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Special Issue on Multidisciplinary Sciences and Advanced Technology 2026
Guest Editors: Prof. Paul Andrew
Deadline: December 31, 2026

Special Issue on Advances in Engineering, Computing and Physical Sciences
Guest Editors: Dr. Pallavi Ranja, Dr. Horace Charles Mingo, Dr. Taif Alawsi, Dr. Ercan Atagü, Prof. Sergey V. Parshi, Mr. Hari K.C, Dr. Mohammad Karimi, Prof. Erdal Fıra, Mr. Ginanjar Wiro Sasmito, Dr. Ahmed Salman Ibraheem, Mr. Istiaq Ahmed, Dr. Ku Noor Dhaniah Ku Muhse, Dr. Tan Woan We, Dr. Mohsen Mhadhbi, Dr. Yasir Bu, Dr. Abdelkarim Ballouti, Prof. Noureddine Boumdouha, Dr. Alejandro Medina Santiago, Dr. Priyanka Kajal, Dr. Emmanuel Ampoma Affum, Mr. Rahul Singh, Dr. Kamal Yazzif, Dr. Ziyong Li, Prof. Suroor Moaid Dawood
Deadline: June 30, 2027

Special Issue on Advances in Life, Health and Biomedical Sciences
Guest Editors: Dr. Rajarshi Patel, Dr. Sarah Rajabi, Mr. Shrawan Kumar Singh, Miss Sonia Singla, Miss Sneha Thomas, Mr. Philip Ugbede Ojo Onuche, Dr. Ruchi Pathania, Dr. Ke Zhao, Dr. Omotese I. Oaikhena
Deadline: June 30, 2027

Special Issue on Sustainable Environment, Agriculture, Earth and Society
Guest Editors: Mr. Abdullah All Mamun Anik
Deadline: June 30, 2027

Special Issue on Artificial Intelligence for Energy Transition and Decarbonization 2026
Guest Editors: Dr. Elkhatib Kamal, Dr. Reza Ghorbani, Dr. Ahmed Ragab, Prof. Mohamed Kouki
Deadline: July 31, 2027

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Keyword: Fault diagnosis
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Open AccessArticle
17 Pages, 5,222 KB Download PDF
Bearing Fault Diagnosis Based on Ensemble Depth Explainable Encoder Classification Model with Arithmetic Optimized Tuning

by Kaibi Zhang, Yanyan Wang and Hongchun Qu
Journal of Engineering Research and Sciences, Volume 1, Issue 3, Page # 81-97, 2022; DOI: 10.55708/js0103009
Abstract: In a dynamic and complex bearing operating environment, current auto-encoder-based deep models for fault diagnosis are having difficulties in adaptation, which usually leads to a decline in accuracy. Besides, the opaqueness of the decision process by such deep models might reduce the reliability of the diagnostic results, which is not conducive to the subsequent optimization… Read More

(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2022 and the Section Artificial Intelligence – Computer Science (AIC))

Open AccessArticle
30 Pages, 1,800 KB Download PDF
Graph Neural Networks for Fault Diagnostics in Cyber-Physical Systems: A Survey of Taxonomy, Deployment Architectures and Failure Modes

by Vaibhavi Tiwari, Ola Suaifan, Ramy Othman and Anand Gupta
Journal of Engineering Research and Sciences, Volume 5, Issue 6, Page # 67-96, 2026; DOI: 10.55708/js0506006
Abstract: Graph Neural Networks (GNNs) have emerged as a promising approach for fault diagnosis in complex cyber-physical systems because they can model intercomponent relationships, fault propagation, and system-level anomalies across domains such as industrial automation, smart grids, transportation, and healthcare. This survey presents a multidimensional review of GNN-based fault diagnostics, organizing existing methods according to graph… Read More

(This article belongs to the Section Interdisciplinary Applications – Computer Science (IAC))

Open AccessReview
13 Pages, 1,689 KB Download PDF
The Current Trends of Deep Learning in Autonomous Vehicles: A Review

by Raymond Ning Huang, Jing Ren and Hossam A. Gabbar
Journal of Engineering Research and Sciences, Volume 1, Issue 10, Page # 56-68, 2022; DOI: 10.55708/js0110008
Abstract: Autonomous vehicles are the future of road traffic. In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver. To realize this vision, seven essential technologies need to be evolved and refined including path… Read More

(This article belongs to the Special Issue on Special Issue on Multidisciplinary Sciences and Advanced Technology 2022 and the Section Interdisciplinary Applications – Computer Science (IAC))

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