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

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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Modeling of Memory Dependent Derivative Under Three-Phase Lag in Generalized Thermo-Viscoelasticity

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International Journal of Applied and Computational Mathematics 2021年第6期7卷 1-20页

作者： Singh, Biswajit Sarkar, Smita Pal Barman, Krishnendu Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur Howrah 711103 West Bengal India Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Paschim Medinipur 721102 West Bengal India

In this study, a new generalized model of thermo-viscoelasticity with three phase-lag (TPL) theory concerning memory-dependent derivative (MDD) theory is emphasized. The governing combined equations of the novel model associated with kernel function and time delay are considered in a two-dimensional semi-infinite space. The bounding surface of the medium is assumed to be free of traction and subjected to time-dependent thermal loading. Using Laplace and Fourier Transform techniques, the problem is transformed from the space–time domain and then solved numerically. Suitable numerical technique is used to find the inversion of Fourier and Laplace transforms. In the simulation, the effects of the time-delay parameter and kernel function on the distributions of the displacement components, stresses and temperature field are studied and represented graphically. The results shows that the presence of TPL, the time-delay and kernel function extensively affect all the distributions. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Laplace and Fourier transform Memory dependent derivative Thermo-viscoelasticity Three phase-lag Vector matrix differential equation

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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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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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Research on Reasonable Color Matching Method of Interior Decoration Materials Based on Image Segmentation

Research on Reasonable Color Matching Method of Interior Dec...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Lokesh Kalapala Raghunandan K R D. Shyam Saravanan R K Krishnakumar C. Sivasankar Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed University) Karnataka India Department of EEE Rajalakshmi Engineering College Thandalam Chennai Department of ECE PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Programming Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

In recent years, with the continuous growth of social and economic level, people's living standard has also been constantly improved. Under this background, contemporary people highly respect a good living environment, and the importance of building interior decoration engineering has been fully reflected. As one of the important design contents of interior decoration, the use level and effect of color will directly affect the overall visual effect of interior decoration and the comfort of interior space. Through the rational collocation of colors, the artistic characteristics of interior decoration can be displayed, which can also show the important role of color in interior decoration. In modern interior decoration, the use of colors is gradually enriched, and the use of multiple colors leads to the problem of actual color matching. Image segmentation, which is the premise of image recognition and tracking, divides an image into regions with different characteristics and extracts interesting objects. This technology has been widely used in military affairs, medicine, intelligent transportation, pedestrian detection, product inspection, sports, remote sensing, machine vision and other fields. Therefore, good color matching is very important in the actual interior decoration practice.

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Algorithmic Support for Building a Distributed IoT System in a Cloud Service

Algorithmic Support for Building a Distributed IoT System in...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Mykola Zamkovyi Svitlana Gavrylenko Kyrylo Khatsko Nataliia Khatsko dept. Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

The Internet of Things (IoT) today already connects billions of different IoT devices with the Internet. With the advent of new services and services, new challenges arise: how to make a cloud service publicly available, and reliable, how and where to store data. The article considers an example of the implementation of an IoT application with cloud components, considers the implementation of connecting IoT components to a cloud provider, and presents the IoT architecture. The focus is on the architecture and implementation of data exchange protocols, the implementation of protocols? for data collection and management of remote system components Given the large amount of data transmitted by IoT devices to the cloud, data compression and interaction with cloud services is a very relevant topic. Therefore, the article also offers recommendations for managing the traffic of IoT devices. The purpose of this article is to provide a description and algorithm for connecting IoT devices to the cloud, to bring together disparate information, and make it open and understandable to beginners and academic researchers.

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Research of Intelligent Data Analysis Methods for Identification of computer System State 30

Research of Intelligent Data Analysis Methods for Identifica...

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30th International Scientific Symposium Metrology and Metrology Assurance, MMA 2020

作者： Gavrylenko, Svetlana Chelak, Viktor Hornostal, Oleksii National Technical University Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9781728197197

Classification is one of the methods of the intelligent data analysis (data mining), which allows to study the existing data set and create a model for each class based on its data features. To increase the accuracy of classification, ensemble methods are used, including some of the most effective ensemble methods based on boosting and bagging. This study presents the results of a comparative analysis of machine learning methods for classifying the state of a computer system. The ensemble methods based on boosting and bagging were modified using a special procedure for selecting the main classification parameters: The number of trees (the number of learners), the number of branches (the number of splits or leaves nodes), and the learning speed (the learning rate). Modified methods were tested with the following initial data of the computer system functioning: loading of the central processor and memory, the amount of incoming and outgoing traffic (network utilization), the number of read / write operations to disk, the statistical data of the system events analysis (the number of operations with the system registry, file system, number of processes, etc.). It was found that the classification accuracy with the test sample was more than 99%. © 2020 IEEE.

关键词： bagging decision trees ensemble classification methods boosting

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Calculation of Signal Information Delay in Intelligent Communication Networks 3

Calculation of Signal Information Delay in Intelligent Commu...

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

作者： Kuchuk, Nina Mozhaiev, Mykhailo Kalinin, Yevhen Davydov, Viacheslav Mozhaev, Oleksandr Kuchuk, Heorhii National Technical University 'Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine Computer Technology and Telecommunications Researches Sector National Scientific Center «hon. Prof Bokarius Forensic Science Institute» Kharkiv Ukraine National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine Kharkiv National University of Internal Affairs Department of Cyber Security and Data Technologies Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

An analysis of the signaling systems used in the Intelligent Communication Network has been carried out. The main probabilistic characteristics of signal information delay are determined. A method for calculating these characteristics for such error correction methods is proposed: basic error control method and preventive cyclical control method. The analysis of the delay of signal units from loading the signal network is carried out. Simple formulas are proposed for approximating the load applied to the signaling link. © 2022 IEEE.

关键词： Error correction

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Analysing the Impact of COVID-19 outbreak and Economic Policy Uncertainty on Stock Markets in Major Affected Economies 6

Analysing the Impact of COVID-19 outbreak and Economic Polic...

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6th Smart Cities Symposium, SCS 2022

作者： Matuka, Adelajda Asafo, Shuffield Seyram Eweke, Gamaliel O. Mishra, Pradeep Ray, Soumik Abotaleb, Mostafa Makarovskikh, Tatiana Alhussan, Amel Ali Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Dutta, P.K. Chowdhury, Subrata University of Macerata Department of Economics and Law Italy Niger Delta University Department of Banking & Finance Nigeria J.N.K.V.V. College of Agriculture M.P. Powarkheda India Centurion University of Technology and Management Odisha Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog Egypt School of Engineering and Technology Amity University West Bengal Kolkata India AP Chittoor India

ISBN: (纸本)9781839538544

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Investments

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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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