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共 41538 条记录，以下是101-110 订阅

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Enhancing Learning with Physiological Measures: A Systematic Review of applications in Neuroeducation 17

Enhancing Learning with Physiological Measures: A Systematic...

引用

17th international conference on computer Supported Education, CSEDU 2025

作者： Goussain, Blaha Gregory Correia dos Santos Moura, Roque Antônio de Luche, José Roberto Dale Andrade, Herlandí de Souza Silva, Messias Borges School of Engineering and Sciences Guaratinguetá São Paulo Brazil University of Tennessee Department of Industrial and Systems Engineering KnoxvilleTN United States School of Engineering of Lorena São Paulo Lorena Brazil

ISBN: (纸本)9789897587467

This systematic review explores the integration of neuroscience and education, focusing on physiological monitoring technologies such as Electrodermal Activity (EDA), Heart Rate (HR), and Skin Temperature (ST). These metrics, facilitated by wearable devices and machine learning models, provide real-time insights into student engagement, emotional states, and academic performance. The analysis synthesizes findings from recent studies, highlighting the transformative potential of physiological measures in creating adaptive, student-centered learning environments. The review examines the use of physiological monitoring in education for stress assessment, motivation enhancement, and academic performance optimization, while also addressing challenges in reliability, ethics, and implementation. By identifying existing gaps, it proposes directions for future research to refine these tools and promote their widespread adoption in educational contexts. These advancements underscore the role of physiological insights in fostering emotional well-being and optimizing teaching practices, marking a significant step toward evidence-based, neuroeducation-informed strategies. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Neurons

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Virtual Reality Meets AI: Revolutionizing physical Education with LiDAR and RL 8

Virtual Reality Meets AI: Revolutionizing Physical Education...

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Sareddy, Mohan Reddy Veerandra Kumar, R. Orasys LLC TX United States Saveetha Engineering College Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798331520762

physical education (PE) is changing as a result of the incorporation of cutting-edge technology like LiDAR, virtual reality (VR), and reinforcement learning (RL). The shortcomings of subjective evaluations and environmental restrictions in conventional physical education methods are addressed by these technologies, delivering accurate motion tracking, real-time feedback, and personalised *** a way to improve safety, engagement, and inclusion in physical education, it is necessary to improve motion capture accuracy, optimise abilities using RL, and offer immersive feedback through *** algorithms optimized movement patterns, LiDAR sensors captured 3D motion data, and VR settings provided realistic feedback. With biomechanical analysis to predict the likelihood of injuries and Kalman filtering for data smoothing, the necessary accurate real-time feedback on corrections was generated. This solution increased engagement by 25.8%, injury detection by 34.9%, feedback efficiency by 31.2%, and tracking accuracy by 27.2%. The technique emerged as a good option in motion analysis and augmentation for learning purposes, compared with conventional techniques and other highly advanced technologies. This LiDAR, RL, and VR integration redefine PE in such a way that promises real motion tracking, adaptive feedback and safety. It has got enormous potential for upgrading physical education methods with encouragement for variety and participation. © 2025 IEEE.

关键词： Reinforcement learning

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On the Challenge of Generating Multivariate Time Series Data from Distributed Sensors in IoT-enabled Scenarios 24

On the Challenge of Generating Multivariate Time Series Data...

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14th international conference on the Internet of Things, IoT 2024

作者： Bañuelos, Julián Jerónimo Costa-Requena, Jose He, Jiayuan Salim, Flora D. Sigg, Stephan Department of Information and Communications Engineering School of Electrical Engineering Aalto University Espoo Uusimaa Finland School of Computing Technologies RMIT University MelbourneVIC Australia Cumucore Oy Espoo Uusima Finland School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9798400712852

Smart sensing environments supported by an Internet of Things (IoT) infrastructure are enabling a wide array of possibilities for data-intensive remote monitoring applications. Although nowadays it is possible to access and deploy complex setups of sophisticated sensors, the telecommunications infrastructures are struggling to deal with the massive amounts of generated data for transmission, processing, and storage from the diverse range of IoT devices. As data corruption and the loss of data are realistic scenarios in current IoT deployments, it is relevant to find an alternative solution that circumvents the limitations of the physical infrastructure for dealing with potential missing data in remote sensing environments. In this paper, we analyze the feasibility of reproducing missing sensor data due to a communication failure from correlated sources in the same experimental context. We rely on generative adversarial models for the prediction of acceleration data and evaluate the usefulness of the synthetic data with a standard human activity recognition (HAR) classifier. © 2024 Copyright held by the owner/author(s).

关键词： Time series

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Mobile Edge Computing for Efficient Vehicle Management in Smart City 14

Mobile Edge Computing for Efficient Vehicle Management in Sm...

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14th IEEE international conference on Communication Systems and Network technologies, CSNT 2025

作者： Chanu, Asem Debala Shelar, Saylee Nath, Shubha Brata Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Engineering Ajeenkya DY Patil University Pune India

ISBN: (纸本)9798331531935

Mobile edge computing (MEC) promises to be a useful technology to enable applications in a smart city. However, the task offloading becomes challenging as the MEC infrastructure has limited resources. To solve the challenges of MEC, we have proposed a system architecture with an optimization algorithm to minimize the task delay in MEC. A Genetic Algorithm based task allocation algorithm finds the optimal set of edge servers for task deployment and processing. The proposed system efficiently optimizes the task delay in MEC as analyzed from the experiments performed by taking virtual machines as MEC servers. We show the usefulness of the proposed system by taking an application scenario for efficient vehicle management in a smart city. © 2025 IEEE.

关键词： Genetic algorithms

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Design Process and Development of Automation for Smart Buildings 1

Design Process and Development of Automation for Smart Build...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Sethi, Gunjan Khurana, Deepika Verma, Monika Pant, Surmadhur Chauhan, Priyanka Bhatt, Bhaskar Department of Computer Science and Engineering Delhi Technical Campus Uttar Pradesh Greater Noida India IIMT College of Engineering Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Shivalik College of Engineering Department of ECE Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Mechanical Engineering Dwarahat Almora India

ISBN: (纸本)9798331518578

In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial concerns, and market demands increase, traditional design methodologies are becoming less effective. Current framework's plan and activity have been totally changed by the presentation of automated smart building, which ensures expanded security, better inhabitant solace, and energy effectiveness. With an emphasis on MATLAB-based reproductions, this study offers a careful assessment of the plan interaction and improvement procedures for computerizing savvy structures. The study delineates how MATLAB makes it simpler to depict, reproduce, and investigate savvy building frameworks with certainty. Through the mix of numerous advancements, like sensors, regulators, and correspondence conventions, the research shows the turn of events and check of a mechanized smart building framework. The intelligent building's building automation system prioritizes a humanized interface and uses a combination of mechanical, electrical, and fire control systems, access control systems, and parking management systems to give building occupants a convenient and comfortable place to live and work. The discoveries show prominent additions in functional viability, natural adaptability, and energy utilization. The conventional heuristic and ad hoc design paradigms are becoming ineffective and inadequate for the future due to cost, the requirement to handle growing functional complexity, adaptability, supply chain fragmentation, and time-to-market pressure. © 2025 IEEE.

关键词： Smart homes

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Deep Learning Utility for Gas/Odor Sensor Signature Analysis 2

Deep Learning Utility for Gas/Odor Sensor Signature Analysis

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India

ISBN: (纸本)9798331505745

Gas/odor sensors are integral components of sensor systems used in diverse applications such as food quality control, environmental monitoring, medical diagnostics, odor profiling, industrial safety, agriculture, explosives detection, wine tasting, pharmaceutical quality control, and toxic gas detection. Traditional approaches in this domain of gas/odor sensing technologies face challenges related to accuracy, cross-sensitivity, and drifted real-time performance. The integration of deep learning techniques with gas sensors has shown promise in overcoming these limitations. Moreover, this amalgamation of gas sensing and deep learning strategies enables accurate identification of gases/odors, handling complex patterns, and leading optimal consumption of resources. In this paper, an overview of advancements in deep learning-enabled gas sensing has been presented that focused on methodologies, sensor types, and applications. This review also highlights the state-of-the-art techniques and identifies gaps for future directions for research. © 2025 IEEE.

关键词： Damage detection

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MRAM-Based physical Unclonable Function that is Resilient to Deep Learning Attacks 1

MRAM-Based Physical Unclonable Function that is Resilient to...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Venkadeshwaran, Kuthalingam Alam, Intekhab Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

Electronic devices are everywhere, so strong hardware security is needed to keep private data safe from dangers. A physical Unclonable Function (PUF) device constructed using magneto-resistive random-access memory (MRAM) is shown in this study. The circuit generates appropriate outputs for problem-solving by using built-in properties derived from various manufacturing processes, such as the resistance of the magnetic tunnel junction (MTJ) cell. This proposal differs from Arbiter PUF in that it satisfies the rigorous avalanche criteria (SAC). Additionally, the proposed circuit is impervious to machine learning-based modelling assaults due to its grid-like architecture. Various ML approaches are evaluated for two- and four-array designs, including systems such as support vector machine (SVM), linear regression (LR), and multilayer perceptron (MLP). Accuracy of MLP-attack forecasts remained 53.62% for two array circuit & 49.88% for four array circuit. This demonstrates that the system continues to function well despite substantial process modifications. A convolutional neural network (CNN), a recurrent neural network (RNN), a multi-layer perceptron (MLP), and a Larq are all used in high-dimensional models that are based on deep learning. These nets have an accuracy rating of 50.32%, 50.26%, and 50.32%, along with 50.32%, correspondingly. For a simpler two-array design, the suggested circuit's efficiency at the layout stage is also looked into. Based on the simulations, the suggested circuit has an average intra-hamming distance (HD) of 0.99% and an average inter-hamming distance (HD) of 49.97%. It also has an average diffuseness of 49.08. © 2025 IEEE.

关键词： Hamming distance

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

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6th IEEE international conference on Image Processing, applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Gesture and Voice-Based Automation System for Home Surveillance 1

Gesture and Voice-Based Automation System for Home Surveilla...

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1st international conference on Advances in computer Science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Santhosh, B. Oli, Sanjay Jayanth, C. Suresh Kumar, M. Raj, Preetham N Kandpal, Ankit Dayananda Sagar College of Engineering Department of Electronics and Telecommunication Engineering Bangalore560078 India Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bangalore560078 India Bipin Tripathi Kumaon Institute of Technology Dwarahat Department of ECE Almora India

ISBN: (纸本)9798331518578

Gesture and voice-based automation innovations are being examined because of the developing interest for reliable and smart home security frameworks. In this study, a MATLAB-based home reconnaissance automation framework in light of voice and motion is created and reproduced. To perceive, interpret, and answer user developments and vocal orders, the proposed framework utilizes modern sign handling and AI calculations. Although nonverbal communication through gestures has been around for a while, gesture-based human-computer interaction (HCI) is growing in popularity in the present day. The conventional interface uses a number of tools, including markers, physical controllers, and gloves, to increase the recognition rate. This work offers a novel marker less method for capturing gestures without the use of expensive gear or any obstacles. In order to enable disabled individuals to operate household appliances using their own speech and gesture commands, we present in this paper a speech and Gesture Driven automation system for homes with a central controller. The framework joins programmed reactions with continuous observation highlights to further develop security, openness, and user solace. Framework reenactment, including control rationale, voice order handling, and motion identification, is finished with MATLAB. The accuracy, reactivity, and common sense of the innovation are shown by the recreation results. © 2025 IEEE.

关键词： MATLAB

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Teaching Lean Software Development to Software Engineers 18th

Teaching Lean Software Development to Software Engineers

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18th international KES conference on Agents and Multi-Agent Systems: technologies and applications, KES-AMSTA 2024

作者： Kholmatova, Zamira Succi, Giancarlo Tulkunova, Navruza Faculty of Computer Science and Engineering Innopolis University Universitetskaya Ul. 1 Innopolis420500 Russia Department of Computer Science and Engineering Alma Mater Studiorum – Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy

ISBN: (纸本)9789819764686

This research paper proposes the implementation of a teaching strategy for the Lean software development course targeting third-year undergraduate students. Additionally, the intermediate results of applying the proposed methodology are provided. The primary objective is to suggest an instructional approach and enhance the overall learning experience. The proposed methodology employs a diverse set of research methods, including both qualitative and quantitative approaches, to comprehensively assess the outcomes associated with the Lean software development course. Specifically, the teaching strategy encompasses preliminary grade selection, mid-oral exams, quizzes, project-based learning, and a final exam. The proposed strategy will inform future advancements in curriculum design and instructional strategies, ultimately benefiting both students and the broader software engineering community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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