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All Journal Asian Journal of Science, Technology, Engineering, and Art ALSYSTECH Journal of Education Technology Mikailalsys Journal of Advanced Engineering International International Journal of Education, Management, and Technology
Olobo, Neibo Augustine
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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 1 
Image Contrast Enhancement Using General Histogram Equalization and Homomorphic Filtering Olobo, Neibo Augustine
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 1 (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.v3i1.4244

Abstract

The realm of image processing is characterized by the judicious application of mathematical operations to facilitate image transformation and refinement. By synergizing signal processing techniques, image processing can culminate in an enhanced image or the extraction of salient parameters. This research concentrates on ameliorating the contrast of images beset by low contrast, concomitantly extracting relevant image parameters. Images with subpar contrast can engender flawed outcomes in myriad disciplines, highlighting the necessity of contrast enhancement. This study introduces an innovative image processing system, conducting a comparative analysis of General Histogram Equalization and Homomorphic Filtering. The results unequivocally demonstrate the superiority of Homomorphic Filtering. The system's output manifests pronounced efficiency in elevating image contrast, heralding far-reaching implications.
Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks Asolo, Emmanuel; Chijioke, Jeremiah Henry; Olobo, Neibo Augustine; Oluwagbemi, Isaac Adeolu; Osaro, Chukwuemeka Chukwuma
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.v2i3.4056

Abstract

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.
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.
A Survey of Computer Vision Methods for Financial Information Analysis in Healthcare Applications Olobo, Neibo Augustine; Anthony, Omotola Rahimat; Adebiyi, Olubukola Eunice; Olaide, AbdulKareem Idayat
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.v3i2.5015

Abstract

The integration of computer vision (CV) techniques in healthcare has revolutionized the analysis of medical data, enabling improved diagnostics, treatment planning, and patient care. However, the application of Computer vision methods to analyze financial information within healthcare systems remains an underexplored area. This survey paper provides a comprehensive review of computer vision methodologies applied to financial data analysis in healthcare, focusing on tasks such as invoice processing, expense tracking, fraud detection, and cost optimization. We explore the intersection of Computer vision and financial informatics, highlighting key algorithms, datasets, and challenges. Additionally, we discuss the potential of these methods to enhance financial transparency, reduce operational costs, and improve resource allocation in healthcare systems. This paper aims to serve as a foundational resource for researchers and practitioners working at the intersection of computer vision, healthcare, and financial analytics.
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.
Co-Authors AbdulKareem, Idayat Olaide Adebiyi, Olubukola Eunice Akintayo, Taiwo Abdulahi Anthony, Omotola Rahimat Asolo, Emmanuel Atinuke, Aregbesola Taobat Chijioke, Jeremiah Henry Iyilade, Daniel Olorunfemi Olaide, AbdulKareem Idayat Oluwagbemi, Isaac Adeolu Osaro, Chukwuemeka Chukwuma