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检索条件"任意字段=International Conference on Computer Technology and Development"

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Study and development of Sensor Controlled Arduino Movable Device Using IoT 4

Study and Development of Sensor Controlled Arduino Movable D...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ammisetty, Veeraswamy Shakunthala, B.S. Ande, V.S. Sudhakar Rao Maddinala, Viswanath Veera Krishna Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Vaddeswaram Andhra Pradesh Guntur522302 India Kalpataru Institute of Technology Department of Ise Karnataka Tiptur572202 India Vasireddy Venkatadri Institute of Technology Department of It Nambur Andhra Pradesh Guntur India Sri Vasavi Engineering College Department of Cse Peddatadepalli Andhra Pradesh Tadepalligudem India

ISBN: (纸本)9798350300888

To know that the interaction between human and machine interfaces will increase significantly, bridging the gap between machines and humans, making standardized lives much easier. Robocar is an Arduino controlled car that runs on an Arduino. In fact, this is the archetype of open architecture. Cargobot: Bluetooth-controlled Arduino Robocar performs a variety of functions. Provides powerful and versatile robotic vehicles and reduces labor. The development of flexible and intelligent Arduino-controlled vehicles is critical to supporting the physical distribution of goods. Wi-Fi module technology is designed for wireless communication over a certain distance. © 2023 IEEE.

关键词： Wi-Fi

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Analysis On the Various Approaches Using Machine Learning in Different Businesses 1

Analysis On the Various Approaches Using Machine Learning in...

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1st international conference on Healthcare Innovations, Software and Engineering Technologies, HISET 2024

作者： Desai, Vaibhava Vasantrao Patil, Manisha Thorat, Manisha Kshirsagar, N.S. Kestwal, Mukesh Chougule, Pravin Institute of Management Bharati Vidyapeeth Deemed to Be University Kolhapur India Krishna Institute of Computer Application and Management Karad India Department of Obstetrics and Gynecology Karad India Chief Innovation Officer Iit Ropar Technology and Innovation Foundation Ropar India

ISBN: (纸本)9798350360707

Deep Learning (DL) and Machine Learning (ML) are employed everywhere. The healthcare industry is seeing a rise in the use of ML and DL, especially when paired with big data analytics. Predictive analytics, drug discovery, medical image analysis, personalized medicine, and electronic health record (EHR) analysis are a few applications of machine learning and deep learning in the healthcare industry. It has developed into a very sophisticated and well-liked instrument in the field of computer science. New directions for study and development have been made possible by the development of ML and DL in a variety of sectors. In this research, investigators examine several machine learning approaches that may be utilized to increase an application's intelligence and functioning. As a result, the primary contribution made by this study is to explain the concepts underlying various machine learning approaches and how they can be applied in a wide range of real-world applications, such as e-commerce, cybersecurity platforms, smart towns and cities, healthcare, and agriculture, among many others. Based on our findings, we also highlight the challenges and future research directions. The general purpose of this work is to serve as a technical reference for industry and academic professionals, as well as decision-makers in a range of real-world scenarios as well as application areas. © 2024 IEEE.

关键词： Electronic health record

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Application of Wearables in Healthcare Management: Recent Trends and Futuristic Approach

Application of Wearables in Healthcare Management: Recent Tr...

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2022 OPJU international technology conference on Emerging Technologies for Sustainable development, OTCON 2022

作者： Wagholikar, Smita Wagholikar, Omkar Pune India Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9781665492942

Healthcare management shows revolutionary changes attributed to the technological advances and wearable devices. The wearables are fabricated for accurate measurement of organs and fluids in human beings through sensors, compiling and providing information for healthcare support and diagnosis. Artificial intelligence, developments in telecommunication, online services and wearables have fulfilled requirements of e-health for healthcare services and the bridged the gap between facilities available to city and countryside communities. This study discusses the latest developments and patents in wearables that are specially designed for elderly people, mental health treatment, thermal treatment and more specific applications. Nano materials, smart textiles and wearables operating in a wireless mode are recent trends in healthcare support and management. Artificial intelligence, internet of things and high-speed internet and various types of sensors are key factors in designing smart wearable devices employed in healthcare. Analysis of documents obtained from the Scopus database applying VOSviewer in the healthcare sector is presented. The paper ends by mentioning the research ideas that can be undertaken by future researchers. © 2023 IEEE.

关键词： Wearable sensors

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Research on Chinese Elementary Geometry Text Matching Based on Self-Attention and Bi-LSTM Network 20

Research on Chinese Elementary Geometry Text Matching Based ...

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20th international computer conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Chen, Guoqing School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Text matching can assist computers in understanding and processing large amounts of textual data, thereby providing more intelligent and personalized services. Addressing issues such as missing traditional machine learning mathematical pre- training data and incomplete semantic extraction of mathematical formula features, a Chinese mathematical plane geometry text matching model based on self-attention mechanism and bidirectional long short-term memory networks (SABi-LSTM) is proposed. The model constructs a parsing tree for mathematical expressions, divides semantics, and establishes a Bi-LSTM network for feature extraction. The performance and timeliness of the model's network structure are evaluated, optimizing the Bi-LSTM layers and hidden layer nodes of the proposed network to determine the optimal network structure. A self-attention mechanism is introduced for optimizing weight parameters to reduce the impact of noise data. Experimental results indicate that compared to traditional machine learning algorithms, the Bi-LSTM network with the introduced self-attention mechanism improves the accuracy of Chinese mathematical text matching by an average of 0.55%. After further optimizing the construction of word vectors, the recognition accuracy increases by an average of 0.34%, resulting in a final accuracy of 89.49%. The proposed model effectively determines the matching degree of Chinese elementary geometry text. © 2023 IEEE.

关键词： Semantics

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Parallel Acceleration of Real-time Feature Extraction Based on SURF Algorithm 15

Parallel Acceleration of Real-time Feature Extraction Based ...

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15th international conference on computer Research and development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

Visual feature extraction is the key to target registration calculation. Compared with SIFT, SURF feature extraction algorithm has less computational complexity and better applicability when image signal-to-noise ratio is low. In this paper, an FPGA-based SURF algorithm for real-time feature extraction and parallel acceleration is designed for large-field scene registration applications of space targets. Firstly, the application scenario of space target is analyzed, and the grayscale features of the data containing the target used for registration are counted. Then, the five main parts of SURF algorithm were analyzed, and the algorithm was simplified based on the gray feature of the image. Finally, according to the simplified results, the parallel pipeline architecture of each module is designed based on FPGA. SURF computing modules are also given in this paper the design of the architecture and data flow of information, in XC7K325 completed the whole design of experiment, the results show that the design for 1024 × 1024 pixel image, single frame image processing time need only 51 us, resource usage is not more than 18.6%, power consumption is not more than 7.5 W, The computational efficiency is 87% higher than the previous design. Low signal-to-noise ratio (SNR) was used to simulate space target scene data for simulation test calculation, and the SURF descriptor error was less than 3%, ensuring the system's calculation error while effectively utilizing resources. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Visual Analysis of international Innovation and Entrepreneurship Courses: Exploring Hotspots and development Trends in the Education Field 24

Visual Analysis of International Innovation and Entrepreneur...

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2024 international conference on Intelligent Education and computer technology, IECT 2024

作者： Li, Yanjun Zhou, Jianmei Huang, Yaoping Ji, Zibo Intuitionistic Fuzzy Set Grey Correlation Student Evaluation Topsis China

ISBN: (纸本)9798400709920

[Purpose/Significance] In today's society, driven primarily by innovation, universities have placed unprecedented emphasis on the cultivation of innovative talent through innovation and entrepreneurship courses. This study aims to systematically review, integrate, and analyze the domain of innovation and entrepreneurship courses using visual knowledge mapping technology, providing the academic community with a comprehensive examination of the current state of research in this field. We hope these findings will offer valuable references and guidance for future research and practice in innovation and entrepreneurship education. [Methods] This study employs CiteSpace visualization software for comprehensive bibliometric analysis of core academic literature spanning 19 years[1]. The detailed literature interpretation not only outlines the research trajectory in the field of innovation and entrepreneurship courses but also elucidates key research topics and prominent hotspots[11]. By systematically integrating innovation and entrepreneurship courses, this study delves into their structure, research focuses, and current frontier topics, providing a theoretical foundation and guiding future academic inquiries and practical applications[10]. [Results/Conclusions] Research on university innovation and entrepreneurship courses has matured into a well-developed field and exhibits new trends. The study reveals that prior to 2016, the number of academic papers related to this field grew slowly, but post-2017, there was a noticeable surge. Among the top 20 institutions by publication volume, 18 are from the United States, with the UK and France each contributing two, reflecting the dominant position of the US in this research field and indicating significant research strength and influence from the UK and France. According to keyword evolution sequences, research themes mainly include curriculum system construction, entrepreneurship research, innovation research, teaching model i

关键词： Curricula

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Feature Point Matching for Human-computer Interaction Multi-Feature Gesture Recognition Based on Virtual Reality VR technology 5

Feature Point Matching for Human-Computer Interaction Multi-...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Li, Jinghui Chen, Shuxin Tianjin Renai Coll Tianjin 301636 Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

This paper proposes a system for human-computer interaction using video gesture extraction. The system includes four modules: video acquisition and separation, image preprocessing, gesture extraction and recognition, and virtual reality human-computer interaction. This paper proposes a system for human-computer interaction using video gesture extraction. The system includes four modules: video acquisition and separation, image preprocessing, gesture extraction and recognition, and virtual reality human-computer interaction. Among them, the contour feature should be listed by the LCS description method. This article also uses three methods to describe the characteristics of dynamic gestures, which are static gesture sequence, fingertip movement feature, and palm movement feature. The static gesture sequence refers to a set of ordered static gesture numbers.

关键词： Feature Point Matching Human-computer Interaction Multi-Feature Gesture Recognition Virtual Reality

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Image Recognition of Different Hamster Breeds Using Convolutional Neural Networks 6

Image Recognition of Different Hamster Breeds Using Convolut...

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6th IEEE international conference on Artificial Intelligence in Engineering and technology, IICAIET 2024

作者： Estrella, Allen Paulo S. Lucban, Francis Miguel O. Caya, Meo Vincent C. School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9798350389692

Hamsters exhibit a variety of breeds, making breed identification challenging at first glance. This study proposes a solution using Convolutional Neural Networks (CNNs), specifically VGG16 architecture, to aid pet owners in distinguishing between hamster breeds. Researchers developed an image capturing system and a web application where users can upload hamster images to receive breed identification. The system focuses on recognizing the four most common hamster breeds: Campbell, Roborovski, Syrian, and Winter White, utilizing the VGG16 model trained and tested on a dataset collected by the researchers. The implementation involved a Raspberry Pi 4B with 4GB RAM and a webcam housed in a specially designed box for image recognition of hamsters. Evaluation using a confusion matrix demonstrated an overall accuracy of 91.67%, indicating the effectiveness of the proposed system in identifying hamster breeds. This research contributes to the development of accessible tools for pet owners and enthusiasts interested in hamster breed identification. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning in Renewable Energy Systems 4

Machine Learning in Renewable Energy Systems

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4th international conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Munir, Muhammad Sohail Shahid, Usama Nasir, Usama Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Fatima, Hoor Yaqub, Muhammad Atif Punjab Institute of Management and Professional Development Lahore Pakistan School of Business Computing and Social Sciences University of Gloucestershire Cheltenham United Kingdom Bahria University Lahore Campus Lahore Pakistan University of Central Punjab Lahore Faculty of Information Technology Lahore Pakistan Bahria University Lahore Campus Department of Computer Science Lahore Pakistan National College of Business Administration and Economics Lahore Pakistan Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350391183

From solar and wind power to hydropower and biomass, smart grids, the catalytic industry, and power storage and distribution, this paper covers the gamut of ML's present-day uses. Among the many domains covered by machine learning are classification issues and the methods used to solve them. Classification schemes have recently attracted a lot of interest as a tool for improving the deployment, administration, and optimization of Renewable Energy Systems. The integration of new data sources (such as innovative sensors) and improved algorithms for creating trustworthy data will improve the data flow between ML and systems. The growth of other important areas of data science, such big data analytics, will determine the significance of unsupervised and reinforcement learning in the energy industry. The development of sustainable uses of ML in non-industrial applications for energy management will be aided by massive implementations, specialized algorithms, and new technologies like 5G. This work aims to survey the state-of-The-Art classification algorithms for Renewable Energy situations, including both traditional and cutting-edge methods. Wind speed/power prediction, fault diagnostics in Renewable Energy Systems, power quality disturbance categorization, and other applications in alternative Renewable Energy Systems are some of the particular Renewable Energy topics covered in the paper's extensive literature analysis and discussion. Researchers and practitioners in the area may benefit from the paper's description of categorization algorithms and metrics used to Renewable Energy situations. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Attention-Based Multigranular Remote Sensing Image Extreme Object Detection Algorithm 23

Research on Attention-Based Multigranular Remote Sensing Ima...

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23rd international conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Li, Meng Tsang, Eric C.C. Xu, Wei-Hua He, Qiang School of Computer Science and Engineering Macau University of Science and Technology Taipa China College of Artificial Intelligence Southwest University Chongqing400715 China School of Science Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9798331528041

The detection of small objects and high aspect ratio objects is a challenge for remote sensing image object detection. In this paper, we refer to these objects as extreme objects. The Attention-Based Multigranular Extreme Object Detection Network (AMEODNet) is proposed to address these problems. It consists of two parts. The first part is a multi-granularity information fusion module. The second part is an extreme object extraction section. The proposed method is tested on the NWPU VHR-10 datasets. The results are compared with mainstream object detection algorithms to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Optical remote sensing

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