Engineering Systems and Intelligent Technologies (ESIT)
Engineering Systems and Intelligent Technologies (ESIT) is a peer-reviewed, open-access journal dedicated to advancing engineering systems research supported by intelligent and computational technologies. The journal emphasizes engineering-driven innovation, system-level design, and validated technological solutions addressing real-world engineering challenges. ESIT serves as a scholarly platform for engineers, applied scientists, and industry researchers working at the intersection of core engineering disciplines and intelligent technologies, including artificial intelligence, data-driven modeling, automation, and cyber-physical systems. The journal prioritizes rigor, reproducibility, and practical impact across the full lifecycle of engineering systems. Researchers are welcome to submit papers to ESIT after preparing them according to the SIRG template (Download from here) and following the SIRG Publisher Ethics and Malpractice Statement (See this page). ESIT follows the SIRG Publisher peer-review process in handling all submitted papers (see this page for more details). By submitting their paper to ESIT , authors confirm their agreement to SIRG Publisher terms, conditions, and publication ethics (please check the author page) and (Copyrights page). ISSN (Online): 3071-253X Frequency: 6 Volumes Annually Editor-in-Chief: Prof. Dr. MOHAMED H. ESSAI ALI Novosibirsk State Technical University, Novosibirsk, Russian Federation. Email: mhessai@azhar.edu.eg Prof. Dr. MOHAMED H. ESSAI ALI was born in El-Balyana, Sohag, Egypt, in 1978. He received the B.S. degree in electrical engineering from Al-Azhar University, Egypt, in 2001; the M.S. degree in electrical engineering from Assiut University, Egypt, in 2007; and the Ph.D. degree in communication engineering from Novosibirsk State Technical University, Novosibirsk, Russia, in 2012. ​From 2001 to 2008, he was a Demonstrator and an Assistant Lecturer with Al-Azhar University. From 2009 to 2012, he was a Ph.D. student with Novosibirsk State Technical University. He served as an Assistant Professor with Al-Azhar University from 2012 to 2018, and was a Guest Researcher with Novosibirsk State Technical University from April to December 2014. From 2019 to 2023, he was an Associate Professor with the Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, where he has been a Professor since 2023. ​Dr. Ali is the author of five textbooks and more than 60 articles. His research interests include the theory and applications of robust statistics, wireless communication, channel estimation under a priori uncertainty, optical wireless communication, artificial intelligence-based signal processing, and FPGA-based applications Scopus: https://www.scopus.com/authid/detail.uri?authorId=36801617500 ====================================================== For more details, please get in touch with ESIT Journal editors : Dr. Pawan Whig Vivekananda Institute of Professional Studies, India, IEEE Lifetime Member, Country Head (Threws), Research Consultant Machine Learning Google Certified in Digital Marketing Ex- Dean Research at VIPS-TResearch Area(VLSI Design, AI, ML) ======================= Dr. Amr A. Hassanin Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Sphinx University, Assiut, Egypt. ============================ Assistant Editor: Dr. Mahmoud Khaled Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt. ==================== Contact Email of This Journal: Email: ( editorial.esit@scientificirg.com )
Scientific Innovation Research Group (SIRG)
Engineering Systems and Intelligent Technologies (ESIT) content is published under a Creative Commons Attribution License (CCBY). This means that content is freely available to all readers upon publication, and content is published as soon as production is complete. Engineering Systems and Intelligent Technologies (ESIT) seeks to publish the most influential papers that will significantly advance scientific understanding. Selected articles must present new and widely significant data, syntheses, or concepts. They should merit recognition by the wider scientific community and the general public through publication in a reputable scientific journal.
CVaR-Optimized Distributionally Robust Stacked Ensemble With Range-Based Current Signatures for Reliable Fault Detection in Photovoltaic Farms
Reliable fault detection in photovoltaic (PV) farms is critical to prevent energy losses, ensure safety, and reduce operational costs. However, the inherent variability of operating conditions and overlapping fault signatures pose significant challenges for conventional diagnostic methods. This study proposes a robust machine learning framework for multi-class string-level fault diagnosis. A compact, interpretable feature set is engineered from string current statistics and plant-level DC measurements, emphasizing range-based signatures that capture current imbalance. A heterogeneous stacked ensemble is developed, integrating Extra Trees, histogram-based gradient boosting, and an RBF support vector machine via probability-level fusion to leverage complementary decision boundaries. To enhance reliability under realistic operating shifts, a DRO approach is employed, using a Conditional Value-at-Risk (CVaR) criterion during hyperparameter tuning to prioritize performance in worst-case scenarios. Evaluated on a simulated 250-kW PV plant dataset under variable irradiance, temperature, and fault resistance, the proposed CVaR-optimized stacked ensemble achieves superior performance, with an AROC of 99.3 compared to individual base learners. The method maintains 98.3 specificity, indicating a low false-alarm rate. while ensuring strong sensitivity to fault classes. It demonstrates that combining physically informed feature engineering, stacked ensemble learning, and robustness-aware tuning provides an effective and practical approach to enhancing fault-detection reliability in grid-connected PV systems.
Photovoltaic farm; fault detection; stacked ensemble learning; distributionally robust optimization (CVaR); range-based current signatures; multi-class fault classification.
Copyright (c) 2026 Engineering Systems and Intelligent Technologies (ESIT) https://creativecommons.org/licenses/by/4.0
A Machine Learning Framework for COVID-19 Mortality Risk Prediction Using Clinical and Demographic Data
The COVID-19 pandemic revealed an immediate need for trustworthy mortality prediction tools for effective allocation of health care resources. This paper presents the creation and validation of a machine learning model for predicting mortality risk using clinical and demographic information from the Mexican national registry (1,048,575 records, February 2020-October 2021). In this study we utilized six different algorithms: Logistic Regression, Support Vector Machine, Random Forest, AdaBoost, Decision Tree, and Deep Neural Networks. The models were taught 21 clinically relevant variables (including comorbidities, treatment, and demographic factors). For model assessment, we implemented stratified 80-20 training/testing splits with 5-fold cross-validation, and measured accuracy, F1-score, precision, and recall. The best value of accuracy ( 0.9181 ) was obtained by the Deep Neural Networks, and by Logistic Regression with value 0.9107 and a better value of interpretability. The model performance difference was only 0.7 percentage points which implies that the interpretable models strike a good deal for clinical usage. The more interpretable a model is; the more a clinician is likely to trust it. Importance of certain features measures ( pneumonia, hospitalization type, age ) were found to be predictors of interest, clinically plausible. The framework shows the value of clinically useful interpretable ML models and offers support for their use in sensitive medical decision making
Engineering Applications in Health and Environment
Copyright (c) 2026 Engineering Systems and Intelligent Technologies (ESIT) https://creativecommons.org/licenses/by/4.0/
NSGA-II Optimization of Probabilistic Neural Networks for Robust SQL Injection Attack Detection
SQL injection attacks have been around for years, yet because of the threat they pose to web application backend databases, they have not been mitigated. New and sophisticated attack methods have been developed over the years, using strategies and techniques that increasingly bypass traditional approaches to attack detection. This illustrates increased demand for more advanced Machine Learning models. In this paper, we outline a new approach to provide more efficiency in the detection of SQL attacks. In our approach, we convert raw SQL queries using the Term Frequency-Inverse Document Frequency (TF-IDF) method to create a feature vector that aids in training Probabilistic Neural Network (PNN) classifier. A new approach we introduce in this paper is the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to improve the detection and reduce the complexity of the PNN model. In the end, we will demonstrate the problem of efficiency and the model’s generalization. With our extensive analysis (30,919 SQL queries), findings show that the NSGA-II-optimized PNN provides the most accurate detection at 99.92% and an equal F1 score of 99.94 for the malicious data. The proposed model provides the best detection performance and reduces overfitting in the multi-objective PNN model, making it more stable. The proposal demonstrates that it is a more adaptable solution for real-time SQL injection defense and also addresses the previously identified gap in multi-objective optimization in cybersecurity.
A Data-Driven Computational Framework for Ledge Morphology Prediction in Aluminum Engineering Systems
In complex engineering systems like the Hall-Héroult process, sidewall ledge stability is crucial for thermal balance and aluminum reduction cell longevity. Traditional physics-based approaches such as Finite Element Analysis (FEA) are computationally intensive and operationally complex, limiting real-time decision-making. This paper presents a novel data-driven computational framework that bridges the gap between high-latency simulations and the need for rapid operational diagnostics. The Python-based lightweight tool leverages historical operational data to predict ledge evolution using only the cell’s operational age as temporal input. By integrating validated empirical algorithms, the system accurately forecasts electrolyte temperature, cryolite ratio, and ledge area. Rigorous train-test split validation on industrial data confirms the model’s robustness and generalization capability, achieving high coefficients of determination (R²) on unseen data. This work demonstrates how lightweight, data-driven applications can enhance process diagnostics in heavy engineering industries, offering a fast (<10s), accessible, and reliable alternative to traditional models, aligning with Industry 4.0 principles in metal smelting.
Design and Performance Assessment of a Hybrid Energy Supply for EV Charging Facilities
A rapid shift to electrification is occurring across the entire transportation industry. Even though Electric Vehicles (EVs) greatly reduce tailpipe emissions, the large-scale implementation of EVT will create new issues for existing utility grids, particularly for peak fast-charging. When EVs are charging, there is a sudden and frequent spike in demand, which creates a greater strain on the grids, increases peak demand charges, and decreases the quality of the power supplied. This paper describes the design and operational performance evaluation of a hybrid energy supply system for EV charging stations based on a Hybrid Energy Storage System (HESS). The system under review is proposed to combine photovoltaic (PV) generation, a battery energy storage system (BESS), and the utility grid for a greater flexible and reliable power supply system. The objectives are to manage peak demand, reduce dependence on the grid, and keep the voltage within a predetermined range to avoid the low/high operational range. The proposed hybrid system is capable of reducing dependence on utility grids by approximately 40% while optimizing and maintaining a desired voltage level under extreme/high charging conditions. Therefore, the proposed system is appropriate to meet the anticipated charging demands for EV in growing urban areas.