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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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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning 16

Artificial Intelligence in Medicine for Chronic Disease Clas...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Rakhimov, Mekhriddin Akhmadjonov, Ravshanjon Javliev, Shahzod Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Application Programming Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer System Tashkent Uzbekistan

ISBN: (纸本)9781665451628

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification. © 2022 IEEE.

关键词： Diseases

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Input Data Time Range Impact on Forecast Quality Using Automated Procedures

Input Data Time Range Impact on Forecast Quality Using Autom...

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computer Science (COMSCI), International Scientific Conference on

作者： Kiril Andreev Koparanov Elena Ventseslavova Antonova Ognyan Nakov Nakov Daniela Veleva Minkovska Krasin Krasimirov Georgiev Lyudmila Yordanova Stoyanova Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria Department of Air Transport Faculty of Transport Technical University of Sofia 8 Kliment Ohridski blvd Sofia Bulgaria

Easy access to time series forecasting provides a strong competitive advantage for any company or organization. As automated forecasting environments become more prevalent in the market, it is crucial to explore their capabilities and features to utilize them competently. This study demonstrates the influence of data coverage on prediction accuracy and validates the initial assumption. Based on the findings, recommendations have been presented to enhance the quality of predictive processes.

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The prediction of students' Academic performances with a classification Model Built using Data Mining Techniques 6

The prediction of students' Academic performances with a cla...

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6th Smart Cities Symposium, SCS 2022

作者： Alkattan, Hussein Abotaleb, Mostafa Subhi, Alhumaima Ali Adelaja, Oluwaseun A. Kadi, Ammar Ibrahim Al-Mahdawi, Hassan K. Department of System Programming South Ural State University Chelyabinsk454080 Russia Electronic and Computer Center University of Diyala BaqubahMJJ2+R9G Iraq Information Communication and Technology Department Lagos State University Lagos102101 Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk454080 Russia

ISBN: (纸本)9781839538544

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 - 1.00 which perfectly agrees for most statistical values. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Decision trees

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Benefiting From Power of The Crowd: Contribution of Crowds to the Idea Evaluation Phase

Benefiting From Power of The Crowd: Contribution of Crowds t...

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Engineering, Technology and Innovation (ICE/ITMC), IEEE International Conference on & Management of Technology (IAMOT) Joint Conference, International Association For

作者： Ahmet Çubukcu Özlem Akarçay Tuba Ulusoy Department of Computer Forensics Engineering Necmettin Erbakan University Konya Turkey Department of Computer Programming Program KTO Karatay University Konya Turkey Department of Industrial Engineering Necmettin Erbakan University Konya Turkey

ISBN: (纸本)9781665488181

The Covid-19 pandemic has put the world in a serious crisis by damaging the functioning of many systems (i.e.,health, economy, trade). Online communities have also tried to find solutions to minimize the negative effects caused by the Covid-19 pandemic. One of these communities conducted a program in Turkey to minimize the risky effects brought by the COVID-19 pandemic. The program took place over a three-month period (April 2020 to June 2020) during the open innovation platform powered by innoCentrum. In this process, an accelerator program was carried out to gather, evaluate, and implement innovative ideas. During the evaluation phase of an idea, the assessments of other entrepreneurs who submitted ideas were also taken into consideration by a rating system. The aim of this study is to reveal whether there is a relationship between the contribution levels of crowds (entrepreneurs) and the best ideas selected during the evaluation phase. While the result of the study shows that there is a significant relationship between the average scores given by the crowds with the scores given by the mentors who mainly evaluated the ideas, there is no significant relationship between the number of crowds' comments and the number of crowds' vote with the scores given by the mentors.

关键词： COVID-19 Technological innovation Pandemics

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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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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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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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Parallel Multithreaded Medical Images Filtering

Parallel Multithreaded Medical Images Filtering

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Veska Gancheva Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781665458429

The quality of medical images is paramount. Being of high grade, it guarantees the quality of medical diagnosis, treatment and quality of patient’s life through the means of health care or using automate intelligent systems for medical diagnosing, treatment and monitoring. The paper presents the computational challenges in medical images processing. The great challenges are to propose parallel computational models and parallel program implementations based on the algorithms for medical images filtering. Parallel computational model based on two-dimensional filters is designed. The proposed parallel model is verified by multithreaded parallel program implementation. An investigation of the efficiency of medical images filters based on parallel multithreaded program implementation, applying two-dimensional filters on a given list of compressed jpeg medical images and generating output jpeg images for each type of applied filter. The applied filters are Brightness Control, horizontal and vertical filter of Sobel, Laplace and Blur. A number of experiments have been carried out for the case of dataset consisted of 162 whole mount slide images of Breast Cancer (BCa) specimens scanned at 40x and various number of threads. Parallel performance parameters execution time and speedup are estimated experimentally. The performance estimation and scalability analyses show that the suggested model has good scalability.

关键词： Scientific computing Computational modeling Scalability Instruction sets Filtering algorithms Image filtering Medical diagnosis

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Runtime Verification of Autonomous Systems Utilizing Digital Twins as a Service

Runtime Verification of Autonomous Systems Utilizing Digital...

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Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), IEEE International Conference on

作者： Morten Haahr Kristensen Alberto Bonizzi Cláudio Gomes Simon Thrane Hansen Carlos Isasa Hannes Iven Eduard Kamburjan Peter Gorm Larsen Martin Leucker Prasad Talasila Valdemar Trøjgård Tang Stefano Tonetta Lars B. Vosteen Thomas Wright Department of Electrical and Computer Engineering Aarhus University Denmark Fondazione Bruno Kessler Italy Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany Department of Informatics University of Oslo Norway

ISBN: (数字)9798350389760

ISBN: (纸本)9798350389777

Autonomous Systems (AS) enable systems to adapt to drastic and unprecedented environmental changes, a capability that can be enhanced through the utilization of Digital Twins (DTs). However, the additional capabilities of AS come at the cost of explainability, as the expanding adaptation space complicates the reasoning about the system's behavior. For certain types of systems, it is crucial to ensure that specific properties are upheld despite the system's autonomous behavior. To facilitate the monitoring of these properties, we propose the use of Runtime Verification (RV). This tutorial demonstrates the integration of RV tools into the Digital Twins as a Service (DTaaS) platform to monitor and verify the behavior of AS in real-time. By exploring various methods to incorporate RV tools within a DT context, the tutorial aims to advance the application of RV technologies in autonomic computing and self-adaptive system design. Specifically, we demonstrate how the behavior of a self-configuring DT can be verified utilizing RV. This is accomplished through the DTaaS platform, which supports seamless deployment of DT-based AS.

关键词： Runtime Costs Autonomous systems Tutorials Real-time systems Cognition Digital twins Distributed computing Monitoring System analysis and design

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