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共 3942 条记录，以下是71-80 订阅

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QoE-Based Resource Allocation in Wireless Networks: An Analysis

QoE-Based Resource Allocation in Wireless Networks: An Analy...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Trapty Grover, Madhur Sahu, Madhusmita Ojha, Varun Sunil, M.P. Saravanan, M. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Applications Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Wireless networks are developing quickly and can avoid providing an increasing number of services such as video streaming, online games, or real-time communication. However, the quality of experience when accessing content over these networks is still at stake;end-users are concerned with constraints like network congestion, channel quality, and resources provided. In the literature, several proposals, such as QoE-aware resource allocation strategies, have been suggested to tackle this issue. This technical abstract considers QoE-driven approaches for resource allocation in wireless networks. These approaches are employed to facilitate users' perceived QoE by selectively provisioning the users with adequate network resources according to their QoS demands. For example, the analysis takes a look at some of these strategies as well as the main factors that inform them when designing for user preferences, network conditions, and resource availability. We then discuss the benefits and limitations of other approaches, such as Fuzzy logic-based methods and game theory-based methods. © 2025 IEEE.

关键词： Resource allocation

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Recent Advances in Human Activity Recognition Techniques: A Comprehensive Review

Recent Advances in Human Activity Recognition Techniques: A ...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Sukanya Saha Debapriya Banik Department of Computer Science and Engineering ICFAI University Tripura Agartala India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Human Activity Recognition (HAR) interprets data from healthcare, security surveillance, human-computer interaction (HCI), and sensors to recognize and categorize human actions. The rapid development of artificial intelligence (AI) and the widespread use of sensors have drawn great interest and demonstrated the immense application potential of HAR systems based on non-invasive ambient sensors. Various methods for performing HAR have been reported by researchers. In this survey, it has been noticed that data can be collected in vision and sensor-based formats. The goal of video-based HAR is to automatically identify actions in video clips. Smart wearable sensors are the most popular kind of sensor-based HAR. Surveys have identified HAR tendencies across many contexts and applications. The researchers classified HAR techniques into three categories: online versus offline, multimodal versus unimodal, and feature-based versus learning-based approaches. This survey has focused on the recognition of different activities like sports, yoga, different dance poses, human interaction, and human actions. This survey also concentrated on single-view and multiview datasets. This study compares and contrasts the most recent HAR techniques with a discussion of widely used datasets. Various criteria, including dataset size and assessment metrics, were used to categorize and analyze the chosen studies. This survey also focused on different dataset availability.

关键词： Surveys Reviews Surveillance Medical services Sensor systems Human activity recognition Security Intelligent sensors Wearable sensors Sports

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3D Object Reconstruction from 2D Images Using Variational Autoencoders (VAE)

3D Object Reconstruction from 2D Images Using Variational Au...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Mohamed, Aezeden Nii, Rainier Binga, Kipas Pandey, Alok Kumar Karpagam, J. Nnadhini, T.J. School of Mechanical Engineering Png University of Technology Lae Papua New Guinea School of Mathematics and Computer Science Ng University of Technology Lae Papua New Guinea Angau Memorial General Hospital Lae Papua New Guinea Uttaranchal University Dehradun India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542375

In this research, we present a new method on how to reconstruct 3D object from 2D images by employing Variational Autoencoders (VAE). His generality is a bit stark;however, it is well justified given that classical approaches to 3D reconstruction from multiple perspectives fail to capture detailed geometries from few 2D views. These limitations are surmounted by the proposed VAE based framework our approach learns the distribution of 3D shapes using probabilistic inference leading to accurate reconstruction from one or two 2D images. The model also has a decoder that provides a 3D mesh or voxel grid to effectively be used in computer vision, AR and VR applications. The VAE model takes benefit of a big dataset of 3D object shapes, while the encoder dimension translate the 2D picture in the latent space and the decoder rebuild the 3D objects from this space. From the proposed method evaluation, the results suggest high reconstruction accuracy of the proposed method as compared to the traditional point cloud and mesh-based methods in both quality and time taken. The VAE-based system also improves the model's generalization because it has low computational complexity and creates clear and realistic 3D models. The suggested approach has a massive application to the real-time 3D object reconstruction in the different areas including games, product designing as well as diagnosing tools in medical sciences. © 2025 IEEE.

关键词： Variational techniques

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A Game theory-based Reverse Vickrey Auction for Dynamic Pricing in Edge Computing

A Game Theory-based Reverse Vickrey Auction for Dynamic Pric...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Atharva Rasane Manisha Tapale Dept. Computer Science and Engineering KLE Technological University's Dr. M. S. Sheshgiri College Belagavi India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Low-latency communication is critical for effectively functioning Internet of Things (IoT) systems, including applications in healthcare monitoring, industrial automation, and smart cities. However, the vast number of IoT Devices often overwhelms cloud providers, necessitating the adoption of edge computing to reduce latency and distribute computational load. This paper introduces a novel dynamic pricing mechanism for edge computing based on a reverse Vickrey auction, leveraging game theory to balance competitive pricing with profitability for Edge Servers. We develop utility functions for both IoT Devices and Edge Servers, optimizing the markup to maximize edge server utility while adhering to the budget constraints of IoT Devices. Simulations were conducted with 10 Edge Servers and 50 IoT Devices over 2,000 time units. Results show that the proposed mechanism significantly reduces wait times, with most tasks completed within 10-time units. Moreover, dynamic pricing increases Edge Server profits by up to 10 times in some instances compared to fixed markup approaches. Notably, one server's total profit rose from 7,702 to 76,130 and another from 57,965 to 94,298, underscoring the enhanced profitability and utilization. These findings confirm that our reverse Vickrey auction strategy effectively balances low-latency demand with sustainable operational revenue for Edge Servers, offering a robust and scalable solution for edge-centric IoT environments.

关键词： Adaptation models Profitability Estimation Pricing Internet of Things Servers Resource management Game theory Low latency communication Edge computing

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Advancing Diabetic Retinopathy Research with a Dataset from Aizawl region of Mizoram

Advancing Diabetic Retinopathy Research with a Dataset from ...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： C Vanlalnunpuia Lalhmingliana V. D. Ambeth Kumar Ajoy Kumar Khan R Vanlalawmpuia David Lalmuanpuia Khiangte R. Lalchhanhima Dept. of Computer Engineering Mizoram University Aizawl India Dept. of Information Technology Mizoram University Aizawl India Eye Care Hospital Aizawl India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This study presents a high-resolution Diabetic Retinopathy dataset collected from Eye Care Hospital in Aizawl, Mizoram, highlighting its importance for advancing DR detection in underrepresented populations. The dataset includes 495 retinal images captured using an OCT RS 330 device, with quality maintained through a preprocessing pipeline. Images were filtered for clarity using blur metrics and enhanced with adaptive histogram equalization to emphasize diagnostically relevant features. A baseline deep learning pipeline for multiclass DR classification achieved training, validation, and test accuracies of 50%, 49%, and 56%, respectively, reflecting the dataset's complexity. Advanced augmentations like MixUp and CutMix were applied, setting a benchmark for future research. This work underscores the dataset's unique contributions by providing a foundation for robust diagnostic models. This study supports inclusive and practical AI applications in ophthalmology.

关键词： Training Diabetic retinopathy Hospitals Annotations Pipelines Benchmark testing Retina Complexity theory Ophthalmology Reliability

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Research on Steganography Course with Large Language Model ChatGPT Assisted Learning 24

Research on Steganography Course with Large Language Model C...

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Proceedings of the 2024 3rd international conference on Artificial Intelligence and Education

作者： Min-Shiang Hwang Kanza Fatima Chi-Shiang Chan Chia-Chun Wu Dept. of C.S. and Info. Eng. Asia University Taichung Taiwan Taiwan Dept. of C.S. and Info. Eng. Asia University Taichung Taiwan China Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan Taiwan

ISBN: (纸本)9798400712692

In this study, by integrating practice-oriented teaching methods and combining large language models to assist learning and teaching situations, it is proposed that the Steganography course is practical-oriented and integrates ChatGPT with the Steganography language model teaching and teaching established by the applicant. In addition to focusing In addition to cultivating students with Steganography concepts and practical operational abilities, we hope to cultivate students' abilities of active observation, independent research, and critical thinking so that students can meet the technology industry's professional skills and requirements for Steganography studies. This research explores the pre-test and post-test, of course, students' use of large language models to learn to have higher learning attitudes and achievements towards Steganography theory and practice.

关键词： ChatGPT Social Media Learning

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Using Gamification to Reduce Youth Depression: A Step-by-Step Method

Using Gamification to Reduce Youth Depression: A Step-by-Ste...

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Industrial Electronics: Developments & applications (ICIDeA), IEEE international conference on-

作者： Soumyadeb Bakshi Sourashis Bhaumik Subhra Debdas Saptak Das Deepak Jain Bijay Kishor Shishir Sekhar Pattanaik School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India School of Business Shri Mata Vaishno Devi University Katra Jammu and Kashmir School of Computer Engineering Gita Autonomous College Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Youth depression is a major global health concern due to its alarmingly high occurrence and harmful impact on mental health. Traditional therapy methods frequently struggle with involvement, accessibility, and stigma. This research addresses depression in young people by proposing a novel solution based on gamification concepts. Our method is based on creating a game module integrated with a ML model that is especially designed for young people with depression. It provides a fun and active way to reduce symptoms and postpone thoughts of suicide. The module includes a number of levels and activities that are meant to be enjoyable while also supporting mental and emotional health. Crucially, the game has an adaptable system that is connected to players’ depression levels, modifying job complexity in response to continuous evaluation to guarantee maximum effectiveness and engagement. The key component of our invention is the incorporation of a depression assessment question into the game interface, which allows users’ psychological states to be assessed in real time. The game's algorithm is informed by this data, which constantly adjusts the difficulty and intensity of the challenges to suit each player's demands. Our goal is to improve users’ feeling of agency and accomplishment by personalizing their gaming experience, which will have a beneficial effect on their mental health. Our work emphasizes the value of customized approaches in tackling the complex problems associated with depression and adds to the expanding corpus of research that examines the possibility of gamification in mental health therapies.

关键词： Technological innovation Medical treatment Adaptive algorithms Mental health Games Machine learning Depression Real-time systems Complexity theory

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MetaPix: A Data-Centric AI Development Platform for Efficient Management and Utilization of Unstructured computer vision Data 22nd

MetaPix: A Data-Centric AI Development Platform for Efficie...

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22nd international conference on Software Engineering Research and Practice, SERP 2024, and 23rd international conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Venkatesh, Sai Vishwanath Akandeh, Atra Lokanath, Madhu Ford Motor Company DearbornMI United States

ISBN: (纸本)9783031866432

In today’s world of advanced AI technologies, data management is a critical component of any AI/ML solution. Effective data management is vital for the creation and maintenance of high-quality, diverse datasets, which significantly enhance predictive capabilities and lead to smarter business solutions. In this work, we introduce MetaPix, a Data-centric AI platform offering comprehensive data management solutions specifically designed for unstructured data. MetaPix offers robust tools for data ingestion, processing, storage, versioning, governance, and discovery. The platform operates on four key concepts: DataSources, Datasets, Extensions and Extractors. A DataSource serves as MetaPix top level asset, representing a narrow-scoped source of data for a specific use. Datasets are MetaPix second level object, structured collections of data. Extractors are internal tools integrated into MetaPix’s backend processing, facilitate data processing and enhancement. Additionally, MetaPix supports extensions, enabling integration with external third-party tools to enhance platform functionality. This paper delves into each MetaPix concept in detail, illustrating how they collectively contribute to the platform’s objectives. By providing a comprehensive solution for managing and utilizing unstructured computer vision data, MetaPix equips organizations with a powerful toolset to develop AI applications effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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AIPerfLLM: 3rd international Workshop on Performance Optimization in the LLM world 25

AIPerfLLM: 3rd International Workshop on Performance Optimiz...

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Companion of the 16th ACM/SPEC international conference on Performance Engineering

作者： Kingsum Chow Emilio Incerto Marin Litoiu Zhihao Chang Anil Rajput Khun Ban Daniele Masti Zhiheng Lyu School of Software Technology Zhejiang University Ningbo China IMT School for Advanced Studies Lucca Lucca Italy York University Toronto Canada School of Software Zhejiang University Ningbo China AMD Portland OR USA Intel Corporation Seattle WA USA Gran Sasso Science Institute L'Aquila Italy University of Waterloo Waterloo ON Canada

ISBN: (纸本)9798400711305

Artificial Intelligence (AI) has been widely adopted in various domains (e.g., computer vision, natural language processing, and reliability analysis). However, its use for performance modeling and evaluation remains limited, and its benefits to the performance engineering field are still unclear. Researchers and practitioners have recently started focusing on methods such as explainable or white-box AI-based solutions in performance engineering, but the tools, methodologies, and datasets that enable wider adoption are still lacking. Meanwhile, the rapid rise of large language models (LLMs) such as ChatGPT poses new challenges in performance optimization and cost containment. LLM pre-training is expensive, and the necessary infrastructure also incurs significant carbon footprint. This workshop aims to bridge research and practice by bringing together academia and industry to share experiences and insights in performance engineering for LLM-based services and AI applications. We target techniques and methodologies to optimize performance while reducing energy consumption and cost.

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Physical Layer Security Techniques for Wireless Communication Systems

Physical Layer Security Techniques for Wireless Communicatio...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Nagpal, Manish Pattnaik, Sampa Chau Baxi, Paramjit Hannah Jessie Rani, R. Canessane, R. Aroul Chaudhary, Pavan Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Applications Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

One of the most important issues for secure communication in the physical layer is developing physical layer security techniques to provide confidentiality and data integrity in wireless communication systems. The properties of the physical channel are used in these methodologies to safeguard communication from eavesdropping and some other security threats. This article presents physical layer security methods that can be implemented in wireless communication systems. Artificial Noise (AN) is one of the most popular techniques. The original signal is combined with a generated by the transmitter in such a way that eavesdroppers cannot easily decode the data. Cooperative Anti-jamming: Yet another strategy for jammer localization is to employ cooperative jamming, where multiple friendly users can add noise signals over the signal in an effort to confuse potential eavesdroppers. In addition, additional technologies such as multi-input multi-output beamforming and transmit power control can further improve the security of wireless communication systems. In this regard, strategies based on game theory have been proposed as a viable alternative to enhancing physical layer security. Viewing the interaction between the transmitter, receiver, and eavesdropper as a game allows us to reveal their optimal strategies and security levels. © 2025 IEEE.

关键词： Signaling

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