Mesopotamian Journal of Big Data
type: channel
description: 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.
publisher: Mesopotamian Academic Press
language: en
publication name: Mesopotamian Journal of Big Data
issn: 2958-6453
items: Resolution of Expression of Concern: A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
items: Resolution of Expression of Concern: A Robust Model for Android Malware Detection via ML and DL Classifiers
items: Resolution of Expression of Concern: Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems
items: Resolution of Expression of Concern regarding: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
items: Expression of Concern: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
items: Expression of Concern: Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks
items: Expression of Concern: A Robust Model for Android Malware Detection via ML and DL classifiers
items: Expression of Concern: A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
items: Expression of Concern: Towards Autonomous Optical Fiber Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning
items: Expression of Concern : DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?
items: Expression of Concern: Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images
items: Expression of Concern: Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems
items: Validation Framework for Robust and Explainable Machine Learning Models in Autism Spectrum Disorder Triage
items: Virtual Personalities and Smart Analytics in the Digital Environment: Legal and Applied Framework for Big Data and Financial Transaction
items: Intelligent Forecasting of Solar Atmospheric Disturbances via Capsule Neural Networks and Space Weather Data
items: Concise Comparison of CNN Models On a Specified Dataset
items: A Novel AI-based Dependency-aware Algorithm for Prioritizing Software Requirements in Large-scale Projects
items: Optimized Wavelet Scatter Method for Accurate Classification and Segmentation of Lung Nodules
items: Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks
items: Recognition of Alzheimer’s Disease stages via InceptionV3 and ResNet50
items: Enhancing Throughput in a Network Function Virtualization Environment via the Manta Ray Foraging Optimization Algorithm
items: Transformer-Based Genomic Classification and Simulation of Heat-Responsive Genes in Citrus limon
items: A Robust Model for Android Malware Detection via ML and DL classifiers
items: PSOA-CRL: A Hybrid Multi-Objective Routing Mechanism Using Particle Swarm Optimization and Actor-Critic Reinforcement Learning For VANETs
items: Shared Generator-Based Serverless Multimodal Federated Learning For Medical Image Analysis
items: A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
items: Towards Autonomous Optical Fibre Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning
items: AI-Driven Smart Contract Vulnerability Detection: A Systematic Review of Methods, Challenges, and Future Prospects
items: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
items: UIR: Implementing Deep Neural Networks in addition to Conventional Algorithms for Ultra-Image Recovery

Resolution of Expression of Concern: A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
type: item
description: Resolution of Expression of Concern DOI of this Notice: https://doi.org/10.58496/MJBD/2026/019 Original Article: https://doi.org/10.58496/MJBD/2025/014 Expression of Concern: https://doi.org/10.58496/MJBD/2026/012 Title: A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data Following the publication of the article, A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data, published 2025-09-20 an Expression of Concern was issued by the Mesopotamian Journal of Big Data on 2026-03-03 to alert our readers to concerns regarding methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility The editorial team of the Mesopotamian Journal of Big Data has completed a thorough post-publication review of the article, the supporting data, and the authors' responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The findings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. The Mesopotamian Journal of Big Data thanks the readers who brought these matters to our attention, as well as the authors for their cooperation during the review process.
subject: Articles
  topic: section
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 19
ending page: 19
doi: 10.58496/MJBD/2026/019

Resolution of Expression of Concern: A Robust Model for Android Malware Detection via ML and DL Classifiers
type: item
description: Resolution of Expression of Concern Title :A Robust Model for Android Malware Detection via ML and DL classifiers. DOI of this Notice: https://doi.org/10.58496/MJBD/2026/018 Original Article: https://doi.org/10.58496/MJBD/2025/017 Expression of Concern: https://doi.org/10.58496/MJBD/2026/013 Following the publication of the article, A Robust Model for Android Malware Detection via ML and DL classifiers Published 2025-08-30 an Expression of Concern was issued by the Mesopotamian Journal of Big Data on 2026-03-03 to alert our readers to concerns regarding methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility The editorial team of the Mesopotamian Journal of Big Data has completed a thorough post-publication review of the article, the supporting data, and the authors' responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The findings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. The Mesopotamian Journal of Big Data thanks the readers who brought these matters to our attention, as well as the authors for their cooperation during the review process.
subject: Articles
  topic: section
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 18
ending page: 18
doi: 10.58496/MJBD/2026/018

Resolution of Expression of Concern: Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems
type: item
description: Resolution of Expression of Concern Title : Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems DOI of this Notice: https://doi.org/10.58496/MJBD/2026/017 Original Article: https://doi.org/10.58496/MJBD/2024/017 Expression of Concern:https://doi.org/10.58496/MJBD/2026/008 Following the publication of the article, Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems Published 2024-12-06 an Expression of Concern was issued by the Mesopotamian Journal of Big Data on 2026-03-03 to alert our readers to concerns regarding methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility The editorial team of the Mesopotamian Journal of Big Data has completed a thorough post-publication review of the article, the supporting data, and the authors' responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The findings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. The Mesopotamian Journal of Big Data thanks the readers who brought these matters to our attention, as well as the authors for their cooperation during the review process.
subject: Articles
  topic: section
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
doi: 10.58496/MJBD/2026/017

Resolution of Expression of Concern regarding: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
type: item
description: Resolution of Expression of Concern Title: Resolution of Expression of Concern regarding: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images DOI of this Notice: https://doi.org/10.58496/MJBD/2026/016 Original Article: https://doi.org/10.58496/MJBD/2025/011 Expression of Concern: https://doi.org/10.58496/MJBD/2026/015 Following the publication of the article, Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images Published 2025-08-30 an Expression of Concern was issued by the Mesopotamian Journal of Big Data on 2026-03-03 to alert our readers to concerns regarding methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility The editorial team of the Mesopotamian Journal of Big Data has completed a thorough post-publication review of the article, the supporting data, and the authors' responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The findings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. The Mesopotamian Journal of Big Data thanks the readers who brought these matters to our attention, as well as the authors for their cooperation during the review process.
subject: Articles
  topic: section
rights: Copyright (c) 2026 Eman A. Radhi https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 16
ending page: 16
doi: 10.58496/MJBD/2026/016

Expression of Concern: Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
subject: NOTICE,REOC,2026-04-22,https://mesopotamian.press/journals/index.php/bigdata/article/view/1111
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 15
ending page: 15
doi: 10.58496/MJBD/2026/015

Expression of Concern: Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 14
ending page: 14
doi: 10.58496/MJBD/2026/014

Expression of Concern: A Robust Model for Android Malware Detection via ML and DL classifiers
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
subject: NOTICE,REOC,2026-04-22,https://mesopotamian.press/journals/index.php/bigdata/article/view/1113
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 13
ending page: 13
doi: 10.58496/MJBD/2026/013

Expression of Concern: A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
subject: NOTICE,REOC,2026-04-22,https://mesopotamian.press/journals/index.php/bigdata/article/view/1114
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 12
ending page: 12
doi: 10.58496/MJBD/2026/012

Expression of Concern: Towards Autonomous Optical Fiber Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 11
ending page: 11
doi: 10.58496/MJBD/2026/011

Expression of Concern : DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?
type: item
description: 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.
subject: Articles
  topic: section
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 10
ending page: 10
doi: 10.58496/MJBD/2026/010

Expression of Concern: Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 9
ending page: 9
doi: 10.58496/MJBD/2026/009

Expression of Concern: Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Expression of Concern
  topic: keywords
subject: NOTICE,REOC,2026-04-22,https://mesopotamian.press/journals/index.php/bigdata/article/view/1112
  topic: keywords
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-03-03
publication date: 2026-03-03
starting page: 8
ending page: 8
doi: 10.58496/MJBD/2026/008

Validation Framework for Robust and Explainable Machine Learning Models in Autism Spectrum Disorder Triage
type: item
description: 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.
subject: Articles
  topic: section
subject: Autism Spectrum Disorder (ASD)
  topic: keywords
subject: Triage
  topic: keywords
subject: Robust Machine Learning
  topic: keywords
subject: Adversarial Attacks
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: (XAI)
  topic: keywords
subject: Fuzzy Multi-Criteria Decision- Making (MCDM)
  topic: keywords
creator: Ghadeer Ghazi Shayea
creator: Mohd Hazli Mohammed Zabil
creator: A. S. Albahri
creator: Rula A. Hamid
creator: O .S. Albahri
creator: A .H. Alamoodi
creator: Iman Mohamad Sharaf
rights: 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
date: 2025-11-21
publication date: 2025-11-21
starting page: 448
ending page: 475
doi: 10.58496/MJBD/2025/027

Virtual Personalities and Smart Analytics in the Digital Environment: Legal and Applied Framework for Big Data and Financial Transaction
type: item
description: 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.
subject: Articles
  topic: section
subject: Virtual Personalities
  topic: keywords
subject: Digital Identity
  topic: keywords
subject: Data Protection Law
  topic: keywords
subject: Privacy and Ownership Rights
  topic: keywords
subject: Digital Forensics
  topic: keywords
subject: Transparency and Explainability
  topic: keywords
creator: Hazim Akram Sallal
creator: Faris Kamil Hasan Mihna
creator: Lobna Abdalhusen Easa Al-Seedi
creator: Mustafa Abdulfattah Habeeb
creator: Yahya Layth Khaleel
rights: 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
date: 2025-11-09
publication date: 2025-11-09
starting page: 429
ending page: 447
doi: 10.58496/MJBD/2025/026

Intelligent Forecasting of Solar Atmospheric Disturbances via Capsule Neural Networks and Space Weather Data
type: item
description: 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.
subject: Articles
  topic: section
subject: Solar Flare Prediction
  topic: keywords
subject: Binary Classification
  topic: keywords
subject: Multiclass Classification
  topic: keywords
subject: Capsule Networks
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Class Imbalance
  topic: keywords
subject: Space Weather Forecasting
  topic: keywords
creator: Eman Turki Mahdi
creator: Mohammed E. Seno
creator: Abdullah I. Abdulghafar
rights: Copyright (c) 2025 Eman Turki Mahdi, Mohammed E. Seno, Abdullah I. Abdulghafar https://creativecommons.org/licenses/by/4.0
date: 2025-10-30
publication date: 2025-10-30
starting page: 415
ending page: 428
doi: 10.58496/MJBD/2025/025

Concise Comparison of CNN Models On a Specified Dataset
type: item
description: 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.
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: Convolutional neural network
  topic: keywords
subject: Comparative study
  topic: keywords
subject: CNN models
  topic: keywords
creator: Humam K. Yaseen
creator: Saif S. Kareem
creator: Bashar I. Hameed
creator: Salam K. Abdullah
rights: Copyright (c) 2025 Humam K. Yaseen , Saif S. Kareem, Bashar I. Hameed, Salam K. Abdullah https://creativecommons.org/licenses/by/4.0
date: 2025-10-29
publication date: 2025-10-29
starting page: 369
ending page: 393
doi: 10.58496/MJBD/2025/023

A Novel AI-based Dependency-aware Algorithm for Prioritizing Software Requirements in Large-scale Projects
type: item
description: 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.
subject: Articles
  topic: section
subject: Requirement engineering
  topic: keywords
subject: Requirement prioritization
  topic: keywords
subject: Requirement
  topic: keywords
subject: Analytical hierarchy process
  topic: keywords
subject: Large language models
  topic: keywords
creator: Nahla Mohamed
creator: Waleed Helmy
creator: Sherif Mazen
rights: Copyright (c) 2025 Nahla Mohamed, Waleed Helmy, Sherif Mazen https://creativecommons.org/licenses/by/4.0
date: 2025-10-29
publication date: 2025-10-29
starting page: 394
ending page: 414
doi: 10.58496/MJBD/2025/024

Optimized Wavelet Scatter Method for Accurate Classification and Segmentation of Lung Nodules
type: item
description: 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 C…
subject: Articles
  topic: section
subject: Computer-aided diagnosis
  topic: keywords
subject: Early cancer detection
  topic: keywords
subject: Feature extraction
  topic: keywords
subject: Support vector machine
  topic: keywords
subject: Random forest
  topic: keywords
subject: Decision tree
  topic: keywords
creator: Enas Hamood Al-Saadi
creator: Ahmed Nidhal Khdiar
creator: Nidhal K. El Abbadi
rights: Copyright (c) 2025 Enas Hamood Al-Saadi, Ahmed Nidhal Khdiar, Nidhal K. El Abbadi https://creativecommons.org/licenses/by/4.0
date: 2025-10-28
publication date: 2025-10-28
starting page: 350
ending page: 368
doi: 10.58496/MJBD/2025/022

Uniform Resource Locator Protection Scheme for the Mitigation of Man-In-The-Middle Stripping Attacks
type: item
description: 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.
subject: Articles
  topic: section
subject: SSL Strip
  topic: keywords
subject: HTTPS
  topic: keywords
subject: MITM attack
  topic: keywords
subject: NOTICE,EOC,2026-03-03,https://mesopotamian.press/journals/index.php/bigdata/article/view/1061
  topic: keywords
creator: Duaa Sameer Zhraw
creator: Mohammed Abdulridha Hussain
creator: Zaid Ameen Abduljabbar
creator: Vincent Omollo Nyangaresi
creator: Ali Hasan Ali
creator: Husam A. Neamah
rights: 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
date: 2025-10-28
publication date: 2025-10-28
starting page: 329
ending page: 349
doi: 10.58496/MJBD/2025/021

Recognition of Alzheimer’s Disease stages via InceptionV3 and ResNet50
type: item
description: 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.
subject: Articles
  topic: section
subject: Alzheimer’s disease
  topic: keywords
subject: Deep learning model
  topic: keywords
subject: Image processing
  topic: keywords
subject: InceptionV3
  topic: keywords
subject: ResNet50
  topic: keywords
creator: Iman Aljubouri
creator: Mostafa Ragheb
creator: Mohamad Hamady
rights: Copyright (c) 2025 Iman Aljubouri, Mostafa Ragheb, Mohamad Hamady https://creativecommons.org/licenses/by/4.0
date: 2025-10-27
publication date: 2025-10-27
starting page: 313
ending page: 328
doi: 10.58496/MJBD/2025/020

Enhancing Throughput in a Network Function Virtualization Environment via the Manta Ray Foraging Optimization Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: Network function virtualization (NFV)
  topic: keywords
subject: Manta ray foraging optimization (MRFO)
  topic: keywords
subject: Swarm Intelligence
  topic: keywords
subject: Virtual Network Functions (VNFs)
  topic: keywords
creator: Sanaa S. Alwan
creator: Asia Ali Salman
rights: Copyright (c) 2025 Sanaa S. Alwan, Asia Ali Salman https://creativecommons.org/licenses/by/4.0
date: 2025-10-26
publication date: 2025-10-26
starting page: 295
ending page: 312
doi: 10.58496/MJBD/2025/019

Transformer-Based Genomic Classification and Simulation of Heat-Responsive Genes in Citrus limon
type: item
description: 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.
subject: Articles
  topic: section
subject: Iraqi lemon trees
  topic: keywords
subject: heat stress
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Citrus AI genomics in-silico simulation
  topic: keywords
subject: Transformer model
  topic: keywords
subject: HSP70
  topic: keywords
subject: Gene expression
  topic: keywords
subject: Artificial intelligence
  topic: keywords
subject: Gene regulatory network
  topic: keywords
subject: Plant thermotolerance
  topic: keywords
creator: Ali Fahem Neamah
creator: Zahraa A. Abdalkareem
creator: Mostafa A. Mohammed
creator: Jasim Ibrahim Ahmed
creator: Phaklen Ehkan
rights: Copyright (c) 2025 Ali Fahem Neamah, Zahraa A. Abdalkareem, Mostafa A. Mohammed, Jasim Ibrahim Ahmed, Phaklen Ehkan https://creativecommons.org/licenses/by/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 278
ending page: 294
doi: 10.58496/MJBD/2025/018

A Robust Model for Android Malware Detection via ML and DL classifiers
type: item
description: 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.
subject: Articles
  topic: section
subject: Android Malware
  topic: keywords
subject: Logistic regression
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Artificial Neural Networks
  topic: keywords
subject: NATICUS-Droid dataset
  topic: keywords
subject: NOTICE,EOC,2026-03-03,https://mesopotamian.press/journals/index.php/bigdata/article/view/1060
  topic: keywords
creator: Omar Almomani
creator: Areen Arabiat
creator: Muneera Al Tayeb
creator: Mohammed Amin Almaiah
creator: Mansour Obeidat
creator: Theyazn H. H. Aldhyani
creator: Rami Shehab
creator: Mahmood Rowad
rights: 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
date: 2025-09-24
publication date: 2025-09-24
starting page: 261
ending page: 277
doi: 10.58496/MJBD/2025/017

PSOA-CRL: A Hybrid Multi-Objective Routing Mechanism Using Particle Swarm Optimization and Actor-Critic Reinforcement Learning For VANETs
type: item
description: 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).
subject: Articles
  topic: section
subject: VANETs
  topic: keywords
subject: Routing Protocols
  topic: keywords
subject: Intelligent Transportation Systems
  topic: keywords
subject: Actor-Critic Reinforcement Learning
  topic: keywords
subject: Packet Delivery Ratio
  topic: keywords
creator: Mustafa Maad Hamdi
creator: Baraa Saad Abdulhakeem
creator: Ahmed Adil Nafea
rights: Copyright (c) 2025 Mustafa Maad Hamdi, Baraa Saad Abdulhakeem, Ahmed Adil Nafea https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 241
ending page: 260
doi: 10.58496/MJBD/2025/016

Shared Generator-Based Serverless Multimodal Federated Learning For Medical Image Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: FL
  topic: keywords
subject: SGS-FL
  topic: keywords
subject: Medical imaging
  topic: keywords
subject: Lung nodules
  topic: keywords
subject: Multimodality models
  topic: keywords
creator: Mohammed Adel Al-Shahe
creator: Ahmed Saihood
creator: Mustafa Asaad Hasan
rights: Copyright (c) 2025 Mohammed Adel Al-Shahe, Ahmed Saihood, Mustafa Asaad Hasan https://creativecommons.org/licenses/by/4.0
date: 2025-09-22
publication date: 2025-09-22
starting page: 222
ending page: 240
doi: 10.58496/MJBD/2025/015

A Hybrid Machine Learning Approach for Enhanced Diabetes Prediction: Integrating Image and Numerical Data
type: item
description: 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-…
subject: Articles
  topic: section
subject: Diabetes Mellitus (DM)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Deep Learning (DL)
  topic: keywords
subject: Multimodal Data Integration.
  topic: keywords
subject: NOTICE,EOC,2026-03-03,https://mesopotamian.press/journals/index.php/bigdata/article/view/1059
  topic: keywords
creator: Ahmad Shaker Abdalrada
creator: Ali Fahem Neamah
creator: Huda Lafta Majeed
creator: Ahmed Raad Al-Sudani
rights: Copyright (c) 2025 Ahmad Shaker Abdalrada, Ali Fahem Neamah, Huda Lafta Majeed, Ahmed Raad Al-Sudani https://creativecommons.org/licenses/by/4.0
date: 2025-09-20
publication date: 2025-09-20
starting page: 211
ending page: 221
doi: 10.58496/MJBD/2025/014

Towards Autonomous Optical Fibre Networks: High-Precision EDFA Gain and Spectral Response Prediction via Hybrid CNN-LSTM Deep Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid CNN-LSTM
  topic: keywords
subject: EDFA gain prediction
  topic: keywords
subject: Real-time optimization
  topic: keywords
subject: Spatiotemporal modelling
  topic: keywords
subject: Optical amplifier control
  topic: keywords
subject: NOTICE,EOC,2026-03-03,https://mesopotamian.press/journals/index.php/bigdata/article/view/1058
  topic: keywords
creator: Ibtesam Hussien Htaat
creator: Mudhafar Hussein Ali
creator: Abdulla Khudiar Abass
rights: Copyright (c) 2025 Ibtesam Hussien Htaat, Mudhafar Hussein Ali, Abdulla Khudiar Abass https://creativecommons.org/licenses/by/4.0
date: 2025-08-30
publication date: 2025-08-30
starting page: 195
ending page: 210
doi: 10.58496/MJBD/2025/013

AI-Driven Smart Contract Vulnerability Detection: A Systematic Review of Methods, Challenges, and Future Prospects
type: item
description: Smart contracts (SCs) have become an essential component in the world of decentralized applications, automating transactions across blockchain networks without the need for intermediaries, and with this rise in adoption, the technology has also brought forth growing concern due to security vulnerabilities, which have led to serious financial damage, and the problem is far from being solved. Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain transactions. Given these challenges, researchers have been actively exploring more advanced detection techniques. Despite progress, many existing studies tend to focus narrowly on specific methods, whether static analysis, dynamic testing, or machine learning models, without offering a comprehensive comparison across all available approaches. This fragmented landscape leaves a noticeable gap for practitioners looking for a well-rounded understanding of smart contract security solutions. To address this, our study set out to systematically review the existing body of work, analysing 21 reviewed studies published between 2020 and 2024. The primary aim was to combine the diverse techniques that have been proposed for detecting vulnerabilities in smart contracts, ranging from static and dynamic analyses to more recent AI-driven models, graph-based techniques, and hybrid systems, critically evaluating their strengths, weaknesses, and practical effectiveness. The methodology followed a structured approach. We searched major research databases, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and Scopus—using carefully crafted search queries to ensure that we captured the most relevant and up-to-date papers. Our findings revealed that AI-based methods, especially those leveraging deep neural networks and graph neural networks, have achieved impressive detection accuracy in controlled envir…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Sustainable Growth
  topic: keywords
subject: Security analysis
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Vulnerability detection
  topic: keywords
subject: Ethereum
  topic: keywords
subject: Sustainable Digital Infrastructure
  topic: keywords
subject: Sustainable AI-Blockchain Integration
  topic: keywords
creator: Saad AL Azzam
creator: Raenu AL Kolandaisamy
creator: Ghassan AL Dharhani
rights: Copyright (c) 2025 Saad AL Azzam, Raenu AL Kolandaisamy , Ghassan AL Dharhani https://creativecommons.org/licenses/by/4.0
date: 2025-08-30
publication date: 2025-08-30
starting page: 178
ending page: 194
doi: 10.58496/MJBD/2025/012

Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
type: item
description: Alzheimer's disease (AD) presents significant diagnostic challenges owing to the subtle morphological similarities observed in the early stages, with traditional deep learning approaches often struggling to distinguish between the various stages of disease progression via structural Magnetic Resonance Imaging (MRI) data. Quantum computing offers unique advantages for medical image analysis, leveraging superposition and entanglement capabilities to process high-dimensional feature spaces beyond the limits of classical computation. This study introduces a hybrid quantum-classical neural network architecture (HQC-Net) for accurate four-class Alzheimer's disease classification, which uses quantum processing to detect patterns that are often invisible to classical spatial analysis methods. The proposed framework integrates classical feature extractors, including a custom CNN and modified ResNet18, with six-qubit variational quantum circuits that employ multiaxis rotation encoding (RY→RZ→RX), a quantum Fourier transform for spectral decomposition, and multihead attention for effective quantum-classical feature fusion. Comprehensive evaluations were conducted on the Kaggle dataset (5,121 samples) and the OASIS dataset (20,000 samples), incorporating realistic quantum noise modelling, including depolarising, amplitude damping, and phase damping channels. The modified QResNet18 configuration achieved a test accuracy of 99.67%, with perfect discriminative capability (AUC = 1.0000) on the OASIS dataset. Quantum processing demonstrated superior detection of very mild dementia (99.86% accuracy), which is crucial for early intervention. The proposed approach outperformed existing quantum-enhanced methods by 3.57 percentage points while effectively handling the increased diagnostic complexity associated with four-class classification. This study demonstrates a practical quantum advantage for multiclass neuroimaging classification, achieving superior diagnostic accuracy while maint…
subject: Articles
  topic: section
subject: Alzheimer's disease
  topic: keywords
subject: Quantum machine learning
  topic: keywords
subject: Medical image classification
  topic: keywords
subject: Quantum Fourier transform
  topic: keywords
subject: Hybrid neural networks
  topic: keywords
subject: NOTICE,EOC,2026-03-03,https://mesopotamian.press/journals/index.php/bigdata/article/view/1062
  topic: keywords
creator: Eman A. Radhi
creator: Mohammed Y. Kamil
creator: Mazin Abed Mohammed
rights: Copyright (c) 2025 Eman A. Radhi, Mohammed Y. Kamil, Mazin Abed Mohammed https://creativecommons.org/licenses/by/4.0
date: 2025-08-30
publication date: 2025-08-30
starting page: 156
ending page: 177
doi: 10.58496/MJBD/2025/011

UIR: Implementing Deep Neural Networks in addition to Conventional Algorithms for Ultra-Image Recovery
type: item
description: Many of the images that can be accessed through web search engines or social media networking sites are rare and not high quality because they are endangered or disappear. There must be a way to increase the quality of these images and conduct experiments to reduce noise, remove blur, and make them sharper to reach high-quality surfaces. Approaches that seek to achieve better results compete to increase the efficiency of those low-resolution images and generate images with the same color (RGB) characteristics but with higher quality. Deep learning algorithms, especially the use of convolutional neural networks (CNNs), have achieved advanced results within this context. In this approach, we propose a powerful base model UIR for image recovery by using conventional neural networks (CNNs) added to conventional algorithms for ultrasupper-resolution from low-resolution images by extracting the feature map from a low-resolution image ILR as overlapping superresolution ISR patches, in which every patch represents a high-dimensional vector. The missing features of the pixels that occur during the training process are subsequently compensated via the residual Swin Transformer block (RSTB). The results of quantitative evaluation experiments using PSNR(db)/SSIM metrics were superior to those of state-of-the-art methods on benchmark datasets (Set5, Set14, and BSD100). The selected images have a magnification of x2, resulting in values of (36.86(db)/0.9739, 36.10(db)/0.9656, 34.74(db)/0.9893) and x4, resulting in values of (34.44(db)/0.9784, 27.71(db)/0.8894, and 26.87(db)/0.9915, respectively. The results of the visual comparison also revealed that the texture of the surfaces is sharper, more expressive, less noisy, and blurry than those of the other methods.
subject: Articles
  topic: section
subject: Deep conventional neural networks
  topic: keywords
subject: Superresolution
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subject: High-dimensional vector
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subject: Residual Swin
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subject: Transformer block
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creator: Ali Salim Rasheed
creator: Alaa Hamza Omran
creator: Mohammad Aljanabi
creator: Mohammed Almaayah
creator: Rami Shehab
creator: Mansour Obeidat
creator: Theyazn H. H. Aldhyani
creator: Mosleh Hmoud AlAdhaileh
rights: Copyright (c) 2025 Ali Salim Rasheed, Alaa Hamza Omran, Mohammad Aljanabi, Mohammed Almaayah, Rami Shehab, Mansour Obeidat, Theyazn H. H. Aldhyani, Mosleh Hmoud AlAdhaileh https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
starting page: 144
ending page: 155
doi: 10.58496/MJBD/2025/010