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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Artificial Intelligence
Ukhurebor, Kingsley Eghonghon
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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New developments and trends in 5G technologies: applications and concepts Tyokighir, Silas Soo; Mom, Joseph; Ukhurebor, Kingsley Eghonghon; Igwue, Gabriel
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6032

Abstract

Fifth-generation (5G) wireless technology is the most up-to-date iteration of mobile data networks. This research analyzes the effectiveness of next-generation mobile networks in tandem with mobile communication technologies. Various difficulties encountered at each stage are discussed. With the advent of 5G networks, users may connect to the internet at lightning speeds from almost any location. 5G is one of a kind because of its new characteristics, which allow it to link people and enable them to operate gadgets, machines, and things. 5G mobile technology’s varying speed and capabilities will allow for new user experiences and link new businesses. Companies must know where they can best put 5G to use. This research paper examines and analyzes various topics in great detail, demonstrating how mmWave, massive multiple-input and multiple-output (massive MIMO), microcells, mobile edge computing (MEC), beamforming, diverse antenna technologies, and so on can all work together to improve cellular networks. The primary goals of this article are to demonstrate some of the most recent technical developments and to analyze potential future research directions for the 5G mobile system.
Automatic Pose Recognition in Basketball Videos Using Entropy, Mean and Standard Deviation Paul, Aliga; Nehinbe, Joshua; Ukhurebor, Kingsley Eghonghon
International Journal of Artificial Intelligence Vol 12 No 2: December 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01202.908

Abstract

Most existing models for automatic action recognition in basketball videos lack privacy-friendly analytics, versatility and explainability. So, coaches, players and analysts often invest substantial resources by relying heavily on visual appearance, ball tracking and court context. Unfortunately, this method can be resource-intensive and potentially susceptible to unforeseeable intrusions. This study proposes an entropy-based analytical model for automatic recognition of key basketball actions, designed to optimize the video review process to address the above limitations. The model is implemented with Python programming language to analyze entropy arrays, the mean and standard deviation values derived from 22 basketball game videos. Evaluation suggests that the model flagged basketball_Video2, Video3 and Video9 as containing key moments deserving closer inspection. This has successfully reduced the input datasets to just three critical videos (with mean and standard deviation pairs of 1.96 & 0.33, 2.05 & 0.31, and 1.94 & 0.20) that warrant detailed examination. This targeted filtering significantly improves review efficiency by conserving time and resources and effectively eliminated 19 videos deemed redundant or of lower priority. The approach demonstrates high precision in identifying impactful gameplay moments and addresses a long-standing challenge with workload reduction in basketball analytics without sacrificing review accuracy. Consequently, this method not only supports privacy-conscious analytics but also provides coaches, players and sports analysts with a more focused, resource-efficient framework they can adopt for performance evaluation and strategic decision-making in basketball.
Co-Authors Igwue, Gabriel Mom, Joseph Nehinbe, Joshua Paul, Aliga Tyokighir, Silas Soo