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检索条件"任意字段=IEEE Conference on Local Computer Networks"

共 18608 条记录，以下是91-100 订阅

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural networks (CNN) and Recurrent Neural networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 ieee.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Self Organized Anchor Group based Routing Protocol for IoT Initial Setup 17

Self Organized Anchor Group based Routing Protocol for IoT I...

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17th International conference on COMmunication Systems and networks, COMSNETS 2025

作者： Gupta, Naveen Kumar Aggarwal, Apeksha Yadav, Rama Shankar Kumar Nagaria, Rajendra Dr B R Ambedkar National Institute of Technology Jalandhar Center of Energy and Environment Dept. of Information Technology Jalandhar India Jaypee Institute of Information Technology Dept. of CSE and IT Noida India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj India Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Comm. Engineering Prayagraj India

ISBN: (纸本)9798331531195

Geographical routing approach relies on local geographic information and performs multi-hop routing with the help of local decisions. The nodes are represented by 3D coordinates as some real IoT networks may be deployed in high buildings, hilly areas, underground or underwater. The geographical routing protocols follow the greedy path and suffer from void node problem (VNP). The traditional recovery policies follow a longer path around the boundary nodes of the void region which may go out of energy quickly. Thus, this paper proposes the concept of anchor group and presents an efficient anchor group routing protocol (EAGR) for IoT networks. The anchor group is treated as a sub-destination region where a group of nodes behaves like sub-destinations, which bypass the boundary nodes of the void to minimize the path length and divide the network traffic, in case of void in the network. The paper is also focused on considering the residual energy in the contention-based next-hop selection process. The experimental work has been carried out in the OMNET++ simulator's INET framework and observed that the proposed method is outperforming existing protocols. Moreover, it has reported a longer network lifetime and is able to survive longer than other existing geographical routing protocols on high load. © 2025 ieee.

关键词： Routing protocols

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Bi-clustering of Binary Data via Neurodynamics-driven Binary Matrix Factorization 13

Bi-clustering of Binary Data via Neurodynamics-driven Binary...

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13th International conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Li, Hongzong Wang, Jun Department of Computer Science City University of HongKong Kowloon Hong Kong Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

Bi-clustering, also known as co-clustering, is a powerful data analysis technique that simultaneously clusters rows and columns of a data matrix, revealing hidden patterns. In this paper, we propose a neurodynamics-driven binary matrix factorization approach for bi-clustering binary data. The proposed method utilizes multiple discrete Hopfield networks operating concurrently to explore local optimal solutions. Furthermore, a particle swarm optimization rule is iteratively applied to reinitialize the neuronal states for directing the search toward better solutions. Comparative evaluations across six benchmark datasets reveal that the proposed method outperforms five existing methods in terms of five internal and external indices. © 2025 ieee.

关键词： Particle swarm optimization (PSO)

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Ultrasound Robotic-Assisted Teleoperation Over networks: Performance Evaluation of Commercial 5G, Wi-Fi, and Ethernet 11

Ultrasound Robotic-Assisted Teleoperation Over Networks: Per...

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11th International conference on Mechatronics and Robotics Engineering, ICMRE 2025

作者： Sajadi, Seyed MohammadReza Mathiassen, Kim Solvin, Håvard Brun, Henrik Lehne, Per Hjalmar Elle, Ole Jakob University of Oslo Department of Technology Systems Oslo Norway Fornebu Telenor R&i Norway University of Oslo Department of Informatics Oslo Norway Oslo University Hospital The Intervention Center Oslo Norway

ISBN: (纸本)9798331509293

This study aims to address the challenges of delivering adequate ultrasound examinations in remote areas. It focuses on developing a long-distance teleoperation system for ultrasound examinations using commercially available 5G networks. This is achieved by enhancing a previously developed short-distance teleoperation system. The upgrade involves shifting from a centralized to a distributed architecture, making it suitable for long-distance teleoperation over networks. The performance of the upgraded system was evaluated over commercial 5G, Wi-Fi, and Ethernet networks, with Ethernet serving as the benchmark. An experienced echocardiographer conducted evaluations using these network protocols blindly, assessing the system's bidirectional transparency and dexterity. The results demonstrate that the distributed architecture, combined with 5G technology, can successfully facilitate long-distance teleoperation, allowing smooth manipulation of the robotic system with acceptable transparency over the 5G network. The developed system shows potential for use in remote areas, mobile platforms such as ambulances, and emergency departments of regional healthcare facilities. © 2025 ieee.

关键词： Wireless local area networks (WLAN)

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LBPNET: Detection of Fake Face Currency through LBP-Based Convolutional Neural networks 5

LBPNET: Detection of Fake Face Currency through LBP-Based Co...

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5th International conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Reddy, Y.V. Bhaskar Kumar, C. Satya Ashok, Rayalla Mandlem, Sai Nithin Reddy, P. Ashok Reddy, M.Babu Department of Computer Science and Engineering Mylavaram India Vardhaman College of Engineering Department of Computer Science and Engineering India Krishna University Department of Computer Science Machilipatnam India

ISBN: (纸本)9798331522100

The escalating sophistication of counterfeit face currency demands innovative solutions for robust detection. In this study, we propose LBPNET, a novel approach employing local Binary Pattern (LBP) feature extraction and Convolutional Neural networks (CNNs) to address the challenges posed by realistic fake currencies. LBP descriptors are extracted from currency images, capturing subtle patterns invisible to the human eye. These descriptors serve as inputs for a CNN, named LBPNET, trained to discern genuine from counterfeit face currencies. The model undergoes a comprehensive training phase, incorporating diverse currency images, to establish a generalized and adaptive detection system. In testing, new currency images are evaluated using the trained LBPNET to identify the presence of fake or non-fake currency, showcasing the model's adaptability to emerging counterfeit variations. This approach integrates machine learning and image processing to enhance detection capabilities, offering a promising solution for the evolving landscape of counterfeit face currency across forensic and social media domains. © 2025 ieee.

关键词： Feature Selection

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs 14

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

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14th ieee International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Gupta, Akash Kumar Ranjan, Rakesh Verma, Amit Teja, Panduranga Ravi Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (纸本)9798331531935

Efficient node deployment in Wireless Sensor networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. This paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. The results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments. © 2025 ieee.

关键词： Energy efficiency

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Deep neural network interpretability for image classification based on semantic compressed perception 17

Deep neural network interpretability for image classificatio...

引用

17th ieee International conference on computer Research and Development, ICCRD 2025

作者： Yan, Xuehu Wu, Yichen Zhai, Qingying Zhang, Jinrui Lu, Yuliang Liu, Jingju Yang, Guozheng National University of Defense Technology Hefei China Hefei University of Technology Hefei China

ISBN: (纸本)9798331531881

With the rapid development of deep learning technology, as one of the core tasks in the field of computer vision, the accuracy of image classification has been significantly improved. However, as a black-box model, the decision process of deep neural network for image classification is hard to be intuitively understood and interpreted, which will lead to security risks in applications requiring high transparency and security. In this paper, we initially propose a deep neural network interpretability method for image classification based on semantic compressed perception, aiming to explore the learning and decision mechanism of deep learning models through complex network theory and semantic compressed theory. Compressed perception technology is applied to capture the semantic characteristics of image data. The local clustering coefficient and node centrality in complex network theory are utilized to find the representative sample image instance of each category in the training set, and such that we explore the interpretability of the model from a new perspective. This method is an interpretability method for the synthesis of concept-level features and image-level features, and it is speculated that a deep learning model for image classification may corresponds to an image semantic compressed method. The work in this paper not only helps to understand the internal working mechanism of a model, but also provides a certain basis for further optimizing and improving the model performance. © 2025 ieee.

关键词： Deep neural networks

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Leveraging Machine Learning to Predict Obesity Trends: Insights from Lifestyle and Biometric Data

Leveraging Machine Learning to Predict Obesity Trends: Insig...

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2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Adapala, Rishi Manikanta Kancharla, Yaswanth Shaik, Baba Ameer Jyotsna, C. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Bangalore India

ISBN: (纸本)9798331512965

This is increasingly important as global obesity rates continue to rise: the causes need to be understood, and the ability to predict trends determined. In this study, we use machine learning techniques to predict obesity trends given lifestyle and biometric data from a large obesity dataset. Our methodology starts with data preprocessing that includes transforming categorical variables into numerical variables and standardizes the numerical variables into such a form so that machine learning algos can work with them properly. Supervised machine learning models, such as tree based methods, neural networks and various ensemble techniques are applied, and evaluated. For determining the most effective predictive approach, these models are evaluated by several performance metrics such as accuracy, precision, recall, and F1 score;the best model is chosen based on these metrics. In addition, the study includes explainable AI technique (SHAP - SHapley Additive exPlanations;LIME - local Interpretable Model-Agnostic Explanations) to make the results more interpretable. The results from these methods provide insight into how different lifestyle and biometric factors contribute to the prediction of obesity;such as physical activity, diet patterns, and body mass index. The results from this work demonstrate not only how machine learning can improve prediction accuracy but also how these methods can provide actionable insights into the many contributing factors to obesity. © 2025 ieee.

关键词： Self-supervised learning

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Long-tailed Oracle Character Recognition Based on Convolutional Neural networks and Vision Transformers

Long-tailed Oracle Character Recognition Based on Convolutio...

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2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Zhongyuan Han, Zhiwang Aysa, Alimjan Ibrahim, Ghalip Ubul, Kurban School of Computer Science and Technology Xinjiang University Xinjiang China Xinjiang Key Laboratory of Multilingual Information Technology Xinjiang University Xinjiang China

ISBN: (纸本)9798350368741

Oracle bone inscriptions, which are among the oldest known hieroglyphics in China, encompass rich historical and cultural information. However, the automatic recognition of oracle characters faces substantial challenges due to issues with data quality and long-tail distribution. This study introduces a hybrid model based on a convolutional neural network (CNN) and a visual transformer (ViT), augmented with a multi-expert learning strategy, to enhance the recognition performance of long-tail oracle bone characters. Initially, the CNN extracts local features, while the ViT captures global dependencies, thereby improving the model's feature representation capability. Subsequently, through the multi-expert learning mechanism, the prediction results of multiple models are aggregated, effectively mitigating the effects of data imbalance and alleviating the challenges associated with the long-tail distribution of the dataset. Experimental results demonstrate that the proposed model achieves new state-of-the-art performance on two large-scale Oracle datasets (Oracle-AYNU and OBC306) in terms of Top-1 accuracy, Top-5 accuracy, F1-Score, and average accuracy, with respective values of 87.97% (93.61%), 96.58% (98.84%), 86.83% (93.49%), and 82.81% (85.23%). © 2025 ieee.

关键词： convolutional neural network long-tail distribution Oracle recognition visual transformer

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Advancing Healthcare with Federated Learning: Strengths, Challenges, and Methodological Concerns 6

Advancing Healthcare with Federated Learning: Strengths, Cha...

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6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Marada, Anusha Farooqui, Yassir A. Chauhan, Ankit Department of Computer Engineering PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331522667

Federated Learning (FL), which permits decentralized data analysis while protecting patient privacy, is quickly becoming a crucial paradigm in the healthcare industry. FL complies with strict privacy rules like GDPR and HIPAA by enabling machine learning models to train on local datasets without disclosing sensitive data. In order to in vestigate the use of FL in healthcare, this study summarizes the results of several research, with a particular emphasis on wearable technology data, medical imaging, and Electronic Health Records (EHRs). We as sess several algorithms, including Gradient Boosting Machines (GBMs) and Convolutional Neural networks (CNNs), determining their prowess in managing complex healthcare data as well as the drawbacks they en counter, such as computational demands, high communication overhead, and non-identically Distributed (non-IID) data. conventional assessment methodologies and moral standards. This evaluation outlines the essen tial improvements for FL’s scalability and dependability across varied healthcare contexts, while also highlighting FL’s potential to change healthcare. © 2025 ieee.

关键词： Federated learning

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