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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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Journal : Mikailalsys Journal of Advanced Engineering International

 1 
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.
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.
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