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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Kalinin, Yevhen Kozhushko, Andrii Rebrov, Oleksii Zakovorotniy, Alexander National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Car and Tractor Industry Department Kharkiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Computer Engineering and Programming Department Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

In pattern recognition, depending on how the training sample is represented, a theory associated with the standard representation of the training sample (information) and the structural theory are distinguished. In the first case it is convenient to set and describe objects that are independent of each other, objects that have a rigid, static nature. In the second case, when information is specified in a structural way, it is convenient to study, describe and recognize objects whose basis presents an invariable internal or external structure. This paper considers a fundamentally different case of setting learning information. It deals with objects and processes that are based on conflict. Such features are peculiar to the data sets that an unmanned vehicle receives while driving, which requires minute-by-minute formation and decision-making in the context of the current traffic situation. Naturally, it is necessary to involve game-theoretic models to describe conflicts. It is these issues, i.e., the systematic study of game-theoretic models in the problems of recognition and classification, that the work is devoted to. It is emphasized that the classification of recognizable objects, as some speculative system, must meet the requirements of sufficiency of grounds and coverage of the totality of existing and possible recognizable objects. The most important purpose of classification is to describe the properties of its classes and subclasses, types and subspecies of recognized objects, which makes it possible for it to be used for recognizing specific objects, which subjects encounter in certain areas of activity. The paper introduces the concept of stable classification, formulates and proves the theorems based on the introduced concept, the sufficiency of the conditions for the correctness of the algorithm of pattern recognition, using the game-theoretic approach and evaluation of the degree of stability of the classification underlying it. The generated theorems a

关键词： Decision making

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI

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computers in Biology and Medicine 2025年 190卷 110095-110095页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Iskhakova, Nargiza Khaitov, Aziz Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Gyeonggi-do Seongnam-si461-701 Korea Republic of Department of Systematic and Practical Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan Republican Scientific and Methodological Center for the Development of Education of the Republic of Uzbekistan Uzbekistan

This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Magnetic Resonance Imaging (MRI). The model architecture is meticulously crafted to enhance feature extraction and selectively focus on crucial regions within brain images, thereby improving diagnostic accuracy. A unique component, the MRI Segmentation Block (MSB), is introduced to manage the skull stripping task effectively, highlighting the model ability to learn from complex, multilayered information. We have incorporated a detailed experimental evaluation of the MSB, demonstrating its superior performance in cranial debridement tasks compared to existing methods. The experiments involved a range of MRI scans, assessing the MSB's accuracy through metrics like the Dice Coefficient and Jaccard Index against ground truth annotations by expert radiologists. The results substantiate the MSB's effectiveness, setting a new benchmark for precision in medical imaging diagnostics. The proposed method integrates densely connected neural networks with a connection-wise attention model to extract multiscale features from MRI scans. Furthermore, the attention mechanism is fine-tuned to emphasize salient features significantly associated with various stages of Alzheimer's disease, thereby setting a new benchmark for precision in medical imaging diagnostics. Extensive experiments on the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset demonstrate the superiority of our method over traditional and contemporary approaches, with our model achieving high accuracy and computational efficiency. This makes it suitable for clinical applications where resources are limited. This study represents a significant advancement in the diagnostic process for AD, with potential implications for improving patient outcomes in clinical settings. © 2025 The Authors

关键词： Convolutional neural networks

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Modelling the Process of Determining the Tourist Demand Based on Fishbein's Multi-Attribute Model 15

Modelling the Process of Determining the Tourist Demand Base...

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15th IEEE International Scientific and Technical Conference on computer Sciences and Information Technologies, CSIT 2020

作者： Hovorushchenko, Tetiana Glukhov, Vladyslav Hovorushchenko, Olha Computer Engineering and System Programming Department Khmelnytskyi Ukraine Khmelnytskyi Lyceum No. 17 Khmelnytskyi Ukraine

ISBN: (纸本)9781728174433

The purpose of this paper is automating the analysis of tourist demand by developing a web-service for surveying tourists (both real and potential) and for presenting the survey results in the form of determining the tourist demand. The model of the process of determining the tourist demand based on Fishbein's multi-attribute model has been developed. The most important attributes, by which tourists evaluate their travels, and the relative importance of such attributes are identified. Rules for translating the thoughts and ratings of the user into an acceptable for the developed model 7-point bipolar scale have been developed. The web-service, which provides automating the calculation of tourist attraction of different countries, has been developed. © 2020 IEEE.

关键词： Web services

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Proactive control of rolling stock with traction asynchronous electric motors 2

Proactive control of rolling stock with traction asynchronou...

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2nd IEEE KhPI Week on Advanced Technology, KhPI Week 2021

作者： Dmitrienko, Valery Noskov, Valentin Zakovorotniy, Alexander Mezentsev, Nikolay Leonov, Sergey Gasanov, Magomediemin National Technical University Kharkiv Polytechnic Institute Computer Engineering and Programming Department Kharkov Ukraine Vice-rector for Scientific and Pedagogical Work National Technical University Kharkiv Polytechnic Institute Kharkiv Ukraine

ISBN: (纸本)9781665412056

Proactive management research continues to evolve over time. At the same time, they find applications in various fields of industry. Main approaches of complex objects managing are considered in this article. It is shown that today it is advisable to use approaches related to proactive control to control rolling stock with traction asynchronous electric motors. While using a mathematical model which describes the movement of a diesel train with three carriages, its application for the implementation of proactive control is demonstrated. Approaches related to the identification of the parameters of an object during its operation are proposed in order to clarify the parameters of the existing model in the control system. In this case, it is proposed to identify the parameters of the object at two stages: at the first (lower) level, to determine the parameters which are associated with traction asynchronous electric motors, and at the second (upper) level, the parameters which are associated with the movement of the train. The basic relationships which describe the identification process are given. © 2021 IEEE.

关键词： Parameter estimation

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A lightweight attention-driven YOLOv5m model for improved brain tumor detection

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computers in Biology and Medicine 2025年 188卷 109893-109893页

作者： Muksimova, Shakhnoza Umirzakova, Sabina Mardieva, Sevara Iskhakova, Nargiza Sultanov, Murodjon Cho, Young Im Department of Computer Engineering Gachon University Sujeong-gu Seongnam-si461-701 Korea Republic of Department of Information Systems and Technologies of the Tashkent State University of Economic Tashkent100066 Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent100200 Uzbekistan

Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagnostics, this study introduces a significant enhancement to the YOLOv5m model by integrating an Enhanced Spatial Attention (ESA) layer, tailored specifically for the analysis of magnetic resonance imaging (MRI) brain scans. Traditional brain tumor detection methods, heavily reliant on expert interpretation of MRI, are fraught with challenges such as high variability and the risk of human error. Our innovative approach leverages the ESA layer to acutely focus on salient features, significantly improving the method ability to differentiate between common classes of brain tumors—meningioma, pituitary, and glioma tumors. By processing spatial features with enhanced precision, the model minimizes false positives and maximizes detection reliability. Validated against a comprehensive dataset of 3064 T1-weighted contrast-enhanced MRI images from 233 patients, our modified YOLOv5m architecture demonstrates superior performance metrics compared to the standard model, highlighting its potential as a robust tool in clinical applications for automated and precise brain tumor diagnosis. © 2025 The Authors

关键词： Dynamic contrast enhanced MRI

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Synthesis of The Theory of Motion of Solid Bodies Filled With Bulk Substances For Fault-Tolerant Identification of Their Parameters

Synthesis of The Theory of Motion of Solid Bodies Filled Wit...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Vitalii Kolodnenko Anna Shyman Yevhen Kalinin Nina Kuchuk Department of Agricultural Engineering Sumy National Agrarian University Sumy Ukraine Department of Computer Engineering and Programming «Kharkiv Polytechnic Institute» National Technical University Kharkiv Ukraine Tractors Cars and Bioenergy Resources Department National University of life and environmental sciences of Ukraine Kyiv Ukraine

In the article, the Lagrange equations of motion of a solid body having volumes fully or partially filled with a granular media presented in the form of an ideal liquid. To expand the possibility of applying the theory, it is assumed that the model bulk medium has a surface tension. The first integrals of these equations have been considered. Further, conditions under which there is equilibrium or stationary motion of a solid with a loose medium that are reduced to the conditions of extremality (stationarity) of the potential energy or the modified potential energy of the system are derived from the equations of motion. In practically interesting cases, the problem of the minimum change in potential energy is solved by studying the second variation of the latter, the conclusion of the expression of which is given. In the nonlinear formulation, the theorem on the instability of the equilibrium position of a body with a granular media is proved in the case when the potential energy of the system does not have a minimum in the equilibrium position.

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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The Modified Picture Fuzzy Weighted Geometric Operator for Decisionmaking Processes and its Applications to Ambient Air Pollution

SSRN

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SSRN 2023年

作者： Jaikumar, R.V. Raman, Sundareswaran Pal, Madhumangal Department of Mathematics St. Joseph’s Institute of Technology OMR Chennai India Department of Mathematics Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore India

The aggregation operator is a popular tool for dealing with multi-criteria decision-making (MCDM) problems. An MCDM technique based on the modified picture fuzzy weighted geometric operator (MPFWGO) is proposed in this article. First, the drawbacks of various existing picture fuzzyweighted geometric aggregation operators (PFWGOs) that lead to imprecise outcomes in some exceptional instances are investigated. Second, an MPFWGO is proposed to address the shortcomings of existing PFWGOs. Furthermore, the suggested MPFWGO's properties such as idempotency, boundedness, monotonicity, and commutativity are explored. Numerical examples demonstrate that our proposed operator and corresponding MCDM approach are efficient and reasonable enough to prevent ambiguous results in exceptional instances. Finally, we applied the proposed method to rank the World Health Organization (WHO) regions for air pollution control. © 2023, The Authors. All rights reserved.

关键词： Decision making

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