About the Journal
The Scientific Journal of Research Studies in Future Electrical Engineering is a peer-reviewed academic journal that publishes original research articles, review papers, and scholarly studies in the field of electrical engineering with a future-oriented perspective. The journal aims to promote interdisciplinary research, technological innovation, and applied studies addressing emerging trends, intelligent systems, renewable energy, power systems, and future challenges in electrical and electronic engineering.
Scientific Journal of Research Studies in Future Electrical Engineering is published as a yearbook; and it works in the field of reviewing and publishing scientific research articles in the field of Electrical Engineering in both Persian and English languages. This Journal is ready to receive and review valuable articles from the qualitative and quantitative researches of researchers and professors in this field, and the articles are published after collaborative reviews and Acceptance prints. Respecting the rules of ethics in publications, this publication is subject to the rules of the Committee on Ethics in Publication (COPE) and follows the executive regulations of the Law on Prevention and Combating Fraud in Scientific Works.
This Journal is published based on the license number 94033 of the Ministry of Culture and Islamic Guidance and with ISSN number 3041-9506.
Current Issue
This issue presents scholarly contributions in electrical engineering, industrial energy systems, medical machine learning, and advanced intelligent control systems. The published studies address efficiency enhancement in electrical power distribution within large-scale industrial settings, analyzing practical techniques, operational challenges, and optimization strategies in high-availability process environments.
The issue further evaluates machine learning–based COVID-19 detection systems and provides analytical simulations of advanced deep neural network approaches for PSVT arrhythmia detection, with emphasis on innovative feature extraction and classification techniques. Additionally, the design and implementation of an intelligent adaptive control system for optimizing radiotherapy dosage based on patient biofeedback are examined from both technical and clinical perspectives.
Moreover, performance optimization of distributed control systems (DCS) based on PCS7 architecture using redundant S7-400H controllers in high-reliability process units is investigated as a strategy to enhance operational stability and system resilience.
All contributions have undergone a rigorous peer-review process and aim to deliver technology-driven and application-oriented solutions for industrial efficiency, medical diagnostic accuracy, and advanced control system reliability.
