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APC System: A system for the Blind and Deaf Who Face Visual and Depth Perception Challenges

APC System: A system for the Blind and Deaf Who Face Visual ...

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2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Mubeen, Suraya Ashritha, G.V. Bandru, Sanjeev Sireesha, Marri Bhaskar, Nuthanakanti Kumar, Voruganti Naresh Cmr Technical Campus Dept. of Electronics and Communication Engineering Telangana Hyderabad India Telangana Hyderabad India Sreenidhi Institute of Science and Technology Dept. of Information Technology Telangana Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350306927

In this paper, APC System (Assistive Path Control System) is proposed to provide blind and deaf individuals with the assistance they need to navigate their environment by using the "third eye"technology. As part of the technology, a hand glove and a stick are used to identify obstacles using ultrasonic sensors and to provide haptic feedback through vibrations in order to detect obstacles. Wearers of the glove are alerted through a buzzer and provided with feedback on distance and direction of obstacles in their path, while those of the stick are given feedback on the change in elevation and depth over time. The use of vibrations as a form of feedback is crucial to the functioning of deaf people. Testing of the technology has shown promising results, and future work will focus on optimizing accuracy and accessibility by using machine learning algorithms in order to improve the system. As a primary goal of the APC System is to enhance the quality of life for blind and deaf individuals by providing them with a tool that will help them navigate their environment in the most effective manner. © 2023 IEEE.

关键词： Feedback

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Parallel Path Planning for Multi-Robot Coordination 25

Parallel Path Planning for Multi-Robot Coordination

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25th IEEE international conference on Autonomous Robot Systems and Competitions, ICARSC 2025

作者： Ribeiro, Jorge Brilhante, Marina Matos, Diogo Miguel Silva, Carlos A. Sobreira, Heber Costa, Pedro INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre of Robotics in Industry and Intelligent Systems Porto4200-465 Portugal University of Porto Faculty of Engineering Porto4200-465 Portugal Flowbotic Mobile Systems S.A Viseu3530-259 Portugal

ISBN: (纸本)9798331538606

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A∗ (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA∗ through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm. © 2025 IEEE.

关键词： Multipurpose robots

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A Fast Walsh-Hadamard transform (FWHT) For Detection of Freezing of Gait in Parkinson's disease using Machine learning techniques. 4

A Fast Walsh-Hadamard transform (FWHT) For Detection of Free...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Nancy, R. Thomas George, S. Prasanna, J. Subathra, M.S.P. Karunya Institute of Technology and Science Department of Robotics Engineering Coimbatore India Karunya Institute of Technology and Science Department of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350349856

Freezing of gait stands out as the most severe detrimental motor signs of advanced PD patients with an increased prevalence in the elderly, and frequently leads to falls and injuries among individuals associated with PD. This research proposes an efficient detection method for freezing of gait based on the Fast Walsh -Hadamard Transform (FWHT). The approach involves decomposing the gait signals, and key features are derived by analyzing the statistical parameters and entropy characteristics of the decomposed coefficients. The Artificial Neural Network (ANN) and K-Nearest Neighbor classifier (KNN) are employed to classify the obtained statistical and entropy measures. The tests utilize the benchmark dataset DAPHNET for performance assessment. Remarkably, the K-nearest neighbor (KNN) algorithm demonstrated noteworthy outcomes, achieving 98.42%, 98.24%, 98.57%, 98.38%, and 98.45% accuracy, specificity, sensitivity, positive predictive value, and negative predictive value, respectively. © 2024 IEEE.

关键词： Neural networks

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international conference on advances in Communication technology and Computer engineering, ICACTCE 2023

International Conference on Advances in Communication Techno...

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international conference on advances in Communication technology and Computer engineering, ICACTCE 2023

ISBN: (纸本)9783031371639

The proceedings contain 54 papers. The special focus in this conference is on international conference on advances in Communication technology and Computer engineering. The topics include: Integration of a UHF Fractal Antenna into a 1U CubeSat for Low-Earth Orbit Mission;gesture Recognition Based Virtual Mouse and Keyboard;QCNN—A Conceptual Framework for Duplicate Removal in Big Data Using Quality Assured Crowd Sourcing Coupled with Neural Networks;Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN;a Review on Blockchain Smart Contract Applications;Factors Impacting a UK Based Software Business on international Expansion;predicting Fish Habitat in the Persian Gulf Using Artificial Intelligence;junk Mail Content Detection Using Logistic Regression Algorithm;research Protocol for a Preliminary Evaluation of a Gamified Tool for Adult Wellbeing;initial Evaluation of a Mobile Therapeutic Game for Adolescent Anxiety Disorders;help over Harm: Practical and Ethical Considerations for the Evaluation and Deployment of Therapeutic Games;comparative Analysis on the Use of Teleconsultation Using Support: Vector Regression and Decision Tree Regression to Predict Patient Satisfaction;bibliometric Analysis of Scientific Literature on Mental Health Research in Africa;new Technologies and Interventions to Improve the Mental Health of People with Diabetes;robotics Cyber Security Issues;Improving Data Centre Power Efficiency by Applying the VM Consolidation Approach;development and Evaluation of "Perspective: Counselling Simulator": A Gamified Tool for Developing Risk-Assessment Skills in Trainee Counsellors;Online Buying Behaviour of UK University Students;multi-modal Sentiment Analysis Using Text and Audio for Customer Support Centers;preface.

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Based on Static And Dynamic:Fuzzy Logic-Based Clustering Optimization for Wireless Sensor Networks

Based on Static And Dynamic:Fuzzy Logic-Based Clustering Opt...

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2024 international conference on advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Ali, Adnan Hussein Kareem, Marwa Mahdi Sabbar, Bayan Mahdi Monir, Hind Q. Mohammad Ibrahim, Raed Khalid Mnati, Mohannad Jabbar Institute Of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Middle Technical University Technical Management Institute Baghdad Iraq College Of Engineering And Engineering Technologies AlMustaqbal University Iraq Al-Farahidi University Department Of Medical Instruments Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350371567

Fuzzy C-Means (FCM) clustering, a widely employed and sophisticated data analysis method, offers a robust structure for handling intricate and uncertain data patterns. Its versatility extends to numerous domains, such as data mining, pattern recognition, and image segmentation. Unlike conventional hard clustering approaches, FCM is particularly suitable for scenarios involving ambiguous and overlapping data by assigning data points to distinct clusters with varying degrees of membership. This synopsis provides a summary of FCM, with particular emphasis on its core concepts, iterative optimization process, and capability to handle complex data patterns. It also describes the practical benefits and applications of FCM, emphasizing how it can be utilized to generate flexible and nuanced cluster assignments. Despite the presence of ambiguity and insufficient memberships, FCM continues to be a valuable tool for practitioners and researchers seeking efficient methods to cluster data. © 2024 IEEE.

关键词： Pattern recognition

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Image Processing for Robotic Control in Life science Laboratory Automation 5

Image Processing for Robotic Control in Life Science Laborat...

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5th international conference on robotics and Computer Vision, ICRCV 2023

作者： Yu, Haoying Møller, Vilhelm Krarup Frandsen, Rasmus John Normand Mansourvar, Marjan Department of Electrical Engineering Technical University of Denmark Copenhagen Denmark Department of Biotechnology and Biomedicine Technical University of Denmark Copenhagen Denmark

ISBN: (纸本)9798350326369

In the contemporary age of advanced technology, image processing plays a vital role as an efficient and indispensable tool across industries, enterprises, monitoring systems, and various applications. This study focuses on employing image processing techniques with an Opentrons industrial robot to ensure quality control during production. By leveraging the YOLOv5 model, the robot adeptly recognizes diverse pipette tip types, identifies liquids by their colors, and accurately measures liquid levels to estimate volume percentages within each tip. These detection results then guide a programmable robot, enabling it to perform specific actions on the tips based on predetermined parameters. This paper provides a comprehensive description of the methodology, implementation, and evaluation of our integrated control-robotic system, highlighting its potential to significantly enhance efficiency and precision in laboratory settings. © 2023 IEEE.

关键词： Quality control

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A New Paradigm of Human-robot Dialogue Facilitating Human and Robot Teaming in Sample Exploration

A New Paradigm of Human-robot Dialogue Facilitating Human an...

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2023 IEEE international conference on Real-Time Computing and robotics, RCAR 2023

作者： Fu, Yan Wang, Hao Xue, Shuqi Zhang, Bojun Cheng, Jianmei Gao, Zhengke Huazhong University of Science and Technology School of Mechanical Engineering and Science Wuhan Hubei430074 China Astronaut Research and Training Center of China National Key Laboratory of Human Factors Engineering Beijing100094 China Xi'an Jiaotong University School of Mechanical Engineering Shaanxi Xi'an710049 China

ISBN: (纸本)9798350327182

When human-robot collaboration is used as an optimal for conducting sample collection for lunar missions, the heterogeneity between human and robot spatial information processing modes pose a challenge to the human-robot collaboration. To address the communication obstacles, a conceptual framework, named Co-SRP is constructed, integrating perception of current scenarios, comprehension of elements observed in complete task context, and the prediction of the system and task's status in the near term to achieve spatial co-awareness. The proposed framework's effectiveness was validated on a simulation platform for lunar surface sampling missions. The results indicate that the framework improved the lunar surface sampling robot's ability to understand astronaut guidance commands and increased efficiency in human-robot collaboration. © 2023 IEEE.

关键词： Simulation platform

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Enhanced Diabetes Prediction Using Deep Autoencoder Framework and Electronic Health Records 2

Enhanced Diabetes Prediction Using Deep Autoencoder Framewor...

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Sivakumar, T.B. Malakar, Antara Lekshmi, S. Shailaja, G. Kalaivani, E. Babu, Dhananjaya K. Department of CSE School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering The Assam Royal Global University Guwahati India Basic Science and Humanities Department Mangalam College of Engineering Kottayam Ettumanoor India Department IT CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Bannari Amman Institute of Technology sathyamangalam India Rajampet India

ISBN: (纸本)9798350383867

Type II diabetes, which nowadays has become one of the most common chronic metabolic illnesses, needs cloud-based information systems with great predictive models to ensure timely intervention and proper management. Given the fact that EHRs and deep learning are quite advanced serves as a solution to the given problem. The study is about the application of the improved diabetes prediction model that makes use of prevailing deep learning architecture and EHR data. The framework constitutes a three-step process: It encapsulates a preprocessing step, a feature extraction stage, and a training procedure for a deep autoencoders that learns levels of representation. Demonstration of the system' s effectiveness becomes evident when it shows its improved ability of prediction compared to baseline models which in turn outperform in basic indicators such as accuracy, precision, recall, F1 score, and AUC-ROC. The result of the sensitivity check demonstrates that the model is able to perform well even under different hyperparameter arrangements. Latent feature analysis shows some unequivocal ways of hidings that reflect diabetes risk factors thus making it easy to understand. The clinical validation in a healthcare setting presents high accuracy, the specificity and the predictive values that are known in indicating those who are at the risk of diabetes onset. This research aims at deepening the understanding for the diagnosis of diabetes through the implementation of deep learning methods and generating real-world medical data. It attempts to provide better prognosis, lower healthcare expenditures and alleviate diabetes burden on patients and society. © 2024 IEEE.

关键词： Electronic health record

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Fraud Detection and Analysis System for Car Insurance Claim Using Random Forest Classifier

Fraud Detection and Analysis System for Car Insurance Claim ...

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2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Jain, Aryan Dutta, Tanmay Senthil Kumar, T. Srm Institute of Science and Technology Department of Computing Technology Chennai India

ISBN: (纸本)9798350306927

In recent years, commercial insurers have faced many cases of fraud in all types of claims. Fraud claims have been huge in amount and can cause serious problems. As a result, various organizations that are private or public, including the government, work to identify and prevent fraudulent activities. One of the most prevalent forms of fake insurance claims is related to the auto industry, which are often made through false accident claims. This project aims to solve this problem by developing a machine learning model that uses insurance claim datasets to detect and classify fraud and false claims from the legitimate ones. The project will use the Python PySpark library and the Random Forest Classifying algorithm to label and rank claims and compare their performance using metrics such as soft accuracy, precision, recall and confusion matrix. © 2023 IEEE.

关键词： Python

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Community Detection in Signed Networks Based on Normalized Laplacian Matrix and Convex Non-negative Matrix Factorization 2

Community Detection in Signed Networks Based on Normalized L...

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2nd international conference on Automation, robotics and Computer engineering, ICARCE 2023

作者： Zhang, Yibo Nan, Tian Zhang, Zhaozhi School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Material Company Lanzhou China

ISBN: (纸本)9798350308341

Signed networks can intuitively and flexibly describe the positive and negative interactions among entities in a complex network. Community structure detection is a basic research topic of signed network analysis, which is of theoretical significance and practical value to reveal the organizational structure, functional structure, and individual behavior of complex systems. However, it is still a challenging task to accurately detect the community structure in a signed network. In this paper, we design a community detection algorithm based on normalized Laplacian matrix and convex non-negative matrix factorization for signed networks. The proposed algorithm applies convex non-negative matrix decomposition to a signed network, and decomposes the adjacency matrix of the network into a community membership matrix and a node weight matrix. Meanwhile, the algorithm introduces normalized Laplacian graph regularization terms to reduce noise generated during the decomposition process. Experimental results verify that the proposed algorithm is effective in community detection. © 2023 IEEE.

关键词： Non-negative matrix factorization

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