Attention scholars and researchers in the Big Data realm! The Mesopotamian Journal of Big Data, already with three published issues, invites your cutting-edge contributions to shape the future of this field. Our platform aims to disseminate groundbreaking discoveries and transformative applications in Big Data, emphasizing data analytics, machine learning, and related areas. We encourage interdisciplinary collaboration to drive advancements in this rapidly evolving domain. Your expertise is crucial—join us in this impactful journey.
Expression of Concern: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
EXPRESSION OF CONCERN FOR: A. Radhi, E. ., Y. Kamil, M. ., & Abed Mohammed, M. . (2025). Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images. Mesopotamian Journal of Big Data, 2025, 156–177. https://doi.org/10.58496/MJBD/2025/011 https://mesopotamian.press/journals/index.php/bigdata/article/view/892 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Expression of Concern: Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks
EXPRESSION OF CONCERN FOR: Sameer Zhraw, D., Abdulridha Hussain, M., Ameen Abduljabbar, Z. ., Omollo Nyangaresi, V., Hasan Ali, A., & A. Neamah, H. . (2025). Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks. Mesopotamian Journal of Big Data, 2025, 329–349. https://doi.org/10.58496/MJBD/2025/021 https://mesopotamian.press/journals/index.php/bigdata/article/view/956 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: A Robust Model for Android Malware Detection via ML and DL classifiers
EXPRESSION OF CONCERN FOR: Almomani, O., Arabiat, A. ., Al Tayeb, M., Amin Almaiah, M. ., Obeidat, M. ., H. H. Aldhyani, T. ., Shehab, R., & Rowad, M. . (2025). A Robust Model for Android Malware Detection via ML and DL classifiers. Mesopotamian Journal of Big Data, 2025, 261–277. https://doi.org/10.58496/MJBD/2025/017 https://mesopotamian.press/journals/index.php/bigdata/article/view/918 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Robust Model for Android Malware Detection via ML and DL classifiers”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
EXPRESSION OF CONCERN FOR: Shaker Abdalrada, A. ., Fahem Neamah, A. ., Lafta Majeed, . H. ., & Raad Al-Sudani, A. . (2025). A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data. Mesopotamian Journal of Big Data, 2025, 211–221. https://doi.org/10.58496/MJBD/2025/014 https://mesopotamian.press/journals/index.php/bigdata/article/view/913 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Towards Autonomous Optical Fibre Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Towards Autonomous Optical Fiber Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning
EXPRESSION OF CONCERN FOR: Hussien Htaat, I. ., Hussein Ali, M. ., Khudiar Abass, A . (2025). Towards Autonomous Optical Fibre Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning. Mesopotamian Journal of Big Data, 2025, 195–210. https://doi.org/10.58496/MJBD/2025/013 https://mesopotamian.press/journals/index.php/bigdata/article/view/894 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Towards Autonomous Optical Fibre Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern : DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?
EXPRESSION OF CONCERN FOR: Sallam, M. ., Al-Mahzoum, K. ., Sallam, M. ., Mijwil, M. M. . (2025). DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?. Mesopotamian Journal of Big Data, 2025, 26-34. https://doi.org/10.58496/MJBD/2025/002 https://mesopotamian.press/journals/index.php/bigdata/article/view/706 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images
EXPRESSION OF CONCERN FOR: Kadhim , D. A., Mohammed , M. A. (2025). Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images. Mesopotamian Journal of Big Data, 2025, 1-25. https://doi.org/10.58496/MJBD/2025/001 https://mesopotamian.press/journals/index.php/bigdata/article/view/691 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems
EXPRESSION OF CONCERN FOR: Awaad , A. M., Alheeti , K. M. A., Najem , A. K. A. (2024). Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems. Mesopotamian Journal of Big Data, 2024, 230-240. https://doi.org/10.58496/MJBD/2024/017 https://mesopotamian.press/journals/index.php/bigdata/article/view/658 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Hybrid Model for Forecasting Temperature in Khartoum Based on CRU data
EXPRESSION OF CONCERN FOR: Alkattan , H., Subhi , A. A., Farhan , L., & Al-mashhadani , G. (2024). Hybrid Model for Forecasting Temperature in Khartoum Based on CRU data. Mesopotamian Journal of Big Data, 2024, 164-174. https://doi.org/10.58496/MJBD/2024/011 https://mesopotamian.press/journals/index.php/bigdata/article/view/490 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Hybrid Model for Forecasting Temperature in Khartoum Based on CRU data”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Harnessing the Potential of Artificial Intelligence in Managing Viral Hepatitis
EXPRESSION OF CONCERN FOR: Ali, G., Mijwil, M. M., Adamopoulos, I., Buruga, B. A., Gök, M., & Sallam, M. (2024). Harnessing the Potential of Artificial Intelligence in Managing Viral Hepatitis. Mesopotamian Journal of Big Data, 2024, 128-163. https://doi.org/10.58496/MJBD/2024/010 https://mesopotamian.press/journals/index.php/bigdata/article/view/484 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Harnessing the Potential of Artificial Intelligence in Managing Viral Hepatitis”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty
EXPRESSION OF CONCERN FOR: Ali, B., Alakkari, K., Abotaleb, M., Mijwil, M. M., & Dhoska, K. (2024). MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty. Mesopotamian Journal of Big Data, 2024, 48-67. https://doi.org/10.58496/MJBD/2024/005 https://journals.mesopotamian.press/index.php/bigdata/article/view/386 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection
EXPRESSION OF CONCERN FOR: Ameedeen, M. A., Hamid, R. A., Aldhyani, T. H. H., Al-Nassr, L. A. K. M., Olatunji, S. O., & Subramanian, P. (2024). A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection. Mesopotamian Journal of Big Data, 2024, 175-184. https://doi.org/10.58496/MJBD/2024/012 https://mesopotamian.press/journals/index.php/bigdata/article/view/544 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Automated Water Quality Assessment Using Big Data Analytics
EXPRESSION OF CONCERN FOR: Mohialden , Y. M., Hussien , N. M., Salman, S. A. (2024). Automated Water Quality Assessment Using Big Data Analytics. Mesopotamian Journal of Big Data, 2024, 211-222. https://doi.org/10.58496/MJBD/2024/015 https://mesopotamian.press/journals/index.php/bigdata/article/view/582 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Automated Water Quality Assessment Using Big Data Analytics”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Advanced Machine Learning Approaches for Enhanced GDP Nowcasting in Syria Through Comprehensive Analysis of Regularization Techniques
EXPRESSION OF CONCERN FOR: Alakkari, K., Ali, B., Abotaleb , M., Abttan, R. A., & Dutta , P. K. (2024). Advanced Machine Learning Approaches for Enhanced GDP Nowcasting in Syria Through Comprehensive Analysis of Regularization Techniques. Mesopotamian Journal of Big Data, 2024, 102-117. https://doi.org/10.58496/MJBD/2024/008 https://mesopotamian.press/journals/index.php/bigdata/article/view/476 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Advanced Machine Learning Approaches for Enhanced GDP Nowcasting in Syria Through Comprehensive Analysis of Regularization Techniques”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Agent-Interacted Big Data-Driven Dynamic Cartoon Video Generator
EXPRESSION OF CONCERN FOR: Mohialden, Y. M., khorsheed, A. A., & Hussien, N. M. (2024). Agent-Interacted Big Data-Driven Dynamic Cartoon Video Generator. Mesopotamian Journal of Big Data, 2024, 40-47. https://doi.org/10.58496/MJBD/2024/004 https://mesopotamian.press/journals/index.php/bigdata/article/view/366 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Agent-Interacted Big Data-Driven Dynamic Cartoon Video Generator”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Validation Framework for Robust and Explainable Machine Learning Models in Autism Spectrum Disorder Triage
The development of real-time triage applications for Autism Spectrum Disorder (ASD) is a critical challenge due to the rising prevalence of ASD and the urgent need for efficient resource allocation in healthcare systems. Previous studies have applied machine learning (ML) to ASD triage; however, most approaches overlook robustness against adversarial attacks, provide limited benchmarking across multiple evaluation criteria, and lack explainability to support clinical adoption. Building on our earlier work, which introduced a fuzzy evaluation and benchmarking framework using the 2-Tuple Linguistic Fermatean Fuzzy Decision by Opinion Score Method (2TLFFDOSM), this study proposes a comprehensive five-stage validation and evaluation framework. The framework systematically validates fuzzy-based rankings against raw performance metrics, conducts dual-perspective analysis under normal and adversarial conditions, performs sensitivity analysis across ten weighting scenarios, and integrates explainable AI (LIME, PFI, Integrated Gradients, and PDP) to interpret feature contributions before and after adversarial perturbations. Finally, a checklist benchmarking approach is used to position the framework against five recent studies.
Fuzzy Multi-Criteria Decision- Making (MCDM)
Copyright (c) 2025 Ghadeer Ghazi Shayea, Mohd Hazli Mohammed Zabil, A. S. Albahri, Rula A. Hamid, O .S. Albahri, A .H. Alamoodi, Iman Mohamad Sharaf https://creativecommons.org/licenses/by/4.0
Virtual Personalities and Smart Analytics in the Digital Environment: Legal and Applied Framework for Big Data and Financial Transaction
The rapid progress of artificial intelligence (AI) has altered the very nature of the digital systems that ground today's legal and financial institutions. This study is cross-discipline research that constructs an integrated framework that connects digital identity, AI-based digital wallets, and information governance across a singular legal-technological approach. It considers the consequences of AI reshaping economic exchange, digital identity management, and communication and its mediation by autonomous virtual identities that can carry out actions characteristic of observable real-world social and economic experiences. The study uses a comparison of the regulatory mechanisms in Iraq, Europe, and the US context to convey the efficiency, transparency, security while in need of mitigating regulatory concerns about ownership, privacy, liability, and intellectual property. The findings concede that while virtual identities are devoid of any independent legal status, someone related to those identities is liable. Lastly, the research concludes by recommending Egypt establish a national comprehensive legal framework for AI and big data legislation that gives consideration to protection of data, transparency of algorithmic decision making, and ethical use. Ultimately, the study promotes a hybrid legal and technical regulatory approach to focusing on human values and innovative responsibility.
Copyright (c) 2025 Hazim Akram Sallal, Faris Kamil Hasan Mihna, Lobna Abdalhusen Easa Al-Seedi, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel https://creativecommons.org/licenses/by/4.0
Intelligent Forecasting of Solar Atmospheric Disturbances via Capsule Neural Networks and Space Weather Data
Solar flares represent a major challenge to satellite communications, navigation systems, and terrestrial power grids, making accurate forecasting essential for mitigating their disruptive effects. This study aims to improve the reliability of solar flare prediction by developing a deep learning framework based on Capsule Networks (CapsNet). The proposed approach integrates feature engineering, data preprocessing, and imbalance-handling techniques such as SMOTE and Focal Loss. Using NASA space weather data, we constructed both binary (storm/no storm) and multi-class (C, M, X) classification models across forecasting windows of 6, 24, and 48 hours. The 48-hour binary model achieved 96% accuracy with a True Skill Statistic (TSS) of 0.92, significantly outperforming existing CNN-based approaches. Meanwhile, the 6-hour multi-class model delivered high recall for rare but critical X-class flares (0.86) and strong overall accuracy (92%). These results demonstrate that CapsNet can effectively capture complex spatio-temporal dependencies in space weather data, offering a robust and scalable solution for early-warning systems in solar flare forecasting.
Copyright (c) 2025 Eman Turki Mahdi, Mohammed E. Seno, Abdullah I. Abdulghafar https://creativecommons.org/licenses/by/4.0
Concise Comparison of CNN Models On a Specified Dataset
Recently, interest in Deep Learning (DL), which is a subset of Machine Learning (ML), has emerged. The most famous and used from the DL is the Convolutional Neural Network (CNN). CNN is particularly effective in image processing. There are many duties in image processing that CNN can do, i.e., segmentation, classification, object detection, facial recognition, etc. Image classification is one of the most important applications due to its relevance to various fields, including the healthcare industry and others. One of the challenges researchers face is selecting the appropriate algorithm for the classification task, particularly when dealing with binary or multi-class classification. This paper attempts to compare these algorithms depending on a specific dataset which have four classes, each having a balanced number of medical images. The main thing that the paper focuses on is the power of these algorithms in image classification when placed in the same conditions. This paper also makes a comparison inside the model itself by using three scenarios. The first one involves binary classification, the second uses three classes from the dataset, while the third scenario uses the entire number of classes. The best result among the models is going to AlexNet with an accuracy of 91.92%, and the DenseNet169 with an accuracy of 91.48%. Finally, this paper highlights the differences among state-of-the-art algorithms, particularly in their application to binary and multi-classification tasks.
Copyright (c) 2025 Humam K. Yaseen , Saif S. Kareem, Bashar I. Hameed, Salam K. Abdullah https://creativecommons.org/licenses/by/4.0
A Novel AI-based Dependency-aware Algorithm for Prioritizing Software Requirements in Large-scale Projects
Requirement prioritization (RP) is one of the main activities in software analysis; an incorrect RP process can lead to many software failures. In any software project, the requirements are interdependent. Most current RP techniques almost overlook the requirements dependency (RD) handling while prioritizing. Neglecting dependencies among requirements during the RP task can lead to deadlock and incorrect prioritization results, resulting in high rework and project delays. This motivates us to introduce a novel, scalable, dependency-aware RP algorithm, namely, then Dependency-Aware Enhanced Analytical Hierarchy Process (DA-EAHP), which integrates an RD handling mechanism using fine-tuned large language models (LLMs) into our previously developed RP technique, namely, the Enhanced Analytical Hierarchy Process (E-AHP), to increase the realism and accuracy of the software RP process. The proposed algorithm is assessed against two zero-shot-based LLM models and a fuzzy graph-based model. All were evaluated on various-sized subsets from the PURE dataset, ranging from 25 500 requirements, to compare their dependency analysis accuracy and computational performance. The results show that our proposed algorithm achieves a 7–18% accuracy improvement over the baselines, with an approximate reduction of 52–82% and 71–79% in time and memory, respectively. Moreover, a comparison with another variant of the proposed algorithm without the RD handling process validated its positive impact on the RP process. These results show that DA-EAHP provides a more accurate and efficient RP technique, making it suitable for large-scale software projects with complicated dependencies. Research limitations include the dependence on scalability beyond 500 requirements and expert-annotated ground truth, which are open for future work.
Copyright (c) 2025 Nahla Mohamed, Waleed Helmy, Sherif Mazen https://creativecommons.org/licenses/by/4.0
Optimized Wavelet Scatter Method for Accurate Classification and Segmentation of Lung Nodules
Lung cancer is a significant global health concern due to its high mortality rate. This is primarily due to the difficulty of identifying malignant growths in early-stage computed tomography (CT) images. The thing is, it's often hard to tell the difference between a benign nodule and a malignant one. This makes it tricky to figure out what's going on with the patient. That's where these computer-aided diagnosis (CAD) approaches come in. This study introduces a lightweight and interpretable framework for comprehensively analyzing lung nodules, including their detection, classification, and segmentation. This approach uses the wavelet scattering transform (WST) to extract stable, deformation-invariant features. These features are then evaluated using three traditional classifiers: The Support Vector Machine (SVM), the Random Forest (RF), and the Decision Tree (DT). The methodology follows a structured pipeline. It begins with image enhancement steps, such as resizing, denoising, and gamma-based contrast optimization. Then, it moves on to WST-based feature representation, classification, and segmentation using Otsu’s thresholding technique. Validation experiments were conducted using the SPIE-AAPM Lung CT Challenge dataset in conjunction with the LIDC-IDRI benchmark. This dataset comprises 11,114 annotated CT slices, of which 8,286 are malignant and 2,828 are benign. The results demonstrate the proposed pipeline's effectiveness in delivering accurate and reliable performance. This shows its potential as a supportive tool for early diagnosis and clinical decision-making. Patient-wise separation and stratified 10-fold cross-validation were applied to ensure unbiased evaluation and prevent data leakage. Results show that SVM achieved perfect performance across all metrics (accuracy, precision, recall, and F1-score), while RF and DT yielded 99.33% and 98.18%, respectively. For segmentation, the model reached an Intersection over Union (IoU) of 0.845 and a Dice Similarity Coefficient (DSC) of 0.912. Although the SVM classifier achieved ideal scores, measures such as patient-level grouping, class weighting, and cross-validation were implemented to reduce risks of overfitting. Overall, the integration of WST with classical classifiers provides a lightweight and explainable CAD framework that has strong potential to support early lung cancer detection and clinical decision-making.
Copyright (c) 2025 Enas Hamood Al-Saadi, Ahmed Nidhal Khdiar, Nidhal K. El Abbadi https://creativecommons.org/licenses/by/4.0
Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks
Man-in-the-Middle (MITM) attacks reduce Hypertext Transfer Protocol Secure (HTTPS) to Hypertext Transfer Protocol (HTTP), compromising network communications to potential exploitation. Attackers exploit application-layer vulnerabilities, and the attack often occurs on LAN. This study addresses the problem by introducing a Uniform Resource Locator (URL) protection mechanism that combines encryption with secure key exchange. A browser built with Python and PyQt5 encrypts URLs before transmission. The router decrypts, processes, re-encrypts, and returns data securely. The Diffie–Hellman algorithm generates a new session key for each connection, and the Advanced Encryption Standard with Galois Counter Mode (AES-GCM) technique to encrypt. The system was tested in a VMware host-only environment under four scenarios: normal use, active attacker, system-only, and active attacker with the system enabled. Packet capture and timing analysis evaluated security and performance. The scheme achieved a 100% prevention rate against HTTPS downgrades. Intercepted traffic appeared as unreadable ciphertext. Average execution time increased from 0.05 seconds to 0.11 seconds due to encryption, but it did not affect stability. This research improves application-layer security independently and offers a concrete defense against MITM stripping attacks. In conclusion, the proposed methodology provides a pragmatic and effective strategy for protecting URL traffic in vulnerable local network environments.
Copyright (c) 2025 Duaa Sameer Zhraw, Mohammed Abdulridha Hussain, Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ali Hasan Ali, Husam A. Neamah https://creativecommons.org/licenses/by/4.0
Recognition of Alzheimer’s Disease stages via InceptionV3 and ResNet50
Early and precise detection of Alzheimer’s disease (AD) is essential for successful treatment. This research presents a system that autonomously detects and categorizes the phases of Alzheimer’s disease via brain scans and sophisticated deep learning techniques, including InceptionV3 and ResNet50. These models started with pretrained weights and were augmented by including bespoke classification layers, which consisted of dropout, batch normalization, and dense layers to increase performance and mitigate overfitting. The preprocessing processes included scaling the picture to 224 by 224 pixels, using average filtering for denoising, and converting the color space to guarantee compatibility with the models. Evaluations of the OASIS dataset illustrate the efficacy of the proposed approaches in accurately differentiating among the various phases of Alzheimer’s disease, including four classifications: nondemented, slightly demented, very mildly demented, and moderately demented. ResNet50 outperforms InceptionV3, achieving an accuracy of 93.9%, a micro F1 score of 94%, and a macro F1 score of 96%, demonstrating its efficacy and consistent performance in detecting and classifying all categories. Compared with current models, the suggested technique is more effective.
Copyright (c) 2025 Iman Aljubouri, Mostafa Ragheb, Mohamad Hamady https://creativecommons.org/licenses/by/4.0
Enhancing Throughput in a Network Function Virtualization Environment via the Manta Ray Foraging Optimization Algorithm
Network function virtualization (NFV) has emerged as a transformative paradigm in which traditional hardware appliances are replaced with virtual network functions (VNFs) running on commodity hardware. While NFV offers scalability and flexibility, it faces major challenges in sustaining high throughput and minimizing resource overhead under dynamic traffic conditions. In particular, flooding algorithm-based request propagation often leads to excessive redundancy, congestion, and resource waste. To address this limitation, this study applies Manta ray foraging optimization (MRFO), a swarm intelligence algorithm inspired by natural foraging behaviors, to optimize packet routing and resource allocation in NFV environments. The research employs a Barabási–Albert (BA) scale-free topology model to simulate realistic NFV infrastructures. The performance is evaluated by comparing the conventional flooding algorithm with MRFO-based routing under varying network sizes and time-to-live (TTL) values. The key metrics include throughput, packet delay, CPU and memory utilization, and the success rate. The simulation results demonstrate that MRFO consistently outperforms flooding in medium- and large-scale networks, achieving up to 53.6% improvement in throughput, reduced average delay (2.6 s → 1.7 s), and more balanced resource utilization across NFVs. However, in small-scale networks with limited routing paths, MRFO introduces computational overhead that reduces performance compared with the flooding algorithm. These findings highlight the significance of swarm intelligence for NFV optimization, showing that MRFO is best suited for scalable, dynamic infrastructures such as the cloud, Internet of Things (IoT), and 5G edge networks. This study contributes a novel integration of MRFO with flooding algorithm-based propagation, offering new insights into adaptive and resource-aware NFV optimization strategies.
Copyright (c) 2025 Sanaa S. Alwan, Asia Ali Salman https://creativecommons.org/licenses/by/4.0
Transformer-Based Genomic Classification and Simulation of Heat-Responsive Genes in Citrus limon
Iraqi lemon trees (Citrus limon), vital for regional agriculture and food security, face intensifying threats from extreme heat caused by ongoing climate change in Iraq. Native cultivars often lack thermotolerance due to low expression of protective heat-response genes. This study addresses this critical challenge by developing an AI-assisted framework that integrates real RNA-Seq data, Transformer-based deep learning, and explainable AI to classify and simulate the function of genes associated with heat stress adaptation. The primary objective is to identify key thermotolerance genes and model their biological impact, with a specific focus on indigenous citrus varieties. Using a customized transformer architecture adapted for gene sequence data, the model achieved strong predictive performance (macro F1-score: 0.91, AUC-ROC: 0.96). Among the genes identified, HSP70 and HSFA2 already recognized in the literature as central regulators of heat stress were confirmed as top-ranking candidates in Citrus limon. Their expression patterns and regulatory roles were validated through SHAP-based feature attribution and attention-weight analysis. The study’s contribution lies in its application of transformer and SHAP frameworks to a non-model, underrepresented crop species, offering a novel methodology with explicit reproducibility by clearly defining the datasets used. The results provide a biologically meaningful foundation for gene-level interventions in future breeding and genome editing programs.
Copyright (c) 2025 Ali Fahem Neamah, Zahraa A. Abdalkareem, Mostafa A. Mohammed, Jasim Ibrahim Ahmed, Phaklen Ehkan https://creativecommons.org/licenses/by/4.0
A Robust Model for Android Malware Detection via ML and DL classifiers
The rapid growth of sophisticated Android malware (AM) threats is significant, as Android devices often store private and sensitive personal and financial information. These threats allow stealing of data, interference with device functioning, and network compromise. One of the greatest difficulties in efficient interception systems is ensuring a high level of detection accuracy for distinguishable AM variants. This study focuses on developing a robust Android malware detection model via machine learning (ML) and deep learning (DL) techniques. The model combines ML classifiers, which consist of logistic regression (LR) and decision trees (DTs), and a DL classifier, an artificial neural network (ANN). The model was implemented via an open-source data mining program called Orange. The NATICUSdroid dataset was used to train and test the model, which was measured in terms of accuracy, precision, recall, F-measure and AUC. The experimental findings revealed that the ANN performed the best (accuracy: 98.0%, precision/recall/F-measure: 98.0%, AUC: 0.997) and was better than the LR (accuracy: 96.1%, AUC: 0.989) and DT (accuracy: 96.0%, AUC: 0.971) methods. The results highlight the high potential of DL-based approaches, especially ANNs, to detect Android malware and reinforce their suitability for enhancing mobile security systems.
Copyright (c) 2025 Omar Almomani, Areen Arabiat, Muneera Al Tayeb, Mohammed Amin Almaiah, Mansour Obeidat, Theyazn H. H. Aldhyani, Rami Shehab, Mahmood Rowad https://creativecommons.org/licenses/by/4.0
PSOA-CRL: A Hybrid Multi-Objective Routing Mechanism Using Particle Swarm Optimization and Actor-Critic Reinforcement Learning For VANETs
Vehicular ad hoc networks (VANETs) serve vehicles and infrastructure systems to communicate in real time for critical safety functions and traffic control. The highly mobile nature of VANETs with rapid topology changes, high mobility, and frequent disconnections is a very challenging situation for routing protocols. Most of the current approaches are static and tend to focus on a single metric rather than being flexible in practical environments. This paper introduces a hybrid routing method that is capable of maintaining a high packet delivery rate, low delay, and stable connectivity in VANETs with dynamic traffic situations. To address these problems, in this paper, we propose the PSOA-CRL, which is a hybrid multi-objective routing algorithm that integrates particle swarm optimization (PSO) with actor-critic reinforcement learning (A-CRL). The offline PSO component generates a variety of optimal routes. where the adaptive CRL just-in-time chooses the best available path. The two-way protocol maximizes the trade-off between the packet delivery ratio (PDR), end-to-end delay (E2E), link reliability, energy consumption, and routing overhead. A performance evaluation of PSOA-CRL with benchmarks under multi-objective optimization (MOO) through network metrics reveal the dominance of PSOA-CRL in most of the performance evaluation metrics. The obtained result reveals that the PSOA-CRL has a 97.8% packet delivery ratio, 41.3 ms end-to-end delay, and 96.1% link reliability. These results indicate that the PSOA-CRL is efficient in realizing reliable, real-time VANET routing and can be practically utilized in intelligent transportation systems (ITS).
Copyright (c) 2025 Mustafa Maad Hamdi, Baraa Saad Abdulhakeem, Ahmed Adil Nafea https://creativecommons.org/licenses/by/4.0
Shared Generator-Based Serverless Multimodal Federated Learning For Medical Image Analysis
Medical image analysis constitutes the foundation of the diagnosis of potential life-threatening conditions such as lung cancer. Nevertheless, AI model construction in this context is hindered by strict privacy regulations (e.g., HIPAA, GDPR), the variability of imaging protocols, and the paucity of large annotated datasets. These barriers constrain centralised machine learning and dampen interdisciplinary research. To overcome such barriers, this paper introduces shared generator-serverless federated learning (SGS-FL), a decentralised multimodal medical imaging framework. By employing a shared generator and multidiscriminator architecture, SGS-FL eliminates centralised dependency via cross-modal synthesis, while the communication burden is reduced by embedding a sharing protocol. By employing latent space aggregation with attention and independent component analysis, the interpretability, fairness, and relevance of features are improved. Experimental evaluation was conducted across three lung cancer datasets: LIDC-IDRI CT scans (≈1,018 cases), NODE21 chest radiographs (~10,000 images), and NSCLC radiogenomic PET-CT images (~211 patients). By employing 10-fold cross-validation with 10 independent iterations, SGS-FL achieved 92.5% ± 1.2 accuracy, 0.83 ± 0.02 Dice coefficient, 0.946 ± 0.01 area under the curve, and 21.5 ± 1.1 Frechet inception distance (FID), significantly surpassing benchmark state-of-the-art schemes such as FedACS (~88%) and Federated Transfer Learning (~89%) (p < 0.01). The results indicate that SGS-FL achieves superior scalability, interpretability, and performance and constitutes a sound paradigm of privacy-friendly and clinically trustworthy AI in medical imaging.
Copyright (c) 2025 Mohammed Adel Al-Shahe, Ahmed Saihood, Mustafa Asaad Hasan https://creativecommons.org/licenses/by/4.0
A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
Diabetes mellitus (DM) continues to escalate as a worldwide health emergency issue, with approximately 537 million adults currently diagnosed and forecasts estimating a further increase to 643 million by 2030. Early and precise foretelling of DM remains a decisive factor for timely intervention, thereby mitigating severe downstream sequelae such as cardiovascular disease, peripheral neuropathy, and diabetic retinopathy (DR). Conventional prognostic frameworks typically depend on exclusively either structured tabular measurements or visual medical imagery, which constrains comprehensive diagnostic capacity. This contribution confronts such limitation by advancing a hybrid machine learning (ML) methodology that synergistically combines deep learning—specifically, convolutional neural networks (CNNs) dedicated to retinal photograph scrutiny—with gradient-boosting machines (GBMs) that ingest structured demographic and clinical variables. Two publicly accessible repositories supplied training material: the Pima Indians Diabetes Database for tabular covariates and the Asia Pacific Tele-Ophthalmology Society (APTOS 2019) Blindness Detection corpus for fundus imagery. Retinal studies underwent standardised pre-processing re-scaling, pixel normalisation, Gaussian denoising, and multiplicative augmentation while tabular patient records underwent rigorous feature ranking. Outcome representations from both data strata were concatenated into a consolidated tensor, thereby rendering simultaneous latent-space learning achievable. The experimental results demonstrate that the hybrid model outperforms single-modality models, achieving an accuracy of 96%, a macro average F1 score of 0.96, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.994. The proposed approach offers a comprehensive diagnostic framework by combining systemic and localized disease indicators, thereby enhancing robustness, reducing variance, and supporting more informed clinical decision-making. This work highlights the potential of multimodal ML integration for complex disease prediction and sets the stage for future extensions to other chronic conditions.
Copyright (c) 2025 Ahmad Shaker Abdalrada, Ali Fahem Neamah, Huda Lafta Majeed, Ahmed Raad Al-Sudani https://creativecommons.org/licenses/by/4.0
Towards Autonomous Optical Fibre Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning
This paper introduces a hybrid CNN-LSTM architecture for automatic advantage prediction and optimization in erbium-doped fibre amplifiers (EDFAs), addressing a crucial role in maintaining signal strength over long-distance optical networks; however, existing modelling techniques face significant challenges in balancing accuracy and computational efficiency. The proposed version uniquely integrates convolutional neural networks (CNNs) for spatial-spectral function extraction and long short-term memory (LSTM) networks for temporal dynamics modelling, allowing simultaneous prediction of benefit profiles, 3 dB compression factors, and full-width half-maximum (FWHM) bandwidths from 5 input parameters: pump electricity, signal energy, fibre length, erbium concentration, and wavelength. When validated against OptiSystem simulations across 10 fibre lengths (3–30 m), the framework achieves unheard accuracy (R² >0.999, MSE=0.0032) while decreasing the computational time from hours to 6.1 milliseconds in line with the prediction—a 600,000× speed improvement. Benchmarking in the direction of seven contemporary strategies demonstrates 66–88% stepped forward average performance in essential metrics: 4× decrease in three dB point mistakes (0.18 dBm vs. 0.92 dBm in CNNs), three.4× better FWHM precision (0.36 nm vs. 1.21 nm) for 1 m--20 m as a fibre length, and real-time functionality with 1.28 million parameters. These upgrades permit self-reliant EDF optimization in dynamic optical networks, which remedy the spatiotemporal doped cloth that affects the modern-day process.
Copyright (c) 2025 Ibtesam Hussien Htaat, Mudhafar Hussein Ali, Abdulla Khudiar Abass https://creativecommons.org/licenses/by/4.0