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Evaluation of Modern Generative Networks for EchoCG Image Generation

computers, Materials & Continua 2024年第12期81卷 4503-4523页

作者： Sabina Rakhmetulayeva Zhandos Zhanabekov Aigerim Bolshibayeva Department of Cybersecurity Information Processing and StorageSatbayev UniversityAlmaty050000Kazakhstan Department of Mathematical Computer Modeling International IT UniversityAlmaty050000Kazakhstan Department of Information Systems International IT UniversityAlmaty050000Kazakhstan

The applications of machine learning(ML)in the medical domain are often hindered by the limited availability of high-quality *** address this challenge,we explore the synthetic generation of echocardiography images(echoCG)using state-of-the-art generative *** conduct a comprehensive evaluation of three prominent methods:Cycle-consistent generative adversarial network(CycleGAN),Contrastive Unpaired Translation(CUT),and Stable Diffusion 1.5 with Low-Rank Adaptation(LoRA).Our research presents the data generation methodol-ogy,image samples,and evaluation strategy,followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of the generated *** findings indicate that Stable Diffusion outperforms both CycleGAN and CUT in generating images that are nearly indistinguishable from real echoCG images,making it a promising tool for augmenting medical ***,we also identify limitations in the synthetic images generated by CycleGAN and CUT,which are easily distinguishable as non-realistic by medical *** study highlights the potential of diffusion models in medical imaging and their applicability in addressing data scarcity,while also outlining the areas for future improvement.

关键词： Synthetic image generation synthetic echogcardiography generative adversarial networks CycleGAN latent diffusion models stable diffusion

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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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DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

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computers, Materials & Continua 2024年第10期81卷 229-249页

作者： Sayat Ibrayev Batyrkhan Omarov Arman Ibrayeva Zeinel Momynkulov Joldasbekov Institute of Mechanics and Engineering Almaty050000Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050000Kazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050000Kazakhstan

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage *** study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization *** a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational *** methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the *** findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design *** bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.

关键词： Multi-objective optimization genetic algorithm surrogate model deep learning walking robots

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Comparative effectiveness of models and methods for diagnosing diseases of internal organs 4

Comparative effectiveness of models and methods for diagnosi...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Myrzakerimova, Alua Khikmetov, Askar Kolesnikova, Kateryna Information Systems Department IITU Almaty Kazakhstan Mathematical Computer Modeling Department IITU Almaty Kazakhstan

ISBN: (纸本)9798350374865

Among the various applications of IT in healthcare, automated systems for diagnosing diseases stand out for their utilization of mathematical models in medical decision-making. These systems, capable of analyzing extensive datasets, offer a level of speed and accuracy in diagnosing diseases of internal organs that often surpasses human capability. This paper aims to elucidate and validate the effectiveness of these automated systems through a comprehensive approach involving comparative studies and clinical trials. By examining the accuracy of different mathematical models and highlighting the percentage of cases correctly identified by expert systems, this research underscores the systems' proficiency in rapid data processing and analysis. Drawing on previous research, the complexity of medical diagnostics is explored through the diversity of diagnostic indicators and the development of robust mathematical models based on fuzzy set theory and gradient projection techniques. The paper presents a scheme of the automated diagnostic system, detailing its core components and the innovative use of case-based reasoning. Through a retrospective analysis and clinical trial outcomes, the effectiveness of the proposed system is demonstrated, showcasing its significant potential to enhance diagnostic accuracy and efficiency in the medical field. © 2024 IEEE.

关键词： Expert systems

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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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Development of Pneumonia Detection System on X-Ray Images Using Deep Learning 4

Development of Pneumonia Detection System on X-Ray Images Us...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Nessipkhan, Dias Bazarkulova, Inkar Department of Matematical and Computer Modeling International Information Technology University Almaty Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

This research paper explores the development of an advanced pneumonia detection system utilizing deep learning (DL) techniques on X-ray images, aiming to enhance diagnostic accuracy and efficiency in medical settings. By integrating Convolutional Neural Networks (CNNs), the study leverages the capability of DL to automatically learn and identify intricate patterns within X-ray images that are indicative of pneumonia. The proposed system undergoes rigorous testing and validation on a diverse dataset of chest X-rays, demonstrating its potential to achieve high levels of accuracy, precision, recall, and F-score, surpassing traditional diagnostic methods. Furthermore, the paper discusses the challenges encountered in the development process, including data variability, model interpretability, and the need for extensive dataset annotation. It also highlights the significance of deploying such DL-based systems in clinical environments, where they can provide real-time support to radiologists and healthcare professionals, leading to quicker and more reliable pneumonia diagnosis, ultimately improving patient outcomes and care efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban Area 4

CNN-RNN Hybrid Model For Dangerous Sound Detection in Urban ...

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4th IEEE International Conference on Smart information systems and Technologies, SIST 2024

作者： Momynkulov, Zeinel Omarov, Nurzhan Altayeva, Aigerim Department of Mathematical and Computer Modeling International Inforamtion Technology University Almaty Kazakhstan Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan

ISBN: (纸本)9798350374865

In the realm of audio signal processing, detecting impulsive urban sounds within continuous audio streams remains a complex endeavor. This paper presents an innovative solution via a hybrid Convolutional Neural Network - Recurrent Neural Network (CNN-RNN) framework. The CNN segments of the model target the extraction of spatial hierarchies from the audio, while the RNN components leverage their temporal memory capabilities to recognize and sequence sporadic urban auditory events. Drawing data from a rich collection of urban audio signals sourced from multiple global metropolises, the hybrid model was rigorously trained and evaluated. Findings underscore its superior detection accuracy in comparison to conventional methods, with a marked decrease in false positives. Moreover, the adaptability of the CNN-RNN architecture was evidenced by its consistent performance across diverse acoustic environments. This advancement in impulsive urban sound detection not only accentuates the efficacy of combined CNN-RNN systems but also has broader implications for urban planning, environmental assessments, and public safety initiatives. © 2024 IEEE.

关键词： Recurrent neural networks

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Querying Data Exchange Settings Beyond Positive Queries 31

Querying Data Exchange Settings Beyond Positive Queries

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31st Symposium of Advanced Database systems, SEBD 2023

作者： Calautti, Marco Greco, Sergio Hammad, Hebatalla Mahmood, Tariq Molinaro, Cristian Trubitsyna, Irina Department of Computer Science University of Milan Italy Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Italy Department of Information Engineering and Computer Science University of Trento Italy

Data exchange, the problem of transferring data from a source schema to a target schema, has been studied for several years. The semantics of answering positive queries over the target schema has been defined in early works, but little attention has been paid to more general queries. A few semantics proposals for more general queries exist but they either do not properly extend the standard semantics under positive queries, giving rise to counterintuitive answers, or they make query answering undecidable even for the most important data exchange settings. The goal of this paper is to provide a new semantics for data exchange that is able to deal with general queries. At the same time, we want our semantics to coincide with the classical one when focusing on positive queries, and to not trade-off too much in terms of complexity of query answering. We show that query answering is undecidable in general under the new semantics, but it is coNP-complete when the dependencies are weakly-acyclic. © 2023 CEUR-WS. All rights reserved.

关键词： Electronic data interchange

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Economic and Mathematical Models for Determining the Level of Maturity of an Enterprise in Knowledge Management and Project Management 13

Economic and Mathematical Models for Determining the Level o...

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13th International Conference on Advanced computer information Technologies, ACIT 2023

作者： Chaikovska, Inna Chaikovskyi, Maksym Khmelnytskyi National University Department of Economics Analytics Modeling and Information Technologies in Business Khmelnytskyi Ukraine Khmelnytskyi National University Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine

The article offers an economical and mathematical modeling toolkit to determine the enterprise's knowledge and project management maturity level. In particular, gray relational analysis and the hierarchy analysis method were used in forming a comprehensive assessment of the knowledge management system and determining the level of maturity of a knowledge management enterprise. Fuzzy logic was used in forming a comprehensive evaluation of a project management system and determining the maturity level of a project management enterprise. The following maturity levels are proposed: low, below average, average, above average, and high, and membership functions for these terms are formed. The obtained comprehensive indicators of the enterprise's knowledge management and project management system can be used in modeling functional relationships in the knowledge management system in the operational and project activities of the enterprise to identify problem areas for achieving the target values of performance criteria. © 2023 IEEE.

关键词： Membership functions

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Implementation of Anatomy Constrained Contrastive Learning for Heart Chamber Segmentation 15

Implementation of Anatomy Constrained Contrastive Learning f...

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15th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ukibassov, B.M. Rakhmetulayeva, S.B. Zhanabekov, Zh.O. Bolshibayeva, A.K. Yasar, Ansar-Ul-Haque Information Systems Department International Information Technology University Almaty050040 Kazakhstan Mathematical Computer Modeling International Information Technology University Almaty050040 Kazakhstan Transportation Research Institute Hasselt University Hasselt3500 Belgium

The article covers the implementation of anatomy constrained contrastive (ACC) learning networks on a dataset of echocardiographic images with the purpose of segmenting heart chambers for further study. The dataset was partially synthetic, generated using slicing of 3D models of the heart. The images were generated using cycle-consistent generative adversarial networks (CycleGAN) and contrastive unpaired translation (CUT) approaches. By utilizing both CycleGAN and CUT approaches, the generated images captured a wide range of variations in heart chamber segmentation, allowing for a more comprehensive study of the different anatomical features. Proposed approach augments the implementation of ACC learning networks and partially solves the data scarcity problem when it comes to segmentation implementation. © 2024 Elsevier B.V.. All rights reserved.

关键词： Heart

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