University of Ibadan Journal of Science and Logics in ICT Research

Enhancing Regulatory Compliance in the National FinTech Ecosystem: A Centralized RegTech Approach

Nefishetu Faith ALIU, Abdullahi Braimoh IKHARO , Abayomi Joshua JEGEDE — Mon, 28 Oct 2024 00:00:00 +0000

This study looked at a centralized regtech approach to improving regulatory compliance in the national financial technology ecosystem. The study's specific goals are to design a FinReg/FinTech ecosystem using the new National Financial Ontology (NFO) model and investigate the issue of information overload using a cost effective algorithm in the program. This was tested using the big O time notation to show that the size of the data does not affect processing time. Centralization of regulatory technology (RegTech) is a crucial method to strengthening regulatory compliance and control in the fast evolving financial technology (fintech) sector. The Object-Oriented methodology (OOM), which focuses on encapsulating the behavior and structure of information systems into compact modules, is the approach used in this study.

Enhancing Data Security: Implementing a Step-Count Method for Confidential Communications during Practical Transmission

I. O. Adeyemi , S. O Akinola, S. K Olagunju , F. S. Omotosho — Mon, 28 Oct 2024 00:00:00 +0000

Overall, the improved structure of the data encryption standard algorithm delivers a state-of-the-art solution for
protecting confidential data backed by unrivaled expertise and meticulous design. It is an essential tool in
today's digital landscape, where cybersecurity threats are ever-evolving. Encryption is the encoding of a
statement that only trusted parties can read. Only the authorized recipient understands the encoded message. The
research aims to develop a step-count data encryption model for minimizing information breaches in
transmission. In this research, the "key" length is the same as the original dataset, the cipher length is equal to
the original message, and it has a reduction in encrypting and decrypting times compared to the RSA algorithm.

Botnet Attack Detection in Internet of Things Using Selected Learning Algorithms

B. L Aremu, Aro T. O Aro, K. K Saka, A. A Seriki, R.O Raji — Mon, 28 Oct 2024 00:00:00 +0000

The Internet of Things (IoT) refers to a network of everyday devices, such as smartphones and industrial sensors, all connected to the Internet, allowing them to communicate and share data. IoT networks comprise various devices with different functions, communication protocols, and computational capabilities. This heterogeneity complicates the development of a one-size-fits-all solution for botnet detection. Developing effective botnet detection systems for IoT environments is challenging due to the diversity of devices, each with unique characteristics and behaviors. This study focuses on creating a robust model to identify botnet attacks across various IoT devices. Using the NB-IoT-23 datasets, which include data from five distinct devices, supervised machine learning techniques, namely Logistic Regression, Linear Regression, Artificial Neural Network (ANN), K-nearest neighbours (KNN), and Bagging, were employed to identify the most accurate and efficient method. The research highlights the Bagging ensemble technique as particularly effective. The Bagging model demonstrated remarkable performance, achieving an accuracy of 99.96%, precision of 99.93%, recall of 99.98%, an F1 score of 99.96%, and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 99.96%, all within a training time of 27.59 seconds. These results suggest that the Bagging model is highly effective and very efficient, making it a strong candidate for real-world IoT botnet detection. The model's high accuracy and low computational overhead make it a viable solution for real-world applications of Botnet detection, contributing significantly to the ongoing efforts of stakeholders in securing IoT networks against botnet threats.

Enhancing Intermediate System Network Routing Mechanism for Wireless Sensor Networks through Swarm Intelligence Algorithms Techniques

O.Y. Bello-Sulayman , I. O Mustapha , K. K. Saka , U. B Musa — Mon, 28 Oct 2024 00:00:00 +0000

Wireless Sensor Networks (WSNs) consist of nodes equipped with limited energy resources. These micro
sensors collect and relay data to a central node, but they often encounter challenges related to energy efficiency.
This study examines WSNs' architecture, uses, and energy issues, and introduces Particle Swarm Optimization
(PSO) and Firefly Optimization (FFO) to refine routing protocols. The focus is on enhancing the Intermediate
System Routing Protocol (ISRP) by addressing energy use, transmission delays, and packet delivery. The
method includes node placement, coverage, link stability, and optimization via PSO/FFO. Performance is
assessed through metrics such as energy consumption, delay, packet delivery ratio, and network lifetime
Performance is assessed through energy consumption, delay, packet delivery ratio, and network lifetime. These
research shows that optimizing ISRP with PSO and FFO leads to significant improvements: energy use
decreases from 0.235J to 0.14J, delay reduces by 0.5974s, packet delivery rises from 87% to 96%, and network
lifespan extends from 370s to 576s. This work enhances WSN efficiency and longevity, offering insights for
future studies.

Efficient Tuberculosis Detection Using Chest X-ray Images with Deep Learning Algorithms

journal manager; Olusesan A. Obakunle , Oladimeji A Abiola , Solomon O. Akinola — Mon, 28 Oct 2024 00:00:00 +0000

Tuberculosis is a threat to the existence of the human race due to its substantial mortality rate and it has become a
significant public health concern, which if detected at an early stage could reduce the death rate globally.
Harnessing the potential of machine learning to combat the low detection rate of tuberculosis detection by traditional
methods and promote a faster and more accurate diagnosis of the disease. An online Dataset comprising 11,200
Chest X-ray (CXR) images of different categories of patients that are healthy, Sick but not infected, and those
infected with Tuberculosis with their corresponding bounding box annotations were used for this research, and
feature engineering was carried out on the dataset for effective data cleaning, Image resizing, normalization, and
augmentation to increase the quality of data during data segmentation. The dataset was divided into training,
validation, and testing sets using the RestNet50 model which demonstrated a good performance in classification,
achieving impressive precision, and recall of 98%, and 96% respectively, and YOLOv8 was also used with 68%
precision, 65% recall, and 68% mean average precision respectively which showed that the model needs
improvement to further accurately detect regions infected with tuberculosis.