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共 68658 条记录，以下是441-450 订阅

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Complementary Learning System Theory-based Active Learning for Audio Classification

Complementary Learning System Theory-based Active Learning f...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Geng, Hui Gao, Zijian Wan, Tianjiao Feng, Dawei Wang, Changjian Xu, Kele College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China

ISBN: (纸本)9798350368741

Deep learning has significantly advanced the audio classification, achieving remarkable results. However, these successes often rely on extensive manual annotation of audio, a labor-intensive and costly process. Active Learning (AL) presents a promising solution by minimizing the required amount of annotation through the iterative selection of the most informative audio samples. Current AL methods for audio classification typically depend solely on the latest model checkpoint, overlooking the dynamics of the entire training process. The Complementary Learning Systems (CLS) theory posits that the interplay between short-term and long-term memory systems can effectively measure sample uncertainty, offering a means to capture training dynamics. In this work, we introduce a novel AL framework for audio classification, termed CLS-AL, which addresses the limitations of existing methods by simultaneously maintaining both short-term and long-term memory models. This dual-memory approach allows for a more comprehensive consideration of training dynamics. The divergence in predictions between these memory models provides a new metric for evaluating the uncertainty of unlabeled samples, enhancing the effectiveness of the AL sample selection process. We demonstrate the effectiveness and generalizability of CLS-AL through extensive experiments on a diverse set of audio datasets, showing that CLS-AL obviously outperforms existing state-of-the-art methods. © 2025 IEEE.

关键词： Active Learning Audio Classification Complementary Learning System Uncertainty Estimation

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Analysis of Spatial-Temporal Evolution of Water Surface Area in DaSuGan Lake Based on Multi-source Satellite Images and computer Vision technology 7th

Analysis of Spatial-Temporal Evolution of Water Surface Area...

引用

7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Shang, Liping Du, Yu′e Liu, Baokang Gansu Natural Energy Research Institute Lanzhou China Tianshui Normal University Tianshui China

ISBN: (纸本)9789819624089

Lakes are sensitive indicators of climate change, and obtaining the information of lake water quickly and accurately is of great significance for regional climate change research, the protection and governance of ecological environment. This article is based on multi-source satellite images such as Landsat and GF-6, and uses the method of empirical threshold and single band threshold and water index and other methods to extract the water area of DaSuGan lake. Based on the accuracy evaluation results, the optimal water identification model is selected, and the spatial-temporal evolution law of the water area of DaSuGan lake is analyzed from 1991 to 2020. The results show that: © The Author(s) 2025.

关键词： Lakes

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Detection of Neuronal Pathology in Multiple Sclerosis Using Diffusion Tensor Imaging 5th

Detection of Neuronal Pathology in Multiple Sclerosis Using ...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Padhi, Swarupanjali Prabhu, A. Acharjya, Kalyan Seth, Jyoti Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Diffusion Tensor Imaging (DTI) is a form of magnetic resonance imaging used to stumble on the pathology in neuronal tissue in a couple of Sclerosis (MS) sufferers. DTI uses diffusion-weighted imaging (DWI) to quantify the importance and path of water diffusion within the brain. The diffusion data is then processed to create scalar maps or volumes of fractional anisotropy (FA) values. It can be compared to an ordinary reference dataset to perceive deviations in tissue employers. Evaluating FA values can locate regions of demyelization, axonal damage, and different neural impairments associated with MS. Consequently, DTI can offer beneficial insights into the pathology of MS that could help tell choices related to patient control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diffusion tensor imaging

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Attention-Based Recurrent Neural Networks for Medical Image Classification 5th

Attention-Based Recurrent Neural Networks for Medical Image ...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent Neural Networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Target recognition technology for unmanned ships based on improved YOLOv5 4

Target recognition technology for unmanned ships based on im...

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2024 4th international conference on Intelligent Traffic Systems and Smart City, ITSSC 2024

作者： Yuan, Mingkai Liang, Bingnan Wang, Haoliang Dong, Yixuan School of Marine Engineering Dalian Maritime University Dalian116026 China Department of Automation Shanghai Jiaotong University Shanghai200240 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China

ISBN: (纸本)9781510686304

To enable safe and efficient autonomous navigation of unmanned ships in complex marine environments, particularly when encountering overtaking, head-on, or crossing scenarios, it is crucial to accurately identify and detect unknown unmanned vessels. This paper proposes a lightweight object detection algorithm tailored to these challenges. The lightweight network Ghostnet is used to improve the backbone network of the YOLOv5s algorithm, achieving lightweight target detection. By introducing the ECA attention mechanism, the model can better focus on important information in the image, enhancing the ability of feature representation, and improving the model's capability for complex scene understanding and target recognition and localization. Finally, the MPDIoU is used as the bounding box loss function for the YOLOv5s algorithm, which not only improves convergence speed but also obtains more accurate regression results. Comparative experiments show that the proposed improved algorithm outperforms models such as YOLOv5-BiFPN, YOLOv5-CBAM, and YOLOv5-SE in terms of model size, computational complexity, parameter count, detection speed, and average precision. © 2025 SPIE.

关键词： Marine navigation

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Enhancing Efficiency in 6G Radio Resource Allocation Using Machine Learning Algorithms 5th

Enhancing Efficiency in 6G Radio Resource Allocation Using M...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Nautiyal, Kshitij Taranum, Ayesha Savita Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Using machine gaining knowledge of algorithms with 6G radio resource allocation can assist to enhance network performance by using automating the selection making technique on a cease-to-stop foundation. This paper proposes a more suitable implementation of 6G radio aid allocation the usage of device getting to know algorithms to expand a framework for optimized useful resource utilization. The consequences of the experiments conducted in a real-world testbed validate the effectiveness of the proposed structure in achieving the desired goals consisting of improved community performance and decreased strength consumption. Moreover, the proposed improved implementation might be relevant in a huge range of other resource allocation eventualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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Incorporation of the Self-attention Mechanism into Convolutional Neural Networks for the Target Recognition 5th

Incorporation of the Self-attention Mechanism into Convoluti...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Mishra, Gangeshwar Gupta, Prinima Tanwar, Rohit Department of Computer Science and Technology Manav Rachna University Haryana Faridabad India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

ISBN: (纸本)9789819773701

The study focuses on the increasing complexity of automated target recognition (ATR) in surveillance and reconnaissance. It highlights the need for quick and accurate identification in crucial situations and emphasizes the role of technical improvements in addressing this problem. The study incorporates a self-attention mechanism into Convolutional Neural Networks (CNNs) architectures such as YOLOv8, DenseNet-121, InceptionV4, ResNet-152, and VGG16, with a specific focus on categorizing ground targets in Synthetic Aperture Radar (SAR) pictures. This systematic improvement incorporates self-attention modules carefully positioned at critical spots in the network architectures. The research utilizes the renowned Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset. YOLOv8 is the best classifier, with an outstanding total accuracy of 97.65% and it has high specificity of 98.86% and precision of 96.62% with the testing. This study emphasizes the need to thoroughly assess CNNs architectures to make educated decisions when choosing models designed explicitly for complicated target identification tasks. It offers valuable insights that may contribute to developments in ATR technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Enhanced PIR Scheme Combining SimplePIR and Spiral: Achieving Higher Throughput Without Client Hints 6th

Enhanced PIR Scheme Combining SimplePIR and Spiral: Achievi...

引用

6th international conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Xu, Haoyao Li, Yitong Tian, Haibo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819645657

In recent years, Private Information Retrieval (PIR) schemes capable of rapid response have gotten significant attention, with SimplePIR, for instance, achieving a throughput of approximately 10 GB/s in a single-threaded setting. However, these schemes often require users to download data related to the database, known as client hints, making them unsuitable for scenarios where the database undergoes frequent updates. This paper, building upon the Spiral scheme, incorporates the precomputation approach of SimplePIR to design a query method that leverages simulatable homomorphic ciphertexts for offline precomputation. This enhancement not only improves the efficiency of the Spiral scheme but also addresses the issue of SimplePIR’s necessity for downloading client hints. Experimental results demonstrate that, on a database with 220 entries of 256 bytes each, the proposed scheme achieves a throughput 1.87 times higher than that of the original Spiral scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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Generative Adversarial Networks for Semi-Supervised Learning in Clinical Decision Support Systems 3rd

Generative Adversarial Networks for Semi-Supervised Learning...

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3rd international conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Sengan, Sudhakar Katbar, Nek Muhammad Kothalanka, Amarendra Shelke, Mayura Chinnathambi, Kalimuthan Sharma, Dilip Kumar Ammasai, Viswanathan Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of AI & DS AISSMS IOIT Pune411001 India Deprtment of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819783540

Clinical Decision Support Systems (CDSS) have emerged as crucial tools in healthcare, leveraging vast amounts of data to aid clinicians in their decision-making processes. However, the challenge of efficiently utilizing limited labelled medical data remains. This paper introduces a novel approach using Semi-supervised DC-GAN to address this challenge. Building upon the foundational principles of Generative Adversarial Networks (GANs), this proposed model augments the capabilities of traditional semi-supervised methods, optimizing their performance in the CDSS landscape. Through extensive evaluations, they compared this approach with three widely accepted semi-supervised algorithms: MixMatch, Mean Teacher, and Π-Model. The results indicate that Semi-supervised DC-GAN enhances the data utilization process and significantly improves model training, especially when working with limited labelled datasets. This research underscores the potential of GANs in revolutionizing semi-supervised learning within CDSS, paving the way for more efficient and informed clinical decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

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2025 international conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Da Conceicao, Ariano Tian, Lianfang Du, Qiliang Yuan, Ling School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou511363 China

ISBN: (纸本)9798331506797

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities. © 2025 IEEE.

关键词： LiDAR SLAM Mapping Navigation ORBSLAM 3 ROS RRT

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