Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
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This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Journal of Computing Theories and Applications Methods in Science and Technology Studies
Oluwaseyi Omotayo Alabi
Lead City University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Design and development of arduino-based automation home system using the internet of things Sunday Adeola Ajagbe; Oyetunde Adeoye Adeaga; Oluwaseyi Omotayo Alabi; Adewale Bashir Ikotun; Musa A. Akintunde; Matthew O. Adigun
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp767-776

Abstract

The home automation system described in this paper is low-cost, dependable, and versatile. It uses an Arduino microcontroller and Bluetooth internet protocol (IP) connectivity to allow authorized users to remotely access and control devices. The suggested system employs the internet of things (IoT), which is server-independent, to manage human-desired appliances ranging from industrial machinery to consumer products. In this project, we have taken a Bluetooth module that is programmed through an Arduino Nano to control various devices auto-switching of mechanical devices and monitoring of water level within a range of 130 m using an Android application. This is done to show the effectiveness and viability of this system. Each bulb was switched on/off remotely using a mobile phone successfully. The operation of the water pump attached to the source bucket were controlled from the phone while in manual mode and controlled by an ultrasonic sensor while in automatic mode. It enables remote control of a number of devices, including lights and pumps, and decision-making based on sensor feedback.
Thermal Stability of EVA Nanocomposites for Solar Cell Encapsulation Ganiyu Olamide Ogunsiji; Oluwaseyi Omotayo Alabi; Adeoti Oyegbori Laoye; Saidat Abisoye Salisu; Samuel Adekunle Dada
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.385

Abstract

The long-term reliability and performance of photovoltaic (PV) modules largely depend on the thermal stability and durability of encapsulation materials that protect solar cells from environmental and thermal degradation. Ethylene–vinyl acetate (EVA) is widely used as a solar cell encapsulant due to its excellent optical and mechanical properties; however, its thermal stability and resistance to degradation remain critical challenges under prolonged operating conditions. Although EVA-based nanocomposites have been investigated for solar cell encapsulation, limited studies have systematically examined how different nanoclay fillers and processing conditions influence the thermal stability and encapsulation efficiency of EVA materials. This study aims to optimize the thermal stability of EVA nanocomposites by incorporating different inorganic fillers mica, montmorillonite (MMT), and vermiculite, at varying concentrations and milling cycles. An 8% EVA solution was prepared and blended with these fillers to evaluate their effects on the thermal and structural properties of the nanocomposite materials. Thermal characterization using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) revealed noticeable changes in melting temperature, glass transition temperature, and thermal degradation behavior. The incorporation of nanofillers improved the thermal stability of the EVA matrix and influenced its crystallinity and mechanical properties. The optimized EVA nanocomposite demonstrated enhanced thermal resistance and improved durability compared with neat EVA, although a slight reduction in encapsulation efficiency was observed. These findings provide valuable insights into the formulation and optimization of EVA nanocomposites for solar cell encapsulation, contributing to the development of more thermally stable and durable encapsulation materials for sustainable photovoltaic applications.
RETRACTED: The Use of AI to Analyze Social Media Attacks for Predictive Analytics Temitope Samson Adekunle; Oluwaseyi Omotayo Alabi; Morolake Oladayo Lawrence; Godwin Nse Ebong; Grace Oluwamayowa Ajiboye; Temitope Abiodun Bamisaye
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10120

Abstract

This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to issues within this article, including significant overlap in content, methodology, and visual materials with another previously published article: "Social Engineering Attack Classifications on Social Media Using Deep Learning" (DOI: 10.32604/cmc.2023.032373) published in Computers, Materials & Continua in 2023. Upon thorough investigation, it was found that the article substantially reproduces ideas, methodologies, and figures from the original work without proper attribution, violating the ethical standards of the journal and academic publishing. The authors were contacted and asked to provide an explanation for these concerns. The corresponding author acknowledged the oversight and accepted responsibility for the duplication. Consequently, the authors formally requested the withdrawal of the paper. As per journal policy, the Editor-in-Chief has decided to retract the article due to a breach of publication ethics. The journal sincerely regrets that these issues were not detected during the manuscript screening and review process and apologizes to the authors of the original article, as well as to the readers of the journal. For more information on the journal’s ethical policies, please visit: Retraction Policy.
A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis Timileyin Opeyemi Akande; Oluwaseyi Omotayo Alabi; Julianah B. Oyinloye
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10125

Abstract

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.
Deep learning technique for plant disease detection Temitope Samson Adekunle; Morolake Oladayo Lawrence; Oluwaseyi Omotayo Alabi; Adenrele A. Afolorunso; Godwin Nse Ebong; Matthew Abiola Oladipupo
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p55-62

Abstract

A nation's economy is primarily reliant on agricultural growth. However, several plant diseases seriously impair crop growth, both in terms of quantity and quality. Due to a lack of subject matter specialists and low contrast data, accurate diagnosis of many diseases by hand is highly difficult and time-consuming. The farm management system is therefore looking for a method for automatically detecting early illnesses. To overcome these challenges and correctly classify the different diseases, an efficient and small deep learning-based framework (E-GreenNet) is proposed. A MobileNetV3Small model is used as the foundation of our end-to-end architecture to produce finely tuned, discriminative, and noticeable features. Furthermore, the new plant composite (PC), plantvillage (PV), and data repository of leaf images (DRLI) datasets are used to independently train our proposed model, and test samples are used to evaluate its actual performance. The suggested model achieved accuracy rates of 1.00 percent, 0.96 percent, and 0.99 percent on the given datasets after a rigorous experimental study. Additionally, a comparative investigation of our proposed technique against the state-of-the-art (SOTA) reveals extremely high discriminative scores.
Co-Authors Adenrele A. Afolorunso Adeoti Oyegbori Laoye Adewale Bashir Ikotun Ganiyu Olamide Ogunsiji Godwin Nse Ebong Godwin Nse Ebong Grace Oluwamayowa Ajiboye Julianah B. Oyinloye Matthew Abiola Oladipupo Matthew O. Adigun Morolake Oladayo Lawrence Musa A. Akintunde Oyetunde Adeoye Adeaga Saidat Abisoye Salisu Samuel Adekunle Dada Sunday Adeola Ajagbe Temitope Abiodun Bamisaye Temitope Samson Adekunle Timileyin Opeyemi Akande