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Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation 3rd

Assessing the Reliability of AI-Based Angle Detection for Sh...

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3rd international conference on Optimization, Learning Algorithms and applications (OL2A)

作者： Klein, Luan C. Chellal, Arezki Abderrahim Grilo, Vinicius Goncalves, Jose Pacheco, Maria F. Fernandes, Florbela P. Monteiro, Fernando C. Lima, Jose Univ Tecnol Fed Parana PR Campus Curitiba Curitiba Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Lab Associado Sustenabilidade & Tecnol Reg Mt Sus Braganca Portugal Univ Tras os Montes & Alto Douro UTAD P-5000801 Vila Real Portugal

ISBN: (纸本)9783031530357;9783031530364

Angle assessment is crucial in rehabilitation and significantly influences physiotherapists' decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5 degrees and 17 degrees, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easy-to-implement solution.

关键词： Join Angle Measurement Artificial intelligence Motion Capture robotic Rehabilitation

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Streamlining Machine Learning Model Execution Time with Hyperparameter Optimization 3

Streamlining Machine Learning Model Execution Time with Hype...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Dhivya, P. Jegavardhini, M. Chandru, M. Vaigunth, Krishnan Harish Kumar, S. Rahul, S. Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (纸本)9798350343632

Breast cancer is a common disease that mostly affects women. It can be fatal if not detected early. Early detection and accurate diagnosis can lead to a longer survival rate for patients. Various classification models have been used to identify breast cancer, including logistic regression, decision trees, random forests, k-nearest neighbors, and gradient-boosting algorithms. A Parameterized Random Forest (PRF) classifier has been proposed that chooses the best parameter for classification based on the highest probability of a particular value in the parameters. The proposed model achieves an accuracy of 98.25% with a false negative rate of 1%. It is also more accurate (95.31%) than other classification algorithms when applied to the Hepatitis C dataset. The PRF classifier is a promising new tool for the early detection and accurate diagnosis of breast cancer. It is more accurate than other classification algorithms and can be used to reduce the time required to choose the best parameter value. © 2023 IEEE.

关键词： Artificial intelligence Benign Malignant Random Forest Receiver Operating Characteristics Tuning

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Photovoltaic System based Interleaved Converter for Grid System 3

Photovoltaic System based Interleaved Converter for Grid Sys...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Kavitha, M. Jones Nirmal, L. Jenin Prabhu, R. Srinivasan, P. Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Jeppiaar Nagar Chennai India Department of Electrical and Electronics Engineering Panimalar Institute of Technology Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Veltech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Tamil Nadu Chennai India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9781665462167

The MPPT controller of a photovoltaic (PV) system fluctuates, increasing solar irradiance, and has complex voltage-current properties. To balance the solar PV system towards the load and design the solar energy system on MPPT, a two-cell interleaved DC-DC boost connected with an inverter is utilized. Using just load voltage statistics, a voltage control technique is created, excluding array current monitoring. When compared to non-coupled pair interleaved conversions, the current conversion design has lower ripple contents from the loads and supply sides, better efficiency, as well as lower switch stress. Consequently, a decreased array capacitance value is enough to stabilize the array voltage output. For maximum power point functioning, analytical formulas for the solar supply and interleaved boost converter are constructed. The power interleaved converter is functioning using the control technique of MPPT, the Perturb and Observe (P&O) algorithm is employed, and the power is supplied to the alternating voltage conversion, and it is connected to the voltage source converter. The modeling and experimental findings are presented here to demonstrate how well suited that particular kind of power converter is for the application. In addition, a comparison of coupled and non-coupled interleaved boost converters for solar applications is performed. © 2023 IEEE.

关键词： Maximum power point trackers

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Swarm intelligence based Deep Learning Method for Health Monitoring System using Internet of Things (IoT) 3

Swarm Intelligence based Deep Learning Method for Health Mon...

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3rd international conference on Smart Technologies, Communication and robotics, STCR 2023

作者： Pavithra, R. Venkateshkumar, S. Department of Commerce with It Coimbatore India Department of Cyber Security Coimbatore India

ISBN: (纸本)9798350370867

The research conducted is significant because there is an increasing demand for reliable health monitoring, particularly in underserved and far-flung areas. However, issues with data quality, network optimization, and resource limits might hinder the efficacy of data collected by IoT devices, which otherwise provide until then unimaginable prospects. Swarm intelligence and deep learning together can improve these areas, making IoT-based health monitoring more effective. Efficient sensor placement, data transmission, and processing of massive, frequently noisy health data streams are among the biggest obstacles to widespread adoption of IoT-based health monitoring. Using swarm intelligence algorithms to optimize sensor placement and deep learning for data analysis, the proposed Internet of Things-based swarm health monitoring system (IoT-SHMS) approach addresses the aforementioned challenges. IoT-SHMS uses swarm intelligence techniques, such as Particle Swarm Optimization (PSO), to optimize sensor location and network topologies. This helps to increase the reliability of health prediction while decreasing operational expenses. Constant patient monitoring, early detection of health anomalies, and the detection of falls are a few of the numerous applications of an IoT-SHMS approach in the medical field. It additionally has applications in telemedicine, wellness monitoring, and emergency response systems to give doctors and nurses up-to-date information in a timely manner. Extensive simulation evaluations are presented to prove that IoT-SHMS works as intended. The advantage of our technology over conventional approaches is illustrated by the performance criteria of accuracy, latency, and energy efficiency. Based on these findings, IoT-SHMS appears to be a viable option for healthcare applications in the Internet of Things environment © 2023 IEEE.

关键词： Internet of things

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Analysis of the Metaverse Potential as a Digital Platform for Business Innovation 3

Analysis of the Metaverse Potential as a Digital Platform fo...

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3rd international conference on Digitalization and Management Innovation, DMI 2024

作者： Zlateva, Plamena Petukhov, Igor Steshina, Liudmila Velev, Dimiter University of National and World Economy Sofia Bulgaria Institute of Robotics Bulgarian Academy of Sciences Sofia Bulgaria Volga State University of Technology Yoshkar-Ola Russia

ISBN: (纸本)9781643685823

The Metaverse represents a digital platform that not only changes the way one interacts with the world, but is also a generator of innovation in various business sectors. The Metaverse is emerging as a catalyst for future technological trends, transforming not only digital experiences but also society itself. The paper briefly its key technologies such as virtual and augmented reality, artificial intelligence, blockchain, 5G networks. Based on these technologies, the article seeks to analyze new opportunities for business, economic models, social interactions and insights on how the Metaverse can serve as a platform for continuous innovation, shaping the future of the global economy. © 2025 The Authors.

关键词： 5G AI Blockchain Business Economic Models Innovation VR/AR

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XAI-BattleOps: Explainable AI-based Energy Consumption Analytical Framework for UAV-assisted Battlefield Operations 3

XAI-BattleOps: Explainable AI-based Energy Consumption Analy...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Shah, Khushi Panchal, Henil Shalin Pathak, Lakshin Jadav, Nilesh Kumar Tanwar, Sudeep Yamsani, Nagendar Nirma University Institute of Technology Department of Comp. Sci. & Engg. Ahmedabad India Sr University Department of Cs & Ai Telangana India

ISBN: (纸本)9798350354348

In the rapidly evolving landscape of unmanned aerial vehicles (UAVs), it is crucial to optimize energy consumption for extending its operational capabilities and improving efficiency. In this study, we look at how explainable artificial intelligence can be used to predict the energy consumption of UAVs in military operations. Conventional artificial intelligence (AI) black-box models are powerful but provide no visibility into their judgment calls, creating mistrust and restricting regulation compliance. Explainable artificial intelligence (XAI) techniques were utilized in this paper to develop interpretable models for predicting UAV energy consumption based on various operational parameters. These models provide valuable insights into factors influencing energy usage, thereby enabling stakeholders to make informed decisions, optimize flight plans, and adopt energy-saving measures. Using an extensive dataset, we assess the robustness and accuracy of XAI methodologies. We found out from our analysis that XAI maintains predictive performance and improves model interpretability, thus increasing adoption trust in AI-enabled energy management solutions for UAVs in military applications. © 2024 IEEE.

关键词： Deep learning

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Energy Harvesters based on MEMS for IoT applications 3

Energy Harvesters based on MEMS for IoT Applications

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Ram, G. Dinesh Aravind, T. Reddy, C. V. Tharun Kumar, Bijjam Sunil Sailokesh, Chittamuru Saveetha Engineering College Department of Electronics and Engineering Chennai India

ISBN: (纸本)9781665462167

This research examines several energy harvesting methods based on MEMS (Micro Electro Mechanical Systems) for IoT (Internet of Things) applications. IoT's most important component is its sensor network, which needs wireless power sources. The Internet of Things has been deploying the autonomous power harvesting technology in response to requirements. Thermo, vibratory, and electromagnetic waves can all be harvested as sources of energy by using MEMS-based vibrating devices. Discussions are made on various energy harvesting techniques and vibrating energy sources. The mechanical equiliam of a suspended mass serves as the power generating mechanism for the piezoelectric vibration power, which is presented as a proposed model. A notion to comprehend the behaviors is offered in conjunction with the impedance matching condition and the quality factors, and deliverable power is also analytically addressed with respect to the theoretical limit. For the purpose of mast production, mechatronic power harvesting techniques such as piezoelectric, electromagnetic, and electrostatic induction are studied. The most recent MEMS examples are examined and rated according to their power density. © 2023 IEEE.

关键词： Energy harvesting

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The Realization of a Collaborative robot ACP System for Intelligent Garbage Classification 4

The Realization of a Collaborative Robot ACP System for Inte...

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4th IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2024

作者： Hu, Bin Zhang, Xiaodong Liu, Xinjie Xue, Rurui Cheng, Tao Qin, Minghua Li, Yun Xiong, Gang Dong, XiSong Qingdao Academy of Intelligent Industries China State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences China Yantai Institute of Technology China

ISBN: (纸本)9798350349252

This paper addresses the global waste management crisis by proposing an innovative strategy that combines visual recognition technology with robotics, grounded in the ACP methodology, aiming to create an intelligent garbage classification system. The primary goal of the system is to handle the growing volume of waste by employing the YOLOv7 object detection model in conjunction with a Sawyer/Baxter collaborative robot to achieve effective garbage categorization. The study simulates the application process of ACP in a laboratory setting. robots can select different 3D-printed soft grippers based on needs. Collaborative robots generate new data through actual operations, which is then used to update and improve the model, forming a continuous iterative closed-loop system. Despite environmental challenges, the system's achievements indicate potential for large-scale applications. Future research will focus on refining detection models, enhancing human-robot collaboration, and ensuring scalability for broader integration into waste management systems. © 2024 IEEE.

关键词： Collaborative robots

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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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Fast Point Cloud Registration for Urban Scenes via Pillar-Point Representation 3rd

Fast Point Cloud Registration for Urban Scenes via Pillar-Po...

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Gu, Siyuan Huang, Ruqi Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9789819988495;9789819988501

Efficient and robust point cloud registration is an essential task for real-time applications in urban scenes. Most methods introduce keypoint sampling or detection to achieve real-time registration of large-scale point clouds. Recent advances in keypoint-free methods have succeeded in alleviating the bias and error introduced by keypoint detection via coarse-to-fine dense matching strategies. Nevertheless, the running time performance of such a strategy turns out to be far inferior to keypoint methods. This paper proposes a novel framework that adopts a pillar-point representation based feature extraction pipeline and a three-stage semi-dense keypoint matching scheme. The scheme includes global coarse matching, anchor generation and local dense matching for efficient correspondence matching. Experiments on large-scale outdoor datasets, including KITTI and NuScenes, demonstrate that the proposed feature representation and matching framework achieve real-time inference and high registration recall.

关键词： Point cloud registration Pillar-point representation Semi-dense keypoint matching

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