مجلة الكاظم لعلوم الحاسوب
type: channel
description: 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.
publisher: Imam Al-Kadhim university College (IKU)
language: ar
publication name: مجلة الكاظم لعلوم الحاسوب
issn: 3007-1429
items: Deep Learning Models In The Early Brediction Of Epileptic Seizures
items: Chicken Swarm Optimization based QoS Web Service Composition in Cloud Computing Environment
items: تحليل تدهور القرار الذكي في ظل التوسع الشديد للبيانات في نظم معلومات المؤسسات
items: علوم الحاسوب تحليلات الأداء المدعومة بالذكاء الاصطناعي للعاملين في مجال التعليم: دراسة تجريبية في المدارس العراقية
items: دمج الصور متعددة البؤر باستخدام خوارزمية جينية، مع مراعاة تباين شدة الإضاءة المحلية وخصائص الحواف.
items: Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP
items: MG Experimental Evaluation of Adaptive Multi-Agent AI Models for Detecting Stealthy Cyber Attacks in Dynamic Network Environments based on MADDPG
items: Developing an Enhanced IHBO Algorithm with Chaotic Binary Hashing to Optimize BiGRU for Industry 4.0 IIoT Anomaly Detection
items: Federated Learning for Early Detection of Advanced Persistent Threats in IoT Networks
items: Practical Implementation and Analysis of Software Metrics Impact on Maintainability in Open-Source Systems
items: Explainable Artificial Intelligence Integrated Ensemble Learning Framework for Diabetes Prediction
items: Optimized Hybrid CNN-LSTM Framework with Multi-Feature Analysis and SMOTE for Intrusion Detection in SDN
items: Computationally Efficient Hybrid Framework for MNIST Handwritten Digit Recognition
items: The Connected Classroom: Leveraging EdTech to Enhance Student Engagement
items: Deep Learning-Based Blood Cell Classification with Enhanced Data Preprocessing and Augmentation
items: Cognitive Honeypots AI-Enhanced Deception for Proactive Threat Hunting
items: Analyzing Big Data to Mitigate Cyber Attacks Using Machine Learning Classifications: A Comparative Study of an Efficient Classifier Set
items: Multi-Dimensional Deep Learning for Unusual Detection in Security Footage: A 3D CNN, LSTM, and Attention-Based Approach
items: Pepper Leaf Disease Detection Using Deep Learning Techniques
items: تشفير الصوت وفك تشفيره باستخدام التشفير الأفيني مع لوحة الاستخدام لمرة واحدة
items: تقنيات الذكاء الاصطناعي لفك تشفير وتمثيلات اللغه من نشاط الدماغ القائم على تخطيط كهربية الدماغ:مراجعه شامله
items: Security Content Used To Protect Data From Social Media
items: A A Comprehensive Review of Intrusion Detection Systems in IoT networks Using ML and DL Techniques
items: Fake News Detection: A Comprehensive Taxonomy of Text, Image, Video, and Multi-Modal Techniques
items: التقييم الكمي لأنظمة تخزين الطاقة لتعزيز استخدام الطاقة المتجددة في البيئة الزراعية
items: Hybrid PSO-Bagging Approach for Efficient and Accurate Network Anomaly Detection
items: LV2PA: A Lightweight Verification with Privacy-Preserving Authentication for Vehicular Communications
items: التعرف الآلي على المشاعر باستخدام نماذج CNN-RNN الهجينة على الإشارات الفسيولوجية المتعددة الوسائط.
items: Intelligent Agent-Based Architecture for Low-Light Image Enhancement Using an A3C Framework
items: Incipient Fault Protection Using Artificial Intelligence Techniques

Deep Learning Models In The Early Brediction Of Epileptic Seizures
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description: ملخص لا يزال التنبؤ المبكر بنوبات الصرع يمثل تحديًا في علم الأعصاب السريري نظرًا لعدم القدرة على التنبؤ بالنوبات وما تنطوي عليه من مخاطر نفسية وجسدية على المريض. يُمكن استخدام تخطيط كهربية الدماغ (EEG) كأداة مهمة لأنظمة الإنذار المبكر بالنوبات بهدف تحسين جودة حياة المريض. لذا، تُقارن هذه الورقة البحثية وتقيّم أداء عدة نماذج للتعلم العميق للتنبؤ بالنوبات قبل 15 دقيقة باستخدام بيانات تخطيط كهربية الدماغ. ولتحقيق هذه الغاية، تم اتباع بروتوكول تجريبي موحد لضمان المقارنة بين النماذج: LSTM، وBiLSTM، وشبكة الرسم البياني العصبية، وTransformer. تم تقييم النماذج بناءً على قاعدة بيانات CHB-MIT التي تضم تسجيلات طويلة الأمد من 24 مريضًا. خضعت كل إشارة لخطوات معالجة مسبقة مختلفة مثل الترشيح والتطبيع وتقسيم النافذة الزمنية. بعد ذلك، تم استخراج مجموعة فردية من الميزات، مع موازنة البيانات بين فئتي ما قبل النوبة والنوبة. دُرست النماذج بناءً على مقاييس متنوعة مثل الدقة والنوعية وقابلية التكرار ومعامل F1 والمساحة تحت المنحنى. لذا، تُظهر نتائج نماذج التعلم العميق المستخدمة أداءً متقاربًا. فقد أظهر نموذجا LSTM وBiLSTM دقة عالية في الحالات الإيجابية، بمعدل استرجاع بلغ 0.99. أما نموذج Transformer، فقد حقق أعلى دقة بلغت 0.95 وأعلى خصوصية بلغت 0.76. في حين أظهر نموذج الشبكة العصبية البيانية (GNN) أداءً مماثلاً للنماذج الأخرى، بأداء تنافسي ولكنه لم يتفوق عليها جميعًا. وقد مكّن استخدام الذكاء الاصطناعي القابل للتفسير (XAI) من تقييم أهمية المراحل الزمنية والخصائص في كلٍ من أداء النموذج وقراراته. وبناءً على ذلك، تؤكد النتائج أن نموذج Transformer يتمتع بقدرة عالية على التنبؤ المبكر بالنوبات، وبالتالي، فهو مفيد في تطوير أنظمة إنذار مبكر موثوقة لمرضى الصرع.
subject: Computer Science
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subject: علوم الحاسوب
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creator: shahad sallah
rights: الحقوق الفكرية (c) 2026 shahad sallah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 22
ending page: 36
doi: 10.61710/kjcs.v4i1.149

Chicken Swarm Optimization based QoS Web Service Composition in Cloud Computing Environment
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Dina Alshibani
rights: الحقوق الفكرية (c) 2026 Dina Alshibani (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 37
ending page: 45
doi: 10.61710/kjcs.v4i1.153

تحليل تدهور القرار الذكي في ظل التوسع الشديد للبيانات في نظم معلومات المؤسسات
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Shaymaa kaseb Layus
creator: صالح حاجم جلود
creator: جمال محسن الركابي
creator: محمد كاسب لعيوس الحسناوي
rights: الحقوق الفكرية (c) 2026 Shaymaa kaseb Layus, Salih Hajem Glood , Jamal M. Alrikabi, Mohammad Kaisb Layous Alhasnawi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 55
ending page: 77
doi: 10.61710/kjcs.v4i1.155

علوم الحاسوب تحليلات الأداء المدعومة بالذكاء الاصطناعي للعاملين في مجال التعليم: دراسة تجريبية في المدارس العراقية
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description: تُركز هذه الأخيرة على البحث في تساهم في تحليلات الأداء التقرير في الذكاء الاصطناعي على الكادر التعليمي في المدارس العراقية، وتساعد على جودة التدريس، وإدارة الإدارة، وأداء المؤسسة التعليمية. أبرزت الدراسة إلى مراجعة القصور في إجراءات التقييم التقليدي، التي تعتمد الغالبية على أداء الطلاب في الامتحانات، وتقديم جزئية وبطيئة حول كفاءة التدريس. تم إعداد استبيان مُهيكل لتمثيل تمثيلية من حيث المبدأ والإداريين، ثم تحليل البيانات باستخدام برنامج SPSS الإصدار 25، من خلال السيولة الموثوقة (معامل ألفا لكرون باخ)، واختبارات الربح (معامل ارتباط بيرسون)، والإحصاء الوصفي، وتحليل الإحالة (البسيط والمتعدد) لتفسيرات الدراسة. هناك ارتباط إيجابي قوي بين تطبيق نظام دعم الابتكارات في الذكاء الاصطناعي، وذلك لأنه يساهم في التخطيط المستمر، وتحسين الابتكارات التكنولوجية. وتمثل النقاط الرئيسية الاتجاهات في تطوير دمج الذكاء الاصطناعي في المدارس، وتدريب الكادر التعليمي، تحليلات النحاس الآن لتحسين الموارد وتيسير اتخاذ القرار في الأدلة. يمكن أن تساعد في دعم النتائج المتاحة لصانعي النتائج وتحسين النتائج بشكل كبير بمساعدة التحليلات الذكية.
subject: Computer Science
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subject: علوم الحاسوب
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creator: maherali1976 Ahmed76
rights: الحقوق الفكرية (c) 2026 maherali1976 Ahmed76 (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 88
ending page: 103
doi: 10.61710/kjcs.v4i1.161

دمج الصور متعددة البؤر باستخدام خوارزمية جينية، مع مراعاة تباين شدة الإضاءة المحلية وخصائص الحواف.
type: item
description: تقترح هذه الدراسة منهجًا لدمج الصور متعددة التركيز، يعتمد على تحسين الخوارزمية الجينية لاختيار أوزان الدمج الأنسب، والتي تُستخدم بدورها لرفع جودة الصورة المدمجة الناتجة. يتم استخدام مرشح الانحراف المعياري لاستخراج خصائص النسيج، وتحديد كشف الحواف باستخدام مُعامل سوبل، بالإضافة إلى تحديد خاصية التباين. تُستخدم الأوزان المُحسّنة لتحديد أفضل توليفة من خرائط خصائص هذه الخصائص الثلاث المستخرجة، وذلك لتحقيق عملية دمج دقيقة. تُظهر التجارب أن هذا المنهج يحافظ بنجاح على النسيج والحواف والتباين، مما ينتج عنه صورة مدمجة ذات جودة بصرية محسّنة وغنية بالمعلومات.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Muthana Mahdi
rights: الحقوق الفكرية (c) 2026 Muthana Mahdi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 46
ending page: 54
doi: 10.61710/kjcs.v4i1.154

Lost in Translation? Detecting Emotional Drift in Bilingual Texts of Gilgamesh Using NLP
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description: يُقدّم هذا العمل مفهوم "الانحراف العاطفي" في الترجمة الأدبية، والذي يُشير إلى انحراف أو تحوّل الإشارات العاطفية من النص الأصلي إلى ترجماته. ويركّز هذا البحث على تحليل ثنائي اللغة للترجمتين العربية والإنجليزية لملحمة جلجامش، ويهدف، من خلال الاستفادة من التطورات الحديثة في معالجة اللغة الطبيعية (NLP) مع التركيز على تحليل المشاعر والتعرّف على العواطف، إلى قياس المحتوى العاطفي. وتتمثّل الأهداف النهائية لهذا البحث في تطوير منهجية حاسوبية لاستخراج المحتوى العاطفي من مجموعات النصوص الأدبية ثنائية اللغة ومقارنتها، وقياس الانحراف العاطفي عبر نسخ مختلفة من ملحمة جلجامش، ووضع هذه النتائج في سياق أوسع لأبحاث الترجمة. ويُتيح التقاء اللغويات الحاسوبية ونظرية الترجمة والتحليل الأدبي الرقمي آفاقًا جديدة للدراسات الأدبية المدعومة بالذكاء الاصطناعي، ويُوفّر إطارًا قابلًا للتكرار لدراسة مسألة الدقة العاطفية في الأدب المترجم.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Zaid Al-Araji
creator: Balqees Talal Hasan
rights: الحقوق الفكرية (c) 2026 Zaid Al-Araji (Author); Balqees Talal Hasan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 12
ending page: 21
doi: 10.61710/kjcs.v4i1.146

MG Experimental Evaluation of Adaptive Multi-Agent AI Models for Detecting Stealthy Cyber Attacks in Dynamic Network Environments based on MADDPG
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description: يواجه الأمن السيبراني تحديات متزايدة نتيجة للهجمات المتطورة والسرية التي تستهدف الشبكات الديناميكية. يهدف هذا البحث إلى استكشاف فعالية نماذج الذكاء الاصطناعي متعددة العوامل القابلة للتكيف في كشف هذه الهجمات باستخدام خوارزمية MADDPG (خوارزمية تدرج السياسة الحتمية العميقة متعددة العوامل). وقد صُممت بيئة شبكة ديناميكية لمحاكاة حركة البيانات الطبيعية والهجمات السيبرانية السرية، حيث يراقب كل عامل جزءًا محددًا من الشبكة ويتخذ قرارات فورية لكشف الهجمات أو التخفيف من آثارها.
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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creator: asma ibrahem
rights: الحقوق الفكرية (c) 2026 asma ibrahem (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 78
ending page: 87
doi: 10.61710/kjcs.v4i1.157

Developing an Enhanced IHBO Algorithm with Chaotic Binary Hashing to Optimize BiGRU for Industry 4.0 IIoT Anomaly Detection
type: item
subject: Computer Science
  topic: section
subject: علوم الحاسوب
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subject: Array
  topic: keywords
subject: Array
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creator: SAIF SAAD ALAMSHANI
rights: الحقوق الفكرية (c) 2026 SAIF SAAD ALAMSHANI (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-25
publication date: 2026-03-25
starting page: 1
ending page: 11
doi: 10.61710/kjcs.v4i1.138

Federated Learning for Early Detection of Advanced Persistent Threats in IoT Networks
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: bassam noori
rights: الحقوق الفكرية (c) 2025 bassam noori (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 100
ending page: 108
doi: 10.61710/kjcs.v3i4.140

Practical Implementation and Analysis of Software Metrics Impact on Maintainability in Open-Source Systems
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Nadia Hussien
rights: الحقوق الفكرية (c) 2025 Nadia Hussien (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 87
ending page: 99
doi: 10.61710/kjcs.v3i4.135

Explainable Artificial Intelligence Integrated Ensemble Learning Framework for Diabetes Prediction
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Mohammed Mohammed Abdallazez
rights: الحقوق الفكرية (c) 2025 Mohammed Mohammed Abdallazez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 109
ending page: 126
doi: 10.61710/kjcs.v3i4.141

Optimized Hybrid CNN-LSTM Framework with Multi-Feature Analysis and SMOTE for Intrusion Detection in SDN
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Dalia Shihab
creator: Abbas Abdulazeez Abdulhameed
creator: Methaq T. Gaata Gaata
rights: الحقوق الفكرية (c) 2025 Dalia Shihab, Abbas Abdulazeez Abdulhameed, Methaq T. Gaata (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 55
ending page: 75
doi: 10.61710/kjcs.v3i4.132

Computationally Efficient Hybrid Framework for MNIST Handwritten Digit Recognition
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Dalia Shihab
rights: الحقوق الفكرية (c) 2025 Dalia Shihab (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 76
ending page: 86
doi: 10.61710/kjcs.v3i4.134

The Connected Classroom: Leveraging EdTech to Enhance Student Engagement
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description: تناولت هذه الدراسة متعددة الأساليب تصورات طلاب الجامعات لدور التكنولوجيا في تعزيز المشاركة والتعاون والاستقلالية. جُمعت البيانات من 120 طالبًا (65 طالبة و55 ميلاً) من ثلاث جامعات (الجامعة المستنصرية، وجامعة ديالى، وجامعة واسط) من خلال المشاركة في الاستبيانات وملاحظات الفصول الدراسية والتغذية الراجعة المفتوحة. أظهرت النتائج أن الطلاب لديهم وجهات نظر إيجابية للغاية، حيث كانت المشاركة (م = 4.12) هي الميزة الأكثر ملاحظة، تليها التعاون (م = 3.98) والاستقلالية (م = 3.85). كشف التحليل الاستدلالي عن اختلافات كبيرة في تصورات التعاون: أبلغت الطالبات عن درجات أعلى من الطلاب (ت (118) = 2.04، ص = 0.04)، وأبلغ طلاب جامعة المستنصرية عن تعاون أعلى بكثير من طلاب جامعة أخرى (ف (2,117) = 3.67، ص = 0.028). لم يتم اكتشاف أي اختلافات كبيرة في المشاركة أو الاستقلالية. كشفت بيانات الرصد عن مستويات عالية من التعاون بين الأقران، بينما سلّطت الملاحظات النوعية الضوء على الفرص الرئيسية (مثل مرونة التعلم) والتحديات (مثل مشاكل التواصل، وفجوات المعرفة الرقمية). وتشير النتائج إلى أنه على الرغم من فعالية التكنولوجيا في إشراك الطلاب، إلا أن قدرتها على تعزيز التعاون تعتمد على عوامل ديموغرافية ومؤسسية. وتؤكد الدراسة على أهمية التصميم التربوي المدروس والدعم المؤسسي القوي للاستفادة من إمكانات التكنولوجيا التعاونية بكفاءة وعدالة.
subject: Computer Science
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subject: علوم الحاسوب
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creator: جابر محمد الدليمي
rights: الحقوق الفكرية (c) 2025 Jaber (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 37
ending page: 54
doi: 10.61710/kjcs.v3i4.123

Deep Learning-Based Blood Cell Classification with Enhanced Data Preprocessing and Augmentation
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description: 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.
subject: Computer Science
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creator: marwa raid
rights: الحقوق الفكرية (c) 2025 marwa raid (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 22
ending page: 36
doi: 10.61710/kjcs.v3i4.122

Cognitive Honeypots AI-Enhanced Deception for Proactive Threat Hunting
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description: 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.
subject: Computer Science
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creator: بارق ماهر
creator: كرار ماهر
creator: رؤى رياض
rights: الحقوق الفكرية (c) 2025 bareq maher, Karrar M. Khudhair, Ruaa Riyadh hadi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 55
ending page: 70
doi: 10.61710/kjcs.v3i3.121

Analyzing Big Data to Mitigate Cyber Attacks Using Machine Learning Classifications: A Comparative Study of an Efficient Classifier Set
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description: 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 cont…
subject: Computer Science
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subject: علوم الحاسوب
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creator: ali jawad
rights: الحقوق الفكرية (c) 2025 Ali Jawad (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 1
ending page: 21
doi: 10.61710/kjcs.v3i4.113

Multi-Dimensional Deep Learning for Unusual Detection in Security Footage: A 3D CNN, LSTM, and Attention-Based Approach
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description: لا يزال الكشف الآلي عن الأنشطة غير الاعتيادية في فيديوهات المراقبة يُمثل تحديًا بالغ الأهمية نظرًا للكم الهائل من اللقطات وندرة الأحداث الشاذة. تقترح هذه الدراسة إطار عمل مبتكرًا للتعلم العميق يدمج الشبكات العصبية التلافيفية ثلاثية الأبعاد لاستخراج السمات المكانية الزمنية، وشبكات الذاكرة طويلة المدى قصيرة المدى لنمذجة التبعيات الزمنية، وآلية انتباه للتركيز على الأجزاء البارزة. الهدف الرئيسي هو تحقيق تصنيف ثنائي دقيق للغاية لمقاطع الفيديو إلى فئتين "معتادة" و"غير عادية"، مع معالجة اختلال التوازن بين الفئات والتباين البيئي. تم تدريب النموذج وتقييمه على ثلاث مجموعات بيانات واسعة النطاق، وهي UCF-Crime وXD-Violence وCCTVFights، والتي تتضمن فيديوهات مراقبة تغطي شذوذًا في العالم الحقيقي. تُظهر النتائج التجريبية أن الطريقة المقترحة تُحقق دقة إجمالية تبلغ 97.41% في مجموعة بيانات UCF-Crime، و98.11% في مجموعة بيانات XD-Violence، و98.50% في مجموعة بيانات CCTVFights، بالإضافة إلى دقة عالية، ودرجات تذكر، وF1 في مجموعات البيانات الثلاث المستخدمة في عملية التقييم، متفوقةً بذلك على المعايير الحالية. تشير هذه النتائج إلى أن الجمع بين النمذجة المكانية الزمنية وتجميع السياقات القائمة على الانتباه يُمكن أن يُحسّن بشكل كبير من أداء اكتشاف الشذوذ في سيناريوهات المراقبة المعقدة.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Nadia Ali
rights: الحقوق الفكرية (c) 2025 Nadia Ali (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 1
ending page: 15
doi: 10.61710/kjcs.v3i3.114

Pepper Leaf Disease Detection Using Deep Learning Techniques
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Lafta Raheem Ali Al-khazraji
rights: الحقوق الفكرية (c) 2025 Lafta Raheem Ali Al-khazraji (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 45
ending page: 54
doi: 10.61710/kjcs.v3i3.118

تشفير الصوت وفك تشفيره باستخدام التشفير الأفيني مع لوحة الاستخدام لمرة واحدة
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description: يهدف هذا البحث إلى تصميم وتنفيذ نظام تشفير هجين لتأمين الإشارات الصوتية، بالاعتماد على دمج خوارزميتين تشفيريتين: التشفير الأفيني (Affin Cipher) ولوحة الاستخدام الواحد (OTP). تُعد خوارزمية التشفير الأفيني نموذجًا بسيطًا وفعالًا للتشفير الكلاسيكي، بينما توفر خوارزمية لوحة الاستخدام الواحد مستوى أمان عاليًا عند استخدام مفتاح عشوائي غير متكرر. طُبّق النظام المقترح على إشارة صوتية رقمية، حيث حُوّلت الإشارة أولًا إلى تمثيل رقمي (8 بت)، ثم رُمّمت باستخدام التشفير الأفيني، ثم طُبّقت تقنية تشفير OTP على المخرجات الناتجة. تضمّن التقييم العملي استخدام مقاييس كمية مثل متوسط ​​الخطأ التربيعي (MSE) ومتوسط ​​الخطأ المطلق (MAE) ونسبة الإشارة إلى الضوضاء (SNR)، بالإضافة إلى التوزيع الإحصائي للإشارات في كل مرحلة من مراحل التشفير باستخدام الهيستوغرام. أظهرت النتائج أن الإشارة المفككة مطابقة تمامًا للإشارة الأصلية (MSE = 0، MAE = 0، SNR = ∞)، وأثبت التحليل الإحصائي للمدرج التكراري فعالية النظام في تشويش أنماط تردد الإشارة، مما يعزز مقاومته للهجمات التحليلية. يوفر النظام المقترح توازنًا بين بساطة التنفيذ وكفاءة التشفير، وهو مناسب لتأمين البيانات الصوتية في تطبيقات الوقت الفعلي والأنظمة الحساسة.
subject: Computer Science
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subject: علوم الحاسوب
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creator: siham oleiwi
rights: الحقوق الفكرية (c) 2025 siham oleiwi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 28
ending page: 44
doi: 10.61710/kjcs.v3i3.116

تقنيات الذكاء الاصطناعي لفك تشفير وتمثيلات اللغه من نشاط الدماغ القائم على تخطيط كهربية الدماغ:مراجعه شامله
type: item
description: تتناول هذه الورقة الشاملة تقنيات الذكاء الاصطناعي لفك تشفير اللغة وتمثيلها اعتمادًا على نشاط الدماغ كما يتم قياسه بواسطة تخطيط كهربية الدماغ (EEG)، وهي طريقة شائعة وغير جراحية وتتميز بدقة زمنية عالية لتسجيل النشاط العصبي في الدماغ أثناء معالجة اللغة. وتواجه هذه التقنية عدة تحديات، أبرزها التعامل مع الضوضاء والتعقيد في الإشارات. كما تستعرض الورقة التقدم الكبير في تحليل هذه الإشارات باستخدام تقنيات التعلم الآلي التقليدية مثل آلات المتجهات الداعمة (SVM) والغابات العشوائية (RF)، بالإضافة إلى تقنيات التعلم العميق مثل الشبكات العصبية الالتفافية (CNN) وشبكات الذاكرة طويلة وقصيرة الأمد (LSTM) والمحوّلات (Transformers). وتناقش الورقة أيضًا التطبيقات الرئيسية لهذه التقنيات، مثل توفير أدوات تواصل للأشخاص ذوي الإعاقات، والتشخيص والعلاج الطبي، وفهم الإدراك اللغوي، إلى جانب التحديات المرتبطة بجودة البيانات وتكلفتها وتعقيدها والقضايا الأخلاقية. كما تقدم الورقة رؤى مستقبلية واعدة حول دمج تقنيات متعددة، والتنبؤ بالحالات العصبية والمعرفية، وتطوير واجهات متقدمة بين الدماغ والحاسوب، مما يمهد الطريق لفهم أعمق لآليات معالجة اللغة في الدماغ.
subject: Computer Science
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subject: علوم الحاسوب
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creator: sahar zidan
rights: الحقوق الفكرية (c) 2025 sahar zidan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 15
ending page: 27
doi: 10.61710/kjcs.v3i3.115

Security Content Used To Protect Data From Social Media
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description: 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.
subject: Computer Science
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creator: Zina Alshukri
rights: الحقوق الفكرية (c) 2025 Zina Alshukri (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 67
ending page: 83
doi: 10.61710/kjcs.v3i2.110

A A Comprehensive Review of Intrusion Detection Systems in IoT networks Using ML and DL Techniques
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: fatima Rahim
creator: Asst.Prof.Dr.Saif Ali Abd Alradha Alsaidi
rights: الحقوق الفكرية (c) 2025 fatima Rahim, Asst.Prof.Dr.Saif Ali Abd Alradha Alsaidi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 84
ending page: 95
doi: 10.61710/kjcs.v3i2.111

Fake News Detection: A Comprehensive Taxonomy of Text, Image, Video, and Multi-Modal Techniques
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Hussein Al-Kaabi
creator: Ali Nadhim Kamber
creator: Muhammad Riyad Al-Rikab
rights: الحقوق الفكرية (c) 2025 Hussein Al-Kaabi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 30
ending page: 45
doi: 10.61710/kjcs.v3i2.103

التقييم الكمي لأنظمة تخزين الطاقة لتعزيز استخدام الطاقة المتجددة في البيئة الزراعية
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description: تقدم هذه الدراسة تقييمًا كميًا شاملًا لأنظمة تخزين الطاقة (ESS) لتعزيز دمج الطاقة المتجددة في البيئات الزراعية. ونظرًا للطبيعة المتقطعة لطاقتي الشمس والرياح، تُقدم أنظمة تخزين الطاقة (ESS) حلاً استراتيجيًا لضمان موثوقية الطاقة في العمليات الزراعية الحيوية، مثل الري والمعالجة والتخزين البارد. يستخدم البحث نماذج محاكاة ديناميكية، وبيانات ميدانية من مصر والهند والبرازيل، وتحليلًا تقنيًا واقتصاديًا لمقارنة تقنيات تخزين الطاقة بالبطاريات والطاقة الحرارية والطاقة الكهرومائية. ويتم تقييم مؤشرات الأداء الرئيسية - بما في ذلك كفاءة الطاقة، وتكلفة التخزين المستوية (LCOS)، وخفض الكربون، ورضا المستخدمين - عبر مناطق زراعية مناخية متنوعة. تكشف النتائج عن عدم وجود نظام تخزين طاقة واحد مثالي عالميًا؛ فكل تقنية لها مزاياها الخاصة بناءً على المناخ والتكلفة وقابلية التوسع. وتُسلط الدراسة الضوء على أهمية تصميم نظام مُخصص للمناخ، والدعم المُستهدف، ومبادرات بناء القدرات لدعم اعتماد حلول الطاقة المستدامة في الزراعة. وتهدف النتائج إلى توجيه صانعي السياسات والمهندسين والمزارعين نحو أنظمة طاقة زراعية مرنة ومنخفضة الانبعاثات.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Ahmed Majbel
rights: الحقوق الفكرية (c) 2025 Ahmed Majbel (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 46
ending page: 66
doi: 10.61710/kjcs.v3i2.109

Hybrid PSO-Bagging Approach for Efficient and Accurate Network Anomaly Detection
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: haider Adnan Mohammed
creator: Ahmed Sadeq Jaafar
rights: الحقوق الفكرية (c) 2025 haider aljasim, Ahmed Sadeq Jaafar (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-25
publication date: 2025-03-25
starting page: 1
ending page: 13
doi: 10.61710/kjcs.v3i1.93

LV2PA: A Lightweight Verification with Privacy-Preserving Authentication for Vehicular Communications
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description: 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
subject: Computer Science
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subject: علوم الحاسوب
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creator: Murtadha Alazzawi
creator: Aqeel Luaibi Challoob
creator: Kai Chen, 2Hongwei Lu
rights: الحقوق الفكرية (c) 2025 Murtadha Alazzawi, Aqeel Luaibi Challoob, Kai Chen, 2Hongwei Lu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 1
ending page: 19
doi: 10.61710/kjcs.v3i2.96

التعرف الآلي على المشاعر باستخدام نماذج CNN-RNN الهجينة على الإشارات الفسيولوجية المتعددة الوسائط.
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description: لقد برز التعرف على المشاعر كأحد أحجار الزاوية للتفاعل بين الإنسان والحاسوب، وبالتالي فتح آفاق جديدة في الرعاية الصحية والتعليم والترفيه. إن القدرة على أتمتة عمليات التعرف على المشاعر باستخدام نماذج الشبكة العصبية التلافيفية الهجينة والشبكة العصبية المتكررة توفر طريقًا واعدًا لفك تشفير الحالات العاطفية المعقدة. تطور الدراسة المقترحة نهجًا لدمج تخطيط القلب واستجابة الجلد الجلفانية وتعبيرات الوجه لأداء التعرف على المشاعر بطريقة دقيقة وفعالة. يجمع هذا الهيكل الهجين بين نقاط قوة CNN في استخراج السمات المكانية وRNNs في نمذجة التبعيات الزمنية، مما يوفر بشكل طبيعي علاجًا للتحديات التي يفرضها استخدام البيانات المتعددة الوسائط. تم إجراء تجارب مكثفة على مجموعات البيانات المرجعية المتاحة للجمهور، ويتفوق النموذج الهجين المقترح على الطرق الأحادية والتقليدية الأخرى من حيث دقة التصنيف الأعلى والمتانة. لا تشير هذه الدراسة إلى إمكانات النماذج الهجينة في تعزيز التعرف على المشاعر فحسب، بل إنها توفر أيضًا إطارًا قابلًا للتطوير وقابلًا للتكيف مع التطبيقات في العالم الحقيقي مثل مراقبة الصحة العقلية وأنظمة التعلم التكيفي. وقد أكدت النتائج كيف يمكن لتقنيات التعلم العميق سد الفجوة بشكل كبير بين التجارب العاطفية الذاتية والتحليلات الحسابية الموضوعية.
subject: Computer Science
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subject: علوم الحاسوب
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creator: AMMAR AZEEZ
rights: الحقوق الفكرية (c) 2025 AMMAR AZEEZ (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-25
publication date: 2025-06-25
starting page: 20
ending page: 29
doi: 10.61710/kjcs.v3i2.100

Intelligent Agent-Based Architecture for Low-Light Image Enhancement Using an A3C Framework
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Muthana Mahdi
rights: الحقوق الفكرية (c) 2024 Muthana Mahdi (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-25
publication date: 2024-12-25
starting page: 35
ending page: 47
doi: 10.61710/kjcs.v2i4.95

Incipient Fault Protection Using Artificial Intelligence Techniques
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description: 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.
subject: Computer Science
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subject: علوم الحاسوب
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creator: Shaymaa Hadi Mohammed
creator: Israa Zamil Chyad
creator: Ayodeji Olalekan Salau
rights: الحقوق الفكرية (c) 2025 shaymaa hadi, Israa Zamil Chyad, Ayodeji Olalekan Salau (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-03-25
publication date: 2025-03-25
starting page: 24
ending page: 36
doi: 10.61710/kjcs.v3i1.86