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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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Computers, Materials & Continua 2024年第7期80卷 341-359页

作者： batyrkhan omarov Meirzhan Baikuvekov Daniyar Sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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Artificial Intelligence-Enabled Chatbots in Mental Health:A Systematic Review

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Computers, Materials & Continua 2023年第3期74卷 5105-5122页

作者： batyrkhan omarov Sergazi Narynov Zhandos Zhumanov International University of Tourism and Hospitality TurkistanKazakhstan Alem Research AlmatyKazakhstan

Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few *** chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare *** initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved ***,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical ***,treatments are frequently developed without clear ethical *** AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative ***,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists *** into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial *** this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.

关键词： Chatbot conversational agent machine learning artificial intelligence NLP NLU systematic review

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Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

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Computers, Materials & Continua 2022年第1期70卷 915-934页

作者： Shynar Mussiraliyeva batyrkhan omarov Paul Yoo Milana Bolatbek Al-Farabi Kazakh National University AlmatyKazakhstan CSIS Birkbeck CollegeUniversity of LondonLondonUK

In this research paper,we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus,thereby checking whether it is possible to detect extremist messages in the Kazakh *** do this,the authors trained models using six classic machine-learning algorithms such as Support Vector Machine,Decision Tree,Random Forest,K Nearest Neighbors,Naive Bayes,and Logistic *** increase the accuracy of detecting extremist texts,we used various characteristics such as Statistical Features,TF-IDF,POS,LIWC,and applied oversampling and undersampling techniques to handle imbalanced *** a result,we achieved 98%accuracy in detecting religious extremism in Kazakh texts for the collected *** the developed machine learningmodels in various databases that are often found in everyday life“Jokes”,“News”,“Toxic content”,“Spam”,“Advertising”has also shown high rates of extremism detection.

关键词： Extremism religious extremism machine learning social media social network natural language processing NLP

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A Skeleton-based Approach for Campus Violence Detection

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Computers, Materials & Continua 2022年第7期72卷 315-331页

作者： batyrkhan omarov Sergazy Narynov Zhandos Zhumanov Aidana Gumar Mariyam Khassanova Alem Research AlmatyKazakhstan Al-Farabi Kazakh National University AlmatyKazakhstan International University of Tourism and Hospitality TurkistanKazakhstan Suleiman Demirel University AlmatyKazakhstan Asfendiyarov Kazakh National Medical University AlmatyKazakhstan

In this paper,we propose a skeleton-based method to identify violence and aggressive *** approach does not necessitate highprocessing equipment and it can be quickly *** approach consists of two phases:feature extraction from image sequences to assess a human posture,followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence.A video violence dataset of 400 min comprising a single person’s activities and 20 h of video data including physical violence and aggressive acts,and 13 classifications for distinguishing aggressor and victim behavior were ***,the proposed method was trained and tested using the collected *** results indicate the accuracy of 97%was achieved in identifying aggressive conduct in video ***,the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

关键词： PoseNET skeleton violence bullying artificial intelligence machine learning

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Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

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Computers, Materials & Continua 2023年第9期76卷 3745-3761页

作者： batyrkhan omarov Meirzhan Baikuvekov Zeinel Momynkulov Aray Kassenkhan Saltanat Nuralykyzy Mereilim Iglikova Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmatyKazakhstan Department of Software Engineering Satbayev UniversityAlmatyKazakhstan

Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

关键词： Heart disease detection classification CNN LSTM Conv-LSTM

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Deep Learning-Enabled Brain Stroke Classification on Computed Tomography Images

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Computers, Materials & Continua 2023年第4期75卷 1431-1446页

作者： Azhar Tursynova batyrkhan omarov Natalya Tukenova Indira Salgozha Onergul Khaaval Rinat Ramazanov Bagdat Ospanov Al-Farabi Kazakh National University AlmatyKazakhstan International University of Tourism and Hospitality TurkistanKazakhstan Zhetisu University Named After I.Zhansugurov TaldykorganKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan Brunch Center of Excellence in Taldykorgan TaldykorganKazakhstan

In the field of stroke imaging, deep learning (DL) has enormousuntapped *** clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagingtechniques such as computed tomography (CT) scans. The purpose of thiswork is to classify brain CT images as normal, surviving ischemia or cerebralhemorrhage based on the convolutional neural network (CNN) model. In thisstudy, we propose a computer-aided diagnostic system (CAD) for categorizingcerebral strokes using computed tomography images. Horizontal flip datamagnification techniques were used to obtain more accurate *** Data Generator to magnify the image in real time and apply anyrandom transformations to each training image. An early stopping method toavoid overtraining. As a result, the proposed methods improved several estimationparameters such as accuracy and recall, compared to other machinelearning methods. A python web application was created to demonstrate theresults of CNN model classification using cloud development techniques. Inour case, the model correctly identified the drawing class as normal with 79%accuracy. Based on the collected results, it was determined that the presentedautomated diagnostic system could be used to assist medical professionals indetecting and classifying brain strokes.

关键词： Ischemic stroke hemorrhage CNN deep learning classification

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A Review of Machine Learning Techniques in Cyberbullying Detection

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Computers, Materials & Continua 2023年第3期74卷 5625-5640页

作者： Daniyar Sultan batyrkhan omarov Zhazira Kozhamkulova Gulnur Kazbekova Laura Alimzhanova Aigul Dautbayeva Yernar Zholdassov Rustam Abdrakhmanov Al-Farabi Kazakh National University AlmatyKazakhstan International Information Technology University AlmatyKazakhstan International University of Tourism and Hospitality TurkistanKazakhstan Almaty University of Power Engineering and Telecommunications AlmatyKazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University TurkistanKazakhstan Korkyt Ata Kyzylorda State University KyzylordaKazakhstan

Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning *** only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among *** paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the *** undertake an in-depth review of 13 papers from four scientific *** article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language *** this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or *** article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.

关键词： Cyberbullying hate speech digital drama online harassment detection classification machine learning NLP

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Artificial Intelligence in Medicine:Real Time Electronic Stethoscope for Heart Diseases Detection

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Computers, Materials & Continua 2022年第2期70卷 2815-2833页

作者： batyrkhan omarov Nurbek Saparkhojayev Shyrynkyz Shekerbekova Oxana Akhmetova Meruert Sakypbekova Guldina Kamalova Zhanna Alimzhanova Lyailya Tukenova Zhadyra Akanova Al-Farabi Kazakh National University AlmatyKazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University TurkistanKazakhstan Abai Kazakh National Pedagogical University Kazakhstan NARXOZ University AlmatyKazakhstan

Diseases of the cardiovascular system are one of the major causes of death *** diseases could be quickly detected by changes in the sound created by the action of the *** dynamic auscultations need extensive professional knowledge and emphasis on listening *** is also an unmet requirement for a compact cardiac condition early warning *** this paper,we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning *** system consists of three subsystems that interact with each other(1)a portable digital subsystem of an electronic stethoscope,(2)a decision-making subsystem,and(3)a subsystemfor displaying and visualizing the results in an understandable *** electronic stethoscope captures the patient’s phonocardiographic sounds,filters and digitizes them,and then sends the resulting phonocardiographic sounds to the decision-making *** decision-making systemclassifies sounds into normal and abnormal using machine learning techniques,and as a result identifies abnormal heart *** display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff,but also for patients and recommends further actions to *** a result of the study,we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%.More accurately,the proposed stethoscope can identify normal heart sounds with 93.5%accuracy,abnormal heart sounds with 93.25%***,speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

关键词： Stethoscope phonocardiogram machine learning classification heart diseases PCG

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Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

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Computers, Materials & Continua 2022年第6期71卷 4701-4717页

作者： batyrkhan omarov Azhar Tursynova Octavian Postolache Khaled Gamry Aidar Batyrbekov Sapargali Aldeshov Zhanar Azhibekova Marat Nurtas Akbayan Aliyeva Kadrzhan Shiyapov Al-Farabi Kazakh National University AlmatyKazakhstan International University of Tourism and Hospitality TurkistanKazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University TurkistanKazakhstan Instituto Universitário de Lisboa LisbonPortugal International Information Technology University AlmatyKazakhstan South Kazakhstan State Pedagogical University ShymkentKazakhstan M.Auezov South Kazakhstan University ShymkentKazakshtan Kazakh-British Technical University AlmatyKazakhstan Asfendiyarov Kazakh National Medical University AlmatyKazakhstan Kazakh National Women’s Teacher Training University AlmatyKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan

The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging ***,at the moment,the models for solving this problem using machine learning methods are far from *** this paper,we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3Dcomputed tomography *** use the ISLES 2018(Ischemic Stroke Lesion Segmentation Challenge 2018)open dataset to train and test the proposed *** of the obtained results,as well as the ideas for further experiments are included in the *** evaluation is performed using the Dice or f1 score coefficient and the Jaccard *** architecture may simply be extended to ischemia segmentation and computed tomography image identification by selecting relevant *** Dice/f1 score similarity coefficient of our model shown58%and results close to ground truth which is higher than the standard 3D UNet model,demonstrating that our model can accurately segment ischemic *** modified 3D UNet model proposed by us uses an efficient averaging method inside a neural *** this set of ISLES is limited in number,using the data augmentation method and neural network regularization methods to prevent overfitting gave the best *** addition,one of the advantages is the use of the Intersection over Union loss function,which is based on the assessment of the coincidence of the shapes of the recognized zones.

关键词： Stroke ischemic stroke UNet deep learning segmentation ISLES 2018

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