Iraqi Journal for Computers and Informatics

Introduction: This study represents an important development in human–machine interactions. It aims to utilize the potential of electroencephalograms (EEGs) in recognizing emotions, which is a complex and variable task. This study presents a complete framework for enhancing emotion identification. It provides an intuitive way for humans to interact with machines emotionally by understanding the emotion machine. The process begins with collecting and preprocessing EEG information to use the data for training and testing the proposed system. Optimization, machine learning, and deep learning algorithms are applied in this study. First, particle swarm optimization (PSO) identifies and optimizes critical functions and reduces feature dimensionality. Thereafter, long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) architectures are used in emotion identification. All the applied models are evaluated using common evaluation metrics, such as accuracy, precision, and the F1 score. From various implementations of the different models applied to identify EEG emotion, the LSTM model achieved good results with an accuracy of 98.13%, a precision of 98.15%, and an F1 score of 98.13%. Although the GRU and simple RNN models exhibit good performance in emotion identification, their measurements are less than those of LSTM, which outperforms all the other models. This study incorporates the concepts of the PSO algorithm into a feature selection and deep learning model by using LSTM to enhance EEG emotion identification. The proposed model overcomes difficulties and issues related to EEG signals, leading to an accurate emotion detection system and providing enhanced machine understanding of human–machine interactions.

The goal of this study was to determine the key elements driving the expansion of e-government in Arab nations. One of the econometric models—the cross-sectional time series model (panel)—was employed to accomplish the intended outcome. The following techniques were used to estimate the models: fixed effects model, random effects model, and linear panel data regression analysis. The results revealed that e-government is negatively affected by political stability and the absence of terrorism and violence (PV), positively affected by government effectiveness (GE), positively affected by regulatory quality (RQ), and positively affected by the rule of law (RL).

Healthcare chatbots play a critical role in improving communication between patients and healthcare providers by offering accurate and timely responses. A novel approach is proposed, which leverages a deep learning model that combines long short-term memory (LSTM) neural networks and a sequence-to-sequence (Seq2Seq) architecture to enhance text prediction accuracy in medical dialogue systems. The model leverages the capability of LSTM to capture long dependencies in sequential data alongside the contextual encoding of Seq2Seq, which improves predictive quality in dialogue responses. The encoder–decoder architecture, which utilizes tokenization and padding to standardize input sequences, contributes to the improvement in data processing. The validation accuracy of the model is 0.9766, with a loss of 0.0184. Specifically, the precision is 0.9961, the recall is 0.9981, and the F1 score is 0.9971. The capability of the model for sequence prediction is attributed to its robustness. Other methods of evaluation employing measures such as the Nash–Sutcliffe efficiency coefficient, correlation coefficient, and normalized root mean square error demonstrate that the model is superior to other machine learning algorithms utilizing linear regression and GP regression. Employing callback functions during training ensures the best-fit model is saved, which makes the method viable in different tasks described in the job descriptions.

The 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.

Addressing concerns related to network security involves protecting a network from various threats, including internal and external risks, such as identity theft, collusion, malicious activities, and other attacks. This study aims to address these challenges by introducing the authentication of the identity of nodes that are connected to a network, utilizing blockchain technology and consensus algorithms as integral elements of its security framework. To improve security and deal with identity theft and spoofing attacks in clustered networks, we suggest implementing a blockchain-based authentication and authorization scheme. This scheme ensures that the authentication of nodes or devices relies on blockchain technology, employing a voting algorithm to validate the eligibility of nodes that are seeking access to a network. A new block is generated for each incoming node, and this block contains essential information about that node. The decision to accept a new node into the network is determined through a voting process that considers individual node weights calculated based on three key factors: initial weight, age, and incentive. Java NetBeans IDE 8.0.2 is used to design and implement the simulator. Experimental results comprise the implementation of consensus algorithms and demonstrate the effect of the highest weight nodes. Moreover, these algorithms will launch attacks on the network and are aware of our scheme’s ability to detect such attacks. The simulation results show that our authentication scheme exhibits advantages in terms of security, scalability, and resistance capability against attacks.

Malware 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.

Hypertension, known as the "silent killer," is one of the leading causes of global mortality, with a steadily increasing prevalence. Worldwide, the prevalence of hypertension reaches approximately 30%, with only 50% of cases being diagnosed and a low level of treatment adherence. Hypertension symptoms are often invisible, making early detection crucial to preventing serious complications. This paper aims to develop a hypertension prediction system using the Decision Tree and Random Forest algorithms, which are machine learning techniques used to solve classification and regression problems. These algorithms can predict hypertension risk based on clinical data, such as age, medical history, and lifestyle. The findings of this paper indicate that the Decision Tree and Random Forest algorithms are effective in predicting hypertension risk, achieving accuracies of 99.6% and 99.5%, respectively. This prediction system can provide fast and accurate information, assisting healthcare professionals in designing appropriate intervention strategies while also supporting better medical decision-making.

The proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments.

The Web of Things WoT connects physical objects and displays them in the WWW. The growing number of Internet of Things IoT devices and data sharing has led to the attack on sensitive information and allowing unauthorized persons to access and manipulate it. Therefore, ensuring data privacy and protection is a major challenge for organizations and individuals. This paper presents a new hybrid method for the encryption of information to be more suitable for embedded devices in the WoT environment by modifying the Advanced Encryption Standard AES algorithm and hybridizing it with the Lightweight Encryption Algorithm LEA algorithm as well as the Secure Hash Algorithm version 3 SHA3-256 algorithm for integrity and using four dimensional-NSJR system for generation of chaos keys. The proposed method is designed to decrease encryption /decryption time for the information transmitted in the WoT environment and in areas such as government data that need protection against attacks. The proposed method comprises three sub-layers: Chaos Keys generation layer, Data encryption layer, and Authentication layer. The proposed method passed all 15 NIST ( National Institute of Standards & Technology) tests. The amount of time needed to encrypt and decrypt the proposed method was compared with the original encryption methods for different data sizes and five sensitivity levels, and the proposed encryption method was found to be up to (150%) faster while maintaining security strength.

Land Cover and Land Use studies play an important role in regional socioeconomic development and natural resource management. They support sustainable development by tracking changes in vegetation, freshwater quantity and quality, land resources, and coastal areas. Iraq's Land Use and Land Cover Monitoring with Remote Sensing Data in the Period 2019–2023. This paper performed land use/land cover LULC type classification and time series analysis using Sentinel-2 satellite imagery for the years 2019 and 2023 to identify changes over time. Remote sensing data is used in this paper to address the challenge of detecting land cover change in Iraq through SVM classification. This goal aims to develop a fundamental method of mapping and monitoring these changes, encouraging sustainable land use practices, and achieving the United Nations Sustainable Development Goals. Land cover classes were categorized into five main types: Water, Barren, Building, Vegetation, and Rangeland. The study showed a marked increase in urbanization, and most of this occurring in previously bare soils at the edges of cities. This urbanization was primarily driven by population growth and economic development. What is beneficial for the environment can also be beneficial for us as people humanity as these findings have major implications for urban planning, green space management, and sustainable city development. It seems that there was no change to the existing barren land and buildings, which increased by 8% and 11% respectively, as noted from the data up to October 2023. However, vegetation coverage decreased by 27%, indicating a significant loss of green area. The water category was also up 9%. Results showed satisfactory accuracy assessment (OA: 93.11%) from applying a Support Vector Machine SVM for the LULC classification. The study lays the foundation for ongoing monitoring of LULC changes in Iraq.

Diabetes 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.

Wheat is among the most strategic crops since it is crucial for guaranteeing food security and offering a steady food supply to underdeveloped nations. Low-income families also depend on it mainly for sustenance. (Abdullah et al., 2019). Wheat production, a vital element of Iraq's history, is one of the most significant factors Iraq takes into consideration in terms of its economic growth. Although wheat farming can face several obstacles, it remains an essential part of the food security of the nation growing typically throughout the northern area such as Kirkuk, Salah alDin, and Nineveh (Sarhan, 2011). Wheat is perhaps the most important crop grown around the world because it can be grown in many places. It outdoes other plants thanks to its flexibility. (2017; AlSnbll and ALAlAmeric). For many poor households in Iraq, wheat is an essential food item. It also has a major political and military function. Iraq is importing wheat to satisfy its demands since its output is falling. (Khudhur and Dawood, 2019). By means of agricultural support and production inputs, the government has been able to contribute to wheat growing in Iraq. Still, these actions fall short of shrinking the food gap of the nation. (Hussein and Hashim, 2019).Several quantities, including agricultural output, can be forecasted using a time series model since they are influenced by many distinct natural and economic factors. Policymakers need to anticipate these elements because the agricultural sector is seeing fast economic development, which demands precise future planning. Traditional forecasting methods may not yield dependable results. Improving the precision of agricultural output forecasts, artificial neural networks could help fix this problem. (Namaa, 2023).

The 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.