Iraqi Journal for Computers and Informatics
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Arabic Crime Tweet Filtering and Prediction Using Machine Learning
Full text linkCrime is undeniably rising, thus negatively affecting countries’ economies. Despite several efforts to study crime prediction to reduce crime rates, few studies take the timeline factor into account when extracting crime-related tweets to predict crime. Aiming to predict Arabic crime tweets on Twitter/X, this study predicts crimes after analyzing social sentiment—that is, whether a tweet raises positive, negative, or neutral feelings—and filters the tweets based on crime behavior through an intelligent dictionary built through a genetic algorithm. The study uses a variety of machine learning (ML) models—random forest, logistic regression, and decision trees—which are assessed according to their accuracy, precision, recall, and F1 scores to guarantee robustness and dependability in crime prediction. The accuracy after filtering crimes based on an intelligent dictionary is 97% for decision tree, 97% for random forest, and 94.43% for logistic regression. This research will provide insight into potential crime attitudes and public opinion toward safety and law enforcement
Chronic Kidney Disease (CKD) Diagnosis using Machine Learning Methodology Classifications
Full text linkEarly 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
Latest Developments in Hypercube Network Technology: A Review
Full text linkHypercube 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
Computer Based Detection of Normal and Alcoholic Signals Using Discrete Fourier Transform
Full text linkAlcoholism is a severe, disorder that affects; the functionality of neurons in the central nervous system and leads to the loss of .health and wealth. The suggested technique applies statistical and fractal dimension (FD) features to classify alcoholic and normal subjects using eight channels under an SF-based machine learning architecture. Electroencephalogram (EEG) signals are placed in a framework and separated into different EEG bands using an orthogonal wavelet filter. The following three classification approaches are used to classify the alcoholic and normal patterns of EEG data: least-square support vector machine, vector machine (SVM), and Naïve Bayesian. Results showed that the best classification method was SVM with a sensitivity of 0.9267%, an accuracy of 0.9892%, and a specificity of 0.9916%
Advancing Early Warning Systems for Fire Detection: A Comprehensive Approach in Machine Learning
Full text linkThis research conducts a comprehensive investigation of the efficacy of various machine learning algorithms for fire detection. The algorithms that were examined include logistic regression, decision tree, random forest, support vector classifier, gradient boosting, K-nearest neighbors, Gaussian naive Bayes, multilayer perceptron classifier, and XGBoost classifier. Through in-depth experiments, this study rigorously assesses the performance of these algorithms in identifying and predicting fires based on pertinent input features. Among the algorithms that were investigated, logistic regression is the best performer, with a high accuracy rate of 99%. The findings from this research offer valuable insights for optimizing fire detection systems, providing a nuanced understanding of the practical applicability of machine learning techniques in real-time fire monitoring scenarios. The primary objectives of this study are to elucidate specific challenges in fire detection, evaluate the performance of various machine learning algorithms, and contribute to the foundational knowledge that is essential for enhancing fire management strategies. The research addresses the limited precision of existing fire detection systems and aims to rectify this issue through a systematic exploration of advanced machine learning approaches. The overarching goal is to bolster the foundations of fire management, facilitating the development of proactive measures and prompt responses to mitigate the profound impact of wildfires. By presenting a detailed examination of the strengths and weaknesses of various machine learning algorithms, this research strives to foster a robust and effective approach to fire detection, thereby advancing the field and ensuring the safety of communities at risk.
Routing Protocol Mechanisms in Wireless Sensor Networks using Fuzzy Interface Algorithm
Full text linkNumerous sensor nodes make up a wireless sensor network. Sensor nodes work together to send information about the environment to the base station. Nodes are susceptible to failure during these operations due to power failure, hardware or software failure, etc.. Consequently, delivering dependable packages necessitates achieving two important objectives: energy efficiency and minimized error rate. A novel approach is proposed to distribute the workload among each sensor node to accomplish these objectives. When estimating the scope of communication between nodes, uncertainties are dealt with using the fuzzy logic method. During some weather hazards, the features and cables are particularly challenging to monitor with simple devices in the WAN area. Remote sensing units are used to check and display the feature conditions to resolve this issue. Using machine learning algorithms, the collected data will be analyzed and processed to create a prediction horizon that will be useful for environmental studies and early warning systems. The MatLab2020b simulation program, which has useful tools and learning applications, will be used to carry out this task. The FIS algorithm obtained a cost distance of just 629 m out of the total WSN sensor lengths of 2600 m, which is the best cost among all implemented routing protocol techniques in this study
Detection of Covid-19 and chest pneumonia based on X-ray images using Deep-Transfer Learning
Full text linkلقي العديد من الأشخاص حتفهم نتيجة تفشي فيروس كورونا في عام 2019 (كوفيد-19)، والذي أثر أيضًا على ملايين آخرين في جميع أنحاء العالم. تنتشر العدوى بسرعة. ولذلك، فإن التكنولوجيا التي تتيح الكشف السريع عن الفيروسات ستوفر لمتخصصي الرعاية الصحية المساعدة التي هم في أمس الحاجة إليها. تهدف هذه الدراسة إلى التعرف على مرض كوفيد-19 من صور الأشعة السينية للأشخاص الأصحاء والمصابين بالالتهاب الرئوي باستخدام نموذج VGG16 المعدل. حقق النموذج المقترح نتائج أفضل من الدراسات السابقة المقدمة بدقة 99.13% واستدعاء 99% ودقة 98.70%.Numerous people have died as a result of the coronavirus outbreak in 2019 (COVID-19), which also affected millions of others worldwide. The infection spreads quickly. Therefore, technology that enables quick virus detection will offer healthcare professionals much-needed assistance. This study aims to identify COVID-19 disease from X-ray images of healthy and infected people with pneumonia by using a modified VGG16 model. The proposed model achieved better results than previous studies presented with an accuracy of 99.13%, a recall of 99%, and a precision of 98.70%
Deep Packet Inspection Model Based on Support Vector Machine for Anomaly Detection in Local Area Networks
Full text linkDeep 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
Security Monitoring in Smart Homes Using IoT Data Analytics
Full text linkWith the rapid proliferation of Internet of Things devices in smart homes, the necessity of robust security measures has become clearer than ever. This study aims to apply data analytics to enhance security monitoring within smart home environments. By leveraging the wealth of data generated by IoT devices, the study also aims to create an intelligent system that is capable of proactively identifying and addressing potential security threats through Android mobile devices.Therefore, privacy concerns were addressed through encryption and anonymization methods to protect sensitive information. The study evaluates the effectiveness of the developed security monitoring system through simulated and realistic scenarios, thereby highlighting its ability to detect and mitigate a wide range of security threats. Thus, the research contributes to the development of smart home security by providing a smart, data-driven approach to monitoring security incidents. In the evolving landscape of smart homes, the proposed framework forms a cornerstone for ensuring the safety and privacy of residents within this interconnected ecosystem
Identifying Researchers’ Interest using Text Mining
Full text linkResearchers\u27 interests and academic journals are crucial for advancing scientific inquiry. Journals serve as platforms for sharing and validating discoveries, fostering a symbiotic relationship that advances our collective understanding and pushes the boundaries of human knowledge. Journals, which encompass natural edge research and establish benchmarks for academic rigor. In this paper, an analysis, using text mining, of the publications of Iraqi researchers in scientific journals is used to extract the researcher\u27s interest. In more detail, this paper utilizes the following technologies: pre-processing (tokenization, POS (“Part Of Speech”), normalization, case folding, lemmatization) – filtering (stop word elimination) - feature Extraction (TF-IDF), as well as classification using deep neural network classifier (DNNC), to address the problem of identifying the researcher\u27s interests through texts (title &abstract) analysis. The Iraqi researchers’ data in the field of computer science from the years 2010-2022. As obtained from the Scopus repository, a total of 1170 papers were collected via API- key and scrubber depending on the keyword of computer science and the year. Furthermore, these papers were manually classified based on the hierarchical classification of the ACM journal. Finally, the best results obtained from a classification using DNN and TF-IDF as classifying terms achieved a precision of 90%, Recall of 90%, f1-score of 90%, and accuracy of 90%