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. 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. Brojo Kishore Mishra Professor and Head Department of Computer Science and Engineering NIST University Berhampur, India 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) ======================= 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.
Intelligent Arabic News Classification Systems Using AraBERT Transformer for Digital Media Engineering
The rapid expansion of Arabic digital news has created a pressing need for accurate and scalable automatic news categorization. Arabic natural language processing remains challenging because of the morphological richness of the language, its complex syntax, the prevalence of dialectal variation, and the near-universal absence of diacritics in online text. This paper proposes a transformer-based framework for Arabic news classification centered on fine-tuned AraBERT, a bidirectional encoder pre-trained exclusively on large-scale Arabic corpora. The framework incorporates Arabic-specific text preprocessing, subword tokenization via the AraBERT tokenizer, and a single fully connected softmax classifier appended to the contextual [CLS] representation. Experiments are conducted on the SANAD benchmark dataset, which contains approximately 194,797 Modern Standard Arabic news articles distributed across seven topical categories. The proposed model achieves an accuracy of 98.4%, a macro-averaged precision of 99.1%, a macro-averaged recall of 99.8%, and a macro-averaged F1-score of 99.0%, outperforming fine-tuned multilingual baselines mBERT and XLM-R by substantial margins. Detailed error analysis via confusion matrix and per-class classification reports confirms strong generalization across all categories, with only minor confusion between thematically adjacent domains such as Politics and Finance. The results validate that Arabic-focused pre-training is decisive for high-quality Arabic news categorization and establish a reproducible, scalable pipeline for future research.
Intelligent Engineering Technologies (Applied Only)
Copyright (c) 2026 Engineering Systems and Intelligent Technologies (ESIT) https://creativecommons.org/licenses/by/4.0
Intelligent MITM Attack Detection Systems Using Ensemble Learning for IoT Network Security
The expansion of Internet of Things (IoT) deployments has widened the attack surface available to adversaries, and the man-in-the-middle (MITM) attack remains one of the most damaging threats facing these networks. In a MITM attack, two parties that believe they are communicating directly are in fact exchanging traffic through an intermediary that silently alters or observes the exchange. Common realizations of this threat include ARP spoofing, DNS hijacking, and SSL stripping, each producing a distinct signature in network traffic that a classifier can learn to recognize. This study evaluates and compares five ensemble learning algorithms, Random Forest, Extra Trees, XGBoost, CatBoost, and LightGBM, for the detection of MITM activity in the TON_IoT network traffic dataset. Performance is assessed using accuracy, precision, recall, F1-score, area under the ROC curve, and computational cost. CatBoost obtained the highest detection accuracy (99.2%) and F1-score (0.987), while LightGBM required roughly one third of CatBoost’s training time at a negligible cost in detection quality. Across all five algorithms, boosting methods showed a small but consistent advantage over bagging methods, and detection was effective for every MITM technique considered, with SSL stripping proving the most difficult to identify. The results suggest that the choice among these algorithms in an operational deployment should depend on whether the priority is raw detection accuracy, inference speed, or interpretability, rather than on accuracy alone.
Networks, Communications, and Industrial Connectivity
A Comparative Evaluation of LSTM and Bidirectional LSTM Architectures for Nitrogen Oxide Forecasting in Air Quality Monitoring
Reliable, real-time monitoring of nitrogen oxides (NOx ) is essential for air quality management, yet conventional monitoring networks are limited by sparse spatial coverage and inconsistent predictive reliability. This study compares Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks for forecasting NOx concentrations using the UCI Air Quality Dataset. To assess these architectures rigorously, we benchmark them against a persistence forecast and an ARIMA model, apply expanding-window temporal cross-validation to guard against data leakage, and test the significance of any performance gap with the Diebold–Mariano statistic. Both recurrent architectures attain high accuracy (R2 > 0.999), with BiLSTM showing a marginally higher mean R2 (0.99977 versus 0.99972 for LSTM); however, the Diebold–Mariano test indicates that this difference is not statistically significant (p = 0.384), so the two architectures should be regarded as practically equivalent for this task. Both deep learning models substantially outperform the statistical baselines, most notably ARIMA (R2 = 0.854), which supports the value of recurrent architectures for NOx forecasting while also underscoring the importance of baseline comparison and leakage-aware validation in this line of work.
Computational and Engineering Decision Systems
Robust LSTM-Based Channel State Estimation for Next-Generation Wireless Communication Systems
Long Short-Term Memory (LSTM) networks conventionally employ the hyperbolic tangent (tanh) and sigmoid functions for state and gate activations, respectively. Although numerous alternative activation functions have been introduced for deep neural networks, their potential to improve LSTM performance in wireless communication tasks remains insufficiently explored. This paper investigates the replacement of the tanh state activation function in LSTM architectures with three alternatives: the Bi-tanh1, the Elliott, and the Gaussian Error Linear Unit (GELU). The resulting modified LSTM networks are evaluated as channel state estimators (CSEs) for 5G Orthogonal Frequency-Division Multiplexing (OFDM) systems operating over Rayleigh fading channels. Simulation results demonstrate that all proposed activation-function-based LSTM CSEs substantially outperform conventional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators, particularly under low pilot-density conditions. Among the evaluated configurations, the GELU-based LSTM CSE achieves the lowest symbol error rate (SER) across all tested signal-to-noise ratios (SNRs), establishing GELU as a strong candidate for robust channel estimation in next-generation wireless receivers
A Comparative Analysis of Contrastive and Generative Vision-Language Models for Zero-Shot Behavior Recognition in Surveillance Videos
Vision-language models (VLMs) have recently demonstrated strong zero-shot capability for object recognition and scene classification, yet their suitability for modeling covert human behaviors, such as theft, remains largely unexamined. This paper presents a case study comparing two zero-shot VLM paradigms for cashier theft detection in surveillance footage: a contrastive embedding model (CLIP) and a generative vision-language model (a Llama-3.2-11B-Vision-based pipeline operating in the BLIP/BLIP-2 family of generative architectures). On a set of real cashier-counter recordings, the contrastive model produced near-tied confidence scores between theft and normal-activity prompts (theft confidence 0.504, normal-activity confidence 0.496), indicating weak discriminative margin when intent and temporal context are required. The generative pipeline, in contrast, produced confident and structured binary outcomes (theft confidence 1.000, normal-activity confidence 0.000) accompanied by interpretable natural-language descriptions of the suspect and the event. These results, while drawn from a small, non-benchmarked sample rather than a large annotated corpus, suggest that contrastive similarity scoring is better suited to fast object-level screening, whereas generative reasoning is better suited to behavior-level interpretation. A hybrid pipeline that couples a fast contrastive pre-filter with a generative reasoning stage is proposed as a practical direction for zero-shot surveillance systems that require both efficiency and interpretability
Engineering Applications in Health and Environment
Implicit Geometric-Semantic Fusion for Task-Oriented 6-DoF Grasp Detection via Visual Affordance and Stability Learning
The nature of design-based robotic tasks often results in the mechanical stability of robotic hands in one of two ways: hands either lose or gain their stability in regards to mechanical structure. There have been a number of methodologies created to tackle the issue of interpreting and generating stable robot grasps and affordances. However, tasks related to interpreting and manipulating objects in 3D space are often treated in isolation, which hinders the creation of task-specific robotic manipulation systems. In this paper, we present Implicit Geometric–Semantic Fusion (IGSF), a framework for stable grasp generation in 6-DoF from partial 3D point cloud representations. The IGSF architecture integrates the grasp generation of control points, backward learning of the spatial field and affordances, and the task-based control of the system. The grasp generation module of the framework makes predictions of the placement, orientation (as defined by unit quaternions), and stability without the need for utilizing any fixed grasp anchors. The affordance module uses an attention-augmented Dynamic Graph CNN to learn task-specific functional areas, while the refinement stage adjusts stable grasp candidates to be within the semantically relevant regions and within the constraints of kinematic feasibility. The architecture has been trained using ACRONYM’s characterized and validated grasp data alongside the 3D AffordanceNet’s subset fine-grained affordance annotations, utilizing a balanced multi-objective optimization technique. The experimental assessment of 50 distinct objects and 5 different tasks found an outcome of 100\% feasibility, a 95.1\% success for grasping, a 60\% error reduction with regards to pose (0.018 vs task-agnostic baselines 0.045 m2 with regards to pose error, p < 0.001), and semantic-type alignment. These findings illustrate that integrating geometric stability and semantic affordance within a constrained learning paradigm allows effective 3D task-oriented robotic manipulation systems.
PLONK Simplified: A Pedagogical Zero-Knowledge Proof Framework with KZG Commitments
Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) allow for elegant, privacy-preserving validation of computations. PLONK, a subclass of the zk-SNARKs, is certainly useful, but its complex interactions with permutation arguments, lookup tables, and blinding, among other considerations, make the protocol difficult to follow, let alone understand. This paper describes a framework centered around the core components of zk-SNARKs. In particular, we detail the construction of arithmetic gate constraints, representation of witness polynomials, and the Kate-Zaverucha-Goldberg (KZG) commitment scheme. By removing permutation proofs, lookup, and blinding, we aim to simplify the pedagogy of zk-SNARKs and preserve their essential properties of soundness and completeness. We describe a Python module from the ground up that demonstrates the generation and validation of proofs in a PLONK-modified zk-SNARK. We validate the framework and its foundations with a benchmark of a module generating and validating proofs in a PLONK-modified zk-SNARK. We validate the module against a circuit of 1,000 gates and demonstrate that the system correctly rejects all invalid witnesses. We illustrate the expected asymptotic behavior, with a pro tor of tight the module is quasi-linear, and verification, tight. We justify the foundations of the module and describe tight with zero private inputs. We have also bridged the gap between abstract zk-SNARK theoretical arguments and their practical implementation and research. We have provided a simple, empirically grounded mechanism that describes the key components of PLONK. We have done this in such a way that researchers, developers, and teachers can build on this base module and create production-ready systems without the abstraction.
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.
Infrastructure, Energy, and Mobility Systems
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
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.