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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Chen, Chen-Sheng Chen, Jian-An Yeh, Shih-Hsien Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu Taiwan Chang Gung University Department of Computer Science and Information Engineering Taoyuan Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system

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Streamlining Attendance with Voice Recognition via Gaussian Mixture Model

Streamlining Attendance with Voice Recognition via Gaussian ...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ali, Noor Ali, Khadija Ali, Fatimah Ali, Aaminah Ali, Nisar Ali, Raja Hashim A-levels Lahore Grammer School 55 Main Gulberg Lahore54660 Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad43701 Pakistan Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GIK Institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an AI-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study. © 2024 IEEE.

关键词： Students

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming *** surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement *** proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential *** achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training *** approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical *** study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured ***,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally *** approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Attention-Guided Deep Learning Framework For Movement Quality Assessment 48

Attention-Guided Deep Learning Framework For Movement Qualit...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kanade, Aditya Sharma, Mansi Muniyandi, Manivannan Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India Indian Institute of Technology Madras Department of Applied Mechanics India

ISBN: (纸本)9781728163277

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores. © 2023 IEEE.

关键词： attention mechanism deep learning Movement Quality Assessment performance score generation transformer

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Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 224-236页

作者： Liu, Guannan Xie, Rende Fang, Shih-Hau Wu, Hsiao-Chun Yan, Kun San Jose State University Department of Applied Data Science San JoseCA95112-3613 United States National Taiwan Normal University Department of Electrical Engineering Taipei106 Taiwan Yuan Ze University Department of Electrical Engineering Taoyuan320315 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology School of Information and Communication Guilin541004 China

Automatic human-posture or human-activity recognition is a very important research problem nowadays. In this work, we propose a novel human-posture recognition approach using the 3-D skeletal data acquired by the Kinect V2 sensor. The acquired skeletal data are first segmented using our recently proposed automatic-segmentation technique and each segment can be labeled with a particular kind of human-posture. We propose four different types of node feature matrices extracted from the segmented skeletal data, which can serve as the input features to the advanced graph convolutional network for multiclassification. The realworld experimental results demonstrate that our proposed novel human-posture recognition system can reach a very high average classification-accuracy of 91.56%. In addition, the ablation study of the effect of skeletal-graph variations on the recognition performance is also presented. The average classification-accuracy further reaches up to 92.33% when four confusing joint-nodes are removed from the skeletal graph. Our proposed novel human-posture recognition approach can be very useful for practical applications, such as human-computer interface, intelligent healthcare, robotics, etc. © 2024 IEEE.

关键词： Sensor nodes

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A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

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Journal of Bionic engineering 2023年第4期20卷 1828-1851页

作者： Serdar Ekinci Davut Izci Laith Abualigah Raed Abu Zitar Department of Computer Engineering Batman University72100BatmanTurkey Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi MinhVietnam Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800PenangPulau PinangMalaysia Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates

In this work,we propose a real proportional-integral-derivative plus second-order derivative(PIDD2)controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient *** this regard,this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective *** construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning *** also propose a simple yet effective objective function to increase the performance of the proposed algorithm(CmOBL-AO)to adjust the real PIDD2 controller's parameters *** show the CmOBL-AO algorithm to perform better than the differential evolution algorithm,gravitational search algorithm,African vultures optimization,and the Aquila Optimizer using well-known unimodal,multimodal benchmark ***2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO *** the vehicle cruise control system,we confirm the more excellent performance of the proposed method against particle swarm,gray wolf,salp swarm,and original Aquila optimizers using statistical,Wilcoxon signed-rank,time response,robustness,and disturbance rejection *** also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider *** excellent performance of the proposed method is also illustrated through different quality indicators and different operating ***,we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic *** show

关键词： Aquila optimizer Chaotic local search Modified opposition-based learning Real PIDD^(2)controller Vehicle cruise control system Bionic engineering

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An Integration of Wireless Communications and Artificial Intelligence for Autonomous Vehicles for the Successful Communication to Achieve the Destination 3

An Integration of Wireless Communications and Artificial Int...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bhardwaj, Ankit Pattnayak, Jui Prasad Gangodkar, Durga Rana, Arti Shilpa, N. Tiwari, Prashant University of Connecticut The Math Company Department of Computer Science and Engineering StorrsCT United States JIS College of Engineering Computer Science and Engineering India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India SR University Department of Electronics and Communication Engineering Telangana Warangal India GLA University Institute of Business Management Mathura India

ISBN: (纸本)9798350399264

Multiple smart agents interacting with one another is a fundamental component of the IoT, sometimes referred to as social IoT, is the Internet of Things. such knowledgeable beings, like autonomous vehicles and robots, are furnished with powerful machine learning for mobile operation. Although it should go without saying that wireless networking is crucial in these situations, there aren't enough studies on the subject to offer a comprehensive knowledge of multi-Agent networks that operate wirelessly. We examine how wireless communications and multi-Agent systems interact in this essay interact while using disruptive reinforcement learning to model each artificial intelligence agent. The illustrative system is served by autonomous vehicles that drive across Manhattan's streets. As a result of receiving obtaining data via wireless transmission from other actors, the necessity to adapt policy exchange reinforcement learning is the first unique discovery. It is easy to see the benefit of using wireless communication. We also show how communication mistakes might negatively affect system performance. To draw an initial conclusion in favor of adopting small cell infrastructure, Direct vehicle-To-vehicle and vehicle-To-infrastructure communication is provided by multi-Agent systems in contrasted. Finally, using real-Time ALOHA, we investigate Over multi-Agent systems, multiple access communication is possible. Instant ALOHA rejects the retransmission mechanism, in contrast to conventional to assure timely wireless communication contributions to a multi-Agent system's learning process and acceptable performance, one should consider reliable packet delivery via re-Transmit after collisions. © 2023 IEEE.

关键词： Multi agent systems

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Education 4.0: Considering the Development of Teachers’ Psychological Dispositions Towards the Use of ICT with Kindergarten Children 14th

Education 4.0: Considering the Development of Teachers’ P...

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14th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL 2024

作者： Peretti, Sara Caruso, Federica Pino, Maria Chiara Di Mascio, Tania Centre of Excellence DEWS University of L’Aquila L’Aquila Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Department of Biotechnological and Applied Clinical Sciences L’Aquila Italy

ISBN: (纸本)9783031855603

Since 2020, the COVID-19 pandemic has forced teachers to rapidly adopt Information and Communication Technology (ICT) tools for distance learning, transforming education into an all-digital environment. This shift posed significant challenges, especially for kindergarten teachers accustomed to traditional, face-to-face methods. These changes will have long-term impacts on education, influencing teachers’ attitudes and beliefs, which take time to stabilize and evaluate. The present paper addresses this by posing the question: In 2024, four years after the COVID-19 pandemic, what attitudes have kindergarten teachers developed towards ICT integration in the classroom? For this purpose, the standardized "Intrapersonal Technology Integration Scale" was chosen to assess the beliefs of Italian kindergarten teachers. The study includes 49 kindergarten teachers, all women. Data analysis showed that they have a strong interest in using ICT, indicating a need for ongoing support and training initiatives. However, teachers did not report feeling self-efficacy in using ICT with children in the classroom. This finding is crucial as it can influence their expectations and motivation towards integrating these technologies into daily teaching activities. In conclusion, this study highlights the critical importance of teachers’ attitudes in shaping future learning environments within the Education 4.0 era and provides suggestions to maximize ICT integration starting from early education to better meet the needs of future citizens. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Teaching

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Student Perceptions of AI-Enhanced Adaptive Learning Systems: A Pilot Survey 7

Student Perceptions of AI-Enhanced Adaptive Learning Systems...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Katonane Gyonyoru, Klara Ida Katona, Jozsef Institute of Social Sciences University of Dunaujvaros Department of Organizational Development and Communication Science Dunaujvaros Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring the Growth of Blockchain Literature: Insights from 2016 to 2024

Exploring the Growth of Blockchain Literature: Insights from...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Amit Sharma, Neha Chauhan, Rahul Joshi, Kireet Chythanya, Kanegonda Ravi Ismail Iqbal, Mohammed Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Graphic Era Hill University Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Computer Science & Engineering Uttarakhand Dehradun248002 India SR University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences Petroleum Engineering Department Nizwa Oman

ISBN: (纸本)9798350394412

This bibliographic analysis research addresses the evolving landscape of blockchain research from 2016 to 2024. The introduction identifies the problem of understanding the current state and trends of blockchain literature, given the technology's rapid growth and diverse applications. Research gaps are identified in terms of understanding productivity, collaboration patterns, and emerging topics within the field. To address these gaps, the study conducts a comprehensive analysis using bibliometric techniques on data extracted from the Scopus database. The analysis reveals that China, the USA, and India emerge as the top three contributors, collectively accounting for 68% of the total publications and demonstrating substantial productivity and collaboration within the research community. Moreover, the study highlights the increasing focus on topics such as smart contracts (15%), digital storage (12%), and consortium blockchains (14%), indicating emerging areas of interest and research priorities. Security-related concerns remain prevalent, as evidenced by the frequent co-occurrence of keywords related to network security and cryptography. Additionally, the analysis underscores the diverse range of applications and implications of blockchain technology, spanning industries such as finance, supply chain management, and healthcare. By synthesizing these findings, the study contributes to a deeper understanding of the evolving landscape of blockchain research and provides insights for future research directions and collaborations. © 2024 IEEE.

关键词： Smart contract

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