Iraqi Journal for Computers and Informatics

Data hiding is becoming increasingly important due to the growing threat to the privacy and security of data from intruders and hackers. This situation is accompanied by the advancement in artificial intelligence applications designed to reveal hidden data, making it difficult to choose the most appropriate hiding approach from those presented in literature. The benchmarking method serves as an important roadmap for making decisions. We propose a distinct plain benchmarking method called maximum error insertion (MEI) benchmarking. This approach intends to hide data using maximum error insertion. The MEI refers to the maximum amount of distortion that can be added to host data (such as image or audio) while still ensuring the successful retrieval of hidden data. The maximum error that can be generated by each hiding algorithm is intentionally inserted to the media file, thus giving us maximum error, maximum capacity, and maximum sensitivity to signal processing attacks. Investigation of the two hiding algorithms demonstrates their applicability and precision, and their implementation significantly enhances the reliability of results during the benchmarking stage.

Deep packet inspection is a network security solution that identifies and flags anomalous network traffic patterns in a local network environment. Traditional signature-based techniques for intrusion detection are limited in identifying different attacks or completely new kinds, which makes them unsuitable in some situations. In addition, most previous methods for anomaly detection have low detection rate and high false alarm. In this study, a deep packet inspection model based on support vector machine (SVM) for anomaly detection in local area networks was proposed. The proposed method combined the SelectKBest method and SVM for the categorization of anomaly in a local network environment. Results showed that the proposed method outperformed other related machine learning methods with accuracy, precision, recall, and F1-score of 94.81%, 94.03%, 94.13%, and 94.0799%, respectively. The accuracy result shows that most network traffic can be correctly identified by the SVM using the SelectKBest approach, with minimal false positives or negatives.

Boosting algorithmsarea set of machine learning techniques that arepredicated on the notion that a weak learner's acquisition of multiple basic classifiers might yield results that are superior to those of any one simple classifier used alone. A comprehensive evaluation of regularly used boosting techniques against highly investigated diseases is lacking, despite the fact that boosting approaches have been used for disease prediction in many studies. Thus, the purpose of this workis to highlight themain algorithmsand strategies in the boosting learning. The results of this work will help academics identify a more appropriate boosting approach to predict disease, as well as better understand current patterns and hotspots in diseases prediction models that use boosting learning. The results showed that adaboost algorithm outperformed other algorithms in terms of accuracy, achieving above 90%. This review also demonstrates how combining two boosting methods can increase the basic classifier's accuracy. By using AdaBoost and LightGBM, the accuracy reached99.75%. XGBoost and Gradient Boosting techniques were employed more frequently in researchesthan other boosting algorithms.

International trade is considered the central link in the complex system of contemporary international economic relations. It links all countries of the world in a unified economic system whose goal is to address economic problems at the international level through developing productive capacity, expanding employment opportunities, and enhancing the flow of production factors between countries toachieveeconomic growth. Our study aimed to clarify the considerableimpact of some macroeconomic variables (exchange rate, gross domestic product, public spending, foreign direct investment) on foreign trade in Iraqand to determine the degree of relative importance of the macroeconomic variables affecting foreign trade in Iraq. The study followed the descriptive analytical approach by collecting data related to the study for the period from 2003 to 2020 and then analyzing the data obtained through artificial neural networks.

Early diagnosis of kidney as well as pre-kidney disease is crucial for patients because it allows them to take control of their condition and could potentially avoid or delay more significant consequences that could lower their quality of life. The chance of developing a major disease might be decreased with its assistance. Almost every part of the body could be impacted by chronic kidney disease (CKD). Fluid retention in the lungs, high blood pressure, and swelling of the legs and arms are all potential side effects. This study proposes a model that makes use of machine learning (ML) algorithms for diagnosing kidney disease. The preprocessing dataset, which contains missing values and is preprocessed with the use of mean, delete, and median approaches before data scaling, is the foundation of the suggested model. To achieve the highest classification accuracy, the preprocessing stage receives the results of missing values. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are the two classification algorithms used to classify whether kidney disease is present or absent. Classify the dataset into testing and training (40% and 60%, respectively).The accuracy, F1-score, recall, and precision have been utilized for evaluating the suggested model. The kidney disease data-set has been used to test the outcomes of the suggested model. Without preprocessing any missing values in the dataset, the algorithms SVM and K-NN obtained maximum accuracy (95% and %89). Through deleting missing values from the dataset, the algorithms SVM and K-NN obtained maximum accuracy (%96 and %93). K-NN and SVM algorithms reached a maximum accuracy of %98 when using a mean technique; when using a median method, such algorithms attained an accuracy ranging from %95 to %98.

Accurate traffic prediction poses great difficulties because of the continuously increasing scale and diversity of 5G network traffic, which is driven by user demands. Moreover, certain characteristics of 5G traffic are constantly changing; thus, simulations using traditional models often lead to incorrect estimations or inefficient utilization of available resources. Consequently, we propose a hybrid machine learning model that integrates support vector machine (SVM) and decision tree algorithms to enhance efficiency of 5G traffic prediction. The structure of the hybrid model dynamically adjusts by adding or removing hidden layers and units within the network to improve prediction performance. The efficacy of the proposed model is evaluated using metrics like mean squared error, mean absolute error, and root mean squared error (RMSE). Findings show that the hybrid model consistently achieves lower error rates than SVM alone. Further performance enhancement of the hybrid model in predicting 5G traffic is also supported by comparisons of R-squared values against signal-to-noise ratios. These outcomes show the potential of the proposed method to improve traffic prediction accuracy in 5G networks, serving as a powerful tool for network control.

Data collection and data preprocessing are crucial stages in web usage mining, mainly because of the unstructured, diverse, and noisy nature of log data. During data collection, log file datasets are loaded and merged. Effective and comprehensive data preprocessing plays a vital role in ensuring the efficiency and scalability of algorithms used in the pattern discovery phase of web usage mining. This work aims to address these phases by introducing two innovative approaches. The first approach focuses on determining the device used for accessing the web, distinguishing between computers and mobile devices. The second approach aims to determine user sessions and complete paths by utilizing the referrer URL. The entire preprocessing pipeline has been implemented using the C# programming language, and the source code is available on GitHub at the following link: https://github.com/Mohammed91/Web-Usage-Mining.

This study explores the remarkable advancements in self-driving vehicles achieved through the application of computer vision and machine learning techniques. We examine various algorithms designed for critical functions, such as object detection, image segmentation, behavior prediction, and adaptive learning, which are all integral components of autonomous driving systems. Our research highlights key performance metrics, emphasizing accuracy, efficiency, and safety. Simulated environments and real-world testing are essential for validating the effectiveness of these methodologies.
Our findings underscore the transformative potential of self-driving technology in enhancing transportation safety and its far-reaching effect on numerous industries. Notably, self-driving cars demonstrate the ability to reduce traffic accidents and improve traffic flow, which can lead to substantial economic and social benefits. Moreover, we discuss future research avenues, including the enhancement of system robustness and safety measures, the improvement of human–AI interaction, and the utilization of edge computing and edge AI. We also address the ethical and regulatory challenges associated with the widespread adoption of autonomous vehicles.
Our comprehensive analysis indicates that self-driving technology is poised to revolutionize the transportation sector, offering safer, more efficient, and more accessible mobility solutions. As technology continues to evolve, ongoing research and development will be crucial in overcoming current limitations and realizing the full potential of autonomous driving systems.

Accurate and rapid classification of diabetic retinopathy is critically significant in order to prevent vision loss. The advent of artificial intelligence introduces novel and potent methodologies for enhancing the classification of diabetic retinopathy as derived from medical imaging. Due to the large size of the model make it unsuitable in real world. This research paper is dedicated to the classification of diabetic retinopathy utilizing constrained resources while achieving elevated accuracy levels. We implemented weighted clustering technique within deep convolutional neural networks and transfer learning architectures: VGG 19, DenseNet 121, and EfficientNet B6. To mitigate the challenge posed by considerable model sizes without sacrificing accuracy, the best fit results were observed with EfficientNet-B6, where applying weighted clustering reduced the model size by a factor of 12 while maintaining high accuracy results of 92% for the APTOS-2019 data. This underscores the efficacy of employing lightweight techniques to enhance the practicality of extensive models for the early diagnosis of diabetic retinopathy.

Hypercube networks are very imperative in parallel computing and high-speed networking since it is a multi-dimensional connections that are useful in location dispersion, speed, and capacity. This paper aims to review the state of the art of hypercube network technology in light of the new developments in the field and then highlight the issues of performance enhancement, fault tolerance, and scalability. Recent advancements have significantly integrated the concept of fault tolerance through new methods of redundancy and restoration, interconnection of disjoint cycles for improved traffic flow, and new network indices for enhanced functionality and density. Some emerging trends include AI and ML for optimizing networks and the ability to learn, quantum computing for communication optimization, and the use of ‘green’ networks or self-healing modular networks. In terms of the existing problems in wearable devices, including scalability and energy consumption, the review also defines the future research objectives. This paper hopes to present a clear synthesis of the most recent advances in hypercube networks and evaluate them in the current context of computing and data processing. This review aims to enhance knowledge and application of hypercube network technologies by outlining important developments and future directions.

One of the most important aspects in determining the quality of a software product before placing it on the market is its reliability. The main problem in creating effective software that satisfies the user preferences is that it must be highly reliable. One important factor that has a remarkable influence on the overall reliability of a system is its software. Reliability is a critical aspect of software quality, and the software industry faces many challenges in its quest to produce reliable software at scale. Reliability models are a basic method for quantitatively calculating software reliability. Thus, this paper inspects the reliability of software applications as a substantial feature of this application and helps determine the extent of software reliability in performing specialized functions. This goal is accomplished by calculating the parameters of software reliability growth models (SRGMs). The parameters are evaluated using three algorithms: machine learning decision tree (DT), support vector machine (SVM), and K-nearest neighbors (K-NN). Results show that the SVM model achieves the best mean square error.

This research aims to develop an image classification model by integrating long short-term memory (LSTM) with a convolutional neural network (CNN). LSTM, which is a type of neural network, can retain and retrieve long-term dependencies and improves the feature extraction capabilities of CNN when used in a multi-layer setting. The proposed approach outperforms typical CNN classifiers in image classification. The model’s high accuracy is due to the data passing through two stages and multiple layers: first the LSTM layer, followed by the CNN layer for accurate classification. Convolutional and recurrent neural networks are combined in the recommended model, which demonstrates exceptional performance on various classification tasks. The model achieved a training accuracy of 0.9899 and testing accuracy of 0.9463 using real data, which indicates its success and applicability compared with other models.

The advent of social media has simplified the rapid publishing of explanations on inclusive announcements, movies, politics, and the economy. This growth has led to an increase in the breadth of topics covered. This emotion analysis includes many aspects. Arabic and OMCD survey big data were integrated with data from Twitter to inform this study. Different word embedding methods were implemented, such as Spacy (W2V), FastText, and Arabic Bidirectional Encoder Representation (AraBERT). In the context of sentiment analysis models, convolutional neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) were employed. The evaluation of model performance was based on accuracy. Deep learning (DL) methods using the AST (Arabic sentiment Twitter) dataset yielded 72% and 95% model accuracy rates. The accuracy rates for the OMCD (Offensive Moroccan Comments Dataset) is a dataset containing offensive comments in the Moroccan dialect. dataset fell within the range of 54% to 84%.

Detecting houses through high-resolution satellite images has received increasing attention in recent years and is considered a basic but difficult task in the field of remote sensing. Although, there are many methods that have been developed in this field, there is still a need for an in-depth review of recent articles about Extracting buildings and houses from high-resolution satellite images. This study aims to provide a comprehensive review of articles published in the scientific literature including developments that have occurred in recent years in this field. The topic of house detection is a popular, widespread and rapidly emerging research topic in domain of remote sensing. Because its importance in many fields, including drawing and updating urban maps, monitoring change, detecting damage resulting from environmental disasters, land use analysis, population estimation, and other applications. In addition to Availability of high-resolution images produced by the new generation to satellites. Topics of automatic detection of houses, object-based approaches, machine learning, and deep learning through high-resolution RGB images.

An increasing number of individuals take refuge in telemedicine systems for medical diagnosis and treatment due to their numerous benefits, including reduced healthcare costs, enhanced efficiency, and the ability to treat and prevent a wide range of physical and mental health problems. To improve the health status and clinical findings of older and underserved individuals, healthcare institutions have expanded telemedicine services, integrating them with advanced assisted living systems and environments. Conversational chatbots, or dialogue systems, are software tools designed to emulate human interaction via the Internet. These conversational bots can engage in natural conversations and can be merged into websites, mobile apps, and messaging platforms.

Moreover, they can be used across various fields, such as healthcare, to support and enhance health services. An essential key feature of conversational chatbots is their ability to deliver swift and automated responses. In healthcare, these bots serve multiple purposes, including setting appointments, answering questions, and providing recommendations.

Modern-day conversational chatbots leverage artificial intelligence techniques, such as machine learning and natural language processing, to understand and respond to user inquiries effectively. This study will discuss the objectives of developing chatbot systems, the fundamental methodologies and datasets used, the primary challenges and limitations of existing works, and insights into future trends in chatbot development.

Skin melanoma is one of the most dangerous diseases in the world. Correct classification of skin lesions in the first step can help create clinical judgment by providing an optimal judgment of the disease. As a result, the odds of treating the spread of cancer early may be increased. However, the automatic classification of skin cancer is tough because of the imbalance in most skin cancer images used in training. Several methods based on deep learning have been broadly used recently in skin cancer classification to resolve the problems in classification and attain acceptable outcomes. Nevertheless, reviews containing the aforementioned borderline difficulties in skin melanoma classification are still rare. Thus, this paper presents a summary of the newest deep learning procedures for classifying skin cancer. This paper starts with a discussion of skin cancer types, followed by the presentation of a public dataset available for skin cancer. Subsequently, some pretrained models of CNN used for classification are highlighted. Finally, some opportunities for skin cancer, such as data imbalance and limitation, generative adversarial network, various data sets, and data augmentation, are summarized.

Acute lymphoblastic leukemia (ALL) is a main health problem throughout the world. Therefore, fast and exact diagnosis is the most crucial factor for providing efficient management and treatment methods. The conventional diagnostic tools, based on the morphological and cytochemical investigation of blood and bone smears, are usually not specific and laborious. Thus, they often result in diagnostic errors and delay in treatment initiation. In this paper, ALL-diagnosing methods based on the convolutional autoencoder (CAE) was proposed to reduce the amount of data, and then convolutional neural network (CNN) was applied to identify ALL. The design method employed deep neural networks to recognize the features of the cells in question and then distinguish them as either leukemic or healthy cell types. The proposed laboratory method, with the use of the curated datasets of annotated pathological images of normal lymphoid progenitor cells, aimed to tackle the challenges related to the lack of curated datasets with annotated images of these cells. These challenges are believed to be linked to imprecise and time-consuming leukemia diagnosis and cure process. The simulated results confirmed the efficiency of the suggested technique, where CAE showed a correlation coefficient of 0.987 for lymphoblastic cells and CNN had an accuracy rate of 99.92% in ALL diagnosis. Such data demonstrated the capability of deep-based methodologies to fight leukemia.