Iraqi Journal for Computers and Informatics

This paper presents a newly collected and highly relevant dataset on students' 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.

Because of new multimedia types and advances in technology, protecting data has become extremely valuable. Because of the changes happening around us, cryptography continues to protect information that is crucial for security. Security methods and systems are continually being improved, but we should continue to review data protection methods while data travel through different services. One of the reasons data systems are secure today is because of encryption which makes plaintext into ciphertext. It examines the strengths and weaknesses of various cryptographic algorithms using what has been written by other authors. This paper reviews the use of cryptography in several literatures to make data security better nowadays. Data is protected using encryption during online and other transfers, yet the information may be obtained from an unauthorized attacker who is persistent enough. Two or more approaches to security should be combined, according to what is highlighted in the article. AES encryption and decryption can be improved by adding other algorithms such as the replacement algorithm. AES is used in this study to safeguard a message before it is presented. In 1998, Rijmen and Daemen designed the AES algorithm and named it Rijndael. Many people have started using it because its security is strong and it is not easily broken using brute force. The technique is applied on the plaintext of the AES algorithm. Thanks to this approach, breaking the encryption is extremely difficult, as you need the right key, though AES itself remains a simple system. The performances of both Advanced Encryption Standard and its modified option were tested after they were put into practice. There was an avalanche of 54.69% with the new version of Advanced Encryption Standard compared to the 50.78% observed in the standard AES. The strong encryption

One 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's variation values, the LR and KNN algorithms achieved the highest accuracy (61% and 71%, respectively). The algorithms (LR and KNN) preprocessed the dataset's 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.

Excellent 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 ViT & GAT model establishes results above competing state-of-the-art architectures reaching 92% accuracy on CIFAR-10 and 90% accuracy on CIFAR-100 while attaining 85% accuracy on ImageNet. This framework achieves 99% top-5 accuracy on CIFAR-10 and 94% on ImageNet besides reaching 92% accuracy on CIFAR-10 and 90% on CIFAR-100 and 85% on ImageNet. The proposed model shows excellent ability to generalize while minimizing over fitting and demonstrating resistance against noise and adversarial disturbances. The evaluation with F1-score, AUC-ROC, and confusion matrices proves the effectiveness of ViT & GAT by establishing it as a new benchmark within image classification field.

In the always-evolving and dynamic field of software development, good decision-making is absolutely critical. Developers have to regularly decide how best to apply features, optimize performance and debug issues. This process could be much improved by extracting actionable insights from software code. The presented work explores the tools and metrics available to enable developers to make data-driven decisions, therefore enhancing the development efficiency as well as code quality. Also, it introduces a new automated tool called CodeLens which analyzes software code, extract lines of code (LOC), documentation quality, complexity, and other key criteria. Through a consolidated view of such metrics, the tool helps developers evaluate code fit, spot possible bottlenecks, and prioritize optimization or refactoring efforts. Furthermore, the tool's support of Java and Python languages guarantees general applicability, hence fitting for many software projects.

Artificial intelligence chatbots (e.g., ChatGPT, Claude, and Llama, etc.), also known as large language models (LLMs), are continually evolving to be an essential part of the digital tools we use, but are plagued with the phenomenon of hallucination. This paper gives an overview of this phenomenon, discussing its different types, the multi-faceted reasons that lead to it, its impact, and the statement regarding the inherent nature of current LLMs that make hallucinations inevitable. After examining several techniques, each chosen for their different implementation, to detect and mitigate hallucinations, including enhanced training, tagged-context prompts, contrastive learning, and semantic entropy analysis, the work concludes that none are efficient to mitigate hallucinations when they occur. The phenomenon is here to stay, hence calling for robust user awareness and verification mechanisms, stepping short of absolute dependence on these models in healthcare, journalism, legal services, finance, and other critical applications that require accurate and reliable information to ensure informed decisions.

Electronic 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.

Federated Learning FL is a rapidly evolving machine learning paradigm that enables collaborative model training across decentralized data sources while preserving data privacy. Since its inception in 2016, FL has emerged as a transformative approach in domains such as healthcare, IoT, and edge computing, where data sensitivity and regulatory constraints limit centralized processing. This systematic review consolidates findings from 50 high-quality studies selected from over 250 papers to present a comprehensive synthesis of FL methodologies, core aggregation techniques, privacy-preserving mechanisms, security threats, domain-specific applications, and emerging trends. We categorize challenges into communication overhead, heterogeneity (device, data, model), fairness, trust, and evaluation inconsistencies. We highlight research gaps, notably in standardized evaluation, incentive mechanisms, and deployment scalability. By reviewing recent advances such as vertical federated learning, federated unlearning, and blockchain-based incentives, this paper offers a roadmap for future research and identifies open questions vital for widespread FL adoption.

The ability to navigate safely is a critical issue for people with visual impairments, especially in urban cities where traffic flow is a constant danger. While traditional tools such as white canes and guide dogs provide some assistance, they are often limited in detecting fast-moving hazards (such as a vehicle) and lack integration with modern technologies. The goal of this research paper is to create and test a prototype of a smart system based on the Internet of Things (IoT) that will help blind individuals become more aware of their surroundings and be able to get around on their own. There are two primary pieces to the system: a smartwatch that one can wear and a mobile web-based application. The smartwatch is built on the NodeMCU ESP8266 module and has a GPS module, a buzzer, and an LED indication. All of them are built into a comfortable design that can be worn on the wrist. The system keeps an eye on where the blind person is and gets alerts through a real-time Firebase database. The web program runs on the driver's phone, tracks the car's location, figures out how close it is to the blind user, and sends warnings when the predicted arrival time drops below two minutes. We did field experiments at different speeds (10 km/h, 20 km/h, and 50 km/h) across a distance of 2 kilometers. The results demonstrated a high response accuracy exceeding 99.6%, stable GPS accuracy ranging between 96% and 98%, and an average alert response time of less than 1.5 seconds. These results confirm the system's ability to provide immediate alerts and improve the safety of the blind. By combining real-time cloud services, geolocation, and alerts, this prototype provides an effective system that enhances the safety and mobility of blind people in urban environments. The system's performance indicators support its effectiveness in providing two-way interactive alerts and proactive responses to protect the user.

Blockchain has become a critical enabler of secure data sharing in domains such as healthcare, finance, and digital identity. However, its reliance on classical cryptographic schemes (e.g., RSA, ECDSA, SHA-256) makes current systems vulnerable to emerging quantum computing attacks, raising risks to data confidentiality, integrity, and long-term trust. This paper addresses this challenge by proposing a modular hybrid framework that integrates post-quantum cryptographic (PQC) techniques into blockchain-based personal data sharing. The framework combines lattice-based encryption for protecting off-chain data, hash-based signatures for smart contract authentication, and quantum-safe zero-knowledge proofs and trusted execution environments (TEEs) for privacy-preserving verification and secure key management. To ground this design, we conducted a systematic literature review of 35 studies published between 2018 and 2025, analyzing security, scalability, interoperability, regulatory alignment, and user autonomy. Findings reveal that only 5 out of 35studies (14%) explicitly addressed quantum threats, with over 80% focusing on theoretical resilience without testing implementation constraints. Furthermore, 90% of proposals neglected smart contract compatibility, and only 8% (3/35) incorporated TEEs, underscoring implementation barriers in contract execution, secure key management, and performance integration. Prototype evaluation demonstrated that the framework sustained 1,500 TPSon Hyperledger Fabric, achieved a 75% reduction in storage bloatusing IPFS, and supported GDPR-aligned workflows with 99.98% audit log completion and 95% successful erasure requests. Privacy was further strengthened through zk-STARK proofs, which reduced unauthorized access by 40%, while TEEs improved key management efficiency by ~28%. Although PQC introduced 5–12 seconds of latency, consent revocation was processed in under 2.1 seconds, highlighting both the feasibility and trade-offs of practical post-quantum deployment. This work demonstrates a clear pathway toward quantum-resilient blockchain infrastructures that safeguard personal data, comply with regulatory standards, and maintain user trust in the quantum era.

Automatic Text Summarization is a crucial feature for managing the ever-increasing volume of textual data. However, existing methods often struggle with feature identification for sentence importance, which leads to a lack of maintained narrative coherence and accuracy. In this proposed approach, the summarization process leverages the Chi-square Binary Cuckoo Search (Chi-BCS) method for feature selection, this optimizes text features enhance the summary content and utilizes insights from classification to ensure summaries are contextually relevant and concise. Feature selection aims to improve the performance of machine learning models by reducing the dimensionality of the input data and removing irrelevant or redundant features. Classification, on the other hand, contributes to better summarization by distilling lengthy or redundant content into key points, thereby enhancing both efficiency and accuracy. The proposed approach implements a model that leverages advanced Natural Language Processing and machine learning techniques for effective extractive summarization on both BBC and CNN/DailyMail datasets. Key features extracted from the text include Named Entity Recognition, Cue phrases, TF-IDF, Sentence position, sentiment analysis, etc. Various algorithms are employed to improve classification performance, such as Decision Trees, Support Vector Classifier, Gradient Boosting, Random Forest, K-Nearest Neighbors, and Logistic Regression. Among all the methods evaluated, the Random Forest and Ensemble Hard Voting approach achieved the highest F-score of 96.26 and 0.9322 respectively on the BBC and CNN/DailyMail dataset. In the text summary evaluation, the ensemble method also delivered exceptional results, with ROUGE-2 and ROUGE-L F1 scores reaching 0.799 and 0.818, respectively on BBC. While our ensemble model achieved to high score on ROUGE1 and ROUGE 2, reaching 0.275, 0.5017, respectively on CNN/DailyMail when compared with state of art, highlighting the model's strong performance. These findings demonstrate that the proposed model is highly effective for both the classification and summarization of large-scale textual data.