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Advancements in Human Activity Recognition: A comprehensive Review of Smartphone and Wearable Sensor Integration 7

Advancements in Human Activity Recognition: A comprehensive ...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Dhohada, Cheruku Sumalatha, Konatham Vellore Institute of Technology Computer Science And Engineering Vellore India Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350359299

In recent years, deep learning and machine learning methods have been extensively employed in almost every field due to their capability of data processing and analysis. these are the subdomains of Artificial intelligence that gather information in different formats and from different sources. Human Activity Recognition using smartphones and wearable sensors has evolved into a dynamic field with numerous applications and challenges. Ongoing research efforts continue to refine methodologies, address challenges, and expand the scope of Human activity recognition, making it an exciting area with significant potential. Nowadays everyone is using smartphones, so it is easy to find their applications in health monitoring, fitness tracking, context-aware computing, and security. the ability to automatically detect activities such as walking, running, and sleeping opens avenues for personalized services and interventions that help in people's daily lives. While traditional machine learning and deep learning models offer valuable insights individually, ensemble learning presents an opportunity to further enhance accuracy and robustness. this research work aims to give a concise overview of the various deep learning and machine learning approaches, their convergence, and an analysis of the applications of these algorithms in the field of smartphone sensor-based human activity recognition. © 2024 IEEE.

关键词： Learning systems

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Multi-View Subspace Clustering Based on Affinity Matrix and Low-rank Tensor Decomposition 7

Multi-View Subspace Clustering Based on Affinity Matrix and ...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Yu, Yao Du, Shiqiang Zhang, Kaiwu Song, Jinmei Northwest Minzu University College of Mathematics and Computer Science Lanzhou China Northwest Minzu University Key Laboratory of Chinas Ethnic Languages and Information Technology Ministry of Education Chinese National Information Technology Research Institute Lanzhou China

ISBN: (纸本)9781665478571

Subspace clustering is one of the important methods to solve the problem of high-dimensional data clustering, and the performance of traditional methods based on spectral clustering largely depends on the affinity matrix learned directly from the original data or their computational representation. In this paper, we propose multi-view subspace clustering based on affinity matrix and low-rank tensor decomposition (ATMSC) algorithm, which not only constructs a more meaningful affinity matrix but also makes full use of multi-view complementary information and explores high-order correlation. the Alternating Direction Multiplier Method (ADMM) can efficiently find the optimal solution to the problem. Extensive experiments on real datasets show that our method outperforms other state-of-the-art methods. © 2022 IEEE.

关键词： Tensors

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Deep learning-based method for recognizing violation behaviors of underground personnel 24

Deep learning-based method for recognizing violation behavio...

引用

7th international conference on Signal processing and Machine Learning, SPML 2024

作者： Ma, Tian Zhu, Wenna Shi, Weilu Li, Jiahui Li, Yuancheng College of Computer Science and Technology Xi'an University of Science and Technology China

ISBN: (纸本)9798400717192

Aiming at the complex coal mine environment resulting in impaired image quality and the difficulty of a fixed number of convolutional kernels to capture diverse motion features, coupled with fast-moving personnel deteriorating behavioural coherence, a deep-learning-based method for recognition of violation behaviours of underground personnel is proposed, and a Depth Sensing Wild Attention Convolution Module (DRFA), a Multi-dimensional Feature Extraction Module (MFE), and a Displacement-Aware Module (DAM) are designed, and the method is implemented on the self-constructed Experiments are conducted on the downhole track area violation dataset, and its TOP-1 accuracy reaches 95.10%, which proves that the method can effectively improve the recognition accuracy. © 2024 ACM.

关键词： Coal mines

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A Study of Generative Large Language Model for Healthcare 7

A Study of Generative Large Language Model for Healthcare

引用

7th international conference on information technology, InCIT 2023

作者： Park, Seong-Gyeol Kim, Ahtae Yoon, Taewoong Kamyod, Chayapol Kim, Cheong Ghil Namseoul University Dept of Computer Scicence Choongnam 31020 Korea Republic of Mae Fah Luang University Excellence Ctr. of Indust. 4.0 TextitComputer and Commun. Eng. for Capacity Building Research Center School of Information Technology Chiang Rai57100 Thailand

ISBN: (纸本)9798350358698

the expansion of the 4th Industrial Revolution is making it easier to collect and analyze big data through cloud technology. In addition, advances in semiconductor design and manufacturing technology are accelerating the development of artificial intelligence due to improvements in computer processing speed and performance. these developments in artificial intelligence technology are expected to be applied to the healthcare industry and create new value. Recently, ChatGPT, which has evolved one step further, has emerged, bringing about the development of a new technology called 'Generative AI'. Research is being actively conducted on how AI can replace fields that were considered the exclusive domain of humans with specialized knowledge, such as medicine and law. In this study, we aim to take the opportunity to identify trends in the healthcare field of generative AI. © 2023 IEEE.

关键词： Artificial intelligence

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Scene flow estimation based on Light Field 7

Scene flow estimation based on Light Field

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Wan, Le Zhang, Xudong Hefei University of Technology School of Computer and Information Hefei China

ISBN: (纸本)9781665478571

Light field camera can sample multiple views of the same scene with a single exposure, which has unique advantages in dynamic scene analysis. Existing scene flow estimation algorithms ignore the processing of image noise in natural scenes. To solve this problem, a scene estimation algorithm is designed using the light field focal stack. the bidirectional defocusing in the light field focal stack selects the optimal depth estimation cost and integrates noise processing into the depth estimation. the depth map is applied to the optical flow estimation through full focusing, so that the light rays in all directions of the spatial point are gathered into their respective angular domains, and the complete structural information and direction information of the spatial point can be obtained. When the spatial pixel points are matched, the constraints of the light in different directions of the spatial points are added, which greatly improves the matching accuracy of the algorithm. the anti-noise cost of depth estimation is diffused into the scene flow through all-focus, so that the scene flow algorithm has strong anti-noise performance. the experimental results show that the proposed method has lower sensitivity to noise. © 2022 IEEE.

关键词： Cost estimating

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Strategies for Strengthening Security in Image Watermarking: A Critical Review 7

Strategies for Strengthening Security in Image Watermarking:...

引用

7th international conference on Image information processing, ICIIP 2023

作者： Dewangan, Prachee Swain, Debabala Swain, Monalisa Rama Devi Women's University Dept. of Computer Science Bhubaneswar India Shailabala Women's College Dept. of Computer Science Cuttack India

ISBN: (纸本)9798350371406

the propagation of digital content and its ease of distribution over the internet have raised concerns regarding the protection of intellectual property and its prevention from unauthorized usage. One such effective method for safeguarding digital images is watermarking. However, as digital manipulation and counterfeiting techniques continue to advance, there is a growing need to strengthen the security of image watermarking systems. this paper provides an overview of such key strategies used to enhance security in images using watermarking. © 2023 IEEE.

关键词： Image watermarking

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Rapider-YOLOv7: Lightweight Infrared Object Detection Network 7

Rapider-YOLOv7: Lightweight Infrared Object Detection Networ...

引用

7th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xu, Pucheng Yu, Yuecheng Zou, Rundong Shi, Jinlong Li, Yongzheng School of Computer Science Jiangsu University of Science and Technology Zhenjiang China School of Naval Architecture and Ocean Engineering Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350350890

YOLOv7-tiny, as a lightweight variant of YOLOv7, boasts advantages of fast runtime and fewer parameters. However, when directly applied to infrared object detection, YOLOv7-tiny still faces challenges such as weak extraction of fine details, loss of semantic information, and high resource consumption. To address these issues, we propose a comprehensive balance between detection speed and accuracy in the Rapider-YOLOv7 object detection network. Firstly, it introduces the Exponential Spatial Pyramid Pooling with Channel Spatial Pyramid (Soft-SPPCSP) module to enhance the capability of the original model in extracting low contrast information from infrared images. Secondly, it designs the FO-CA attention module to strengthen the model's feature extraction capability for infrared images with weak textures. Furthermore, it devises the lightweight SFaster-net network, which incorporates SPDconv convolution for feature extraction to accelerate the model's feature extraction speed and enhance its capability to extract features from low-resolution images. Experimental results on the FLIR-v2 infrared dataset demonstrate that the mAP of the Rapider-YOLOv7 model reaches 62.6%, which is 2.9% higher than that of the original YOLOv7-tiny. Moreover, the FPS value on the RTX4060ti reaches 93. © 2024 IEEE.

关键词： Infrared imaging

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An Exploration of Object Detection and Vehicular Communication for Autonomous Vehicles 7th

An Exploration of Object Detection and Vehicular Communicati...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Padmavathi, A. Pullela, Dheeraj Reddy Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9783031699818

Autonomous vehicles represent a transformative technology with the best implications for transportation systems. the key component of their success is their capability to detect and respond to their surroundings. For the benefits to be realized, the fusion of advanced computer vision techniques, such as object detection and vehicular communication, is essential. this paper explains the techniques in object detection and vehicular communication in autonomous vehicle applications for future advancements based on related works. the research highlights the need for creative solutions in integrating communication and computer vision tasks and presents idea of work flow for combining object detection with vehicular communication. the main motive of this research is to present about idea of integrating object detection model and vehicular communication. Additionally, we explore vehicular communication systems and their integral role in ensuring safe and efficient autonomous driving. the idea of object detection and vehicular communication is not only for autonomous vehicles but also for ADAS (Advanced Driver Assistance System)-equipped vehicles. © IFIP international Federation for information processing 2024.

关键词： Advanced driver assistance systems

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Semi-end-to-end Based ECG abnormality detection method 7

Semi-end-to-end Based ECG abnormality detection method

引用

7th international conference on computer information Science and Application technology, CISAT 2024

作者： Zhang, Wanli Gao, Cuiyun Huang, Songqun Luo, Binwen Zhou, Yingjie Zhao, Yang Anhui Jianzhu University Power Quality Analysis and Load Detection Technology Electronic and Information Engineering School Hefei China Second Military Medical University Department of Cardiovasology Changhai Hospital Shanghai China

ISBN: (纸本)9798350375107

To solve the problem of information redundancy or loss caused by ECG abnormality detection in fixed-length samples, our team proposed a feature extraction and classification method based on 3R samples and TSL. Although this method solves the above problems, it also has two defects: the current algorithm is based on the results obtained for each 3R sample, and it does not regression to the complete sample, which will lead to multiple classification results for a complete sample in some cases. In addition, the original algorithm needs to screen high-quality signals, in which the double-threshold threshold method can not adapt to the signals from different devices. we proposes a semi-end-to-end ECG abnormality detection and quadratic modelling regression algorithm in this paper. In the pre-processing part, the high-quality screening process is omitted, and the noise samples are classified and recognized as a class of ECG samples. Meanwhile, for the problem that the original 3R classification results are not reduced to the complete samples, this study proposes a secondary modelling approach based on the 3R classification probability matrix and the complete sample labels to regress the classification results. twelve-category classification and three-category classification experiments are carried out on Cardiol Set dataset, and the results show that the method in the paper has significant improvement over the original method. © 2024 IEEE.

关键词： Long short-term memory

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CA-UNet: A Brain MRI Segmentation Model Based on U-Net with Attention Mechanism 7

CA-UNet: A Brain MRI Segmentation Model Based on U-Net with ...

引用

7th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xinya, Song Xingguang, Duan Xujia, Wang Fengxinyun, Fang Jiexi, Tian Changsheng, Li School of Medical Technology Beijing Institute of Technology Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350350890

Brain tissue segmentation is of paramount importance in the field of medical image processing, as its accuracy directly impacts subsequent diagnosis and treatment processes. However, the intricate structure of brain tissue makes it difficult to achieve precise segmentation. To address these issues, we propose a neural network based on UNet with attention mechanism and context fusion module, named CA-UNet. In the network, a Convolutional Block Attention Module (CBAM) is added in encoder and decoder to enhance the model's feature extraction capabilities. then, the encoded information is sent to the Multi-scale Context Fusion Module (MCFM) for multi-scale context information fusion. In essence, CA-UNet refines the original UNet by incorporating attention mechanisms to better identify and segment small regions within brain MRI images, making it more effective for medical image analysis tasks. the results of this study indicate that the proposed method achieves an average Dice Similarity Coefficient of 95.77%, with white matter being 96.67%, cortical gray matter being 93.63%, basal ganglia and thalami being 95.00% and ventricular cerebrospinal fluid being 96.79%. the introduction of attention mechanism and contextual fusion module is an effective approach to enhance brain tissue segmentation performance. © 2024 IEEE.

关键词： Medical image processing

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