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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

共 37967 条记录，以下是261-270 订阅

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Web Based Application to Recognize Diseases and Providing Consultant to Patients 3

Web Based Application to Recognize Diseases and Providing Co...

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3rd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ajitha, P. Sathish Kumar, P.J. Navaneethakrishnan, Sundara Rajulu Sivasangari, A. Surendran, R. Sathyabama Institue of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Trichy India Sathyabama Institue of Science and Technology School of Computing Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

One of the most important aspects of civilization is human wellness. The best and most accurate condition analysis seeks to offer the necessary assistance as soon as practical. The clinical research factor necessitates knowledge from other fields, such as computer science and statistics, as this comprehension is frequently difficult. The need to pursue novel approaches places a heavier burden than normal on these fields. The variety of new strategies allows for a comprehensive investigation while avoiding specific elements. As a solution to this issue, a systematic evaluation of human illnesses with system mastering is proposed. This study centers on current techniques for system learning advancement applied to the prognosis of human diseases in the medical field in order to discover exciting trends, make light predictions, and assist in decision-making. This initiative includes a scientific chatbot that uses synthetic intelligence to answer patients' questions. This article explains a one-of-a-kind set of device learning algorithms that are used in healthcare programmes to provide excellent preferred choice assistance. This article is intended to cover a research gap in the development of practical choice aid structures for scientific applications. © 2024 IEEE.

关键词： Clinical research

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Survey on LED lamps for residential applications: Characterization of performance 23

Survey on LED lamps for residential applications: Characteri...

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23rd International scientific conference on Electric Power engineering, EPE 2023

作者： Hudač, Tomáš Drapela, Jiri Kukačka, Leoš Meyer, Jan Stiegler, Robert Brno University of Technology Faculty of Elect. Eng. and Communication Dept. of Electrical Power Engineering Brno Czech Republic Institute of Mechatronics and Computer Engineering Technical University of Liberec Liberec Czech Republic Institute of Electrical Power Systems and High Voltage Engineering Technische Universität Dresden Dresden Germany

ISBN: (纸本)9798350335934

Aim of the paper is to conduct a survey of the present market, focusing on the best-selling retrofit LED lamps and to analyze experimentally their fundamental EMC performance for subsequent classification of the LED drivers. Two stage experimental characterization testing approach is adopted. First, input current and output luminous flux in steady-state under nominal supply conditions is acquired. Next, transfer of input supply voltage interharmonics to output visible flicker components in terms of gain factor curves is obtained. Based on the achieved and discussed testing results, classification of the LED lamps is developed. © 2023 IEEE.

关键词： Light emitting diodes

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Automated Requirements engineering in Agile Development: A Practitioners Survey

Automated Requirements Engineering in Agile Development: A P...

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2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Umar, Muhammad Aminu Lano, Kevin King's College London Department of Informatics United Kingdom Ahmadu Bello University Department of Computer Science Zaria Nigeria

ISBN: (纸本)9798350322972

Requirements engineering (RE) in the context of agile development is the process of discovering, analysing, validating, and managing software requirements through engaging with the stakeholders. RE activities are central to software development and its success. Advances in automation have been explored in requirements engineering activities and processes in the industry. However, the application of automated support is still limited in practice. In this study, we examine automated RE support in agile development through a qualitative analysis using industry experts. We have found that the application of automated RE support tools is primarily in the experimental stage, largely due to the limited practical implementation in industry settings. The current focus has been on research experiments, with relatively fewer instances of real-world industry application. Although experts are optimistic about its huge potential to reduce development efforts and costs if automation is harnessed. Therefore, we anticipate that these findings will be beneficial for practitioners responsible for developing RE tools for agile development, as well as for the researchers. © 2023 IEEE.

关键词： Requirements engineering

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Distinguishing Abstracts of Human-Written and ChatGPT-Generated Papers in the Field of computer science 14

Distinguishing Abstracts of Human-Written and ChatGPT-Genera...

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14th International conference on computer and Knowledge engineering, ICCKE 2024

作者： Arzani, Mohsen Vahdat-Nejad, Hamed Hossein-Pour, Matin PerLab Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

ISBN: (纸本)9798331511272

In the era of artificial intelligence, advanced technologies such as text deep fakes have emerged, utilizing AI and deep learning algorithms to generate text contents that convincingly mimic reality. Text-based deep fakes, commonly found in emails, articles, news, and social media posts, pose a significant threat to public trust. Our research proposes a method to distinguish between fake and genuine scientific abstracts in the field of computer science using a specialized dataset and deep learning models. The proposed detection model can differentiate between real and fake abstracts with 97.5% accuracy. Additionally, we employed metrics such as precision, recall, accuracy, and F1 score to measure the performance of our system in detail. ©2024 IEEE.

关键词： Contrastive Learning

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Algorithms for Detection of Anomalies in Data From Clinical Studies and Medical Registries

Algorithms for Detection of Anomalies in Data From Clinical ...

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2024 International conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Patil, Harshal Swati Sudheendramouli, H.C. Pratyush, Kumar Parikh, Swapnil Vishwakarma, Rajesh Kumar Sri Balaji University Maharashtra India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand 248002 India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science and Application Mysuru570016 India Shri Vile Parle Kelavani Mandal's Institute of Pharmacy Department of Pharmaceutical Chemistry Maharashtra 424001 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat India Jaypee University of Engineering and Technology Department of Electronics and Communication Engineering Madhya Pradesh 473226 India

ISBN: (纸本)9798350375237

Anomaly detection is a burgeoning trend in data quality, underpinned by the fundamental principles of honesty and truthfulness in scientific research. Adherence to these principles is critical for advancing science and ensuring public trust in scientific findings. Deviations from these principles can lead to scientific misconduct or fraud, with potentially severe consequences, particularly in clinical research where human lives may be at stake. This work introduces an implemented algorithm for anomaly detection, leveraging basic machine learning concepts such as training on original datasets and testing on new datasets. Two training methods, resubstitution, and external validation (hold-out), were employed. To assess classification accuracy, the input dataset was deliberately augmented with anomalous records, and the detection results were compared against a vector of artificially generated anomalies. Prior studies may have overlooked factors such as varying anomaly occurrence rates and the need for manual threshold adjustments, which our algorithm addresses. By addressing these limitations, the algorithm contributes to the advancement of anomaly detection methodologies in clinical research and beyond. © 2024 IEEE.

关键词： Clinical research

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A DCAT Profile Approach for Citizen science 23rd

A DCAT Profile Approach for Citizen Science

引用

23rd International conference on Web engineering (ICWE)

作者： Alvarez Luna, Reynaldo Zubcoff, Jose Garrigos, Irene Univ Informat Sci Havana Cuba Univ Alicante Alicante Spain

ISBN: (纸本)9783031503849;9783031503856

Many citizen science projects generate large amounts of data in the life sciences. In fact, the most successful and mature projects study species movements or behaviour patterns through spatio-temporal observations. However, the use of these data is difficult in some scenarios due to the heterogeneity of the sources and the lack of consensus on how to share them across different scientific communities. This work focuses on defining a DCAT profile for citizen science based on PPSR Core to promote consistency and interoperability with other DCAT-based systems, such as data repositories and search engines. This can facilitate the discovery, sharing and reuse of citizen science data across different life science projects.

关键词： Citizen science Data sharing DCAT PPSR Core

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Volleyball game tactical data prediction system using computer vision technology 2

Volleyball game tactical data prediction system using comput...

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2nd International conference on mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Xie, Jingguang Cheng, Xinnian Guangdong University of Science and Technology Department of Physical Education Dongguan China Jieyang Polytechnic Dept. Arts & Sports Guangdong Jieyang522000 China

ISBN: (纸本)9798350386639

This paper aims to create a highly automated computer vision aided volleyball tactical prediction system, which revolutionizes the flow of data analysis in volleyball games. The system relies on the advanced technology of computer vision and machine learning to extract detailed tactical information directly from the game video stream without manual intervention. This model integrates advanced visual recognition algorithms to generate an adversarial network, which can track player movements, ball trajectories and field layout in real time, and accurately decode the dynamic changes of the game. Using deep neural networks, especially in combination with convolutional neural networks (CNNS) and recurrent neural networks (RNN), the system not only recognizes basic movements, but also analyzes tactical behavior in depth, such as offensive strategy, defensive formation changes, and even predicts the moment of offensive and defensive transition. A key breakthrough is that we have optimized the algorithm to deal with fast movement and occlusion, ensuring high recognition accuracy in complex match situations. Through the introduction of multi-modal fusion and self-attention mechanism, the system can learn and extract rules from massive data, effectively predict the team's tactical preferences and potential scoring opportunities, and provide real-time tactical advice to the coaching team. Experimental verification shows that the system is far superior to the existing solutions in the accuracy of tactical recognition and match result prediction. The application of this model marks a new stage of sports data analysis, brings scientific support for sports optimization, training strategy and competition strategy, and promotes the frontier development of sports science and technology. © 2024 IEEE.

关键词： Generative adversarial networks

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Domain Adaptation Using Generative Adversarial Networks for Medical Image Synthesis 5th

Domain Adaptation Using Generative Adversarial Networks for ...

引用

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

作者： Ganesh, D. Verma, Upendra K. Patil, Smita Alam, Intekhab Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India 3School 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

Latest advancements in state-of-the-art scientific photograph evaluation depend heavily on the availability of large new datasets of cutting-edge annotated scientific photographs for version training. Unfortunately, obtaining massive samples of cutting-edge categorized scientific picas is ultra-modern high-priced, or impossible. Area edition is a way to generalize a version educated on a selected dataset to a brand new area, wherein the model turned into not directly educated. Generative adverse Networks (GANs) were used to generate realistic artificial photographs. It has enabled the opportunity present day the usage of GANs to bridge the domain hole via generating artificial medical photos, which could then be used for education fashions. On this paper;we evaluate the latest progress in making use of area edition the use of GANs for scientific photosynthesis, with an emphasis on applications including pathology, scientific imaging, and optical acceptable imaging. We talk about the benefits of ultra-modern using GANs for area adaptation and present processes for each present day of the above programs. We also speak about various challenges and future directions in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Exploring the Experimental Possibilities of AI based Real- Time Virtual Reality Assisted Touchless Home Appliance Control 1

Exploring the Experimental Possibilities of AI based Real- T...

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1st International conference on Innovative Sustainable Technologies for Energy, mechatronics and Smart Systems, ISTEMS 2024

作者： Kumar, C. Ashok Gnanasekar, A. Ramya, R. Narmatha, S.U. Arunadevi, R. Ravi, C.N. Engineering Mlr Institute of Technology Department of Electronics and Communication Hyderabad India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Physics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350384246

The design and execution of home automation systems have been greatly enhanced by recent technological breakthroughs, which have increased the capabilities and usefulness of embedded systems. There is a significant demand for smart home automation systems that include Virtual Reality (VR). One possible use of the proposed technology is to let the owner operate their home appliances through virtual reality. Using this is a breeze. In this work, we offer a modular, practical, and secure automation control structure for smart homes that is tied to virtual reality and has a cheap price tag. In this study, we take a simple look at how virtual reality may be used to regulate voltage for common home equipment including lights, fans, air conditioners, motor controls, and more. An AI-based Virtual Appliance Control System (AIVACS) is the name given to the proposed system because of its foundation in AI logic. In order to determine how effective the proposed model is, it is cross-validated using a standard Home Appliance Control System (HACS). The hardware has been assembled and tested in a scaled-down prototype with three options for controlling it: illumination, motors, and a fan in a domestic setting. The primary rationale for this research is the significant practical benefit that this VR-based hands-free operation would provide to physically impaired and elderly individuals, who wouldn't be required to physically go to the location of the switch. © 2024 IEEE.

关键词： Domestic appliances

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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