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.