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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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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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Traffic and road sign recognition using deep convolutional neural network

Traffic and road sign recognition using deep convolutional n...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Kuchkorov, Temurbek Khamzaev, Jamshid Allamuratova, Zamira Ochilov, Temur Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Department of Computer Systems Tashkent Uzbekistan Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Joint Faculty TUIT-BSUIR Department of Information-computer Technologies and Programming Tashkent Uzbekistan

ISBN: (纸本)9781665432580

Automatic road sign recognition is one of the most important steps to help a driver prevent accidents. In this paper, the deep convolutional neural network (Deep CNN) was used for the autonomous traffic and road signs detection and recognition system. The proposed system works in real-time recognition of the image of road signs. The contribution of this article is that it consists of a newly developed datasets for 42 different road signs using combination of existing datasets and collected local road signs. The images were taken from different angles and included other parameters and conditions. More than 30K images were collected to create the dataset, which is named Uzbekistan traffic and road signs (UZ-TRS-2021). The CNN architecture was used with various parameters to achieve the best recognition speed. Experimental results show that the proposed CNN architecture achieved 98% accuracy. © 2021 IEEE.

关键词： Convolutional neural networks

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Social Engineering Attacks Detection Approach

Social Engineering Attacks Detection Approach

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

作者： Sergii Lysenko Volodymyr Vorobiov Oleksandr Bokhonko Piotr Gaj Oleg Savenko Jacek Wołoszyn Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy.

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An EOQ Model with Breakable Items Considering Stock Dependent Demand and Lead Time Dependent Credit Period

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International Journal of Applied and Computational Mathematics 2021年第6期7卷 1-26页

作者： Mallick, Rabin Kumar Patra, Kartik Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore 721 102 W.B. India Department of Mathematics Department of Mathematics Vivekananda Satavarshiki Mahavidyalaya Manikpara Paschim Medinipur 721513 W.B. India

In this paper, an EOQ model for breakable items to a retailer has been studied. Here, demand of the business is stock dependent. The breakability of the items is dependent on stock and holding cost. Here, a credit period has been offered to the retailer, which depends on lead time. Stock dependent demand and breakability are balanced by the lead time dependent credit period. The main intention of the present model is to detect the optimal order quantity, optimal lead time and to maximize the total profit of the whole business. To test our proposed model, two numerical examples are also illustrated. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Breakability Credit period Lead time Supply chain

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Comparative Analysis of Minimum Chip Thickness, Surface Quality and Burr Formation in Micro-Milling of Wrought and Selective Laser Melted Ti64

SSRN

引用

SSRN 2023年

作者： Yalçın, Bekir Karakılınç, Uçan Ergene, Berkay Aslantaş, Kubilay Isparta University of Applied Sciences Department of Computer Programming Isparta32200 Turkey Pamukkale University Department of Mechanical Engineering Denizli20160 Turkey Afyon Kocatepe University Department of Mechanical Engineering Afyonkarahisar 03200 Turkey

Selective laser melting (SLM) is a three dimensional (3D) printing process which can manufacture functional parts with complex geometries as an alternative to using traditional processes (i.e. via machining of wrought metal). The fabricated parts can be then machined if precision and high surface finish are required, particularly when miniature channels/geometries smaller than 1 mm are to be made on the part. Therefore, micro milling has a significant role in the production of such miniscule geometries. In this experimental effort, the micro machinability of Ti-6Al-4V (Ti64) parts produced via SLM compared with wrought Ti64. The aim is to study the effect of micro milling parameters: feeds per tooth of 0.05, 0.1, 0.25, 0.5, 1, 2, 5 and 10 μm/tooth, cutting depths of 25, 50, 100 and 200 μm and spindle speeds of 5000, 10000, 20000 and 50000 rpm © 2023, The Authors. All rights reserved.

关键词： Surface properties

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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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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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Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers

arXiv

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arXiv 2022年

作者： Daggitt, Matthew L. Kokke, Wen Atkey, Robert Arnaboldi, Luca Komendantskya, Ekaterina Department of Computer Science Heriot-Watt University Edinburgh United Kingdom Mathematically Structured Programming Group University of Strathclyde Glasgow United Kingdom Laboratory for Foundations of Computer Science University of Edinburgh Edinburgh United Kingdom

Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of networks with hundreds of thousands of nodes. In theory this opens the door to the verification of larger control systems that make use of neural network components. However, although work has managed to incorporate the results of these verifiers to prove larger properties of individual systems, there is currently no general methodology for bridging the gap between verifiers and interactive theorem provers (ITPs). In this paper we present Vehicle, our solution to this problem. Vehicle is equipped with an expressive domain specific language for stating neural network specifications which can be compiled to both verifiers and ITPs. It overcomes previous issues with maintainability and scalability in similar ITP formalisations by using a standard ONNX file as the single canonical representation of the network. We demonstrate its utility by using it to connect the neural network verifier Marabou to Agda and then formally verifying that a car steered by a neural network never leaves the road, even in the face of an unpredictable cross wind and imperfect sensors. The network has over 20,000 nodes, and therefore this proof represents an improvement of 3 orders of magnitude over prior proofs about neural network enhanced systems in ITPs. © 2022, CC BY.

关键词： Problem oriented languages

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A recommender system for WordPress themes using item-based collaborative filtering technique 29

A recommender system for WordPress themes using item-based c...

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29th International Scientific Conference Electronics, ET 2020

作者： Ujkani, Berat Minkovska, Daniela Stoyanova, Lyudmila Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9781728174266

Recommender systems allow making personalized recommendations for items or products while browsing online, using numerous algorithms and techniques to predict and recommend potentially useful items to a specific user. This paper presents a system built for the purpose of filtering out WordPress themes trough the ratings or reviews of other similar users using item-based collaborative filtering technique and provide user-specific recommendations for the largest online marketplace of web templates called Themeforest, part of Envato marketplaces. © 2020 IEEE.

关键词： Collaborative filtering

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