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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and Machine Learning 3

Detailed review on Integrated Healthcare Prediction System U...

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3rd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. sensors connected through wireless networks make it possible to track the actions of a whole population. sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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Photovoltaic abnormal data cleaning based on fuzzy clustering-quartile algorithm

Photovoltaic abnormal data cleaning based on fuzzy clusterin...

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Industrial Cyber-Physical systems (ICPS)

作者： Yidong Li Danyun Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

The irradiance-power curve is an important basis for examining the operating status of photovoltaic power stations. In the actual operation process, sensor failure, abnormal communication and equipment damage will bring a large number of abnormal values to the output data of photovoltaic power plants. It will have a significant impact on a variety of applications based on photovoltaic output data. This paper analyzes the typical outliers on the irradiance-power curve and proposes a photovoltaic output data cleaning method based on fuzzy clustering algorithm and quartile algorithm. By comparing with the quartile method, it is proved that this method can effectively identify abnormal data when there are a large number of outliers in the photovoltaic output data.

关键词： Photovoltaic systems Training Clustering algorithms Prediction algorithms Cleaning Filling Data models

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TacLink-Integrated Robot Arm toward Safe Human-Robot Interaction

TacLink-Integrated Robot Arm toward Safe Human-Robot Interac...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Quan Khanh Luu Alessandro Albini Perla Maiolino Van Anh Ho Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan Oxford Robotics Institute University of Oxford Oxford England Japan Science and Technology Agency (JST) PRESTO Kawaguchi Saitama Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Recent developments in vision-based tactile sensing offer a simple means to enable robots to perceive touch interactions. However, existing sensors are primarily designed for small-scale applications like robotic hands, lacking research on their integration for large-sized robot bodies that can be leveraged for safe human-robot interactions. This paper explores the utilization of the previously-developed vision-based tactile sensing link (called TacLink) with soft skin as a safety control mechanism, which can serve as an alternative to conventional rigid robot links and impact observers. We characterize the behavior of a robot integrated with the soft TacLink in response to collisions, particularly employing a reactive control strategy. The controller is primarily driven by tactile force information acquired from the soft TacLink sensor through a data-driven sim2real learning method. Compared with a standard rigid link, the results obtained from collision experiments also confirm the advantages of our "soft" solution in impact resilience and in facilitating controls that are difficult to achieve with a stiff robot body. This study can act as a benchmark for assessing the efficiency of soft tactile-sensitive skins in reactive collision responses and open new safety standards for soft skin-based collaborative robots in human-robot interaction scenarios.

关键词： Human-robot interaction sensor phenomena and characterization Observers Robot sensing systems Skin Safety Collision avoidance Robots Standards Resilience

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advanced Uses of Micro Electro Mechanical systems-Based Devices in Healthcare and Robotics Design

Advanced Uses of Micro Electro Mechanical Systems-Based Devi...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International conference on

作者： Hari Mohan Rai Kaustubh Kumar Shukla Hasan Janashin Mohd Ammar Devina Gandhi Vinayak Uniyal School of Computing Gachon University South Korea Department of ECE Dronacharya Group Of Instutions Greater Noida India Department of ECZ Dronacharya Group Of Instutions Greater Noida India Department of ECZ Dronacharya Group Of Institutions Greater Noida India Department of CSE Dronacharya Group Of Institutions Greater Noida India Department of CSIT Dronacharya Group Of Instutions Greater Noida India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

This research aims to provide affordable and accessible devices for elderly and physically handicapped individuals, addressing self-reliance and societal impact. Micro-Electro-Mechanical systems (MEMS) is a popular technology, but limited in India due to limited resources. Here primary aim is to create novel devices that can improve design and analysis efficiency. The proposed sensor will normalize mobility for wheelchair bound patients and find industrial applications in robotics. The project also focuses on developing wearable devices for gait detection and analyzing reliability, sensitivity, and device response. The research uses multi-physics concepts and FEM to design and analyze sensing materials for MEMS/BioMEMS-based devices. It also focuses on the development of high-sensitivity MEMS-based microstructure devices for gait detection in wheelchair-bound patients. The sensor can be used in industrial robotics and medical sectors. The study also aims to analyze the reliability, sensitivity, and device response of the MEMS-based device, which could be used for the diagnosis and treatment of gait-related diseases. The goal is to make this technology accessible to the elderly and physically handicapped, addressing various healthcare and environmental issues.

关键词： Micromechanical devices Sensitivity Service robots Wheelchairs Robot sensing systems Finite element analysis Mobile robots Biomedical monitoring Wearable devices Robots

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14th International conference on applications and Techniques in Information Security, ATIS 2024

14th International Conference on Applications and Techniques...

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14th International conference on applications and Techniques in Information Security, ATIS 2024

ISBN: (纸本)9789819797424

The proceedings contain 24 papers. The special focus in this conference is on applications and Techniques in Information Security. The topics include: Optical Neural Networks – A Strategy for Secure Quantum Computing;guarding Against Quantum Threats: A Survey of Post-Quantum Cryptography Standardization, Techniques, and Current Implementations;cryptographic Distinguishers Through Deep Learning for Lightweight Block Ciphers;detection and Mitigation of Email Phishing;securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques;Design of Image Encryption Technique Using MSE Approach;Low Latency Binary Edward Curve Crypto Processor for FPGA Platforms;Augmenting Security in Edge Devices: FPGA-Based Enhanced LEA Algorithm with S-Box and Chaotic Functions;machine Learning Approach for Malware Detection Using Malware Memory Analysis Data;DDOS Attack Detection in Virtual Machine Using Machine Learning Algorithms;an Unsupervised Method for Intrusion Detection Using Novel Percentage Split Clustering;HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control systems Using MLPNN and Attention Mechanisms;silent Threats: Monitoring Insider Risks in Healthcare Sector;Enhanced Deep Learning for IIoT Threat Intelligence: Revealing advanced Persistent Threat Attack Patterns;Adaptive Data-Driven LSTM Model for sensor Drift Detection in Water Utilities;Enhancing FGSM Attacks with Genetic Algorithms for Robust Adversarial Examples in Remote Sensing Image Classification systems;GAN-Enhanced Multiclass Malware Classification with Deep Convolutional Networks;IOT Based Locker Access System with MFA & Remote Authentication;a Secure Authentication Scheme between Edge Devices Using Hypergraph Hashing Technique in Iot Environment;enhancing Access Control and Information Sharing in Cloud IoT with an Effective Blockchain-Based Authority System.

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Optimizing RF-Sensing for Drone Detection: The Synergy of Ensemble Learning and sensor Fusion

Optimizing RF-Sensing for Drone Detection: The Synergy of En...

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International conference on Distributed Computing in sensor systems (DCOSS)

作者： Laiba Tanveer Muhammad Zeshan Alam Maham Misbah Farooq Alam Orakzai Ahmed Alkhayyat Zeeshan Kaleem Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Pakistan Wah Pakistan Department of Computer Science Brandon University Canada Brandon Canada Department of Computer Technical Engineering College of Engineering Islamic University Najaf Iraq

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Unmanned Aerial Vehicles (UAVs) find extensive applications across various industries, surveillance, and communication services. However, concerns regarding their potential misuse have prompted the development of counter-drone measures. In this paper, we propose a counter-UAV approach centered on radio frequency (RF) signal sensing. Upon the detection of an RF signal, our system employs a Short-Time Fourier Transform (STFT)-based spectrogram (SP) generation process. This SP is further refined through adaptive windowing and logarithmic tuning to extract multi-intensity features. To classify the complex RF time-domain signals and STFT spectrograms, we utilize two deep learning classifiers: RF-Network and SP-Network, facilitating a multi-class classification process by using deep neural networks (DNN). To enhance the overall accuracy of our model, we leverage an ensemble neural network (EN-Net) by combining predictions from the RF-Network and SP-Network classifiers. Fusing data from a single sensor in both time and frequency domains enhances DNN accuracy by providing complementary information, improving robustness, and reducing overfitting, resulting in increased model performance and a deep understanding of the data. Our results demonstrate a notable improvement in accuracy—specifically, a 36% increase for multi-class models when compared to single-class models. This proves the effectiveness of our EN-Net model in addressing security threats posed by UAVs through advanced RF signal analysis and classification.

关键词： Radio frequency Accuracy Computational modeling RF signals Artificial neural networks sensor fusion Feature extraction

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3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive Clustering and 3D Point Spatial Histograms

3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Junaid Baber Olivier Aycard GIPSA-LAB University of Grenoble Alpes France

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

The advent of 3D LiDAR technology has revolutionized object detection in applications such as autonomous driving, robotics, and advanced driver assistance systems. However, existing methods often require substantial computational resources, limiting their practicality for real-time applications on devices with constrained hardware capabilities. This paper presents an efficient and lightweight 3D LiDAR object detection framework, 3D-PSH, that combines adaptive clustering with 3D Point Spatial Histograms (3D-PSH) and classical classification techniques to address these challenges. Our framework begins with an adaptive clustering algorithm that segments the point cloud data into distinct clusters, representing potential objects. 3D Point Spatial Histograms (3D-PSH) are then computed from these clusters and subsequently quantized into a Bag of Visual Words (BoVW) to create a compact and informative representation. These representations are then classified using robust classical classification methods to identify object types, such as pedestrians and vehicles. This multi-step approach ensures a balance between computational efficiency and detection accuracy, making it suitable for real-time deployment. Extensive experiments on the KITTI dataset and our live sensor data demonstrate the effectiveness and efficiency of our proposed framework. The results indicate that our method achieves competitive accuracy while significantly reducing computational requirements compared to traditional approaches. This framework offers a practical solution for deploying 3D object detection in a wide range of applications, particularly where computational resources are limited.

关键词： Support vector machines Histograms Visualization Three-dimensional displays Laser radar Accuracy Object detection Robot sensing systems Real-time systems Computational efficiency

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Trade-off Analysis of Gateway Strategies for Embedding of CAN Frames into Ethernet Packets

Trade-off Analysis of Gateway Strategies for Embedding of CA...

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International conference on Information and Communication Technology Convergence (ICTC)

作者： Sungwon Bae Hanyoung Park Kanghyun Ko Ji-Woong Choi Department of Electrical Engineering and Computer Science DGIST Daegu South Korea Korea Automotive Technology Institute (KATECH) Cheonan South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

The advancement of advanced driver assistance systems (ADAS) has increased the demand for more sensor data within vehicles, necessitating faster data transmission. To meet this requirement, in-vehicle networks (IVNs) with higher link speeds are essential, as the data rates of traditional IVN protocols are insufficient for future applications. To address these demands, an IVN architecture that integrates both controller area network (CAN) and automotive Ethernet is being considered. The coexistence of CAN and automotive Ethernet necessitates the use of gateways, which introduces latency when transferring data from CAN to automotive Ethernet. However, many studies have not thoroughly explored the strategies and characteristics of such gateways and their impact on performance. Therefore, in this paper, we analyze the processing latency and network efficiency associated with different gateway strategies for embedding CAN frames into Ethernet packets. The findings of this study show the trade-off between latency and network efficiency and highlight the potential for optimizing CAN-Ethernet gateways.

关键词： Protocols Ethernet Logic gates Throughput Real-time systems Information and communication technology Controller area networks Vehicle dynamics Optimization Payloads

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Real-Time GPS Tracking and Health Monitoring for Animals Using IoT and Mobile Application

Real-Time GPS Tracking and Health Monitoring for Animals Usi...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： V. Usha Sandeep Karagatla P. Sheela Gowr P.M.G Jegathambal A. Lizy V. Sathya Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of CSE (AI&DS) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Vels Institute of Science Technology & Advanced Studies Chennai Dept. of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala (R&D) Institute of Science and Technology Chennai India Dept of Information Technology Velammal Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Internet of Things (IoT) grounded solution of Real Time Global Positioning System (GPS) tracking with Health Monitoring of Animals for Owners to track their animals like cows, buffaloes, dogs etc. This GPS module and a heartbeat sensor are attached to the animals in order to be able to follow them closely. This data is sent to a mobile app, and the owner can see their heart on the map as well as monitor its pulse. Based on analysis of such heart rate variations, the system determines when it is seeing an abnormal activity related to e.g. stress (increased heart rate) or potential health risks (no sign of a heartbeat), which could be interpreted as fear or death. Each animal has an initial heart rate level that is set, and when this rate exceeds it, the system sounds an alert to indicate danger. This method is especially helpful for keeping an eye on animals in urban areas, areas close to forests, and areas with high rates of theft and loss. The project demonstrates the successful execution of a reliable, real-time animal monitoring system with practical applications in both urban and rural environments.

关键词： Animals Tracking Heart beat LoRa Real-time systems Mobile applications Safety Internet of Things Monitoring Global Positioning System

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Integration of C-V2X with an Obstacle Detection System for ADAS applications

Integration of C-V2X with an Obstacle Detection System for A...

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IEEE International conference on Communication, Networks and Satellite (ComNetSat)

作者： Jungme Park Mohammad Hasan Amin Haswanth Babu Konjeti Yunsheng Wang ECE Kettering University Flint USA Mobility Systems Kettering University Flint USA Computer Science Cal Poly Pomona Pomona USA

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

Cellular Vehicle-to-Everything (C-V2X) technology is essential for enhancing road safety by connecting vehicles, roadside infrastructure, and networks. It improves safety by transmitting vital warnings and traffic updates that vehicle sensors might miss. This paper presents a novel C-V2X-based object detection system designed for intersections, leveraging cutting-edge 5G sidelink technology and advanced Deep Neural Networks (DNNs) deployed on edge computing units. The system utilizes Basic Safety Messages (BSMs), which convey timestamps and location data of detected objects, sent from a Roadside Unit (RSU) to On-Board Units (OBUs) via 5G sidelink. The proposed system was tested using an outdoor testbed at the GMMRC testing track at Kettering University. The system continuously monitors the intersection, with the RSU broadcasting detection results to nearby vehicles equipped with OBUs. Utilizing the depth perception capabilities of the stereo camera, the system accurately determines the distance of objects from the camera. Performance evaluations conducted through real-time experiments on the test track demonstrated the system's efficiency, achieving an average latency of just 1 millisecond in V2X communication across various scenarios. The 5G-enabled communication offers minimal latency, high data rates, and reliable connections, making it exceptionally well-suited for C-V2X applications. These results highlight the considerable potential of this technology for advancing vehicular communication and safety across diverse applications.

关键词： Vehicle safety Sidelink Object detection Artificial neural networks Cameras Real-time systems sensor systems sensors Vehicle-to-everything Testing

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