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15th International Conference on Knowledge and Systems Engineering, KSE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Hao-Wei Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu City Taiwan Ph.D. Program in Engineering Science Chung Hua University Hsinchu City Taiwan Bachelor Program in Applied Foreign Language and Smart Commerce Chung Hua University Hsinchu City Taiwan

ISBN: (纸本)9798350329742

This paper proposes a fitness movement evaluation system using deep learning. The system uses a deep convolutional neural network (CNN) to extract features from pictures of fitness movements. The features are then used to classify the movements into different categories. The system is evaluated on a dataset of pictures of fitness movements. The results show that the system can accurately classify the movements into different categories. The system is designed to provide feedback to users on their fitness movements. The proposed system is a valuable tool for fitness enthusiasts. The main contribution of this paper is to propose a way to give users a score for their fitness movement. It can help users improve their fitness and track their progress over time. The system is also a valuable tool for fitness professionals. It can help professionals develop new fitness programs and provide feedback to their clients. © 2023 IEEE.

关键词： Convolutional neural networks

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An Application-Aware Clustering Strategy in Vehicular Ad Hoc Network 6

An Application-Aware Clustering Strategy in Vehicular Ad Hoc...

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6th International Conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Wei, Chi-Hung Chang, Chin-Chih Chu, Chien-Hao Hsiao, Sean Chung Hua University Ph.D. Program in Engineering Science Hsinchu City Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu City Taiwan Chung Hua University Bachelor Program in Applied Foreign Language and Smart Commerce Hsinchu City Taiwan

ISBN: (纸本)9798350340921

VANET (Vehicular Ad Hoc Network) was developed to address the unique challenges of communication in a mobile vehicular environment. This technology can enhance driving safety and traffic control. Moreover, similar principles can be applied to unmanned aerial vehicles and robotics. When transmitting data in moving vehicles, it is challenging as access points and base stations keep changing, requiring handovers between different access points and base stations. Clustering is a popular approach used to address this challenge. Common clustering strategies are based on the proximity, communication range, the density, the application requirements of the moving vehicles, or hybrid of them. In this paper, we propose a clustering strategy based on the application sublayer in the vehicle communication protocol. If two computing devices on the move run the same application, they can be grouped together. The contribution of this research is to develop a clustering mechanism based on the application with very low overhead. We also validate the proposed method through simulations. The simulation results indicate that the proposed method is feasible. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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A system for detecting scratches on the surface of small-sized metal shafts after the grinding process using optical techniques combined with computer vision.

A system for detecting scratches on the surface of small-siz...

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International Symposium on applied science 2024, ISAS 2024

作者： Dau, Sy Hieu An, Khang Le Nguyen Thi, Phuc Dang Department of Applied Physics Applied Sciences Faculty Ho Chi Minh city University of Technology Vietnam National University HCMC Ho Chi Minh City Viet Nam Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

During the grinding process of metal machine parts requiring a high degree of smoothness, the use of diamond-surfaced grinding tools, specialized grinding stones, or high-hardness metals are common choices. However, during grinding, surface tool damage or misalignment between grinding surfaces can cause defects such as cracks, scratches, or complete surface destruction. Detecting small scratches is one of the biggest challenges due to their size and distribution, making it difficult to quickly identify them using the human eye. Automated systems, on the other hand, struggle to highlight scratches because automatic optical lighting systems face strong reflections from the metal surface, especially cylindrical surfaces after being polished. As a result, the images obtained lack controlled reflection positions and contrast between normal and scratched areas, leading to inefficiency in detecting these scratches. In this study, we propose an optical system combined with lighting techniques to highlight scratches on the surface of a cylindrical machine part after polishing, thereby detecting and classifying these defects to make pass-fail decisions in practical production line applications. © 2025 Institute of physics Publishing. All rights reserved.

关键词： Grinding (machining)

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Measuring Electromagnetic Performance and Configuration of Networks in High Interference Environments 15

Measuring Electromagnetic Performance and Configuration of N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Khan, A.Z. Panwar, Neeraj Rayaguru, N.K. Shelke, Priya M. Giri, Rakesh Kumar Jaishankar, P. Yeshwantrao Chavan College of Engineering Applied Physics Department Nagpur India University Gehu Dehradun Department of School of Computing India Vel Tech Rangarajan Dr sagunthala R & D Institute of Science and Technology Department of Eee India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Sunrise University Department of Computer Science & Engineering Alwar India Sastra Deemed to be University Department of School of Civil Engineering India

ISBN: (纸本)9798350370249

Measuring the electromagnetic overall performance and configuration of networks in high-interference environments can be challenging. In those environments, the indicators of hobby can be infected with unwanted heritage indicators, i.e., noise, leading to degraded overall performance and misguided size readings. This paper investigates the most reliable techniques for measuring networks' electromagnetic performance and configuration in these problematic environments, utilizing introducing a unique multistage framework that combines sign-processing strategies with some community parameters. In particular, the framework includes four degrees: sign reception, sensitivity analysis, pass-correlation, and community configuration optimization-an analysis of the results of varying environmental conditions on sign reception and sensitivity change into conduct. A cross-correlation approach has evolved to decide the extent of correlation among one-of-a-kind alerts and to locate the specific configurations of the particular networks. Sooner or later, an optimization set of rules was carried out to locate the network's optimum configuration. The final result of this research is a reliable method for measuring the electromagnetic performance and configuration of networks in high-interference environments. The proposed technique is anticipated to help researchers and engineers better understand the electromagnetic behaviors of complicated networks in actual-world scenarios. © 2024 IEEE.

关键词： Configuration Electromagnetic Environmental Performance Technique

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High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

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Geoscience Frontiers 2022年第1期13卷 364-373页

作者： Guocheng Hao Juan Guo Wei Zhang Yunliang Chen David AYuen School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Department of Mathematics Duke UniversityDurhamNC 27708USA Department of Applied Physics and Applied Mathematics Columbia UniversityNew YorkNY 10027USA

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and *** change tendency may be related to the occurrence of earthquake *** of the underlying intensity trend plays an important role in the analysis of data and disaster *** chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field *** main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field *** verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data *** to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function ***,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.

关键词： Earth’s natural pulse electromagnetic field Chaos theory Radial Basis Function neural network Forecasting model

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Machine learning sparse tight-binding parameters for defects

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npj Computational Materials 2022年第1期8卷 1081-1091页

作者： Christoph Schattauer Milica Todorović Kunal Ghosh Patrick Rinke Florian Libisch Institute for Theoretical Physics TU Wien1040ViennaAustria Department of Applied Physics Aalto UniversityP.O.Box 11100AaltoFI-00076Finland Department of Mechanical and Materials Engineering University of TurkuFI-20014TurkuFinland Department of Information and Computer Science Aalto UniversityP.O.Box 15400AaltoFI-00076Finland

We employ machine learning to derive tight-binding parametrizations for the electronic structure of *** test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding *** Multi-layer perceptrons(i.e.,feed-forward neural networks)perform best we adopt them for our further *** demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure,local density of states,transport and level spacing simulations for two common defects in single layer *** machine learning approach achieves results comparable to maximally localized Wannier functions(i.e.,DFT accuracy)without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation *** is general and can be applied to a wide range of other materials,enabling accurate large-scale simulations of material properties in the presence of different defects.

关键词： structure electronic spacing

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A hybrid optimization approach for securing cloud-based e-health systems

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Multimedia Tools and Applications 2024年第16期84卷 1-36页

作者： Altherwi, Abdulhadi Ahmad, Mohammad Tauheed Alam, Md. Moddassir Mirza, Hidayath Sultana, Nazia Pasha, Amjad Ali Sultana, Nishat Khan, Asif Irshad Alam, Md Mottahir Azim, Rezaul Department of Industrial Engineering College of Engineering and Computer Science Jazan University PO Box. 706 Jazan45142 Saudi Arabia College of Medicine King Khalid University Abha Saudi Arabia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering and Computer Science Jazan University Jazan45142 Saudi Arabia Government Medical College Siddipet Ensanpalli Siddipet District Telangana502114 India Aerospace Engineering Department King Abdulaziz University Jeddah21589 Saudi Arabia Department of Business Administration Applied College Jazan University P. O. Box 706 Jazan45142 Saudi Arabia Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz Jeddah21589 Saudi Arabia Department of Physics University of Chittagong Chattogram4331 Bangladesh

In modern healthcare, cloud-based e-health technology offers substantial benefits but faces significant security challenges. Sensitive patient data is vulnerable to cyber threats during transmission and storage, potentially enabling unethical collaboration between healthcare professionals and cloud storage providers. This paper aims to develop a robust security framework that safeguards patient privacy and prevents unauthorized access to sensitive medical information in remote patient health monitoring systems. The study presents an Artificial Hybrid Optimization with an Attribute-Based Encryption (AHO-ABE) model. The model leverages cryptographic techniques and hybrid optimization to enhance data security. Specifically, whale optimization (WO) is used for efficient key generation, while sheep flock optimization (SFO) is employed for continuous network surveillance. Experimental results demonstrate that the AHO-ABE model significantly enhances data security, outperforming existing RPM-MEC, AID, AI-HCS, and IoT-HMS approaches. The model exhibits superior resilience against unauthorized access and data breaches, achieving a 5 ms execution time, 850 kbps throughput, 0.121 mJ energy usage, and 4 s latency. Combining cryptographic techniques with hybrid optimization algorithms, the AHO-ABE model provides a robust framework for safeguarding sensitive medical information. This approach ensures the integrity and confidentiality of patient data in remote healthcare settings, offering an effective solution against high-performance cyber attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Control of Magnetic Skyrmions in an Exchange Biased van der Waals Ferromagnet

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1710-1711页

作者： McCray, Arthur R C Lebedev, Dmitry Arpaci, Sevdenur Te Velthuis, Suzanne G E Lopez-Dominguez, Victor Amiri, Pedram Khalili Hersam, Mark C. Petford-Long, Amanda K. Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program Northwestern University Evanston IL United States Department of Materials Science and Engineering Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

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MACAIF: Machine Learning Auditing for Clinical AI Fairness 23

MACAIF: Machine Learning Auditing for Clinical AI Fairness

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1st International Symposium on Trustworthy Autonomous Systems, TAS 2023

作者： Barnard, Pepita Bautista, John Robert Krook, Joshua Liu, Anqi Menéndez, Héctor Schmidt, Aurora Sookoor, Tamim University of Nottingham School of Computer Science Nottingham United Kingdom University of Texas at Austin School of Information Austin United States University of Southampton School of Electronics and Computer Science Southampton United Kingdom Johns Hopkins University Department of Computer Science Baltimore United States King's College London Department of Informatics London United Kingdom Johns Hopkins University Applied Physics Lab LaurelMD United States

ISBN: (纸本)9798400707346

Artificial intelligence in the form of machine learning algorithms is driving the latest industrial revolution, leading to disruptive changes in the ways we communicate, interact, design, collect information, and express ourselves. While these changes offer new possibilities for our societies, they may also introduce biases that can lead to unfair decisions. This issue is particularly critical in the context of medical diagnosis, as bias can jeopardize patient treatment and health. To mitigate these biases, it is essential to such biases and involve all relevant stakeholders in the design of fair machine learning algorithms. In this context, the MACAIF project aims to develop user-centred interfaces that allow stakeholders, including doctors, to challenge the fairness of machine learning algorithms based on demographics, such as gender or race. Our project proposes a methodology to engage with stakeholders and incorporate their concerns during the design of a dashboard based on MLighter - an adversarial tool which is applied to identify fairness-related issues in machine learning models. © 2023 Owner/Author.

关键词： Patient treatment

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