technology that balances modern conveniences with core religious values, bridging generational gaps and ensuring the continued vitality of religious communities in the digital age.
| 4 |
Author(s):
Oladejo Samuel Adetunji, Waheed A. A. (Ph.D.).
Page No : 73-84
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Improvement on Cybersecurity Techniques and Risk Mitigation of Information Systems Used in Internet Banking and Mobile Banking.
Abstract
Abstract: Cybercrime committed on financial institutions are rapidly and steadily becoming more sophisticated and more widespread. The rise in occurrence and extent of cyber-attacks can be linked to a number of factors, such as ineffective risk management systems within banking sectors, ICT technological infrastructure and staff competency and awareness about cybercrimes attacks. As a result of the vulnerabilities in the systems, organized criminals take advantage to breach financial institution’s systems to steal money. This study makes an effort to look into ways to improve improved multi-tier threat and risk management system for internet and mobile banking. The completion of this project marks a significant milestone in the development of a modern banking management application system. The Banking Management Application System, integrated with Flutter, DRF, and MySQL, demonstrates the capabilities of these technologies in building cross-platform mobile applications with an intuitive and visually appealing user interface, robust backend API, and efficient database management. By implementing essential banking functionalities, the system aims to enhance the banking experience for customers, providing convenience, security, and efficiency in managing their accounts and conducting transactions. The successful implementation of the Banking Management Application System with DRF and MySQL confirms the feasibility and effectiveness of this technology stack. The system's architecture, database design, user interface design, and the powerful features of DRF and MySQL contribute to its overall functionality and user satisfaction. Through this project, we have gained valuable insights into software design, system implementation, and the utilization of the Dart-Flutter-DRF-MySQL stack for developing a comprehensive and feature-rich banking management application system.
| 5 |
Author(s):
Abbas Sani, Faisal Rasheed, Bachcha Lal Pal, Ajay Singh Dhabariya, Usman Haruna, Babangida Salisi Mu'az, Abdulgaffar Abubakar Yahya.
Page No : 85-93
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Comprehensive Review on Natural Language Generation for Automated Report Writing in Finance.
Abstract
The financial industry is transforming with the advent of Natural Language Generation (NLG), a subset of Natural Language Processing (NLP), which automates data conversion into coherent and contextually relevant narratives. This paper presents a comprehensive review of NLG's application in financial report automation, tracing its evolution from template-based methods to advanced deep learning and knowledge graph techniques. We discuss the relevance of NLG in automating report generation, its role in enhancing data analysis and decision-making, and its potential to improve investor communications and compliance with regulations. The paper identifies research gaps, including the need for optimization, accuracy improvement, and the integration of machine learning models for better classification and prediction. A proposed methodology for structured report generation is outlined, leveraging deep learning architectures such as RNNs and LSTMs. Future work aims to address these gaps and further integrate NLG into financial reporting, promising to streamline processes, reduce costs, and provide more personalized and insightful financial narratives.
| 6 |
Author(s):
Usman Haruna, Bachcha Lal Pal, Ajay Sing Dhabariya, Faisal Rasheed, Asifa Farooq Shah, Abbas Sani, Babangida Salisu Mu'azu, Abdulgaffar Abubakar Yahya.
Page No : 94-106
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Review on Temporal Convolutional Networks for Electricity Theft Detection with Limited Data.
Abstract
Electricity theft detection using artificial intelligence (AI) and machine learning techniques has shown significant promise in recent research. However, practical implementation and widespread adoption of these advanced methods face several persistent challenges, particularly when dealing with limited data. This review delves into the computational complexity, data requirements, overfitting issues, and scalability and generalizability concerns associated with popular techniques such as Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Deep Convolutional Neural Networks (DCNN), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Artificial Neural Networks (ANN). Computational complexity and resource constraints affect the training times and convergence of TCN, LSTM, and DCNN, while high data needs and parameter tuning hinder MLP and GRU. The ANN-based method utilized by the Electricity Company of Ghana underscores overfitting and data duplication, further exacerbated by limited data availability. Moreover, the scalability and generalizability of TCN, LSTM, and DCNN across different regions and larger datasets are limited, with effectiveness varying based on electricity consumption patterns and theft tactics. Addressing these challenges through optimizing computational efficiency, improving data quality and utilization, and enhancing scalability and generalizability is crucial, especially in data-constrained environments. Continued research and development in these areas will be essential for realizing the full potential of AI-based electricity theft detection systems with limited data.
Keywords: Electricity Theft Detection, Artificial Intelligence, Machine Learning, Limited Data, Computational Complexity, Data Quality, Scalability, Generalizability, Overfitting
| 7 |
Author(s):
Ahmad Umar Labdo, Ajay Singh Dhabariya, Zainab Mukhtar Sani, Musa Abubakar Abbayero.
Page No : 107-117
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A Review of Task Offloading Algorithms with Deep Reinforcement Learning.
Abstract
Enormous data generated by IoT devices are handled in processing and storage by edge computing, a paradigm that allows tasks to be processed outside host devices. Task offloading is the movement of tasks from IoT devices to an edge or cloud server –where resources and processing capabilities are abundant– for processing, it is an important aspect of edge computing. This paper reviewed some algorithms of task offloading and the techniques used by each algorithm. Existing algorithms focus on either latency, load, cost, energy or delay, the deep reinforcement phase of a task offloading algorithm automates and optimizes the offloading decision process, it trains agents and defines rewards. Latency-aware phase then proceeds to obtain the best offload destination in other to significantly reduce the latency.
| 8 |
Author(s):
Abdulgaffar Abubakar Yahaya, Bashir Muhammad Ahmad, Faisal Rasheed, Usman Haruna, Abbas Sani, Babangida Salisu Muaz, Ismail Abubakar Yahaya, Aliyu Hamza Idris.
Page No : 118-131
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Application of Machine Learning in Education: Recent Trends Challenges and Future Perspective.
Abstract
In recent times, Machine learning (ML) is one of the most valuable fields of artificial intelligence (AI) that is transforming education. The application of ML in education provides a promising benefit both to the scientists and researchers and this is the focus of this study. This paper reviews recent trends and advancements of ML in education focusing on areas such as personalization of learning, predictive analytics, plagiarism detection, intelligent tutoring systems, gamification of learning, and recommendation systems. After conducting the literature review, we found the current benefits and challenges of ML in education. The paper also provides insight into the applications and recommendations to address the challenges of ML in education.
