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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Chen, Jianwen Tsai, Meng-Shiun Lin, Wan-Ju Hung, Che-Lun Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Mechanical Engineering National Taiwan University Taipei106319 Taiwan Department of Mold and Die Engineering National Kaohsiung University of Science and Technology Kaohsung807618 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan

ISBN: (纸本)9798400716416

Automated tool wear condition monitoring is essential for ensuring product quality. However, achieving both high detection precision and utilizing edge computing are critical for advancing intelligent manufacturing. Consequently, this article develops a tool wear detection system utilizing edge computing techniques. Firstly, a novel end-to-end ensemble UNet-based model is proposed. Through end-to-end feature fusion training, it effectively addresses the robustness and accuracy issues inherent in single models. Secondly, multiple neural computing sticks are involved to reduce the need for cloud transmission and preserve data privacy, thereby enabling scalability and flexibility in deployment. Experiments demonstrate that mean intersection over union (mIoU) and mean dice coefficient (mDice) in tool wear detection can be improved by over 3% and 2%, respectively. Furthermore, the execution time can be accelerated by a factor of 4. © 2024 ACM.

关键词： Smart manufacturing

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Tele-Rheumatology-Advanced Neural Network Model for Remote Assessment and Management of Rheumatic Conditions 10

Tele-Rheumatology-Advanced Neural Network Model for Remote A...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Aghalya, S. Sarasu, R. Mishra, Nilamadhab Arunachalam, G. Gobinath, D. Naganathan, Suresh Babu Thandullu St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Spatial Informatics Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority. © 2024 IEEE.

关键词： Neural network models

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Comparative Analysis of the Use of Variational Autoencoders and Generative Adversarial Networks for Anomalous Image Detection Tasks 7

Comparative Analysis of the Use of Variational Autoencoders ...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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Interdependency patterns of potential seaport risk factors in relation to supply chain disruption in Indonesia

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Journal of Shipping and Trade 2023年第1期8卷 6页

作者： Do. Bagus, Muhammad Reza Hanaoka, Shinya Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152-8550 Japan Department of Information System College of Informatics and Computer Management Tidore Mandiri (STMIK Tidore Mandiri) Tidore Indonesia

As an integral part of the global supply chain network, Indonesian supply chain entities should understand conditional seaport risk factors that could lead to seaport threats that affect supply chain continuity. This study aims to provide a procedure for evaluating the interdependencies, implications, and correlations among various seaport risk factors for supply chain threats, specifically by investigating current practices in the developed economic region of Indonesian seaport operations. The study uses a rough set method to solve feature selection problems and multivariate analysis of variance to assess the correlation between dependent and independent variables. We find 39 conditional seaport risk factors that are potentially influenced by about 21 dependent factors related to seaport-fulcrum supply chain entities. Furthermore, threats from the planning process, infrastructure, seaport service process, distribution process, financial costs of nuclear enterprises and security existed and affluent highest potential risk in Indonesia. © 2023, The Author(s).

关键词： Association rule mining Genetic algorithm Interdependency risk analysis Rule rough set theory Seaport risk factors

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Human Activity Recognition in Smart Cities from Smart Watch Data using LSTM Recurrent Neural Networks 1

Human Activity Recognition in Smart Cities from Smart Watch ...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kandpal, Meenakshi Sharma, Bhisham Barik, Rabindra K. Chowdhury, Subrata Patra, Sudhansu Shekhar Dhaou, Imed Ben KIIT Deemed to Be University School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India KIIT Deemed to Be University School of Computer Applications Bhubaneswar India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because to integration. The advent of smart watch technology and deep learning techniques has engendered a potential increase in performance and accuracy of such tasks. Our paper explores the application of recurrent neural networks particularly LSTM for action recognition task using smart watch sensor data, i.e., gyroscope and accelerometer data. We have taken into consideration some general activities like standing, walking, sitting, etc. and implemented a LSTM network for the action recognition job using the time sequences sensor data. This model can be useful for other health-based applications which can monitor the health conditions of a user by keeping record of his activities. © 2023 IEEE.

关键词： Watches

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Two Layer Symmetric Cryptography Algorithm for Protecting Data from Attacks

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computers, Materials & Continua 2023年第2期74卷 2625-2640页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Shahab SBand Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathilticsSlovak University of Technology in BratislavaSlovakia

Many organizations have insisted on protecting the cloud server from the outside,although the risks of attacking the cloud server are mostly from the *** are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server ***,the attackers have designed even better mechanisms to break these security *** cryptography is the best data protection algorithm that exchanges data between authentic *** this article,one symmetric cryptography algorithm will be designed to secure cloud server data,used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure ***,the XOR function will be applied to plain text,and then salt technique will be ***,a reversing mechanism will be implemented on that data to provide more data *** decrypt data,the cipher text will be reversed,salt will be removed,andXORwill be *** the end of the paper,the proposed algorithm will be compared with other algorithms,and it will conclude how much better the existing algorithm is than other algorithms.

关键词： Cryptography symmetric algorithm encryption decryption cipher text cloud security asymmetric algorithm

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Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

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computers, Materials & Continua 2023年第2期74卷 2641-2659页

作者： Muhammad Nadeem Ali Arshad Saman Riaz SyedaWajiha Zahra Muhammad Rashid Shahab S.Band Amir Mosavi Department of Computer Science Abasyn UniversityIslamabad44000Pakistan Department of Computer Science National University of TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliuYunlin64002Taiwan Institute of Information Society University of Public ServiceBudapest1083Hungary John von Neumann Faculty of Informatics Obuda UniversityBudapestHungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaSlovakia

Cloud computing is one of the most attractive and cost-saving models,which provides online services to *** computing allows the user to access data directly from any *** nowadays,cloud security is one of the biggest issues that *** types of malware are wreaking havoc on the *** on the cloud server are happening from both internal and external *** paper has developed a tool to prevent the cloud server from spamming *** an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)*** will block those IP addresses that the attacker will use and prevent spamming ***,the KNN classifiers will determine which area the spammer belongs *** the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

关键词： Intrusion prevention system spamming KNN classification spam cyber security botnet

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Optimization Algorithm for Estimating the Velocity Characteristics of Complex Medium

Optimization Algorithm for Estimating the Velocity Character...

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2022 IEEE International Multi-Conference on engineering, computer and Information Sciences, SIBIRCON 2022

作者： Pinigina, Daria L. Oksana, Kopylova A. Novosibirsk State Technical University Department of Computer Engineering Novosibirsk Russia Laboratory of Geophysical Informatics Department of Computer Engineering Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9781665464802

An optimization computational grid algorithm and the results of its application for estimating the velocity characteristics of complex medium based on experimentally obtained seismic wave arrival times are presented. An increase in the resolution of the algorithm for detecting inhomogeneities is achieved by adaptively selecting the grid step in areas with a pronounced heterogeneity of the medium structure. The algorithm automatically identifies zones of inhomogeneity, taking into account non-linear variations in the hodograph of seismic waves. The restriction on the minimum allowable grid step is deter-mined taking into account the upper limiting frequency of sounding oscillations generated by the vibration source and the number of intersections of seismic wave fronts at the grid nodes. The proposed algorithm has been tested on experimental data obtained from vibration sounding of mud volcano zones and the Baikal Rift Zone (BRZ). The consistency of the reconstructed theoretical velocity hodograph with the geological structure of the heterogeneity of the structure of medium in the areas of field works is shown. © 2022 IEEE.

关键词： Seismic waves

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MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems 32

MAX CUT in Weighted Random Intersection Graphs and Discrepan...

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772143

Let V be a set of n vertices, M a set of m labels, and let R be an m × n matrix of independent Bernoulli random variables with probability of success p;columns of R are incidence vectors of label sets assigned to vertices. A random instance G(V, E, RT R) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely [RT R]v,u) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given G(V, E, RT R) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of G(V, E, RT R) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, m = nα, α Θ(1)n), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in G(V, E, RT R) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system Σ with incidence matrix R (i.e. minimum imbalance over all sets in Σ). We exploit this connection by proposing a (weak) bipartization algorithm for the case m = n, p = Θ(1)n that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in a set system with incidence matrix R. In fact, with high probability, the latter 2-coloring corresponds to a bipartition with maximum cut-weight in G(V, E, RT R). Finally, we prove that our (weak) bipartization algorithm terminates in polynomial time, with high probability, at least w

关键词： Graph theory

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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