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All Journal Journal of Multidisciplinary Science: MIKAILALSYS Asian Journal of Science, Technology, Engineering, and Art ALSYSTECH Journal of Education Technology Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics International Journal of Education, Management, and Technology
Akintayo, Taiwo Abdulahi
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Agriculture, Biological Sciences & Forestry Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Intelligent Recycling Facilities with IoT Sensors and Data Analytics for Environmental Justice and Sustainable Materials Processing in Low-Income Areas Akintayo, Taiwo Abdulahi; Enabulele, Ewemade Cornelius; Paul, Chadi; Okereke, Ruth Onyekachi; Sobajo, Moses Sodiq; Afolabi, Olasunkanmi John; Joel, Ogundigba Omotunde; Nnadiekwe, Oluchi Anthonia; Queenet, Madumere Chiamaka; Abdulyekeen, Rilwan; Emoshoriemhe, Akpaibor Favour; Oyefemi, Oyero Muqadas; Godwin, Agbonze Nosa; Ebuka, Eguzoroh Emmanuel
Journal of Multidisciplinary Science: MIKAILALSYS Vol 2 No 3 (2024): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v2i3.3827

Abstract

This research seeks to transform waste management in low-income communities like Nigeria by introducing intelligent recycling facilities equipped with IoT sensors and data analytics. These innovative facilities will optimize recycling processes, monitor material flows, and provide valuable insights on waste reduction and environmental impact. The goal is to address the pressing issue of waste production, which has become a significant concern in developing nations due to rising food consumption and population growth. In Nigeria, inadequate waste collection and disposal methods have led to environmental pollution and health crises. The common practice of dumping garbage on roads has resulted in unsightly piles of refuse, hindering the nation's beauty. To combat this, we propose the adoption of sustainable smart bins with efficient IoT applications. These smart bins will provide a futuristic solution for waste management, enabling remote monitoring and optimization of waste levels. The benefits of this IoT-based system include (1) Remote access for efficient level control (2) Improved time and energy efficiency (3) Reduced congestion in waste bins. By developing a low-cost, intelligent waste bin system with IoT technology, we can create a green and clean atmosphere within cities. This innovative approach will inform policy and practice, advancing environmental justice and sustainable development in marginalized areas.
Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology Olope, Olumide Innocent; Akintayo, Taiwo Abdulahi; David, Fakokunde Babatunde; Chiamaka, Kalu Henrietta
Journal of Multidisciplinary Science: MIKAILALSYS Vol 2 No 3 (2024): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v2i3.3841

Abstract

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.
Transforming Data Analytics with AI for Informed Decision-Making Akintayo, Taiwo Abdulahi; Paul, Chadi; Queenet, Madumere Chiamaka; Nnadiekwe, Oluchi Anthonia; Victoria, Shittu Sarah; David, Fakokunde Babatunde; Joel, Ogundigba Omotunde; Agada, Olowu Innocent; Ngozi, Egenuka Rhoda; Arinze, Ugochukwu Ukeje; Ojemerenvhie, Grace Alele; Oluwadamilola, Adebesin Adedayo; Nnamani, Chinenye Cordelia; Olayinka, Usman Wasiu
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.3812

Abstract

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.
Computing Performance Optimization Through Parallelization: Techniques and Evaluation Akintayo, Taiwo Abdulahi; Olobo, Neibo Augustine; Atinuke, Aregbesola Taobat; AbdulKareem, Idayat Olaide
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.4210

Abstract

Parallelization has become a cornerstone technique for optimizing computing performance, especially in addressing the growing complexity and scale of modern computational tasks. By leveraging concurrent processing capabilities of multi-core processors, GPUs, and distributed systems, parallel computing enables the efficient execution of large-scale problems that would otherwise be computationally prohibitive. This paper explores various parallelization techniques, including data parallelism, task parallelism, pipeline parallelism, and the use of GPUs for massive parallel computations. We also examine the key performance evaluation metrics such as speedup, efficiency, Amdahl’s Law, scalability, and load balancing that are critical in assessing the effectiveness of parallelization strategies. Through case studies in scientific simulations, machine learning, and big data analytics, we demonstrate how these techniques can be applied to real-world problems, offering significant improvements in execution time and resource utilization. The paper concludes by discussing the trade-offs involved in parallel computing and suggesting future avenues for optimizing parallelization methods in the context of evolving hardware and software technologies.
The Impact of Machine Learning on Fraud Detection in Digital Payment Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 2 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i2.4900

Abstract

The rapid adoption of digital payment systems has revolutionized financial transactions, but it has also introduced significant challenges in combating fraud. Traditional rule-based fraud detection methods are increasingly inadequate against sophisticated and evolving fraud schemes. This research explores the transformative impact of machine learning (ML) on fraud detection in digital payments. By leveraging advanced ML techniques such as supervised learning, unsupervised learning, and deep learning, financial institutions and payment platforms can analyze vast amounts of transaction data in real-time, identify complex patterns, and adapt to emerging threats. Case studies from industry leaders like PayPal, Stripe, and Mastercard demonstrate the effectiveness of ML in reducing false positives, improving detection accuracy, and enhancing scalability. However, challenges such as data quality, model interpretability, and adversarial attacks remain critical concerns. This study highlights the benefits, limitations, and future trends of ML in fraud detection, emphasizing its potential to create a more secure and resilient digital payment ecosystem. As fraudsters continue to innovate, the integration of machine learning with emerging technologies like explainable AI (XAI) and blockchain promises to further strengthen fraud prevention efforts, ensuring the safety and trust of digital payment systems worldwide.
Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization Akintayo, Taiwo Abdulahi; Olobo, Neibo Augustine; Iyilade, Daniel Olorunfemi
Mikailalsys Journal of Advanced Engineering International Vol 1 No 3 (2024): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v1i3.3811

Abstract

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.
Assessing the Cybersecurity Risks Associated with the Internet of Things (IoT) Devices Akintayo, Taiwo Abdulahi; Asolo, Emmanuel; Nnamani, Chinenye Cordelia; Felix, Omojola Ayogoke; Osaro, Chukwuemeka Chukwuma; Atinuke, Aregbesola Taobat
Mikailalsys Journal of Advanced Engineering International Vol 1 No 3 (2024): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v1i3.3862

Abstract

The rapid rise of the Internet of Things (IoT) in our daily lives has brought significant cybersecurity concerns to the forefront, emphasizing the need for both active and proactive measures. This research provides a comprehensive review of the literature on the cybersecurity challenges and threats faced by various IoT devices. It outlines proposed solutions and structural frameworks while also exploring different methods for detecting and identifying potential threats. Additionally, it highlights research gaps within the industrial and economic sectors of IoT applications. Our findings reveal that the main issues affecting IoT systems include cybercrime and privacy violations. While Artificial Intelligence holds great promise for enhancing cybersecurity, many attacks, particularly those focused on authentication and confidentiality, are still inadequately addressed by existing solutions. This indicates a pressing need for further research and practical testing of the recommended defenses.
A Comparative Study of AI-Powered Virtual Assistants in Banking: Features, Benefits, and Challenges Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi
ALSYSTECH Journal of Education Technology Vol 3 No 2 (2025): ALSYSTECH Journal of Education Technology
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/alsystech.v3i2.5191

Abstract

This study examines the adoption of AI-powered virtual assistants in Nigerian banking, focusing on their features, benefits, and challenges. Through a comparative analysis of selected banks, including GTBank, Zenith Bank, and Access Bank, the research highlights key features such as 24/7 customer support, multilingual capabilities, and transaction processing. Benefits include cost reduction, improved customer service, and operational efficiency for banks, as well as convenience and personalized services for customers. However, challenges such as technical issues, low digital literacy, and regulatory compliance hinder widespread adoption. The study concludes with recommendations for stakeholders to enhance the effectiveness of AI-powered virtual assistants, fostering financial inclusion and digital transformation in Nigeria.
The Impact of Artificial Intelligence on Risk Management in Banking and Finance Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi; Adanna, Arinze Betsy
Mikailalsys Journal of Advanced Engineering International Vol 2 No 2 (2025): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v2i2.5195

Abstract

This research explores the transformative role of Artificial Intelligence (AI) in risk management within the banking and finance sector. It examines how AI technologies such as machine learning, natural language processing, and predictive analytics are enhancing risk assessment, fraud detection, and regulatory compliance. The study also highlights challenges such as data privacy, algorithmic bias, and the need for skilled professionals. The findings suggest that AI is revolutionizing risk management but requires careful implementation to mitigate associated risks.
Developing an AI-Driven Predictive Model for Stock Market Forecasting in the Banking Sector Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi; Adanna, Arinze Betsy
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 2 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i2.5197

Abstract

This study develops an AI-driven predictive model for stock market forecasting in the banking sector, using LSTM, Random Forest, and Linear Regression. Historical stock prices, macroeconomic indicators, and banking sector metrics were analyzed, with data preprocessing techniques applied to enhance accuracy. Model performance was evaluated using MAE, RMSE, and R², with LSTM achieving the best results (R² = 0.92). Findings suggest AI models can improve investment decisions, trading strategies, and risk management. Future research should explore real-time data integration, sentiment analysis, and hybrid AI models for enhanced forecasting accuracy.
Co-Authors AbdulKareem, Idayat Olaide Abdulyekeen, Rilwan Adanna, Arinze Betsy Afolabi, Olasunkanmi John Agada, Olowu Innocent Akinnagbe, Olayiwola Blessing Arinze, Ugochukwu Ukeje Asolo, Emmanuel Atinuke, Aregbesola Taobat Chiamaka, Kalu Henrietta David, Fakokunde Babatunde Ebuka, Eguzoroh Emmanuel Emoshoriemhe, Akpaibor Favour Enabulele, Ewemade Cornelius Felix, Omojola Ayogoke Godwin, Agbonze Nosa Iyilade, Daniel Olorunfemi Joel, Ogundigba Omotunde Ngozi, Egenuka Rhoda Nnadiekwe, Oluchi Anthonia Nnamani, Chinenye Cordelia Ojemerenvhie, Grace Alele Okereke, Ruth Onyekachi Olayinka, Usman Wasiu Olobo, Neibo Augustine Olope, Olumide Innocent Oluwadamilola, Adebesin Adedayo Osaro, Chukwuemeka Chukwuma Oyefemi, Oyero Muqadas Paul, Chadi Queenet, Madumere Chiamaka Sobajo, Moses Sodiq Victoria, Shittu Sarah