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All Journal Journal of Information Systems and Technology Research
Gonten, Fidelis
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pre-Review Convolutional Neural Network for Detecting Object in Image Comprehensive Survey and Analysis Gonten, Fidelis; Nfwan, Fidelis; Ya’u Gital, Abdulsalam
Journal of Information Systems and Technology Research Vol. 3 No. 2 (2024): May 2024
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v3i2.799

Abstract

The Convolutional neural network (CNN) has significantly exposed a great performances and growing desire in the field of image processing within the research community, through relevant innovations in object detection by magnificent capacity in transfer learning and feature learning. With the advancement of CNN in object detection, huge amount of data is process with great speed. In respect to CNN, object detection has greatly advanced and become popular in the research community, security experts, traffic experts, and remote sensing community etc. In this review, comprehensive study of various CNN architecture for object detection in images based on conventional approached, novelty, and achievement were analysed in details. Therefore, it is an important review on how to achieve high performance in object detection via CNN. We first introduced the basic idea on CNN models and their improvement in detecting object. Secondly, we review CNN and its variant such as, ResNet, VGG, GoogleNet and other CNN architectures. Thirdly, we mention some performance metrics used for object detection. Lastly, we analyse some main contribution of CNN algorithm with their remarkable achievement and further analyse the challenge and its future direction
Systematic Survey Analysis of the Application of Artificial Intelligence Base Network on Grid Computing Techniques Nerat Jakawa, Jimmy; Gonten, Fidelis; Emmanuel, Datti Useni; Pandok, Dakur Atiku; Maikano, Ponfa Canfa
Journal of Information Systems and Technology Research Vol. 3 No. 3 (2024): September 2024
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v3i3.908

Abstract

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. In order to increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. With the goal of integrating the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has achieved great strides toward modernizing and expanding it. In this study, researchers used the Systematic literature review method which identifies, evaluates and interprets all relevant research results related to certain research questions, certain topics, or phenomena of concern.  The study review on how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.
Co-Authors Emmanuel, Datti Useni Maikano, Ponfa Canfa Nerat Jakawa, Jimmy Nfwan, Fidelis Pandok, Dakur Atiku Ya’u Gital, Abdulsalam