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.
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
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
Computer Science 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
MG 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
Audio Encryption and Decryption using the Affine Cipher with the One-Time Pad (OTP)
This research aims to design and implement a hybrid encryption system to secure audio signals, based on the integration of two cryptographic algorithms: Affine Cipher and One-Time Pad (OTP). The Affine Cipher algorithm is a simple and efficient model of classical encryption, while the One-Time Pad algorithm provides a high level of security when using a random, unrepeated key. The proposed system was applied to a digital audio signal, where the signal was first converted to a numerical representation (8-bit), then encrypted using Affine Cipher, followed by the application of OTP encryption to the resulting outputs. The practical evaluation included the use of quantitative measures such as the average quadratic error (MSE), the average absolute error (MAE), and the signal-to-noise ratio (SNR), in addition to the statistical distribution of signals at each stage of the encryption by histogram. The results showed that the loose signal was exactly identical to the original signal (MSE = 0, MAE = 0, SNR = ∞), and statistical analysis of the histogram proved the effectiveness of the system in blurring the frequency patterns of the signal, which enhances its resistance to analytical attacks. The proposed system provides a balance between simplicity of implementation and efficiency in encryption, and is suitable for securing audio data in real-time applications and sensitive systems
Copyright (c) 2025 siham oleiwi (Author) https://creativecommons.org/licenses/by/4.0
AI- Techniques for Decoding Language Representation from EEG-Based Brain Activity: A Comprehensive Review
This comprehensive paper examines artificial intelligence techniques for decoding and representing language from brain activity, as measured by electroencephalography (EEG), a common, non-invasive, and high-temporal-resolution method for recording neural activity from the brain during language processing. This technique faces several challenges in dealing with noise and complexity. It also reviews the significant progress in analyzing this signal using traditional machine learning techniques such as SVM, RF, as well as deep learning techniques like CNN, LSTM, and Transformers. This paper also discusses the main applications of these technologies in providing communication tools for people with disabilities, medical diagnosis and treatment, understanding linguistic perception, as well as the challenges related to data quality, cost, complexity, and ethical issues. It also offers promising future insights into the integration of multiple technologies, predicting neurological and cognitive conditions, and developing advanced brain-computer interfaces, paving the way for a deeper understanding of language processing mechanisms in the brain.
Copyright (c) 2025 sahar zidan (Author) https://creativecommons.org/licenses/by/4.0
Security Content Used To Protect Data From Social Media
Abstract Social media networks have revolutionized global connectivity, enabling billions of users to engage in virtual communities and mutual interactions. However, their widespread adoption has attracted malicious actors who exploit platform vulnerabilities to compromise user security and privacy. Despite preventive measures, cyber-attacks targeting social media have surged, necessitating advanced intrusion detection systems (IDS) to mitigate risks. Although these platforms fetch never-seen convenience, users do not have the technical ability to understand the privacy implications of their shared content. As a result the use of available privacy settings fall short compared to the general practice of security. This study initiates the development of a holistic framework for social media security that integrates Policy-driven safeguards: Use strong passwords, keep updating your credentials often, share data carefully, use antivirus software, and stick to your own software. Artificial Intelligence (AI) and Machine Learning (ML)-driven solutions:: Machine learning algorithms for user sentiment analysis, disinformation detection, combating illicit activities such as child trafficking, and adversarial machine learning-based enhancement of intrusion detection. Ethical AI integration: Aligning with "AI for Good" efforts can help cut down biases and ensure fairness in automated security setups. The paper takes a close look at the latest improvements in social media security, stressing how important it is to protect private information as more breaches happen that could harm economic stability and confidence in using these platforms. This helps connect tech creativity with strict rules and offers a new way to strengthen platform trustworthiness and ability to bounce back in a digital world that is becoming more competitive.
Copyright (c) 2025 Zina Alshukri (Author) https://creativecommons.org/licenses/by/4.0
A A Comprehensive Review of Intrusion Detection Systems in IoT networks Using ML and DL Techniques
The Internet of Things (IoT) is growing at an extremely rapid rate, impacting all aspects of our lives and extending to various fields, including wearable technology, smart sensors, and home appliances. However, the rapid growth is coupled with serious security concerns that render these technologies vulnerable to hacking opportunities and erode user privacy, as well as data protection, especially as cyber-attacks become more complex. Intrusion detection is a crucial aspect for tracking and thwarting such attacks. Machine learning (ML) and deep learning (DL) algorithms have ever-increasing efficiency in automating procedures like these. This study aims to provide researchers with a comprehensive overview of contemporary Intrusion Detection System (IDS) techniques employed in the IoT environment, highlighting strengths and weaknesses. It also gives direction to future research by suggesting that more adaptive, lightweight, and efficient intrusion detection systems can be developed to address the unique constraints of IoT networks.
Asst.Prof.Dr.Saif Ali Abd Alradha Alsaidi
Copyright (c) 2025 fatima Rahim, Asst.Prof.Dr.Saif Ali Abd Alradha Alsaidi (Author) https://creativecommons.org/licenses/by/4.0
Fake News Detection: A Comprehensive Taxonomy of Text, Image, Video, and Multi-Modal Techniques
The widespread dissemination of fake news across digital platforms has posed significant challenges for information integrity, social stability, and public trust. Traditional fake news detection approaches, primarily based on text analysis, are no longer sufficient, as misinformation now integrates multi-modal content, including images, videos, and manipulated metadata. This paper presents a comprehensive taxonomy of fake news detection techniques, categorizing existing methods into text-based, image-based, video-based, and multi-modal approaches. We review the evolution of detection methodologies, from traditional machine learning models to advanced deep learning architectures, including transformers, convolutional neural networks (CNN), and hybrid AI models. Additionally, we analyze the growing challenge of adversarial attacks, where malicious actors manipulate text, images, and videos to bypass detection systems. Finally, we highlight emerging research directions, such as adversarial-resilient AI models, cross-modal fact verification, and human-AI hybrid fact-checking systems, which are crucial for developing trustworthy, explainable, and robust fake news detection frameworks. This study serves as a foundation for researchers and practitioners in advancing multi-modal misinformation detection and strengthening AI-driven fact-checking mechanisms.
Copyright (c) 2025 Hussein Al-Kaabi (Author) https://creativecommons.org/licenses/by/4.0
Quantitative Assessment of Energy Storage Systems For Enhanced Utilization of Renewable Energy in Agricultural Setting
This study presents a comprehensive quantitative assessment of Energy Storage Systems (ESS) to enhance the integration of renewable energy in agricultural settings. Given the intermittent nature of solar and wind energy, ESS offers a strategic solution to ensure energy reliability for critical farming operations such as irrigation, processing, and cold storage. The research employs dynamic simulation models, field data from Egypt, India, and Brazil, and techno-economic analysis to compare battery, thermal, and hydropower storage technologies. Key performance indicators—including energy efficiency, Levelized Cost of Storage (LCOS), carbon reduction, and user satisfaction—are evaluated across diverse agro-climatic zones. Results reveal that no single ESS is universally optimal; each technology has trade-offs based on climate, cost, and scalability. The study highlights the importance of climate-specific system design, targeted subsidies, and capacity-building initiatives to support the adoption of sustainable energy solutions in agriculture. Findings aim to guide policymakers, engineers, and farmers toward resilient, low-emission agricultural energy systems.
Copyright (c) 2025 Ahmed Majbel (Author) https://creativecommons.org/licenses/by/4.0
Hybrid PSO-Bagging Approach for Efficient and Accurate Network Anomaly Detection
The surge in internet usage has triggered a substantial increase in network attacks, raising serious cyber security concerns. Fog computing, which enhances cloud computing by providing low-latency services to mobile users, is particularly susceptible to these threats due to its proximity to end users and limited computational resources. Traditional Intrusion Detection Systems (IDS) designed for conventional networks may not directly apply to fog computing environments, where the ability to process and analyze large volumes of data efficiently is crucial. This paper presents a novel approach for network anomaly detection within fog environments, utilizing a Particle Swarm Optimization (PSO) -based Wrapper feature selection method combined with the Bagging technique. By applying this methodology to the NSL-KDD dataset, our approach effectively reduces computational complexity and improves the accuracy of intrusion detection models. The proposed system demonstrates superior performance compared to existing methods, achieving an impressive 98.3% accuracy and a low false positive rate of 1.5%. These results underscore the potential of the PSO-Bagging framework to enhance the security of fog computing systems, offering a robust solution to the growing problem of network attacks in distributed computing environments.
Copyright (c) 2025 haider aljasim, Ahmed Sadeq Jaafar (Author) https://creativecommons.org/licenses/by/4.0
LV2PA: A Lightweight Verification with Privacy-Preserving Authentication for Vehicular Communications
Security and privacy must be taken into account for vehicular ad-hoc networks (VANETs) due to the fact that broadcasting occurs through an open communication channel. This work offers a Lightweight Verification with Privacy-Preserving Authentication (LV2PA) approach for vehicular communications to overcome these challenges. To satisfy security and privacy requirements, the proposed LV2PA approach employs not only the cryptographic hash function, but also a Bloom filter and the Chinese remainder theorem. During the mutual authentication of the LV2PA scheme, only the first roadside unit (RSU) and on-board unit are required to communicate with a trusted authority (TA) due to the changeover use, however the other RSUs in vehicular communications do not require TA communication. Consequently, bottleneck problems for the TA are avoided. In addition, the RSU updates the shared group key whenever a vehicle joins or departs the group; hence, the proposed LV2PA provides complete forward secrecy and backward secrecy for vehicular communications. The formal (Burrows–Abadi–Needham (BAN) logic) and informal security analyses demonstrate that the proposed LV2PA scheme is legitimate and meets the security and privacy requirements, respectively. In terms of computing and communication expenses, the performance evaluation of the proposed LV2PA scheme has advantageously low overhead and low latency compared to state-of-the-art schemes
Copyright (c) 2025 Murtadha Alazzawi, Aqeel Luaibi Challoob, Kai Chen, 2Hongwei Lu (Author) https://creativecommons.org/licenses/by/4.0
Automated Emotion Recognition Using Hybrid CNN-RNN Models on Multimodal Physiological Signals.
Emotion recognition has emerged as one of the cornerstones of human-computer interaction, thus opening new frontiers in healthcare, education, and entertainment. The ability to automate emotion recognition processes using hybrid Convolutional Neural Network-Recurrent Neural Network models offers a promising avenue for decoding complex emotional states. The proposed study develops an approach for the integration of electrocardiogram, galvanic skin response, and facial expressions for performing emotion recognition in an accurate and efficient manner. This hybrid architecture combines the strengths of CNNs in spatial feature extraction and RNNs in modeling temporal dependencies, which naturally provides a remedy for challenges inherently brought about by the use of multimodal data. Extensive experiments have been conducted on benchmark datasets publicly available, and the proposed hybrid model outperforms other unmoral and traditional methods in terms of higher classification accuracy and robustness. This study points not only to the potential of hybrid models in advancing emotion recognition but also provides a scalable framework adaptable for real-world applications such as mental health monitoring and adaptive learning systems. The results underlined how deep learning techniques can dramatically bridge the gap between subjective emotional experiences and objective computational analyses.
Copyright (c) 2025 AMMAR AZEEZ (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Agent-Based Architecture for Low-Light Image Enhancement Using an A3C Framework
Low-light image enhancement (LLIE) is an important area of research as many applications such as photography, video surveillance, and security are confronted with image degradation in low-light environments. This paper presents an intelligent agent-based method for LLIE using the Asynchronous Advantage Actor-Critic (A3C) framework. The enhancement task is effectively cast in this work into the framework of a Markov Decision Process. This method enables an agent to learn a policy that successively improves image quality. In the agent, features are extracted by a Fully Convolutional Network (FCN), a policy network for choosing an action, and a value network for estimating the reward. In training, non-reference loss functions are also used to measure image quality without the availability of the reference image or ground truth images. Such functions include spatial consistency loss, exposure control loss, and illumination smoothness loss, and the approach achieves end-to-end enhancement without reference image. The experimental results on LOL and MIT-Adobe dataset also show that the proposed technique enhances image brightness, Contrast, and structure much better as compared to other state-of-the-art methods. Especially, the methodology proposed scored 25.93 PSNR, 0.932 SSIM, and 0.053 LPIPS on the LOL dataset, achieving better results than related strategies. The designed agent-based approach works under a wide range of low-light situations. This approach allows obtaining enhancement results that will be satisfactory in terms of the users’ preferences and needs of the specific applications. The findings highlight the method's robustness and flexibility, making it suitable for various practical applications. This work demonstrates that reinforcement learning agents have promising applications in improved image processing capabilities, and establishes a new record for low-light image improvement.
Copyright (c) 2024 Muthana Mahdi (Author) https://creativecommons.org/licenses/by/4.0
Incipient Fault Protection using Artificial Intelligence Techniques
In the field of incipient fault protection, various sources can cause failures, such as lightning, switching transients, mechanical imperfections, and chemical breakdown. To guard against these errors, Buchholz relays and pressure relief devices have been utilized. However, in recent years, preventive health measures have gained more attention. One popular approach is the implementation of the Dissolved Gas Analysis (DGA) system, which detects incipient faults by analyzing the gases dissolved in the transformer oil. In this context, the use of artificial neural networks (ANN) and artificial neural networks combined with expert systems (ANNEPS) has shown promise for power transformer protection against incipient faults using DGA. Power transformers, especially large oil-filled ones, are commonly subjected to DGA for identifying and diagnosing early-stage faults. By analyzing the dissolved gases and employing interpretation systems, such as (ANNEPS), unexpected failures can be prevented. The objective of this research is to identify internal problems within transformers, and an (ANN) structure has been specifically developed for this purpose. The ANNEPS approach combines the outputs of ANN and expert systems to ensure rapid and accurate identification of various types of transformer failures. By comparing the results of both computational methods, a reliable assessment can be made, enhancing the effectiveness of incipient fault protection strategies. Overall, the combination of (DGA) and advanced techniques like (ANN) and (ANNEPS) provides a robust approach to detect and prevent incipient faults in power transformers. These methods offer improved accuracy and promptness in identifying transformer failures, ultimately contributing to the reliability and efficiency of power systems.
Copyright (c) 2025 shaymaa hadi, Israa Zamil Chyad, Ayodeji Olalekan Salau (Author) https://creativecommons.org/licenses/by/4.0