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检索条件"任意字段=14th International Conference on Computer Systems and Technologies, CompSysTech 2013"

共 1942 条记录，以下是41-50 订阅

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A Federated Learning Based Hybrid Deep Learning Framework for Enhanced Human Activity Recognition 14

A Federated Learning Based Hybrid Deep Learning Framework fo...

引用

14th international conference on computer and Knowledge Engineering, ICCKE 2024

作者： Azmoudeh, Jamileh Dandani, Samaneh Arghaee, Sajjad Havangi, Iman Valizadeh, Parisa Yaghmaee, Mohammad Hossein Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran

ISBN: (纸本)9798331511272

Human Activity Recognition (HAR) has become increasingly important with the advent of mobile computing and sensor technologies. Traditional HAR systems, relying on centralized data processing and supervised learning techniques, face significant challenges related to data privacy, scalability, and the need for extensive labeled datasets. Federated Learning (FL) has emerged as a promising solution to address these limitations by allowing collective model training without centralizing sensitive user data, thereby enhancing privacy and personalization. this paper introduces a novel framework that integrates FL with hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models to improve the accuracy and robustness of HAR. Our approach leverages the strengths of CNNs in capturing spatial features and LSTMs in modeling temporal dependencies, while maintaining data privacy through FL. Extensive experiments on real-world HAR datasets demonstrate that our proposed framework not only preserves privacy but also achieves high recognition rates with limited labeled data, showcasing its potential for practical applications in healthcare, fitness, and smart home environments. ©2024 IEEE.

关键词： Federated learning

来源：评论

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Effective Node Deployment in Wireless Sensor Networks Using Reinforcement Learning 14

Effective Node Deployment in Wireless Sensor Networks Using ...

引用

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

作者： Priyadarshi, Rahul Teja, Panduranga Ravi Vishwakarma, Anish Kumar Ranjan, Rakesh Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798331531935

Wireless Sensor Networks (WSNs) are vital for applications like environmental monitoring and smart cities, but effective node deployment remains challenging. Traditional methods often fail in dynamic scenarios. this paper proposes a Reinforcement Learning (RL)-based framework to optimize WSN node deployment, maximizing coverage while minimizing energy use and extending network lifetime. the RL model adapts to environmental changes and varying network conditions, ensuring robust performance in static, dynamic, and high-density environments. Experimental results show the RL approach outperforms conventional methods, achieving better coverage, lower energy consumption, and improved network longevity. the study demonstrates RL’s adaptability and effectiveness in addressing WSN deployment challenges, enabling intelligent, self-optimizing networks for dynamic real-world applications. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

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GlobalLearn: An Efficient Multilingual Textual Summary for Education Videos 14

GlobalLearn: An Efficient Multilingual Textual Summary for E...

引用

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

作者： Shetty, Pratham R. Prateek, M. Krishna, Anirudh Ranjive, R. Begum, M. Farida Dept. Computer Science and Technology PES University Bengaluru India

ISBN: (纸本)9798331531935

Educational videos have become a preferred medium for learning, offering advantages such as self-paced study, the ability to revisit concepts, and a distraction-free environment. However, the effective- ness of such videos can be amplified through concise, multilingual textual summaries. this paper introduces GlobalLearn, an AI powered educational video to text summarization application that integrates speech recognition, optical character recognition, and NLP-driven large language models(LLMs) to extract and summarize key concepts from educational videos. By integrating audio and visual transcripts, time-stamped summaries, and multilingual capabilities, GlobalLearn ensures acces- sibility for diverse global audiences. this user-centric approach not only supports efficient learning and revision, but also bridges linguistic barriers, fostering an inclusive educational ecosystem. the paper explores GlobalLearn’s design and technical architecture, and core functionalities to outline its potential impact on modern education. © 2025 IEEE.

关键词： Speech recognition

来源：评论

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Accelerated MR image reconstruction using non convex regularized non-subsampled Contourlet transform and total variation based prior 14

Accelerated MR image reconstruction using non convex regular...

引用

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

作者： Dhengre, Nikhil Ranjan, Rakesh Motdhare, Sagar Department of ECE IIIT Maharashtra Nagpur India School of Computer Science UPES Uttarakhand Dehradun India

ISBN: (纸本)9798331531935

In the domain of compressive sensing MRI (CS-MRI), MRI image is reconstructed using partially acquired k-space data, in order to shorten the acquisition time of MRI. Various priors and their combinations have been examined for compressive sensing MRI application. In this paper, we propose a CS-MRI method comprised of the combination of non convex regularized non-subsampled contourlet transform (NSCT) based sparsity and hybrid total variation as a prior. the NSCT captures multi-directional and multi-scale image features effectively, while hybrid total variation preserves edges better while suppressing artifacts. the non conex regularization provides better measure of sparseness and preserve structural details better compared to convex regularization. Alternating direction method of multipliers is used to solve the proposed method. the experimental results of the proposed method are analyzed for different undersampling patterns. © 2025 IEEE.

关键词： Contourlet transform

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Network and Security Solution using Jaya Algorithm-based graph search for Drone Transportation 14

Network and Security Solution using Jaya Algorithm-based gra...

引用

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

作者： Patel, Pramod Kumar Khan, Nikhat Raza Mishra, Aishwarya Agrawal, Akrati Daravai, Archana Department of Electronics and Communication Engineering IES College of Technology Bhopal India Department of Computer Science and Engineering IES College of Technology Bhopal India

ISBN: (纸本)9798331531935

this study reports the suitability of several algorithms to identify the best course in a network of lines that is utilized to guide an Unmanned Arial Vehicle (UAV) aircraft in a safety configuration. It utilizes a waypoint graph using Jaya algorithm for this purpose, which is based on the GPS coordinates of flight route movement locations in three dimensions. It performs a comparative analysis of the drone path prediction using a dynamic adjacency matrix that represents the shortest path in the graph. In the context of the area observation mode, evaluate the benefits and drawbacks of the two routing methods. © 2025 IEEE.

关键词： Target drones

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Novel Deep Learning Approach for Tooth Segmentation in Orthodontic Treatment Using STU-RestNet 14

Novel Deep Learning Approach for Tooth Segmentation in Ortho...

引用

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

作者： Kapoor, Saurabh Maheshwary, Priti Department of Computer Science and Engineering Rabindranath Tagore University M.P. Bhopal India

ISBN: (纸本)9798331531935

In this paper we introduce the novel approach in dentistry deep learning method for image segmentation which is helpful for orthodontics treatment. In oral diagnosis treatment the image segmentation plays an important role to determine caries for accurate diagnosis in dental treatment. the previous traditional method failed in the clinical segment in labeling the process of tooth i.e. both label and unlabel data. We address these types of issues using the Swin Transformer UNet and Rest Net method. It is a self-learning-based algorithm named as STU-RestNet-Swin Transformer U-Net RestNet method demonstrated the performance on the dataset which proving its performance and effectiveness, robustness, accuracy in tooth segmentation. Our architecture is based on the segmentation approach with novel mechanisms to decode the various regions and deep supervision technique using STU- RestNet. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

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Enhancing Socio-Economic Development through Digital Literacy: a Strategic Imperative for Rural Communities 14

Enhancing Socio-Economic Development Through Digital Literac...

引用

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

作者： Aryan, Priyam Gaba, Bhavya Katiyar, Akarsh Pathak, Shashwat Ambar Kumar, Manish Srivastava, Anubhava Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331531935

Modern society requires digital literacy because it operates under IT leadership. Various locations across rural territory continue to experience technology shortages alongside insufficient training regarding proper IT utilization. the communities encounter multiple obstacles because they have inadequate internet connectivity and insufficient infrastructure alongside poor cybersecurity capabilities. A mobile-based digital platform targeting rural needs was proposed to solve this problem. Digital literacy training helps people gain abilities that enhance their access to education and healthcare services and government programs. the research examines digital literacy fundamentals alongside practical applications and educational and social effects that digital literacy creates for employment alongside society at large. the research evaluates methods which can improve digital literacy knowledge delivery to different population segments. © 2025 IEEE.

关键词： Economic and social effects

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Video Trailer Generation using Multimodal Data Analysis 14

Video Trailer Generation using Multimodal Data Analysis

引用

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

作者： Praveen, Pranathi Rajesh, Shreeja Murthy, Prajna Rama Giridhar, Nikhil Narayan, Surabhi Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798331531935

Trailers have been a vital part of the entertainment industry to pique audience interest. the process of trailer generation has been evolving to provide effective trailers to the public. In this paper we propose a novel approach by utilizing the audio and video modalities to extract key trailer segments that are compiled to create an effective trailer which highlights the impactful scenes of the short film. In contrast to previous work in this domain, the novel approach gives greater emphasis to the auditory features, since auditory features have a significant impact on the film in the horror genre. the dataset is a compilation of 311 short films and their respective trailers from various reputable public sources. Our approach introduces an audio guided visual model that compliments the acoustic features ensuring that the extracted segments are key moments both in terms of audio and video. the results obtained indicate that a significant proportion of predicted segments are deemed highly trailer-worthy. © 2025 IEEE.

关键词： Entertainment

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions 14

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

引用

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

作者： Ansari, Shaharyar Alam Ali, Salman Luqman, Mohammad Ahmad, Shahnawaz School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (纸本)9798331531935

the Internet of things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. the research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. the proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. the initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). the system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance. © 2025 IEEE.

关键词： Recurrent neural networks

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments 14

Deep Learning Techniques for Intrusion Detection Systems in ...

引用

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

作者： Alam, Mahfooz Ashraf, Zubair Singh, Priya Pandey, Bishwajeet Rehman, Kaleemur Aldasheva, Laura Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida201306 India College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida201306 India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (纸本)9798331531935

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. this paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. the goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems. © 2025 IEEE.

关键词： Network intrusion

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