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共 1333 条记录，以下是101-110 订阅

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Indian Sign Language Recognition Using Classical and machine learning Techniques - A Review 2

Indian Sign Language Recognition Using Classical and Machine...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Kalava, Mani Deepika Kadiyala, Likhita Kommineni, Sai Atla, Ramana Reddy Thadikemalla, Venkata Sainath Gupta Electronics and Communication Engineering V R Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350310719

Humans need to communicate in order to emphasize their thoughts and feelings, collaborate with others, and elevate society as a whole. A hearing-impaired person uses sign language to communicate and this language develops naturally within them. However, the non-signer community doesn't somehow acknowledge it and hence this remains as a significant barrier that negatively impacts living quality. To bridge the gap, effective sign-language recognition (SLR) system is required and is still an unsolved research issue. New technologies have been developed for the past few years to counter the problem of recognition and were mainly developed using sensors and hardware equipment based on gloves. As contrary to earlier technology, this review presents that, there is no need for expensive and complex hardware in order to recognize sign language, only a modern device with a camera is sufficient. This is accomplished by using Google's MediaPipe framework and machine learning techniques. In this paper, we had presented various techniques developed for Indian sign language and our future goal is to deliver a reliable SLR system with computer vision and AI due to its self-learning capabilities and increased accuracy. © 2023 IEEE.

关键词： Audition

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Research on the Optimal Action Time Window of Financial Support Policy Based on Deep learning 2

Research on the Optimal Action Time Window of Financial Supp...

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2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Wang, Nan Li, Feng Shenyang Institute of Engineering Shenyang China Peking University Beijing China

ISBN: (纸本)9798350368888

As China's financial market opens up and innovation accelerates, regional financial risks have gradually emerged and become one of the key concerns of current financial regulation. This project takes Liaoning Province as an example, takes the spatio-temporal correlation between financial regulation and economic development as the research object, and selects a total of 16 mixed-frequency sample indicator pools representing the state of economic and financial development from five dimensions. Recurrent neural network (RNN) in deep learning is used as the basis for model construction to fit the trend of GDP under different circumstances. The effectiveness of regional financial regulation policies is explained from a quantitative perspective. The optimal time window for the introduction of financial support policies is found through model generalization. This research will play a crucial role in systematically preventing regional financial risks, giving full play to the incentive effect of policies, and better leading economic development. © 2024 IEEE.

关键词： Decentralized finance

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Design of a Sanskrit Devanagari Script Recognition and Sanskrit to Chinese Translation System Based on Deep learning 2

Design of a Sanskrit Devanagari Script Recognition and Sansk...

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2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Shi, Biying Tang, Jialin Wu, Shihong Zhuhai College Beijing Institute of Technology Dept. of Information Zhuhai China YGsoft Inc. Ai Business Unit Zhuhai China

ISBN: (纸本)9798331515669

This study endeavors to enhance the accuracy of Sanskrit Devanagari script recognition through advanced deep learning techniques and to achieve accurate conversion from Sanskrit to Chinese using machine translation technology, in support of the digitization of ancient literature and the preservation of cultural heritage. A combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is used for text recognition, along with a neural machine translation system based on the Transformer model for translating Sanskrit to Chinese. Experimental results show that the OCR system employed in this study achieves an accuracy rate of 89%, a recall rate of 77%, and an F1 score of 0.826 in recognizing Sanskrit text. The machine translation system effectively converts Sanskrit into semantically accurate Chinese text, with translation quality quantified by BLEU and METEOR evaluation metrics, reaching scores of 41% and 30% respectively. In summary, the findings of this paper not only optimize the text processing workflow for Sanskrit but also provide a similar framework and practical experience for text recognition and translation of other similar languages, contributing to the further advancement of cultural exchange and linguistic research. © 2024 IEEE.

关键词： Chinese Devanagari Neural machine translation Optical character recognition Sanskrit

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proceedings of the 2nd international conference on signal processing, Computer Networks and Communications, SPCNC 2023

Proceedings of the 2nd International Conference on Signal Pr...

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2nd international conference on signal processing, Computer Networks and Communications, SPCNC 2023

ISBN: (纸本)9798400716430

The proceedings contain 75 papers. The topics discussed include: bearings-only passive location mathematical model for UAV cluster based on three points;towards radar emitter recognition in changing environments with domain generalization;codebook-based digital fountain encoding and decoding method;a review of intrusion detection research based on deep learning;design of short wave anti fading monitoring system based on large aperture polarization sensitive array;federated learning with sample-level class balancing aggregation;acceleration signal integration method based on variational mode decomposition;exploration of 5G wireless network coverage scheme in cement factory;and research on voice recognition and control technology for the elderly at home.

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Non-contact Detection of Sleep Apnea Event based on Respiratory signal using Deep learning 2

Non-contact Detection of Sleep Apnea Event based on Respirat...

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2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Xu, Yan Zhuang, Zhongxu Hong, Hong Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing China

ISBN: (纸本)9798331515669

Sleep apnea is a common sleep disorder. In recent years, numerous studies have concentrated on developing automated diagnostic methods for sleep apnea based on machine learning and deep learning. However, most of these methods use the signals from PSG sensors, which usually obstruct the patients' breathing and cause discomfort. Radar-based contactless detection techniques overcome these limitations, but there are still some drawbacks. For example, most focus on sleep apnea classification and cannot localize events precisely. In this study, we propose a fine-grained, non-contact sleep apnea detection method based on respiratory signals. Our method combines convolutional neural networks and multi-head self-attention mechanisms. We evaluate the method on clinical datasets, and the results demonstrate that this method achieves an accuracy of 83.7% in 1-second-level sleep apnea segment detection. Additionally, the Intraclass Correlation Coefficient (ICC) for the Apnea Index (AI) is 0.915, and the Mean Absolute Error (MAE) is 6.34. It has the potential to serve as an at-home monitoring method. © 2024 IEEE.

关键词： Sleep research

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Anomaly Detection in Electro-Mechanical Devices Using MFCC 2

Anomaly Detection in Electro-Mechanical Devices Using MFCC

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Chuphal, Mayank Singh, Kiran Bisht, Ayush Sharma, Vikrant Awasthi, Shashank Vats, Satvik Graphic Era Hill University Computer Science and Engineering Dehradun India Gl Bajaj Institute of Technology and Management Department of Cse Greater Noida India G.L. Bajaj Institute of Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

This research introduces an innovative approach to detecting anomalies in electric motor-driven fan systems. Mel-Frequency Cepstral Coefficients (MFCCs) are employed for sound signal feature extraction. A specialized artificial neural network (ANN) model is trained using these extracted features to differentiate between normal and anomalous sound patterns based on their acoustic characteristics. The results demonstrate the efficacy of the proposed methodology in distinguishing normal sounds from defective sounds of electric motor-driven systems. This research contributes to the enhancement of maintenance practices and operational reliability within industrial settings, the field of electric motors, and anomaly detection using MFCCs. © 2024 IEEE.

关键词： ANN Audio classification Electric motor anomaly machine learning MFCC signal processing

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Research and Application of a Graphical User Interface System for Peripheral Blood Cell Detection and Identification Based on Deep learning Model YOLOv5 2

Research and Application of a Graphical User Interface Syste...

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2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Lu, Yan Ma, Xingxing Zhou, Yun Zhao, Shilei Zhang, Haiyan Zhang, Mingyi Chen, Hao Song, Yang Gao, Yunfei Inner Mongolia University of Science and Technology Baotou Medical College Baotou City014060 China

ISBN: (纸本)9798350368888

With the advancements in machine learning and deep learning technologies, significant breakthroughs have been achieved in the field of target recognition. This paper aims to research and apply a graphical user interface (GUI) system for blood cell detection and identification based on the deep learning model YOLOv5. The system enhances the accuracy of blood cell detection and identification by medical professionals, thereby improving diagnostic efficiency. This study mainly encompasses the collection and preprocessing of a blood cell image dataset, the training and optimization of the YOLOv5 model, and the design and development of the GUI system. Firstly, a substantial dataset of blood cell images is collected and subjected to preprocessing steps such as data cleaning and image augmentation to improve the robustness and accuracy of the training model. Secondly, the YOLOv5 model, based on deep learning target recognition, is applied for the detection and identification of blood cells. Then, the model is trained and optimized to enhance the accuracy of blood cell detection and identification. Lastly, a GUI system featuring a user-friendly interface and interaction is designed and developed. The trained model is then applied to real-world tasks of blood cell detection and identification, enabling medical professionals to conveniently use and operate the system, thereby improving work efficiency. Experimental results indicate that the GUI system for blood cell detection and identification, based on the deep learning model YOLOv5, can accurately detect and identify blood cells. This holds significant implications for blood analysis and related disease diagnosis in the medical field. © 2024 IEEE.

关键词： Graphical user interfaces

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A Study on Student Behavior Recognition Based on YOLOv7 2

A Study on Student Behavior Recognition Based on YOLOv7

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2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Wang, Lin Chen, Liang Liu, Wenhong Shengyang Ligong University College of Automation and Electrical Engineering Shenyang China Northeastern University College of Information Science and Engineering Qinghuangdao China

ISBN: (纸本)9798350368888

Student classroom behavior is an important part of classroom instructional research, and student behavior reflects their level of attention and participation in teaching content, providing valuable feedback to educators. Based on the research on student classroom behavior recognition based on deep learning, this paper combines the complex environment of university classrooms to accurately identify student behaviors. The research and innovation of this paper are as follows: (1) Establish a dataset. Taking the behavior data of students in a school classroom as the research object, three behaviors of "learning", "sleeping"and "playing with mobile phones"were studied.(2) The lightweight MobileOne is used to replace the YOLOv7 backbone feature extraction network, which reduces the number of parameters and greatly shortens the detection time. (3) BiFPN is used at the neck of the network to enhance the feature representation ability through adaptive feature fusion and context-aware mechanism, so as to improve the learning ability of the model. The network model proposed in this paper analyzes the experimental results on this self-defined data set, and the average detection accuracy reaches 88.67%, which is improved by 8%;the parameters of the model are only 32.58 MB, which is decreased by 42.51%;the detection speed is 64.33 f/s, and the network parameters are reduced by 42.51%, increased by about 5.37 f/s. It has faster recognition speed and accuracy, and is more suitable for class student behavior classification detection. © 2024 IEEE.

关键词： Contrastive learning

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Research on Quantum machine learning Technology Based on Regenerating Kernel Hilbert Space 2

Research on Quantum Machine Learning Technology Based on Reg...

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2nd international signal processing, Communications and Engineering Management conference, ISPCEM 2022

作者： Tian, Jincheng Chen, Xinyun Li, Chentao School of Computer Science and Engineering Macau University of Science and Technology 999078 China School of Computer Engineering Jimei University Jimei District Fujian Province Xiamen City361021 China

ISBN: (纸本)9781665492713

In the era of big data, software algorithms are improving rapidly, and machine learning algorithms are also widely used. In doing so, transmitting signals from quantum devices will also contribute to research on machine learning technology. Traditional machine learning networks face the problem of low efficiency and slow response when processing large amounts of complex data. Quantum computing technology offers supercomputing power to compensate for this shortcoming. Based on the advanced development of quantum machine learning and quantum computing technology, this paper combines machine learning technology. Integrate the regenerated kernel Hilbert space to complete the composition of the architecture, effectively improving the computational performance of machine learning. © 2022 IEEE.

关键词： learning algorithms

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Exploring the Impact of learning Management System (LMS) Adoption on Online learning: An Educator-Centric Perspective 2

Exploring the Impact of Learning Management System (LMS) Ado...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mathur, Priyanka Sharma, Asha Mamraj Manipal University Jaipur Jaipur India

ISBN: (纸本)9798350366570

learning Management Systems (LMS) are used in the context of online learning and it has to be significant from the perspective of educators. It is crucial to comprehend how LMS adoption affects teaching methods, instructional strategies, and overall learning experiences as technology continues to transform educational environments. Utilizing a qualitative research approach, the study collects data from instructors who are actively involved in online learning through surveys and Think aloud observations. The primary objective of this research is to unravel the multifaceted implications of LMS adoption on the teaching profession. The paper addresses several key aspects, including the integration of LMS into pedagogical strategies, changes in teaching methodologies, and the perceived effectiveness of LMS in facilitating student engagement and comprehension. Special attention is given to the challenges and opportunities that emerge for educators as they navigate the complexities of incorporating LMS into their teaching repertoire. The findings of this study contribute to the existing body of knowledge by shedding light on the nuances of LMS adoption from a teacher-centric perspective. Common themes and patterns in teachers' experiences with LMS, such as the impact on workload, professional development needs, and the role of LMS in fostering collaborative learning environments is identified. Moreover, the alignment between teachers' expectations and the actual outcomes of LMS integration, offering insights into areas that may require further refinement or support is being done. © 2024 IEEE.

关键词： Collaborative learning

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