Iraqi Journal for Computers and Informatics
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273 research outputs found
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Post-Quantum Secure Blockchain-Based Federated Learning Framework for Enhancing Smart Grid Security
Full text linkEmerging technologies have accelerated the digitalization of smart grids, improving demand-side management, sustainability, and operational efficiency. The attack surface is widened by this interconnection, though, leaving vital smart grid data and systems vulnerable to online attacks. Single points of failure, privacy violations, and a lack of robustness against sophisticated attacks persist in centralized data processing. Traditional cryptographic techniques are further threatened by the development of quantum computing, which raises significant security risks for smart grids. With a focus on post-quantum cryptography (PQC) resilience, this study examines 206 peer-reviewed research articles on blockchain-based federated learning (BFL) in smart grids that were published between January 2023 and July 2025. It assesses the advantages, limitations, and compromises of the current BFL models in this field. The paper suggests a unique post-quantum secure BFL (PQS-BFL) framework that integrates federated learning (FL), lightweight PQC protocols, and a scalable blockchain architecture to solve the vulnerabilities that have been uncovered. This design enables decentralized, private, and impenetrable cooperation among grid nodes. The results demonstrate that the system mitigates quantum-resilient attacks and inference threats while improving data integrity, key management, and secure model aggregation. A path for creating safe, scalable PQS-BFL solutions for upcoming smart energy systems is provided in the paper\u27s conclusion, along with an overview of the main research issues. This study shows that using PQC, blockchain, and FL to secure next-generation smart grids is both feasible and important
Abnormal Behavior in Online Exam: Distance Learning Assessments Dataset
Full text linkThis paper presents a newly collected and highly relevant dataset on students\u27 abnormal behavior in online exams. This dataset focuses on assisting research in building machine-learning models that allow for maintaining academic integrity during the era of online exams. Properly, more than 8,500 annotated images of normal and abnormal behaviors of students during remote examination are held in the dataset hosted at the Harvard Dataverse repository. The dataset has two versions: the original and the augmented. We utilize semantic segmentation and deep learning techniques in the applied data augmentation; this dataset provides a crucial foundation for developing and benchmarking intelligent proctoring systems. We evaluate the dataset using YOLO5 and our improved SPL-YOLO5 model, and the resulting mean average precision (mAP) is close to 1.0
Securing DNA Profiles Using AES Cryptography: New Approach to Encrypted Biometric Authentication in EHR Systems
Full text linkElectronic Health Records EHRs have revolutionized healthcare by storing patient data in a digital format, increasing accessibility and efficiency. However, the flaws of traditional authentication methods necessitate the development of advanced security solutions. This study presents a novel methodology integrating AES-256 encryption with DNA-based steganography to enhance biometric verification in Electronic Health Records EHRs. The approach involves extracting Short Tandem Repeats STRs and Single Nucleotide Polymorphisms SNPs from DNA profiles, encoding the genetic data into binary format, and securing it with AES-256 encryption to ensure high confidentiality. Encrypted DNA profiles are embedded in MRI images by using the Discrete Cosine Transform DCT, which ensures the concealed data remains both imperceptible and secure against unauthorized alterations, and during the authentication phase, and the encrypted genetic information is extracted, decrypted, and matched with reference samples for verification, the experimental results indicate that the system significantly improves both biometric security and medical data protection, with average processing time of approximately 320 milliseconds, and as it exhibits strong resistance to tampering, achieving has a 99.8% success rate in preventing unauthorized modifications. The embedding method maintains a high level of image quality, reflected by a Peak Signal-to-Noise Ratio PSNR of around 47 dB, confirming that the diagnostic utility of MRI images remains unaffected. This approach effectively combines biometric security with medical data safeguarding, providing a dependable and scalable solution for patient authentication in electronic health record EHR systems
Optimized Security for Blockchain Edge-Fog Systems Performance Analysis and Optimization Strategies
Full text linkThe trends of resource consumption and optimization mechanisms for blockchain-enabled security in edge-fog computing environments. While blockchain provides robust security for fog networks in a decentralized fashion, its demand for resources creates tremendous challenge in resource-constrained settings. Through in-depth examination of a Practical Byzantine Fault Tolerance PBFT-based blockchain deployment across 50 edge devices and 10 fog nodes. The study reveals the most critical resource bottlenecks and proposes an adaptive resource management framework that maximizes the tradeoff between security requirements and operational efficiency dynamically. The proposed work shows that data-type-based optimization and intelligent workload distribution can reduce CPU utilization by 27%, memory by 22%, and network bandwidth by 38% without sacrificing security assurance. The introduction of a novel dynamic resource allocation algorithm that adjusts consensus participation and cryptographic strength to current system conditions, demonstrating that security-performance trade-offs can be optimally resolved through context-sensitive optimization. These advancements are a move towards resource-constrained security architectures for edge-fog computing, enabling the broader applicability of blockchain security in resource-poor IoT environments
A Comparative Analysis of the Effectiveness of Multiple Models for Predicting Heart Failure using Data Mining
Full text linkOne of the most fatal and well-known diseases worldwide, heart disease claims the lives of many people every year. In order to preserve lives, early detection regarding such disease is essential. One of the quickest, practical, and affordable methods of disease detection is Data Mining DM, an artificial intelligence AI technology. Human life is saved by healthcare services through prompt and efficient decision-making. For forecasting, decision-making, and disease prediction, DM technologies are essential. This research predicts heart disease using DM algorithms. There are 14 attributes in Cleveland dataset, including blood fat, blood pressure, gender, and age. The probability regarding patients developing heart disease in the future can be forecasted by analyzing such parameters. For classifying if heart disease is present or absent, two classification algorithms are used: Logistic Regression LR and K-Nearest Neighbor KNN. The precision, accuracy, f-score, and recall of the suggested model are evaluated. The outcomes of suggested model were tested using the heart disease dataset. Without preprocessing the dataset\u27s variation values, the LR and KNN algorithms achieved the highest accuracy (61% and 71%, respectively). The algorithms (LR and KNN) preprocessed the dataset\u27s variation values to get the highest accuracy (90% and 93%). In order to improve data driven medical decision-making, the presented research demonstrates how well DM algorithms work to increase heart disease prediction accuracy
Image-Based Malware Detection Using Deep CNN Models
Full text linkMalware or malicious software represents one of the most remarkable threats to cybersecurity, as it compromises the integrity, confidentiality, and availability of computer systems and networks. Traditional malware detection methodologies frequently prove inadequate in identifying innovative and sophisticated malware variants. Deep learning (DL) presents a promising strategy for malware detection by utilizing advanced algorithms that are capable of discerning intricate patterns from extensive datasets. This study presents a model based on deep learning with Convolutional Neural Network (CNN) for malware classification. This research utilized the Malimg dataset, which includes 9,339 malware samples from 25 distinct families. The approach requires resizing malware images to a resolution of 64 x 64 pixels and normalizing these images for model training. The selection of a 64 × 64 size frame reduces network complexity while speeding up training without sacrificing important information. The architecture of the proposed CNN primarily consists of more than one convolutional layer, max-pooling, dropout to mitigate overfitting problem, fully connected layers for achieving better classification results. The proposed model established an impressive accuracy of 96%. For model evaluation, the following measures of accuracy were used: precision, recall, F1-score, and accuracy. This research shows that CNN-based methods can have a high level of effectiveness in detecting obfuscated malware
Discussion on techniques of data cleaning, user identification, and session identification phases of web usage mining from 2000 to 2022
Full text linkThe data preprocessing step is an important step in web usage mining because of the nature of log data, which are heterogeneous, unstructured, and noisy. Given the scalability and efficiency of algorithms in pattern discovery, a preprocessing step must be applied. In this study, the sequential methodologies utilized in the preprocessing of data from web server logs, with an emphasis on sub-phases, such as session identification, user identification, and data cleansing, are comprehensively evaluated and meticulously examined
An Efficient Categorization of Diabetes Imbalanced Data Using SMOTE-ENN With Fine-Tuned LS-SVM Algorithm
Full text linkDiabetes has been recognized as a major cause of death. Diabetes is a chronic disease. In recent years, the impact of diabetes has increased dramatically, and it has become a global threat. Machine learning is a part of computational algorithms designed to imitate human intelligence by learning from the surrounding environment. Type 2 diabetes is indicated by deviation high blood glucose levels attributable to insulin resistance and reduced pancreatic insulin production. In this study, two diabetes datasets are used, the Pima Indians diabetes and Iraqi Society Diabetes ISD datasets. They are collection of data on diabetes which characterized by an imbalanced distribution and the presence of outliers. The diabetes data sets are preprocessed. Many methods, including data resampling have been proposed to address the data sets imbalance issue. We utilized the resampling SMOTE-ENN technique to address the imbalance diabetes datasets issue and imputation. The classification of imbalanced datasets is a crucial field in machine learning. The machine learning approach that is used in this study is the Least Square Support Vector Machine LS-SVM to categorize the diabetes patients. Machine Learning ML algorithms are constructed by a set of hyperparameters. Thus, hyperparameters values should be carefully chosen. We used grid search algorithm to optimize LS-SVM algorithm hyperparameters. The classification results were improved. In addition, we could enhance the performance of the fine-tuned LS-SVM with the used resampling technique, SMOTE-ENN, that processes diabetes datasets. The performance metrics that evaluate the proposed algorithm SMOTE-ENN and fine-tuned LS-SVM are accuracy, recall and precision. The metrics measurements obtained were much better and higher when the proposed algorithm was used to categorize diabetes patients
Deep Learning Model for COVID-19 Diagnosis: Improving Accuracy and Sensitivity in Early Detection
Full text linkThe continuous COVID-19 pandemic, caused by the SARS-CoV-2 virus, required fast and efficient diagnostic tools. This work presents a deep learning-based system, using convolutional neural networks, for the detection and diagnosis of COVID-19 through computed tomography tests, aiming to assist specialized medical professionals. A total of 746 Computed Tomography images (CT), were used in this work, one of the largest publicly available chest computed tomography dataset for research into COVID-19. Our proposed technique showed the accuracy of more than 99% for the training set, with high sensitivity and specificity, and achieved 97% on the validation set. Such results would hint at the very possible implementation of our deep CNN approach in clinical diagnostic settings, particularly for COVID-19 testing, to enhance early detection and management for patients
Enhancing Image Classification using Graph Attention Networks
Full text linkExcellent performance in artificial intelligence image classification leads to extensive applications throughout areas such as healthcare facilities, robotic systems and multimedia platforms. The research field has evolved through new developments in both Vision Transformers (ViTs) alongside Graph Neural Networks (GNNs). A new image classification method utilizes integrated Vision Transformers (ViTs) and Graph Attention Networks (GATs) to improve results for difficult dataset types. The hybrid architecture made possible by combining ViTs with GATs successfully captures complex relationships within visual data because ViTs deliver powerful global feature extraction while GATs establish strong patch-level dependencies. The implementation of GATs via their built-in attention mechanism allows dynamic region prioritization for both accurate recognition and better interpretability of images. The experiments using benchmark datasets CIFAR-10, CIFAR-100, ImageNet, Fashion-MNIST, and SVHN show that ViT + GAT outperforms Swin Transformer and ConvNeXt for state-of-the-art architectures. The proposed method delivers prominent improvements in all classification metrics including accuracy and both accuracy and resistance to noise interference and adversarial perturbations. Model reliability and task generalization capabilities are demonstrated through the precision, recall, F1-score and AUC-ROC metrics. This project integrates smartphone-level ViT technology with deep social modeling GAT components to redefine image classification methods. The method\u27s outstanding performance proves itself as a promising solution for complex visual recognition challenges on multiple scale levels