It is a free journal published by Imam Al-Kadhim university College (IKU), Baghdad, Iraq. It is open access in computer science. The publication of this journal contributes to the development of science and its impact. In addition, it provides a means of discussion for science researchers.
Cyberdefense Powered by Generative AI: A Comprehensive State –of- the -Art Review
Cyberattacks are becoming more advanced, challenging the traditional defenses based on signature-based and rule-based approaches. The current literature on generative artificial intelligence (GenAI) in the context of cybersecurity is disjointed based on model family or area of application, and it does not combine technical performance indicators with practical implementation limitations, making a comprehensive picture impossible. To fill this gap, the study conducts a comprehensive review of peer-reviewed articles and conference papers (2021-2026) indexed in Scopus and ScienceDirect, and Springer databases, involving the use of GenAI as a defensive cybersecurity tool. These papers are divided into (I) GenAI model family: generative adversarial networks (GAN), large language models (LLM), variational autoencoders (VAE), diffusion models, and hybrid GenAI; (II) application domain: intrusion detection, malware detection, anomaly detection, threat intelligence, privacy-preservation, vulnerability detection, and phishing and spamming detection; and (III) defense strategy: reactive, proactive, and adversarial. GenAI typically increases the accuracy of detection and data efficiency and provides active defense. Nevertheless, the practical validation is usually limited to offline tests that apply imprecise metrics. The paper provides the performance–efficacy trade-off model, which relates technical standards and realistic implementation limitations. It also identifies a research roadmap in the future focused on creating autonomous, privacy-protective, and trustful GenAI-powered cyber defenses and suggests a living review platform to keep track of advances in the fast-changing area.
Copyright (c) 2026 Ahmed Alsamman, Najla Badie Al Dabagh (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Achievement of Participation in E-Governance Using Machine Learning: A Socio-Technical Approach to Programming Language Education
This paper explores how e-governance principles and machine learning (ML) technologies can be integrated to support participatory design in educational systems, and, in particular, learning the programming language curriculum. Based on the socio-technical systems theory of Mumford and the ETHICS methodology, we are able to propose a new framework, which will utilize three supervised ML classifiers, including Support Vector machine (SVM), Random Forest (RF) and Decision Tree (DT), to systematically analyze the data of teacher questionnaires collected via e-governance channels. The data set included the answers of 94 teachers in Baghdad, Iraq, and was used to categorize the needs of instructional improvement into six categories: Hardware Development (DH), Demonstration Tools Development (DDT), Teacher Experience Development (DTE), Redistribution of Teacher Sessions (RDTS), Syllabus Development (DS) and Student Ability Development (DSA). The results of the experiment show that SVM attains the maximum classification accuracy of 99.90% in training and 98.00% in testing. The analysis of the results of questionnaires has shown that the most pressing areas of improvement are Syllabus Development (85%), and Student Ability Development (75%). This paper highlights that an active participatory, socio-technical approach to e-governance, one that actively engages educators in decision making, can go a long way in improving the quality and relevance of the educational policy outcomes
Copyright (c) 2026 shakir Fadhil Shabram (Author) https://creativecommons.org/licenses/by/4.0
Adaptive deep learning framework for detecting fake faces
Abstract The rapid development of face generation technologies in recent years has enabled the creation of stunningly realistic human face images, effectively blurring the lines between authentic and synthetic content. While these technologies offer positive applications in education, entertainment, and digital art, their potential for misuse—particularly on social media—is profound. The proliferation of deep-fakes poses a significant threat to digital trust, facilitating electronic fraud and identity manipulation, which necessitates the development of effective detection solutions. This paper addresses this escalating threat by proposing an adaptive detection framework based on pre-trained Convolutional Neural Networks (CNNs). Unlike traditional methods that struggle with unseen data and environmental noise, our approach optimizes ResNet50, DenseNet201, and Alex Net architectures through customized data preprocessing and hyper parameter tuning. Evaluated on the rvf10k dataset—selected for its diverse illumination and ethnic representation—the proposed model achieves a peak accuracy of 99.89%. Furthermore, this work provides a comprehensive analysis of computational cost and inference speed, demonstrating robustness against over-fitting via cross-validation techniques. The results confirm that the proposed framework significantly enhances detection reliability and contributes to securing digital content integrity.
Copyright (c) 2026 satar Shaker (Author) https://creativecommons.org/licenses/by/4.0
A High-Performance Hybrid Entropy Complexity Learning Framework for Robust Detection of Stealthy Cyber Attacks
Cyber-attacks are advancing toward secretive behaviors that avoid classical signature detection systems and single-metric abnormality approaches. This paper is used to be proposed a framework-based Hybrid Entropy–Complexity Learning (HECL) for strong detection of cyber-attacks based on networking information models. The proposed approach cooperatively analyzes the entropy-based Shannon scheme, statistical complexity, and learning adaptability for characterizing normal and abnormal traffic activities. Different from conventional systems-based detection entropy, the proposed method utilizes the temporal coupling between both the entropy and complexity, allowing the correspondence of low-rate and whitewash attacks. Comprehensive simulations illustrate that the proposed model performs outstanding detection accuracy, speedy convergence, and a lower rate-based false-alarm when it is compared to the standalone entropy and approaches-based Machine Learning (ML). The key results emphasize that integrating an information-theoretic system with the learning-based indicators gives an influential and lightweight security technique appropriate for recent information security systems. The experimental results on the CICIDS2017 dataset show that it outperforms the state of the art deep learning techniques with 98.85% accuracy and 0.95% false positive rate.
Copyright (c) 2026 Zaydon L.Ali (Author) https://creativecommons.org/licenses/by/4.0
A Lightweight Hybrid Encryption Algorithm for Internet of Things Security using ASCON and AES with Chaotic System
The recent rise in the use of Internet of Things (IoT) devices has further increased the pressure on cryptographic solutions that will meet the challenge of high-security standards, yet nearly meet the extreme resource requirements imposed by embedded computers by nature. The paper introduces a new hybrid encryption architecture that is aimed at meeting these conflicting requirements of the IoT setting. The scheme suggested is a variant of AES-r cipher combined with the ASCON authenticated-encryption primitive giving the AESASCON algorithm, and extended with integrity verification by the SHA3-256 hash to create the AESASCONH construction. Stochastic key material is calculated using a four-dimensional NoseSprott-Jerk-Rossler (4D-NSJR) chaotic system, which is highly sensitive to initial conditions causing the effective key space to become large and making brute-force enumeration challenging. The architecture is segmented into three layers that collaborate with one another and they are chaotic key generation, symmetric data encryption and authenticated-integrity verification. General testing against the complete NIST SP 800-22 statistical test is done that indicates that all the 15 randomness tests have been passed. Experiments with Five data sizes are used in experiments to demonstrate speedups ranging from 42% for large data payloads (1.5 MB) to 175% for small data sizes compared to AES. This demonstrates that the suggested approach provides efficient and secure protection for IoT. and entropy values of data of up to about the theoretical maximum of 8.0 bits per symbol, and thus confirm speed improvements as well as cryptographic strength is available to support high-assurance IoT deployments.
Copyright (c) 2026 wasan alaa Hussain (Author) https://creativecommons.org/licenses/by/4.0
Scalable Video Encryption for Real-Time Applications Using Parallel Computing Models
Abstract It is necessary in various real-time applications such as teleconferencing, live streaming, and remote monitoring. However, applying standard encryption methods on high resolution video streams can cause computational delay. We propose a scalable video encryption framework, based on AES-256 and multi-threaded parallel processing to boost the performance of cryptography without compromising security in the paper. Video frames are sliced into independent chunks and assigned to available CPU cores so that encryption can occur in parallel. In order to recover frame ordering correctly, we use a synchronization mechanism. Extensive experimental evaluation on Full HD video streams shows that the proposed framework improves throughput and results in a four-times reduction in encryption time when compared to sequential execution. Using eight threads with small CPU utilization efficiency, the system achieved a speed up as high as 4.62×. These results show that parallel software encryption on CPU is a realistic method for secure real-time video applications.
Copyright (c) 2026 baydaa alkhafaji, Suad abed alwahab,, Maha talib Abdullah, Israa Akram Alzuabidi, Wasan abed alwahab (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning Models In The Early Brediction Of Epileptic Seizures
Abstract Prediction of early epileptic seizures is still a challenge in clinical neuroscience due to the unpredictability of seizures and their psychological and physical dangers to the patient. EEG can be used as an important modality for seizure warning systems with the goal of improving patient quality of life. Hence, the paper will compare and evaluate the performance of several deep learning models for seizure prediction 15 minutes in advance using EEG data. To this end, a uniform experimental protocol was followed in order to ensure a comparison between models: LSTM, BiLSTM, Graph Neural Network, and Transformer. Models were evaluated based on the CHB-MIT database that consisted of long-term recordings from 24 patients. Each signal was subjected to different pre-processing steps such as filtering, normalization, and time window segmentation. Then, an individual set of features was extracted, while data between pre-ictal and ictal classes were made balanced. Models were studied based on various metrics such as accuracy, specificity, reproducibility, F1 coefficient, and area under the curve. Therefore, the results from the deep learning models used show converging performance. The LSTM and BiLSTM models showed high accuracy in positive cases, with a retrieval rate of 0.99. The Transformer model showed the highest accuracy of 0.95 and highest specificity of 0.76. The Graph Neural Network (GNN) showed comparable performance to the other models, with competitive performance but not outstanding in any of them. The use of Explainable AI (XAI) enabled the assessment of the importance of the temporal phases and features in both the performance and the decisions of the model. Hence, the findings confirm that the Transformer model has a high capacity for early seizure prediction, and thus, it is useful in the development of reliable early warning systems in patients with epilepsy.
Copyright (c) 2026 shahad sallah (Author) https://creativecommons.org/licenses/by/4.0
Comparative Evaluation of Machine Learning Techniques for Handwritten Digit Classification Using the MNIST Dataset
Machine learning (ML) and deep learning (DL) have advanced at a very rapid pace; thus, improving image classification in many fields. MNIST data is one of the most popular benchmark datasets to compare classification algorithms and it is represented by 70,000 grayscale images of handwritten digits (09). This paper will make a comparative analysis of a number of machine learning and deep learning algorithms and models, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) using the MNIST dataset. All of the models were trained and tested using the same preprocessing methods, including normalization and feature scaling. Several evaluation metrics were applied to evaluate the performance, and they included accuracy, precision, recall, F1-score, and confusion matrix. The findings prove that conventional machine learning models, especially SVM and Random Forest, perform competitively. Nonetheless, CNN is more effective as compared to classical models because it is capable of learning hierarchical features of space automatically using image data. The results indicate the significance of choosing suitable models depending upon the nature of data and computing limitations. This paper presents a comparative study in detail and in a systematic manner that would improve the comprehension of the trade-offs between the model performance and computational efficiency and could act as a valuable source of information to researchers and practitioners in image classification exercises.
Copyright (c) 2026 Aysar Tuaimah (Author) https://creativecommons.org/licenses/by/4.0
Predictive GIS-Based Decision Support System for Forecasting Teacher Demand and Optimizing Educational Human Resource Allocation
Abstract - Unavoidably, educators world over are struggling to address the issue of supply and demand of teachers in geographically dispersed regions. The conventional workforce planning methods are based on reactive, manual and non-spatial forecasting processes which in most cases leads to shortage of teachers in the rural sections and excesses in the urban centres. In this paper, the author suggests a Predictive Geographic Information System (GIS)-Based Decision Support System (DSS) to combine machine learning forecasting, spatial analytics, and optimization modeling to improve the efficiency of teacher demand prediction and allocation. The suggested framework will use historical data on enrollments as well as demographic data, records of teacher attrition, and geospatial data to project future staffing needs. Random Forest and Gradient Boosting models are compared in terms of forecasting performance and geographic differences are defined by spatial clustering and hotspots analysis. The optimization model of an integer linear programming minimizes the cost of staffing imbalance and allocation with regulatory and budget constraints. The results of the experiment indicate higher accuracy in prediction (R^2 = 0.92) and 27% lowering of the staffing imbalance than the standard allocation procedures. The suggested system offers a data-driven and spatially intelligent planning tool of strategic educational workforce planning in the hands of policymakers.
Copyright (c) 2026 maherali1976 Ahmed76, Nisreen Mustafa Sajid (Author) https://creativecommons.org/licenses/by/4.0
Chicken Swarm Optimization based QoS Web Service Composition in Cloud Computing Environment
The increasing volume of data in the cloud computing environment has led to a situation where a single, abstract cloud web service has lost its ability to meet the complex requirements of various customers. Therefore, a service composition is becoming a necessity in the cloud computing environment. In this paper, Chicken Swarm Optimization (CSO) based QoS Web Service Composition in Cloud Computing Environment CSOQSC_CCE algorithm has been proposed. CSO is a metaheuristic algorithm that inspired from the hierarchy and foraging habits of the chicken swarm. The proposed CSOQSC_CCE algorithm is validated through using the Quality of Web Service (QWS) which is a real-world service dataset. The investigation results show that the proposed CSOQSC_CCE algorithm overcome the existing approaches in terms of response time and delay.
Copyright (c) 2026 Dina Alshibani (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Decision Degradation Analysis under Extreme Data Scalability in Enterprise Information Systems
This paper provides a computer framework describing a research on the deterioration of the decision making of AI based enterprise information systems in reaction to the extreme data scale issues. The pattern of erosion of the decision-making accuracy of four AI architectures, that is, neural networks, random forests, support vector machines, and ensemble approaches, was studied by means of controlled multi-scenario simulation. These experiments were performed in four cases of scalability of linear growth, exponential burst, step-wise expansion and random volatility. One of the most important innovations in our approach is a simulation engine which will be employed in order to measure degradation using various performance measures i.e. decision accuracy, response time, uniformity and computational load. We marked important threshold limits in our analysis, where the reliability of the system will be very low and where an early warning of the looming collapse would be detected. We also came up with predictive models that we used to predict degradation patterns. The results show that the ensemble methods are much stronger with the average accuracy of 94.3 percent even in extreme stress conditions, which are not present in traditional architectures. On the other hand, exponential bursts exhibit the greatest performance disparities, and after their thresholds are exceeded, at least 25 performances are required to initiate execution. Finally, this work provides new techniques for assessing recovery dynamics, resilience, and decay rates. We provide organizations that are having trouble simplifying their architectures with practical, hands-on guidance. The most important lessons are to maintain decision-making for workloads involving a lot of data by offering proactive system layout policies. The established threshold detection and preemptive intervention techniques are part of a new paradigm of predictive system management that we have developed.
Copyright (c) 2026 Shaymaa kaseb Layus, Salih Hajem Glood , Jamal M. Alrikabi, Mohammad Kaisb Layous Alhasnawi (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Performance Analytics for Educational Staff: An Empirical Study in Iraqi Schools
This quantitative study investigates the impact of AI-based decision support systems on teacher performance and administrative efficiency in Iraqi secondary schools. A structured questionnaire was administered to 350 teachers and administrators across four governorates (Sulaimaniyah, Nineveh, Baghdad, and Al-Nasr District). Data were analyzed using SPSS v25 through reliability tests (Cronbach's alpha), validity tests (Pearson correlation), descriptive statistics, and regression analysis. The results revealed a strong positive correlation between AI-based systems and teacher performance (R = 0.939, R² = 0.875, p < 0.001), indicating that 87.5% of the variance in teacher performance is explained by AI system use. Additionally, AI tools significantly improved administrative planning efficiency (R² = 0.694) and decision quality (R² = 0.570). Staff acceptance of AI technologies also showed a substantial impact on institutional performance (R² = 0.642). The study recommends promoting AI integration in schools, enhancing technological infrastructure, and providing training programs for educational staff. These findings offer evidence-based insights for policymakers seeking to modernize educational management through smart analytics.
Copyright (c) 2026 maherali1976 Ahmed76 (Author) https://creativecommons.org/licenses/by/4.0
Hybrid local intensity variation and edge features map-based multi-focus image fusion using Genetic algorithm
This study proposes a multi-focus image fusion approach that considers Genetic Algorithm (GA) optimization to achieve the selection of the most appropriate fusion weights that are used to increase the quality of the resulting fused image. local intensity variations with a standard deviation filter used to extract texture features, edge detection with the Sobel operator is defined, and a variance feature is also determined. The optimized weights are used to identify the best combination of feature maps of these three extracted features to achieve an accurate fusion process. Experiments demonstrate that this approach successfully retains texture, edges, and variation, resulting in a fused image with improved visual quality and information richness.
Copyright (c) 2026 Muthana Mahdi (Author) https://creativecommons.org/licenses/by/4.0
Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP
The present work introduces the notion of "emotional drift" in literary translation, which refers to the diversion or transformation of emotional signals from source text to its translations. A bilingual analysis of the Arabic and English translations of The Epic of Gilgamesh serves as the focus of this paper, which, by utilizing recent expansions in Natural Language Processing (NLP) with an emphasis on sentiment analysis and emotion recognition, aims to quantify emotional content. The eventual objectives of this research are to develop a computational pipeline whereby emotional content can be extracted from and compared across bilingual literary corpora, to quantify emotional drift across varying versions of the Gilgamesh, and to contextualize these findings against the larger backdrop of translational research. The intersection of computational linguistics, translation theory, and digital literary analysis opens up a new avenue for machine-assisted literary scholarship and provides a replicable framework for inquiry into the issue of affective fidelity concerning translated literature.
Copyright (c) 2026 Zaid Al-Araji (Author); Balqees Talal Hasan (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid CNN–ViT Deep Learning Framework for Detecting and Classifying Image Manipulation in Online News Media
The tremendous growth in the flow of information online, and its quick evolution in the digital age has led to serious challenges. Fake news, particularly that which relies on dubious or manipulated images (such as using Adobe Photoshop or GAN networks), deepfake techniques, has become a clear danger to credibility and public opinion are among the most dangerous practice of media manipulation, because to their ability to directly influence the public and weaken trust in media sources and news organizations .The purpose of this work was to develop a hybrid system model for detecting misleading and manipulated images on online news websites. This was through an integrated model based on the Vision transducer (ViT) combined a model that integrates with convolutional neural networks (ResNet50 and EfficientNetB3).The suggested model leverages the capabilities of convolutional the networks for feature extraction and the analysis of long-range relevance inside an image, utilizing the capabilities of the Vision Transducer. This provides for more accurate visual portrayal. The model was developed using pre-trained weights and translational learning, and then father enhanced by adding leakage layers to reduce over-allocation. The proposed model was training and achieved a test accuracy of 78.2%. The stability of performance during the verification phase confirms the model’s effectiveness and reliability in verifying the authenticity of images in digital media before publication.
Copyright (c) 2026 marwa raid (Author) https://creativecommons.org/licenses/by/4.0
Twitter Bot Detection Using Relational Graph Convolutional Networks and Convolutional Neural Networks
The increasing fake number accounts and bots on Twitter threaten cyber security, information integrity, and public discourse. modern methods to define and block bots', primarily depend on user information metadata, content, and behavioral heuristics, have confirmed inactive against more developed bots that simulate actual user behavior. Graph Neural Networks (GNNs) can help. User interaction and Social media can be modeled as a graph, where users are represented as nodes and their interactions as edges, and GNNs can mesh individual and relational data into a single entity. This paper analyzes these accounts from two various methodological viewpoints looked at Conventional Neural Networks (CNNs) and Relational Graph Convolutional Networks (R-GCNs). While convolutional neural network (CNN) models have shown high efficiency in recognizing linguistic indicators and metadata, their reliance on surface features has limited their ability to handle computations designed to accurately simulate human texts and metadata. The R-GCN model architecture is the most outstanding, outperforming all models particularly in terms of F1 and ROC-AUC, by successfully capturing different relationship patterns—follow, rewet and mention. this paper highlights the quality and limitations of current detection schemes and proposes modern methods for automatic interpretation, multimedia integration, and cross-platform adaptation.
Copyright (c) 2026 Hind Mohammed (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid AI Model for Early Detection of Heart Disease Using Clinical and ECG Data
One of the main causes of morbidity and death is still cardiovascular diseases (CVDs) thereby stressing the need for developing effective early diagnostic tools. This paper proposes a hybrid approach using AI that combines deep ECG feature learning with clinical data to enhance early heart disease diagnosis. The ECG signals were preprocessed, divided into 2-second epochs, and then represented as 128-dimensional embedding using a 1D CNN-based feature extractor. At the same time, a set of 13 clinical risk factors was normalized and used to construct a lightweight clinical prediction branch. Both were then fused using a multimodal neural network that learns both ECG and clinical features. The findings of the experiment have demonstrated the effectiveness of the proposed hybrid framework, which has achieved 97.56% accuracy, 0.996 AUC, 99.0% sensitivity, and 96.0% specificity, significantly outperforming the individual models. Furthermore, the SHAP values-based explain ability method has assisted in revealing the most important clinical variables and ECG embedding features, thereby making the prediction results interpretable and meaningful from the clinical perspective. In summary, the proposed approach has shown how multimodal AI systems might help with early heart disease diagnosis and offers a promising solution for future real-time applications.
Copyright (c) 2026 Hayder Ali Kadhim (Author) https://creativecommons.org/licenses/by/4.0
Experimental Evaluation of Adaptive Multi-Agent AI Models for Detecting Stealthy Cyber Attacks in Dynamic Network Environments based on MADDPG
Cybersecurity faces increasing challenges due to sophisticated and covert attacks targeting dynamic networks. This research aims to explore the effectiveness of adaptive multi-agent AI models in detecting these attacks using an algorithm MADDPG (Multi-Agent Deep Deterministic Policy Gradient) a dynamic network environment was designed to simulate natural data traffic and covert cyberattacks, where each agent monitors a specific part of the network and makes immediate decisions to detect or mitigate attacks .
Copyright (c) 2026 asma ibrahem (Author) https://creativecommons.org/licenses/by/4.0
Developing an Enhanced IHBO Algorithm with Chaotic Binary Hashing to Optimize BiGRU for Industry 4.0 IIoT Anomaly Detection
The rapid expansion of the Industrial Internet of Things (IIoT) in Industry 4.0 has increased the need for reliable anomaly detection to protect industrial services and critical operations. Deep learning–based IDS solutions can achieve strong performance, yet practical deployment may be affected by tuning effort and suboptimal search behavior when optimization is trapped in local optima, particularly when feature selection and model optimization are handled as separate stages. This paper develops an enhanced Improved Honey Badger Optimization (IHBO) algorithm and integrates it with a Bidirectional Gated Recurrent Unit (BiGRU) model for IIoT anomaly detection. To strengthen exploration and maintain population diversity during optimization, the proposed IHBO incorporates a logistic chaotic map as a controlled diversification mechanism. In addition, chaotic binary hashing is used to map continuous candidate representations into discrete binary decisions, enabling effective wrapper-based feature selection within the same optimization framework. The main novelty lies in performing integrated feature selection and BiGRU optimization within a single framework rather than treating these steps independently. Experiments on two benchmark datasets—an industrial IIoT dataset and UNSW-NB15—show that the proposed BiGRU–IHBO approach achieves 94.70% accuracy on the IIoT dataset and 99.29% accuracy on UNSW-NB15, with consistent improvements in precision, recall, and F1-score compared with baseline models reported in the same experimental setting.
Copyright (c) 2026 SAIF SAAD ALAMSHANI (Author) https://creativecommons.org/licenses/by/4.0
Federated Learning for Early Detection of Advanced Persistent Threats in IoT Networks
In the era of connected IoT devices, ensuring cybersecurity while preserving data privacy is increasingly critical. Federated learning offers a promising approach by enabling collaborative training of detection systems without sharing raw data. This paper presents a novel federated Intrusion Detection System (IDS) based on XGBoost algorithm, and for the first time designed to detect initial compromise (I.C.) phase of Advanced Persistent Threats (APTs) in distributed Internet of Things (IoT) environments. By leveraging the federated framework, the IDS achieves robust detection across multiple devices while maintaining privacy and minimizing computational overhead. Extensive simulation results indicated that our proposed method achieved a precision of 97%, recall of 100%, and F1-score of 98%, providing a practical and efficient solution for real-world IoT security challenges.
Copyright (c) 2025 bassam noori (Author) https://creativecommons.org/licenses/by/4.0
Practical Implementation and Analysis of Software Metrics Impact on Maintainability in Open-Source Systems
Common software metrics and maintainability measures in open-source Java are examined in this research.The authors test the major object-oriented metrics—Coupling Between Objects (CBO), Lines of Code (LOC), Weighted Methods per Class (WMC), Lack of Cohesion of Methods (LCOM), Depth of Inheritance Tree (DIT), and Cyclomatic Complexity—against real-world maintainability indicators like bug counts, code modifications, and developer turnover. The authors use Python data analysis and visualization to find statistically significant patterns in Spearman correlation analysis. The findings indicate that metrics such as CBO and cyclomatic complexity are very predictive in terms of maintenance effort, whereas others, such as DIT, provide little insight. In addition to theoretical justification, this work provides a practical, reproducible workflow to be implemented by software engineers to give priority to code quality and make more intelligent maintenance decisions. Finally, this study ties the strengths of academic measures and the real-world aspects of daily software engineering.
Copyright (c) 2025 Nadia Hussien (Author) https://creativecommons.org/licenses/by/4.0
Explainable Artificial Intelligence Integrated Ensemble Learning Framework for Diabetes Prediction
Accurate prediction of diabetes based on clinical and demographic indicators is essential, as early prediction of this chronic metabolic disorder plays a critical role in preventing long-term organ complications. However, existing research continues to face significant challenges, including pronounced class imbalance, the scarcity of large and diverse datasets, and limited integration of explainable artificial intelligence. This research compares several ensemble learning methods (Decision Tree, Random Forest, AdaBoost, Gradient Boosting, Histogram based Gradient Boosting, extremely randomized Trees, and XGBoost) on a large imbalanced dataset (87,664 negative vs. 8,482 positive samples). To mitigate imbalance, we evaluate six resampling approaches including (Random Over Sampling, Random Under Sampling, Synthetic Minority Over-sampling, Adaptive Synthetic Sampling, Tomek Links Removal Sampling and (Synthetic Minority Over-sampling with Edited Nearest Neighbors). We assess models using metrics robust to class imbalance (precision, recall, F1, AUC-ROC, and AUC-PR) and calibration measures. The Extra Trees classifier achieved the highest measured accuracy (0.994); with Random Over Sampling for balancing dataset. also, these results were compared with several previous works and number of machine learning algorithms, and the results showed superiority. Explainability is performed at both global and local levels: permutation and SHAP for global feature importance, and (Local Interpretable Model-Agnostic Explanations) force plots for instance-level reasoning. however, we analyzed this result using sensitivity, specificity, PR-AUC and calibration, we report detailed experiments showing how resampling method, hyperparameter tuning, and stratified validation influence performance. Finally, we provide clinical-relevant insights from SHAP analyses and discuss limitations and future directions for deploying interpretable models in screening workflows.
Copyright (c) 2025 Mohammed Mohammed Abdallazez (Author) https://creativecommons.org/licenses/by/4.0
Optimized Hybrid CNN-LSTM Framework with Multi-Feature Analysis and SMOTE for Intrusion Detection in SDN
There is a growing trend toward the use of software-defined networks (SDN), which presents new security challenges requiring advanced intrusion detection systems (IDS). This paper proposes a deep learning-based hybrid system combining convolutional neural networks (CNNs) and long-term short-term memory networks (LSTMs) that can be used for effective intrusion detection in SDN environments. The model uses CNNs to acquire the spatial features of network traffic data and LSTMs to learn temporal patterns, enabling the identification of complex attack patterns. We evaluate our model using an InSDN dataset and test its performance using various feature sets, ranging from 6 to 83 features in our model. Experimental results indicate that our model has a high multi-class classification accuracy of 99.63% when using all 83 features in Group1. Furthermore, we use a Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance which considerably enhances detection accuracy of minority attack classes which is reaching 99.76%. It is established that the presented hybrid CNN-LSTM model is powerful and successful solution to SDN security improvement.
Copyright (c) 2025 Dalia Shihab, Abbas Abdulazeez Abdulhameed, Methaq T. Gaata (Author) https://creativecommons.org/licenses/by/4.0
Computationally Efficient Hybrid Framework for MNIST Handwritten Digit Recognition
This research work suggests a combination classification technique for handwritten digit recognition based on MNIST dataset. The design embraces Principal Component Analysis (PCA) to reduce the level of dimensionality, KMeans clustering to get the pattern of the framework, and Random Forest to make the final prediction. By compressing the feature space into a 50 dimensional space and adding in cluster information, we are able to attain an accuracy of about 93.1 percent. The significant contributions of this paper are to prove the PCA-KMeans integration useful to improve classification, offering a computationally efficient solution as alternative to deep learning model, and to prove that feature augmentation using clustering outcomes leads to better discrimination of visually similar digits. The data suggest that the referred to hybrid model is stable and feasible approach to image identification and pattern recognition, which could be applied to more complicated dataset.
Copyright (c) 2025 Dalia Shihab (Author) https://creativecommons.org/licenses/by/4.0
The Connected Classroom: Leveraging EdTech to Enhance Student Engagement
This mixed-methods study examined university students' perceptions of the role of technology in promoting engagement, collaboration, and autonomy. Data were collected from 120 students (65 females and 55 miles) from three universities (Al-Mustansiriya University, University of Diyala, and University of Wasit) through participation in questionnaires, classroom observations, and open-ended feedback. Results showed that students had highly positive viewpoints, with engagement (M=4.12) being the most strongly observed advantage, followed by collaboration (M=3.98) and autonomy (M=3.85). Inferential analysis revealed significant differences in perceptions of collaboration: female students reported higher scores than male students (t (118) =2.04, p=.04), and students from Al-Mustansiriyah University reported significantly higher collaboration than those from another (F (2,117) =3.67, p=.028). No significant differences were discovered for engagement or autonomy. Observational data revealed high levels of peer collaboration, while qualitative feedback highlighted key opportunities (e.g., learning flexibility) and challenges (e.g., communication issues, digital literacy gaps). The results indicate that while technology is an effective tool for student engagement, its ability to enhance collaboration depends on demographic and institutional factors. The study underscores the importance of thoughtful pedagogical design and strong institutional support to leverage the potential of collaborative technology efficiently and equitably.
Copyright (c) 2025 Jaber (Author) https://creativecommons.org/licenses/by/4.0
Deep Learning-Based Blood Cell Classification with Enhanced Data Preprocessing and Augmentation
Classification of blood cells accurately is an extremely important task in the field of hematology, for the diagnosis of blood disorders and for guiding decision making in the clinical context. In this paper, we use a high-quality dataset of 17,092 microscopic peripheral blood cell images from the Hospital Clinic of Barcelona, encompassing eight different cell types, all of which have been annotated by expert pathologists. To improve model performance and to tackle the class imbalance in the dataset, we developed a strong data preprocessing and data augmentation pipeline which includes contrast enhancement, normalization, geometric and photometric transformations, injection of noise, and mixup style synthetic data. We develop two state-of-the-art deep learning models (EfficientNet-B0, ResNet50) to enable benchmarking of the proposed pipeline. In our experimental results, EfficientNet-B0 achieved overall accuracy of approximately 98.3% and ResNet50 achieved accuracy of 98.6%, with very good precision, recall, and F1-scores for all classes. These preliminary results demonstrate the effectiveness of the designed data preprocessing and data augmentation strategies, as well as provide a benchmark for managing blood cell images in hematology for future research.
Copyright (c) 2025 marwa raid (Author) https://creativecommons.org/licenses/by/4.0
Cognitive Honeypots AI-Enhanced Deception for Proactive Threat Hunting
The rapidly increasing complexity of cyber threats, including AI-powered attacks, is forcing a shift in defense strategies from reactive to proactive approaches. Traditional honeypots remain largely static and easily identifiable, while newer AI-enhanced models emphasize realism but still lack deep understanding of attacker cognition. To address this gap, this paper introduces CogniTrap, a novel framework that combines a high-interaction honeypot with an AI-driven cognitive deception engine. CogniTrap dynamically creates and adapts “cognitive decoys” designed to exploit attackers’ biases and reasoning flaws. A prototype of CogniTrap was developed and deployed, where decoy placements and adaptations were optimized using reinforcement learning informed by live analysis of attacker tactics, techniques, and procedures (TTPs). Intelligence gathered from triggered decoys was transformed into proactive hypotheses for threat hunting in production environments. Experimental results from comparative 30-day live deployments showed that CogniTrap increased attacker dwell time by 45% compared to a standard high-interaction honeypot and generated higher interaction rates with deceptive assets. Furthermore, it was able to produce high-fidelity threat hunting queries based on attacker cognitive patterns, validating its practical utility. This research marks the first implementation-based framework for adaptive cognitive honeypots, bridging the gap between theoretical cognitive security concepts and operational proactive threat hunting. By providing architecture, algorithms, and empirical validation, CogniTrap establishes a new paradigm for intelligent cyber defense.
Copyright (c) 2025 bareq maher, Karrar M. Khudhair, Ruaa Riyadh hadi (Author) https://creativecommons.org/licenses/by/4.0
Analyzing Big Data to Mitigate Cyber Attacks Using Machine Learning Classifications: A Comparative Study of an Efficient Classifier Set
In this research, we address a set of methodological approaches and approaches in scientific research in the field of cybersecurity and big data analysis using machine learning techniques, which have made the data analysis process easier to understand and mitigate cyberattacks. This method involves data processing and analysis in a series of stages, or in the form of analytical protection layers, including analysis and organization of large databases,. Training and qualifying classifiers is a method for reducing the dimensionality and skewness of feature vectors according to basic procedure analysis.. Using the principal components in the analysis, we employ various binary classifiers for vector K-nearest neighbors and many other algorithms, such as Bayesian algorithms and others, analyze vector machines, as well as artificial intelligence, which work to increase the efficiency and accuracy of attack detection devices. Artificial neural networks are also highly efficient in detecting cyberattacks and analyzing all network disturbances. Through our research, the basic idea was to classify and segment data and deal with each type of data separately to facilitate processing. Another approach is to combine data classifiers. This idea relies on two options: soft voting and majority voting. Each approach has its own method and method for specific use. methods for intrusion detection and penetration of large databases. In the first approach, we analyze and process data in parallel by classifying and dividing it into several subsets, and assigning each subset a separate path. In this method, we partition the problem, making the solution simpler and the data less complex. In the other approach, we also use sensors to collect client data within search servers. The sensor contains a set of parallel paths, each path analyzes the data and information for the client, and is conducted through a parallel network that detects anomalies through two different data sets. One group contains computer network traffic, which includes examining data and hosts from distributed denial of service (DDOS) attacks. The other group contains the Internet of Things and its data.
Copyright (c) 2025 Ali Jawad (Author) https://creativecommons.org/licenses/by/4.0
Multi-Dimensional Deep Learning for Unusual Detection in Security Footage: A 3D CNN, LSTM, and Attention-Based Approach
Automated detection of unusual activities in surveillance videos remains a critical challenge due to the vast volume of footage and the rarity of anomalous events. This study proposes a novel deep learning framework that integrates 3D convolutional neural networks for spatiotemporal feature extraction, Long Short-Term Memory networks for modeling temporal dependencies, and an attention mechanism to focus on salient segments. The primary objective is to achieve highly accurate binary classification of video clips into “usual” and “unusual” categories, while addressing class imbalance and environmental variability. The model is trained and evaluated on three large-scale datasets, UCF‑Crime, XD-Violence, and CCTVFights, which comprise surveillance videos covering real-world anomalies. Experimental results show that the proposed method achieves an overall accuracy of 97.41% on the UCF-Crime dataset, 98.11% on the XD-Violence dataset, and 98.50% on the CCTVFights dataset, in addition to high precision, recall, and F1 scores on the three datasets used in the evaluation process, outperforming existing benchmarks. These findings indicate that combining spatiotemporal modeling and attention-driven context aggregation can significantly enhance anomaly detection performance in complex surveillance scenarios.
Copyright (c) 2025 Nadia Ali (Author) https://creativecommons.org/licenses/by/4.0
Pepper Leaf Disease Detection Using Deep Learning Techniques
Early diagnosis of pests and plant diseases is crucial for preventing significant crop losses. This study proposes a leaf disease detection system using MobileNetV2 integrated with multiple optimization techniques (Adam and learning rate scheduling). Evaluated on the Pepper PlantVillage dataset, the MobileNetV2 model employs patch embedding and attention mechanisms for feature extraction, with SoftMax used for final classification. The model was further validated on a multi-class Apple PlantVillage dataset. Results demonstrate high accuracy: 97.03% for pepper and 94.63% for apple classification. Comparative analysis with CNN architectures shows superior efficiency and faster convergence for our model, outperforming Inception v3 (96.81%) and VGG-19 (94.93%) on the pepper dataset.
Copyright (c) 2025 Lafta Raheem Ali Al-khazraji (Author) https://creativecommons.org/licenses/by/4.0