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.
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
AI-Driven Smart Contract Vulnerability Detection: A Systematic Review of Methods, Challenges, and Future Prospects
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 environments. For example, models such as ContractWard and SCVDIE-ENSEMBLE reported Micro-F1 scores of 98.48% and 95.46%, respectively, but these models also have a trade-off—they demand high computational resources, which limits their real-world deployment in resource-constrained settings. On the other hand, lighter tools such as Slither and NeuCheck offer faster detection and lower resource usage but might fall short in regard to identifying more complex or new vulnerabilities. We also noticed a growing trend towards real-time monitoring tools, such as SODA and GPTScan, which aim to strike a balance by reducing false positives while providing proactive security measures. However, several challenges remain unresolved where many AI-driven models still rely heavily on labelled datasets, which may not generalize well to novel attack patterns. Scalability is another concern, especially for models that are computationally intensive.
Copyright (c) 2025 Saad AL Azzam, Raenu AL Kolandaisamy , Ghassan AL Dharhani https://creativecommons.org/licenses/by/4.0
Hybrid Quantum-Assisted Deep Learning Model for Early-Stage Alzheimer’s Disease Classification Based on MRI Images
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 maintaining computational efficiency and clinical deployment feasibility under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.
Copyright (c) 2025 Eman A. Radhi, Mohammed Y. Kamil, Mazin Abed Mohammed https://creativecommons.org/licenses/by/4.0
UIR: Implementing Deep Neural Networks in addition to Conventional Algorithms for Ultra-Image Recovery
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.
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
Precise Kidney Stone Localization in Medical Imaging via a Capsule Network
Traditional convolutional neural networks (CNNs) face significant limitations in medical imaging when detecting small, spatially variable objects such as kidney stones, primarily due to their inability to preserve pose information and spatial correlations through max pooling operations. While previous CNN-based studies achieved approximately 93% accuracy in kidney stone detection, they struggled with the precise localization of small or partially obscured stones, creating a critical research gap in automated urological diagnostics. This study develops and evaluates a capsule network (CapsNet) framework that leverages dynamic routing and vector-based capsules to increase kidney stone localization accuracy in computed tomography (CT) images while maintaining spatial coherence and reducing false positives. The CapsNet model incorporates convolutional layers, primary capsules, and stone capsules via dynamic routing algorithms. The approach was systematically evaluated via a publicly accessible kidney stone CT dataset from the Mendeley repository, comprising 512 anonymized abdominal CT slices preprocessed to 256×256 pixels. The dataset was partitioned into training (70%), validation (15%), and test (15%) sets. The performance was compared against that of a baseline CNN under identical conditions using 50 epochs and the Adam optimizer. The results demonstrate CapsNet's superior performance across all the metrics: 96.5% accuracy, 96% precision, 97% recall, 96% F1 score, 0.93 Dice coefficient, and 0.89 IoU, significantly outperforming the CNN baseline (92% accuracy, 0.84 Dice coefficient, 0.78 IoU). CapsNets enhance kidney stone localization and generalization by preserving spatial and pose information, improving diagnostic accuracy in medical imaging.
Copyright (c) 2025 Omar Hammad Jasim, Daniah Abdul Qahar Shakir , Mohammed Shihab Hamad , Waleed Kareem Awad https://creativecommons.org/licenses/by/4.0
Advances and Insights in Image Texture Analysis : A Review
Texture analysis is an essential step in image analysis, and subsequent applications such as medical imaging, remote sensing, and scene understanding are highly important in image processing. Although it is vital, the field presents its own set of research challenges, especially when manipulating variations in texture patterns and requirements for properties that remain unaffected by related transformations, such as rotation, scaling, and translation. This review provides an in-depth description of key activities in the field of texture analysis, including classification, segmentation, synthesis, and image retrieval, along with their strengths and limitations. The approaches are classified as structural, statistical, and model-based and are discussed in consideration of their appropriateness and performance. The regular textures most favour structural methods, whereas statistical and model-based methods are more flexible, although they sometimes require more computational resources. Other challenges that are outlined in the review include a lack of support for real-time and transform-invariant applications. These findings can aid in determining the appropriate techniques to use and in developing lightweight yet durable methods for texture analysis. Overall, the review offers profound insights into the field and provides a course for future research and creativity.
Copyright (c) 2025 Ghaith H. Alashour, Nidhal K. El Abbadi https://creativecommons.org/licenses/by/4.0
Intelligent Techniques for Autism Spectrum Disorder Diagnosis: A Review
Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose prevalence has increased drastically around the world due to the shortcomings of the traditional method of diagnosis, which has been shown to be unsustainable since it is usually time-consuming, expensive, and subjective to clinical interpretation. These difficulties make finding more scalable, efficient, and objective methods of diagnosis incredibly expedient. This review explores the impacts of intelligent technologies such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and Internet of Things (IoT) sensor-supported systems and the revolution they are bringing into the diagnosis of ASD. This work reviews the recent developments in the use of multisensor platforms (e.g., eye tracking, electroencephalography (EEG), speech processing, and computer vision) and computational models to increase accuracy, accessibility, and speed. The systematic review approach was utilized, where only peer-reviewed journal articles published from 2019-2025 were considered and retrieved from major scholarly databases. Seven research questions that addressed diagnostic performance, algorithm innovation, data sources, dimension reduction, and clinical significance guided the review. Even with fewer than 128 sensors and similar sensors incorporated into diagnostic models, an accuracy rate of 85–95% is achieved, which at least meets or surpasses previous standards. Generalizability, fairness, and data privacy are increased because of federated learning and explainable AI systems. Openly accessible resources such as ABIDE-III, SFARI Genomes 2.0, and NDAR-2024 have been essential in terms of discovering robust biomarkers and enabling the validation of models in various ethnicities and populations. These findings indicate the potential of intelligent systems for early detection and accurate and personalized ASD diagnosis. These technologies make it possible to screen for autism noninvasively, in real time, and at an affordable cost, hence opening up avenues to more inclusive and fairer approaches to autism care across the world.
Copyright (c) 2025 Ekraam Jabier, Ali Fadhil Marhoon, Ammar A. Aldair https://creativecommons.org/licenses/by/4.0
A Comparative Study of RFID System Performance in Large-Scale Network Planning Facility
Big data in manufacturing fields present several challenges leads to reduce profitability and missed opportunity for innovation. One of the used strategies is the use of radio frequency identification system. considered a business strategy to increase productivity, speed up decision-making, and enhance production monitoring and control while preserving the structure and integrity of current manufacturing systems. The present research compares five artificial inelegant algorithms based on RFID system in facility layout design to investigate the fitness of each algorithm in manufacturing big data processing. The objective functions have been used are the minimum number of required readers, minimum readers overlap, and maximum tags coverage. The contribution in this work is the workability of each algorithm in different facility design condition based on design alternatives. the results present that cuckoo search (CS) has the optimum fitness reach to 74.68% in big data and large area condition while particle swarm optimization (PSO) observed optimum fitness 74.46% in small data and large area. The simulation results illustrate the applicability and robustness of the proposed method, with the characteristics maintaining exceptional approximation capabilities even in high-dimensional spaces.
Copyright (c) 2025 Ali Abdulqader Al Qisi , Azli Bin Nawawi , Adel Muhsin Elewe https://creativecommons.org/licenses/by/4.0
Coupling a dual tree - complex wavelet transform with K-means to clustering the epileptic seizures in EEG signals
Electroencephalography (EEG) signals are routinely recorded in clinical settings for the diagnosis of epilepsy, that is, brain electrical disorders, by a neurologist. Nevertheless, both the reliability and safety of GB analysis of EEG parameters are not satisfactory. Therefore, identifying the effectiveness of EEG for diagnosis is a considerable challenge for hospitals. This study was conducted to improve the efficiency of detecting epileptic seizures in EEG signals by combining three methodologies: the double tree complex wavelet transform (DT-CWT), K means clustering, and the ChaCha20 encryption algorithm. EEG segments are initially partitioned into regular segments, each of which is further partitioned into smaller clusters by K-means. To analyse the EEG waves to extract frequency information and select six discriminable statistical features, these clusters are examined via the DT-CWT. After feature extraction, epileptic seizures are discerned on the basis of K-means, which can enable very accurate detection of the seizures. The final results are encrypted via the ChaCha20 standard to ensure patient data confidentiality at the send and receipt stages. The findings presented in this study show that the proposed approach has a good clustering accuracy of 99% to help doctors diagnose patients with epilepsy and prescribe the best treatment to cure patients to maintain privacy and prevent data from being seen by unauthorized persons with an overall accuracy of 96.3%. Through the improvement of the accuracy of neurological disease identification, this method opens up the possibility for further progress in the domain of EEG signal analysis.
Copyright (c) 2025 Raid Lafta, Rasha Hallem Razzaq https://creativecommons.org/licenses/by/4.0
Overview of the CICIoT2023 Dataset for Internet of Things Intrusion Detection Systems
The rapid expansion of the use of the Internet of Things (IoT) has encouraged many attackers to exploit the vulnerabilities in these networks to violate data privacy or disrupt service; they are easy targets due to the diversity of devices within the network, which has led to the loss of unified security standards. intrusion detection system (IDS) play a pivotal role in securing IoT networks by monitoring inbound and outbound traffic to these networks and issuing a security alarm when there is an attack; moreover, they respond directly to these security threats to prevent them from harming the network and violating data privacy. To design an IDS capable of performing work with high efficiency, an appropriate dataset must be chosen to train and evaluate the designed model. This dataset works as a fundamental task in the success of these systems because it plays a major role in training the system, feature engineering, evaluating the performance of the model, and other tasks. This paper focused on one of the modern datasets used in training and evaluating IDS models, that is, the CICIOT2023 dataset. The CICIOT2023 dataset is distinguished from other datasets, such as CICIDS2017, UNSW-NB15, and KDD1999. It focuses on the IoT environment, unlike other datasets that focus on data traffic in traditional networks, and it uses a variety of devices and protocols; moreover, it contains modern and complex attacks and a balance between the data of those attacks and normal traffic. This paper discusses the structure of the dataset, the kinds of attacks it contains, the applications and fields in which it is used, the strengths that distinguish it from other datasets, its role in developing cybersecurity research, the most important studies that have been written and dealt with this dataset, and finally, the future visions for developing the dataset.
Copyright (c) 2025 Wisam Ali Hussein Salman , Chan Huah Yong https://creativecommons.org/licenses/by/4.0
Bridging Law and Machine Learning: A Cybersecure Model for Classifying Digital Real Estate Contracts in the Metaverse
The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, plot location, transaction value, and contract duration. Preprocessing of data included encoding categorical data, standardization of numerical variables, and UTF-8 encoded text to preserve data quality. Two classification models were used: Logistic Regression and Random Forest. The model's evaluation used accuracy, precision, recall, and F1-score as evaluation criteria. The Random Forest outperformed with a perfect classification score showing that it may have been better suited to dealing with the complexity and dimensionality of the dataset. The outcomes of the study highlight the role AI could play in automating the analysis of contracts, at the same time highlighting that cybersecurity practices are important when working with data. The framework of this study seeks to support the development of a regulatory regime and add further transparency to real estate contracts in the metaverse - as a scalable tool for future digital real estate management.
Copyright (c) 2025 Faris Kamil Hasan Mihna , Hazim Akram Sallal , Lobna Abdalhusen Easa Al-Seedi , Hasan Ali Al- Tameemi , Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel , Dheyaa A. Mohammed https://creativecommons.org/licenses/by/4.0
DeepSeek: Is it the End of Generative AI Monopoly or the Mark of the Impending Doomsday?
The rise of superintelligent open-source generative AI (genAI) heralds both extraordinary potential and unprecedented risk, exemplified by the rapid emergence of DeepSeek as a global AI innovator. This perspective article examines the dual-edged nature of open source genAI technologies, highlighting their capacity to democratize innovation while exposing critical vulnerabilities. By providing affordable, high-performing, and openly available models like DeepSeek-R1 and DeepSeek-V3, this Chinese AI company has disrupted the proprietary dominance of Western AI giants. These advancements are expected to empower researchers in resource-limited settings, foster global collaboration, and enable breakthroughs across numerous fields. Open-source AI, as illustrated by DeepSeek, has the potential to redefine the technological landscape by making advanced capabilities accessible to underrepresented communities and encouraging ethical and inclusive innovation. However, the openness that drives such progress is fraught with existential risks. Superintelligent open-source models, accessible to anyone with minimal resources, lower barriers for misuse by malicious actors. From automated cyberattacks and disinformation campaigns to destabilizing critical infrastructures, the potential for harm is vast and unprecedented. Beyond immediate security concerns, these technologies threaten economic stability by displacing entire workforces and exacerbating inequalities, and they undermine human agency by enabling manipulation on an individual and societal level. This perspective seeks to explore the profound benefits of open-source superintelligent AI while critically addressing the urgent need for ethical and regulatory frameworks to mitigate its risks. The story of DeepSeek underscores the fragile balance between innovation and destruction in an era where technological progress outpaces safeguards. Humanity’s ability to harness the transformative power of open-source AI without succumbing to its destructive potential is not just a technological challenge—it is an existential imperative. This perspective argues for vigilance, responsibility, and global cooperation to ensure that the promise of open-source AI serves humanity rather than imperiling it.
Copyright (c) 2025 Malik Sallam, Kholoud Al-Mahzoum, Mohammed Sallam, Maad M. Mijwil https://creativecommons.org/licenses/by/4.0
Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images
Kidney cancer, particularly renal cell carcinoma (RCC), poses significant challenges in early and accurate diagnosis due to the complexity of tumor characteristics in computerized tomography (CT) images. Traditional diagnostic approaches often struggle with variability in data and lack the precision required for effective clinical decision-making. This study aims to develop and evaluate machine learning (ML) models for the accurate classification of kidney cancer using CT images, focusing on improving diagnostic precision and addressing potential challenges of overfitting and dataset heterogeneity. Two ML models, Support Vector Machines (SVM) and Multi-Layer Perceptrons (MLP), were employed for classification. Key attribute extraction techniques, including grayscale-level co-occurrence matrix (GLCM) and Gabor filters, were utilized to capture texture and structural features of CT images. Data normalization and preprocessing ensured consistency and enhanced model reliability. The SVM model achieved an accuracy of 93%, while the MLP model demonstrated superior performance with a 99.64% accuracy rate. These results highlight the MLP model's ability to capture complex patterns in the data. However, the exceptional accuracy of the MLP model raises concerns about potential overfitting, warranting further evaluation on more diverse datasets. This study underscores the potential of ML techniques, particularly MLP, in enhancing the accuracy of kidney cancer diagnosis. Integrating such advanced ML models into clinical workflows could significantly improve patient outcomes.
Copyright (c) 2025 Dhuha Abdalredha Kadhim , Mazin Abed Mohammed https://creativecommons.org/licenses/by/4.0
Deep Learning-based English-Arabic Machine Translation for Sulfur Manufacture Texts
The field of machine translation (MT) has seen significant advancements with deep learning (DL) techniques for translating texts among different languages. Despite the wealth of studies, there exists a noticeable gap in significant research dedicated to its translate Sulfur manufacture texts, primarily hindered by resource scarcity and the intricate grammatical structures inherent to these texts. This paper explores the application of transformer-based Arabic MT for sulfur manufacture texts, including its attention mechanisms and encoder-decoder framework, focusing on the new model ability to handle the linguistic and syntactic complexities inherent in these languages, such as morphological richness and context, and how the transformer's self-attention mechanism addresses these issues. It discusses the specific challenges of our proposed translation model, the obtained results indicate that this model is effective and has an accuracy of 90.7% in comparison with Mishraq application, which has 84.9% for the same test samples.
Copyright (c) 2024 Diadeen Ali Hameed , Belal Al-Khateeb https://creativecommons.org/licenses/by/4.0
Anomaly-Based IDS (Intrusion Detection System) for Cyber-Physical Systems
Cyber-physical systems (CPS) are critical infrastructures that integrate physical processes with computational components. The security of CPS is paramount, as any breach can lead to severe consequences. Anomaly-based intrusion detection systems (IDS) have emerged as a promising approach to safeguard CPS against cyber threats. This paper presents an anomaly-based IDS designed specifically for CPS, leveraging machine learning techniques to establish a baseline of normal system behaviour and promptly detect deviations indicative of malicious activities. The proposed system incorporates multiple classification techniques, including KNeighbors, RandomForest, XGB, DecisionTree, SGD, SVM, LGBM, AdaBoost, Bagging, and MLP Classifier, to enhance detection accuracy and robustness. Key components of the IDS, such as data collection, feature extraction, anomaly detection, and alert generation, are thoroughly outlined. The system's performance is evaluated, highlighting its effectiveness in accurately identifying intrusions while maintaining low false positive rates. The proposed anomaly-based IDS aims to provide a robust and reliable solution for enhancing the security of CPS and protecting critical infrastructure from cyber threats.
Copyright (c) 2024 Ahmad Muter Awaad , Khattab M Ali Alheeti , Abdul Kream A.H. Najem https://creativecommons.org/licenses/by/4.0
Healthcare Intelligence and Decision Making: Big Data’s Role in Predictive Analytics for Clinical Decision-Making
Technology and data are transforming healthcare systems. The use of big data in predictive analytics to anticipate healthcare outcomes, make accurate diagnoses, and improve care is a major advance. Predictive modeling analyzes patient data from EHRs, genetic data, and wearable devices to improve early diagnosis, targeted treatment, and efficiency. For example, predictors of chronic diseases like diabetes and heart disease can identify high-risk groups and treat them early, improving outcomes and saving money. A backpack matched to the patient's genetics and surroundings is also used. Privacy, system integration, and algorithmic transparency remain major issues. Predictive analytics may transform healthcare and overcome adoption hurdles in early detection and individualized care, as this article shows.
Copyright (c) 2024 Sahar Yousif Mohammed, Noora Saleem Jumaah , Estabraq Abbas , Haifaa Mhammad Ali, Mohammad Aljanabi https://creativecommons.org/licenses/by/4.0