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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47594 条记录，以下是101-110 订阅

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Exploring Students' Attitudes Towards robotics technology in STEM Education: A Machine Learning-Based Prediction Analysis 4

Exploring Students' Attitudes Towards Robotics Technology in...

引用

4th IEEE international conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： El Aziz, Mahmoud I. Abd Elshewey, Ahmed M. Tarek, Zahraa El-Kenawy, El-Sayed M. Ghazy, Ahmed M. Bhatnagar, Roheet Shams, Mahmoud Y. Kafrelsheikh University Faculty of Education Department of Curriculum Instruction and Educational Technology Kafrelsheikh33511 Egypt Suez University Faculty of Computers and Information Computer Science Department Suez Egypt Mansoura University Faculty of Computers and Information Computer Science Department Mansoura35561 Egypt Delta Higher Institute of Engineering and Technology Dept. of Communications and Electronics Mansoura35111 Egypt Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India Kafrelsheiku University Faculty of Artificial Intelligence Dept. of Machine Learning 33516 Egypt

ISBN: (纸本)9798350387063

The aim of this paper is to predict the extent to which students will accept robotics technology in future teaching plans. The study involves students engaging in a two-week project-based learning program on robotics technology, after which they will decide whether it is suitable for them. To achieve this, the paper uses several prediction algorithms based on Machine Learning (ML), including Linear Regression (LR), Ridge Regression (RR), Elastic Net (EN) regression, and K-Nearest Neighbor (KNN) models. The sample group consists of 35 students from STEM high schools in Egypt. The research findings indicate that the students have gained confidence and satisfaction in robotics technology and believe it will have significant value in the future. The prediction results obtained from the ML regression models support these findings. The dataset used in the study includes seven input features and one target output representing the total prediction values of accepting and rejecting robotics technology in STEM schools. The regression models LR, RR, EN, and KNN achieve determination coefficients (R2) of 99.99%, 98.80%,99.20%, and 85.60%, respectively. © 2024 IEEE.

关键词： Prediction models

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Cloud-based Data Analytics for Automated Coastal Cleanup Robots with Convolutional Neural Network 8

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Babu, A. Ramesh Basavaraddi, Chethan Chandra S. Ramesh, S. Mouleeswaran, S.K. Sahoo, Santosh Kumar Solainayagi, P. Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India R&D Don Bosco Institute of Technology Bangalore Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Bangalore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

The growing danger of coastal pollution requires novel, automated remediation methods. This paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. The robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. The proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. The cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. The technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. The data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. The proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity. © 2024 IEEE.

关键词： Cloud-based Infrastructure Environmental Monitoring Image Recognition Real-time Processing Sensor Technologies

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Deep Learning Approaches for Detecting and Predicting Knee Osteoarthritis Severity 2

Deep Learning Approaches for Detecting and Predicting Knee O...

引用

2nd IEEE international conference on Recent advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Karegoudra, Shilpa Veeresha, R.K. Yendapalli, Vamsidhar Department of Computer Science and Engineering Udupi574110 India Department of Robotics and Artificial Intelligence Udupi574110 India GITAM University GITAM School of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350354461

Knee osteoarthritis stands as one of the most prevalent musculoskeletal ailments globally. Its initial signs include joint stiffness, discomfort, and limitations in functionality, marking it as a significant joint disorder. Older adults are more likely to have arthritis, and it normally gets worse with age. If there are no early, effective therapies used, it can lead to impairment in older people. Therapy must be started as soon as possible;otherwise, a full knee replacement may be necessary. The very subjective nature of the standard procedures creates a barrier to early illness progression detection. This study involves in-depth exploration and examination of the application of deep learning techniques for knee osteoarthritis detection and severity prediction. In this work EfficientNet-B6 was chosen for its superior performance in balancing accuracy, speed, and resource efficiency. Unlike models such as MobileNet or ResNet, X-ray images are used to train the model to predict the severity of knee osteoarthritis. The model classifies the data with an accuracy of 87%. A website for OA prediction is developed, the user needs to drag and drop an X-ray image in the dialog box given. The predicted output along with the severity will be shown within a few seconds. This website is very easy to use. © 2024 IEEE.

关键词： Knee prostheses

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Research and design of intelligent food delivery robot on campus under epidemic situation 2

Research and design of intelligent food delivery robot on ca...

引用

2nd Asia conference on Advanced robotics, Automation, and Control engineering, ARACE 2023

作者： Xu, Yanhui Yang, Dunzhao Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Class 2 Electronic Information Engineering Grade 19 China

ISBN: (纸本)9798350328363

In order to improve the efficiency of campus food delivery under the epidemic situation, an intelligent food delivery robot with Raspberry PI (ROS system) and STM32F103VET6 as the control core be designed in this paper, which can achieve the goal of fully automatic food delivery in the dormitory. The Raspberry PI (ROS system) sends the food delivery command through WIFI, the system receives the command and responds, sends the speed command to the controller installed in the robot chassis and advances to the designated position according to the predetermined route, and sends the command to the system to prompt the students to take the food. The system mainly includes radar navigation, ultrasonic obstacle avoidance and voice prompt system and other modules to work together to complete automatic food delivery. © 2023 IEEE.

关键词： Motion planning

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Cutting Tool Condition Monitoring Based on a Machine Learning Approach by Using Vibration Signals 1

Cutting Tool Condition Monitoring Based on a Machine Learnin...

引用

1st international conference on robotics, engineering, science, and technology, RESTCON 2024

作者： Tiwari, Saurabh Gunasegaran, V. Amarnath, M. Chelladurai, H. India Institute of Information Technology Design and Manufacturing Dept. of Mechanical Engineering Jabalpur India

ISBN: (纸本)9798350348842

In metal cutting operations, tool wear could result in higher energy utilization, excessive tool vibration, and deteriorated surface finish quality. Therefore, tool wear monitoring is critical to enhance manufacturing productivity and quality. This study proposes a new approach for monitoring and classification of tool wear with 192 data sets of turning experimental tests at different cooling approaches. The Daubechies wavelet transform is used to state the level of flank wear at different cooling approaches. The acquired vibration signals were processed in both the time and frequency domain with various features extracted. An artificial neural network (ANN) model was applied to detect the flank wear at dry, flood, and minimum quantity lubrication(MQL) cooling states. The results confirmed the ability of the monitoring system to detect the flank wear of the cutting insert by utilizing the tool vibration signals. © 2024 IEEE.

关键词： Neural networks

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Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities 7

Robotic Restroom Hygiene Solutions with IoT and Recurrent Ne...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Nasreen, A. Kathija Shenbagapriya, M. Seeni, Senthil Kumar Veda, Parthiban Meenakshi, B. Murugan, S. Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science And Technology Centre For Yoga College of Engineering And Technology Chenagalpattu Chennai Tamil Nadu Kattankulathur India Sri Sairam Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

Public restrooms needs to be frequently cleaned to maintain public health and hygiene. Traditional bathroom cleaning techniques may not solve current cleanliness issues. This research proposes an advanced robotics, Internet of Things (IoT), and Recurrent Neural Networks (RNNs) solution to these problems. The proposed system uses autonomous robots with IoT sensors and cameras. Robots identify cleaning and maintenance needs in restrooms. A central system receives data from IoT sensors on cleanliness indicators, including toilet paper, soap, and foot movement. Recurrent Neural Networks (RNN) processes this data to predict and prioritize cleaning requirements. The RNN monitors toilet conditions in real time and reacts to changing use and cleaning needs. This dynamic technique optimizes cleaning resource allocation and maintains facility cleanliness. The device also warns cleaning personnel when certain areas need quick attention. This novel technique makes restroom care more cost-effective, responsive, and environmentally friendly by combining robots, IoT, and RNNs. This study advances smart facility management, which uses technology to improve public space cleanliness, user experience, and resource usage. © 2024 IEEE.

关键词： Internet of things

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Mobile-Based Fitness Monitoring System: A Smartphone-Centric Tracker without External Accessories 15

Mobile-Based Fitness Monitoring System: A Smartphone-Centric...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Devi, P. Jayasri Archana Ilampiray, P. Kalaivani Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani India

ISBN: (纸本)9798331300579

This paper delves into the transformative impact of modern technology on human health, exploring the shift towards utilizing mobile applications as integral tools for health monitoring, control, and management. As society becomes increasingly interconnected and reliant on technology, concerns about the potential health implications arise. This paper investigates the nuanced relationship between technology and health, specifically focusing on the development of mobile applications as a paradigm shift from traditional external gadgets. © Grenze Scientific Society, 2024.

关键词： Electronic health record

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A Comprehensive Study of Gesture Recognition in Indian Sign Language

A Comprehensive Study of Gesture Recognition in Indian Sign ...

引用

2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Malviya, Shashi Mahajan, Anita Sethi, Kamal Kumar Ai & Ds Acropolis Institute of Technology and Research Indore India Cse Acropolis Institute of Technology and Research Indore India

ISBN: (纸本)9798350388800

Numerous studies have explored the domain of Indian Sign Language (ISL) recognition, often relying on datasets curated by researchers. This review examines various research endeavors, focusing on classification methodologies and the range of gestures analyzed. By surveying diverse studies, we assess how gesture interpretation is approached and the methods used to gather data, along with their associated limitations. This comprehensive analysis sheds light on the nuances of ISL recognition, offering insights into the state of the field and avenues for future research. © 2024 IEEE.

关键词： Gesture recognition

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Autonomous Mobile Medical Robot Based on SLAM Algorithm 5

Autonomous Mobile Medical Robot Based on SLAM Algorithm

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5th international conference on advances in Electrical engineering and Computer Applications, AEECA 2024

作者： Yao, Xinyi Shanghai Maritime University College of Transport & Communications Shanghai China

ISBN: (纸本)9798350355253

This paper aims to develop an autonomous navigation medical robot suitable for indoor medical environments, equipped with a Mecanum wheel AGV platform and the Hector-SLAM algorithm for accurate localization and map building. The robot uses laser radar and vision systems for environmental perception, and employs the A∗ algorithm and the Dynamic Window Approach (DWA) for path planning and obstacle avoidance. Experimental results show that Hector-SLAM outperforms traditional methods in terms of accuracy and adaptability, effectively enhancing the level of automation in medical settings. The research confirms that the medical robot's navigation system has significant application potential in complex indoor environments, offering a new direction for future medical rescue. © 2024 IEEE.

关键词： Medical robotics

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An Extensive Analysis of Pattern Identification and Categorization

An Extensive Analysis of Pattern Identification and Categori...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Tiwari, Abhishek Computer Science and Engineering Parul University Vadodara India Computer Science and Engineering Apex Institute of Technology Mohali India

ISBN: (纸本)9798350388800

Pattern recognition and classification standards are important for data interpretation and decision-making processes in many fields. This article provides a comprehensive review of these two intersections, exploring their evolution, approaches, recent developments, and practical applications. We take a closer look at various applications in information modelling and classification, highlighting their advantages and limitations. We discuss simple processes such as supervised and unsupervised learning, emphasizing the role of features in effective representation. Classification algorithms their advantages, and limitations are described. The review continues to discuss the intersection of these areas and shows how they complement each other in solving problems. We also explore their diverse uses, from medical to autonomous driving, to demonstrate their transformative potential. This review aims to provide insight into recognition and classification standards, encouraging further research and innovation in these important areas. © 2024 IEEE.

关键词： Adversarial machine learning

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