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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8045 条记录，以下是61-70 订阅

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Cross-Session EEG Motor Imagery Classification Method Based on Transfer Learning 5

Cross-Session EEG Motor Imagery Classification Method Based ...

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5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Xiong, Chaolin Li, Hualiang Wang, Yalong Lin, Ziyue Yang, Wenhan Fan, Shengping Wang, Yifan Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou Guangdong510080 China Guangdong Power Grid Co. Ltd. Guangzhou Guangdong510080 China

ISBN: (纸本)9798331533694

Motor imagery (MI) electroencephalogram (EEG) signals exhibit cross-session variability, which hinders traditional EEG classification models from achieving satisfactory performance in cross-session EEG data classification. therefore, further adjustments and optimizations are needed. To resolve this problem, the research develops a transfer learning-based approach for cross-session motor imagery EEG classification. To comprehensively analyze EEG data, this study introduces an attention-optimized separable CNN framework for multi-domain feature extraction (spectral, spatial, and temporal). In parallel, supervised learning is applied to reduce the classification error of source domain data. Meanwhile, transfer learning is leveraged to enhance the alignment of feature representations between source and target domains, enabling the training of a neural network capable of effectively processing cross-session EEG datasets. Finally, the proposed algorithm is compared with several selected advanced algorithms and evaluated on the publicly available 2008 BCI Competition IV 2a dataset. the results show that without any manual feature extraction, the proposed cross-session EEG motor imagery classification method based on transfer learning achieves an average accuracy of 73.1% and a Kappa value of 64.8% for the four-class classification dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on simulation of human brain neural network and signal processing technology 5

Research on simulation of human brain neural network and sig...

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5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Wang, Haochen Xuteli College Beijing Institute of Technology Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

With the cross development of computational neuroscience and artificial intelligence, research on human brain neural network simulation and signal processing technology has become a hot topic of common concern in both academia and industry. this study used a multi-level and multi-scale computational model and high-precision algorithm to simulate the dynamic behavior of human brain neural networks, exploring the complex interactions between neurons and their impact on information processing capabilities. Real time dynamic simulation of billions of neurons was achieved on a simulation platform using ultra large-scale integrated circuit chips, simulating signal processing patterns similar to those of the human brain. By using an improved backpropagation algorithm to decode and reconstruct neural signals, the efficiency of the algorithm and the accuracy of signal processing have been improved. On this basis, combined with experimental data obtained from functional magnetic resonance imaging, the similarities and differences between neural network simulation results and actual human brain activity were compared and analyzed, revealing the potential connection between cognitive function and brain network activity patterns. this study not only achieved new breakthroughs in simulation technology and signal processing algorithms, but also provided a new quantitative tool and theoretical support for related neuroscience research, which is of great significance for the development of brain computer interfaces and intelligent information processing systems. In addition, the study also delved into the balance between ensuring model complexity and processing efficiency, as well as the challenges and opportunities brought by interdisciplinary collaboration in the field of neuroscience. through cross validation and error analysis of simulation experiments, the effectiveness of the model and the accuracy of prediction results were ensured. Based on this, feasible suggestions for opt

关键词： Magnetic resonance imaging

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Tracking of Fragmented Particles with Neural networks 5th

Tracking of Fragmented Particles with Neural Networks

引用

5th international conference on Information Technology in Geo-Engineering, ICITG 2024

作者： Zhu, Zhiren Wang, Jianfeng Department of Architecture and Civil Engineering City University of Hong Kong Hong Kong

ISBN: (纸本)9783031765278

In this paper, a novel particle tracking method is proposed to investigate the kinematics of Leighton Buzzard sand (LBS) particles exhibiting slight- and medium-level fragmentation by combining PointConv and PointNetLK networks. Firstly, a series of image processing algorithms were employed on the raw CT slices to facilitate reproduction of particle morphology. Subsequently, all particles were represented as point sets, down sampled and grouped, yielding substantial datasets for neural network training and testing. Additionally, Gaussian noise was generated and introduced into the particle point sets to enhance the network robustness. then, the PointConv network was implemented to efficiently match the particles under different strains. this success was attributed to the good preservation of morphological features of these particles and the excellent capacity of PointConv to capture morphological features. Next, the PointNetLK network was trained and incorporated with the particle correspondence obtained by PointConv, aiming to determine the optimal transformation matrix for the corresponding particles. Finally, the predictive results are evaluated based on the visualization of the transformed point cloud, and the particle kinematics is analyzed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computerized tomography

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Liver Tumour Detection in Computed Tomography Images through Image processing and Deep Learning 5th

Liver Tumour Detection in Computed Tomography Images Through...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Irukulla, Sandeep Bodasingi, Pavan Kumar Maurya, Naveen Kumar Ram, G. Challa Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534202 India

ISBN: (纸本)9783031770777

this work presents detection of liver tumours in computed tomography (CT) images based on image processing and deep learning. the existing systems for liver tumour detection are based upon support vector machine (SVM) and random forest approach, which are less interpretable, computationally expensive, and require more parameters. the proposed approach commences with a comprehensive pre-processing stage encompassing resizing, histogram equalization, and bilateral filtering for noise reduction. In addition, K-Means image-based segmentation has been employed to extract the CT images’ features effectively. the pivotal element of this approach is based on the utilization of convolutional neural networks (CNNs), a potent deep learning tool, to achieve accurate tumour identification by classifying them as benign or malignant. By capitalizing on the strengths of deep learning, specifically CNNs, this method aims to significantly elevate the precision of liver tumour detection within CT scans. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Convolutional neural networks

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Enhanced Multimodal Conversational AI Using Speech and Image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

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

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

the development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. the emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. the study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. this study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional Neural networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Elastic Force sensor Based on Velostat® Characteristics Investigation 5th

Elastic Force Sensor Based on Velostat® Characteristics Inv...

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5th Polish Congress of Mechanics and 25th international conference on Computer Methods in Mechanics, PCM-CMM 2023

作者： Kciuk, Marek Faculty of Electrical Engineering Department of Mechatronics Silesian University of Technology ul. Akademicka 10a Gliwice44-100 Poland

ISBN: (纸本)9783031731600

Velostat® is carbon black based elastic foil which changes its resistance based on applied force, so it may be used as a force sensor. It enables to produce elastic toys and wearable electronics products for human health monitoring. As well as it enables us to design matrix sensors which offers pressure distribution monitoring even on irregular shapes. the biggest advantage of Velostat® is its flesibility which gives wide variety of applications. On the other hand, the material has strongly nonlinear behavior with hysteresis loop. the purpose of the presented research is designing, implementation and measurement of electro-mechanical characteristics of pressure sensor made. the Velostat® was used as a sensing material for self-made pressure sensor in matrix structure. In this scope only one sensory cell is taken under measurement during one measurement procedure. Automated measurement stand is presented as well as measurement procedures and results with analysis. Research confirmed high nonlinearity and hysteresis loop. In addiction the hysteresis significantly depends on velocity of force stimulus. Exemplary, for velocity of force decreasing equal to 100 mm/min resistance returns to its nominal value after force achieves 0 N. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Product design

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Integrating Long Short-Time Period Reminiscence networks for Medical Photograph Classification 5th

Integrating Long Short-Time Period Reminiscence Networks for...

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

作者： Agarwal, Chhaya Kamalraj, R. Singh, Vaishali Dadhich, Atul Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

L lengthy short-time period memory (LSTM) networks are present-day recurrent neural networks that have been proven effective in numerous regions such as natural language processing, speech popularity, and computer vision. Recently, there was a growing hobby within the software cutting-edge LSTM networks to scientific image type. the gain of ultra-modern usage of LSTM networks in the scientific image category is that they must remember the temporal styles modern day information and effectively integrate the local and worldwide representations of today's photograph. Moreover, they have been proven to be a powerful device to capture temporal patterns in longitudinal, multi-modal information. In this paper, we overview the current methods for clinical picture type and LSTM network usage and the demanding situations and possibilities related to this approach. We additionally provide a top-level view of cutting-edge ability packages for modern LSTM networks in clinical imaging and provide some critical guidelines for future studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Efficient Deep Fake Detection Technique on Video and Audio Dataset Using Deep Learning 5th

Efficient Deep Fake Detection Technique on Video and Audio D...

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5th international Ethical Hacking conference, eHaCON 2024

作者： Vadishetty, Rahul Wayne State University DetroitMI United States

ISBN: (纸本)9789819784561

the latest advances in computer vision processing need tools for creating realistic deepfakes. Deepfake technology has made it viable to provide very practical-looking fake audio, video, and images. In the recent era, we have faced issues that include impersonation and disseminating false information. the spread of fake media streams causes chaos in social networks and has the potential to harm a person's or a group’s reputation, manipulating public sentiment, as well as mindsets about the individual or community. To address these challenges and detect deep fake video images and voices, a deep learning approach has been adopted in this study by using two publicly available datasets containing audio, videos, and images for research and experiment. Six models such as CNN, LSTM, and Pre-trained models VGG-16, Alex-net, Google-Net, and Mobile-net have been analyzed for deepfake detection. Our proposed CNN approach achieved an accuracy of 98.89% on the face and an accuracy of 98.96% on the voice dataset and LSTM achieved an accuracy of 95.11% on the face and 92.67% on another voice dataset. A comparative performance analysis of the Deepfake analysis results obtained in this study is performed with related works to show the supremacy of the current study. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Novel Approach for Duty Cycle Optimization in Designing Scalable Wireless sensor networks 5th

A Novel Approach for Duty Cycle Optimization in Designing Sc...

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5th international conference on Electrical and Electronics Engineering, ICEEE 2024

作者： De, Sudip Kumar Banerjee, Avishek Majumder, Koushik Department of Information Technology Asansol Engineering College WB Asansol India Department of Computer Science and Engineering MAKAUT WB Kolkata India

ISBN: (纸本)9789819791118

this paper aims to enhance the efficiency of wireless sensor networks (WSNs) within an area of interest (AOI) by optimizing duty cycles. Focusing on a specific AOI divided into clusters, each with a dynamically selected cluster head, the study emphasizes establishing a time-synchronized duty cycle optimization protocol in the communication between cluster heads and the base stations. Unlike conventional approaches that randomly select nodes for duty cycle optimization, this paper introduces a method to identify the optimal cluster head (CH) of each cluster based on maximum remaining energy and a shorter distance from the Base Station (BS) to maximize the durability of the network. By introducing a time-synchronized Duty Schedule, the proposed methodology ensured that nodes that are not in use will enter a sleep mode, conserving energy. To ensure effective communication between the Data Centre and multiple Base Stations (BSs) of AOIs, the Particle Swarm Optimization (PSO) has been also used. through simulation and analysis, the effectiveness of the proposed duty cycle optimization method is evaluated, demonstrating its potential to enhance WSN performance in terms of energy efficiency, network longevity, and communication. this research contributes to advancing the design and operation of WSNs, particularly in scenarios where energy conservation and data transmission are paramount concerns. the suggested duty cycle model assures a 20.44% increase in the network’s lifetime as compared to other available studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy efficiency

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

5th International Conference on Medical Imaging and Computer...

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5th international conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

ISBN: (纸本)9789819638628

the proceedings contain 53 papers. the special focus in this conference is on Medical Imaging and Computer Aided Diagnosis. the topics include: Deep Learning Frameworks for Histopathological Image processing in Colorectal Cancer Diagnostics;Improving Knee Osteoarthritis Detection through a Multitask Learning Method from 2D MRI Slices;enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study;retNet: A Two-Stage Deep Neural Network for Clinically Traceable Retinal Disease Classification;Attention-Based CNN for Enhanced Detection of Arsenic Exposure;deep Learning for Lymph Node Metastasis Detection in Pancreatic Ductal Adenocarcinoma;Knowledge Distillation for Enabling Efficient AI-Based Skin Cancer Detection in Resource-Constrained Environments;classification Method in Vision Transformer with Explainability in Medical Images for Lung Neoplasm Detection;BFuse-Net: Bonferroni Mean Operator-Aided Fusion of Neural networks for Medical Image Classification;increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches;Synth-to-Segment: MRI Brain Tumor Segmentation with Diffusion Transformers and Attention U-Net;(ExMod) Model for Medical Image Segmentation Using Scribble Annotations;LV-Mamba: Integrating Denoising Mechanism with Mamba for Improved Segmentation of the Pediatric Echocardiographic Left Ventricle;LoRA-MedSAM: Efficient Medical Image Segmentation;dependency-Related Skin Lesion Bed and Periwound Segmentation Trained on Partially Annotated Clinical Images;rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion;Explainable Deep Learning Framework for Ground Glass Opacity (GGO) Segmentation from Chest CT Scans;Multi-statistical Features-Based 4D Spatiotemporal Level Set Model for CTP Image Segmentation;deep Learning Based Segmentation of Magnetic Resonance Cardiac Images;generic Liv

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