Volume 3, Issue 10 - 7 Articles
This issue presents seven research articles that explore how advanced technologies like AI, machine learning, and data analytics are helping solve real-world problems. These studies cover a wide range of areas including environment, health, communication, transportation, and education. Topics include improving prediction models, building smarter network systems, making autonomous vehicles safer, enhancing data analysis methods, and supporting student mental health. Each article shows how modern tools can lead to better decisions, stronger systems, and more personalized solutions. Together, they highlight the growing importance of combining technology with practical needs to create a smarter and more connected future.
Front Cover
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # i–i, 2024
Editorial Board
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # ii–ii, 2024
Editorial
by Paul Andrew
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # iii–iv, 2024
Table of Contents
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # v–v, 2024
An Integrated Approach to Manage Imbalanced Datasets using PCA with Neural Networks
by Swarup Kumar Mondal and Anindya Sen
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 1-12, 2024; DOI: 10.55708/js0310001
Abstract: Imbalanced dataset handling in real time is one of the most challenging tasks in predictive modelling. This work handles the critical issues arising in imbalanced dataset with implementation of artificial neural network and deep neural network architecture. The usual machine learning algorithms fails to achieve desired throughput with certain input circumstances due to mismatched class… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Artificial Intelligence – Computer Science (AIC))
Conceptual Business Model Framework for AI-based Private 5G-IoT Networks
by Laurence Banda
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 13-20, 2024; DOI: 10.55708/js0310002
Abstract: The fusion of fifth generation (5G) networks, Internet of Things (IoT) and artificial intelligence (AI), referred to as intelligent connectivity by most industry experts, can be seen as a crucial success factor for sustainable digitalization. Until recently, research into these key triad technologies has been conducted in isolation. One of the promising applications of intelligent… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Telecommunications (TEL))
Navigating the Autonomous Era: A Detailed Survey of Driverless Cars
by Vaibhavi Tiwari
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 21-36, 2024; DOI: 10.55708/js0310003
Abstract: The incorporation of cutting-edge technologies like sensor networks, artificial intelligence (AI), and vehicle-to everything (V2X) communication has hastened the rollout of autonomous vehicles (AVs), offering significant possibilities for the future of transportation. This document offers an extensive overview of AV technology, covering essential elements such as technological infrastructure, degrees of automation, cybersecurity threats, societal impacts,… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Transportation Science & Technology (TST))
On a Kernel k-Means Algorithm
by Bernd-Jürgen Falkowski*
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 37-43, 2024; DOI: 10.55708/js0310004
Abstract: This is the extended version of a paper presented at CISP-BMEI 2023. After a general introduction kernels are described by showing how they arise from considerations concerning elementary geometrical properties. They appear as generalizations of the scalarproduct that in turn is the algebraic version of length and angle. By introducing the Reproducing Kernel Hilbert Space… Read More
(This article belongs to the Section Artificial Intelligence – Computer Science (AIC))
Evaluation of Equivalent Aacceleration Factors of Repairable Systems in a Fleet: a Process-Average-Based Approach
by Renyan Jiang, Kunpeng Zhang, Xia Xu and Yu Cao
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 44-54, 2024; DOI: 10.55708/js0310005
Abstract: Research on repairable systems in a fleet is mainly concerned with modelling of the failure times using point processes. One important issue is to quantitatively evaluate the heterogeneity among systems, which is usually analyzed using frailty models. Recently, a fleet heterogeneity evaluation method is proposed in the literature. This method describes the heterogeneity with the… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Operations Research and Management Science (ORM))
Biclustering Results Visualization of Gene Expression Data: A Review
by Haithem Aouabed, Mourad Elloumi and Fahad Algarni
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 54-68, 2024; DOI: 10.55708/js0310006
Abstract: Biclustering is a non-supervised data mining method used to analyze gene expression data by identifying groups of genes that exhibit similar patterns across specific groups of conditions. Discovering these co-expressed genes (called biclusters) can aid in understanding gene interactions in various biological contexts. Biclustering is characterized by its bi-dimensional nature, grouping both genes and conditions… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Mathematical and Computational Biology (MCB))
Enhancing Mental Health Support in Engineering Education with Machine Learning and Eye-Tracking
by Yuexin Liu, Amir Tofighi Zavareh and Ben Zoghi
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 69-75, 2024; DOI: 10.55708/js0310007
Abstract: Mental health concerns are increasingly prevalent among university students, particularly in engineering programs where academic demands are high. This study builds upon previous work aimed at improving mental health support for engineering students through the use of machine learning (ML) and eye-tracking technology. A framework was developed to monitor mental health by analyzing eye movements… Read More
(This article belongs to the Special Issue on SP5 (Special Issue on Multidisciplinary Sciences and Advanced Technology 2024) and the Section Artificial Intelligence – Computer Science (AIC))