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The Convergence of Artificial Intelligence, internet of Things, and Healthcare: Legal Considerations for a Connected Future 5th

The Convergence of Artificial Intelligence, Internet of Thin...

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5th international Ethical Hacking conference, eHaCON 2024

作者： Ghosh, Jayanta Sinha, Vijoy Kumar Nanda, Ardhendu Sekhar The West Bengal National University of Juridical Sciences Kolkata India Centre for Regulatory Studies Governance and Public Policy The West Bengal National University of Juridical Sciences Kolkata India National Institute of Securities Markets and Maharashtra National Law University Mumbai India

ISBN: (纸本)9789819784561

AI and IoT may supplant human actions, and results have had a more profound impact on bioethics across the globe. technology-based healthcare using AI and IoT has exploded in the global health business. Human-to-human care has succeeded in many domains, from industrial efficiency to human life. Gaps formed between the human psyche to allow AI and IoT may be scrutinized, and ethical problems raised. Initial testing revealed loopholes in meta-data collecting, threatening digital privacy, and disrupting healthcare protection and insurance. A minor error can cost a human life. Low risk of physical injury, precise outcomes, and speedier healthcare data governance make IoT services popular. Big data ensures unpredictability in data handling cloud storage solutions, which may be very confidential. Government regulations may struggle to maintain unique health IDs and considerable tracking of health services. The newest applications of mind-based deductions to real life put both parties’ mental health at risk. The global market for digital healthcare has surpassed 5.5 billion USD. The healthcare industry is trying hard to replace human intervention with human growth and development. AI and IoT technologies are available to aid healthcare practitioners with administrative activities, clinical documentation, patient outreach, image analysis, medical device automation, and remote patient monitoring. This research will examine AI and IoT in healthcare, both from a practical and international legal standpoint. The international legal scenario will help to make a synthetic analysis for future legal directions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Accident Detection and Reporting System for Powered Two-Wheelers Using internet of Things and Edge AI augmented with MCUSUM features

Accident Detection and Reporting System for Powered Two-Whee...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Viswanath, Vijay Unnikrishnan, S. Saeed, S. Dhanya, V.S. Deepa, P.P. Selvan, Kalai Emerging Technologies Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798331507541

According to data provided by the Ministry of Road Transport and Highways (MORTH), Government of India, about 1.68 lakh people have lost their lives in accidents and nearly 4.4 lakh people have suffered injuries [1]. The number of accidents has increased by approximately 12% year on year. The data also show that people affected by accidents are likely to be in the age group of 15 to 49 years. This age group is virtually the pillar that supports the nation's development. Therefore, road fatalities can indirectly influence the nations development. In this work, we have implemented a system aimed towards powered two-wheelers (PTWs) that detects accidents and sends an SOS signal to a preconfigured contact. The system, upon detecting an accident/critical event, can attempt to convey the SOS signal in multiple modalities for redundancy. The SOS signal includes the last known GNSS coordinates of the vehicle to be able to pinpoint the location of the incident. The system also has a manual SOS trigger option that can be used as a panic button. Detection is performed by a Convolutional Neural Network (CNN) based classifier running on the system using Edge AI capability that uses sensory output from an inertial measurement unit (IMU) and its derivative data for input features. In this work, we have experimented with the possibility of improving the classification performance of the classifier by augmenting the input feature set with Multivariate Cumulative Summation (MCUSUM) [2] control chart statistic of the IMU data. The results show that this technique can give marked improvements in the overall accuracy of the classification system. © 2025 IEEE.

关键词： Emergency services

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Assessing signal Noise Effects on Machine Learning Models for ECG-based Cardiac Diagnosis

Assessing Signal Noise Effects on Machine Learning Models fo...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Guglielmi, Emanuela Russo, Davide Donato Trinchese, Pasquale Laudato, Gennaro Scalabrino, Simone Testa, Gianluca Oliveto, Rocco Department of Bioscience and Territory University of Molise Italy Department of Medicine and Health Sciences University of Molise Italy

The internet of Medical Things (IoMT) plays a vital role in healthcare by enhancing preventive careand chronic disease management through continuous monitoring using smart sensors and wearable devices. However, the reliability of IoMT systems can be compromised by noise in the acquired vital signals, which can negatively impact the accuracy of Machine Learning (ML) models used for anomaly detection. This study evaluates the impact of various disturbances on the performance of ML models in predicting cardiac conditions, with a focus on assessing the reliability and effectiveness of these systems in real-world applications. We investigated the effects of three types of noise—baseline wander, muscle artifact noise, and electrode motion artifact—on the performance of two advanced ML models designed to predict cardiac conditions, specifically atrial fibrillation (AF) and ventricular tachycardia (VT). Our analysis centered on how different noise intensities (i.e., the "loudness" of the noise) and durations (i.e., the length of time the noise persists) impacted the classification performance of these models. The VT detection model showed robust performance, with minimal impact even under intense and prolonged noise conditions. In contrast, AF detection was affected by all types of noise, with classification accuracy decreasing by up to ∼59% in the most challenging scenarios. © 2025 by SCITEPRESS – Science and technology Publications, Lda.

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Bag-Level Multiple Instance Learning for Acute Stress Detection from Video Data

Bag-Level Multiple Instance Learning for Acute Stress Detect...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Brügge, Nele Sophie Korda, Alexandra Borgwardt, Stefan Andreou, Christina Giannakakis, Giorgos Handels, Heinz German Research Center for Artificial Intelligence AI in Medical Image and Signal Processing Lübeck Germany Translational Psychiatry Department of Psychiatry and Psychotherapy University of Luebeck Lübeck23562 Germany Heraklion Greece Department of Electronic Engineering Hellenic Mediterranean University Chania Greece Institute of Agri-food and Life Sciences University Research and Innovation Center Hellenic Mediterranean University Heraklion Greece Institute of Medical Informatics University of Luebeck Lübeck Germany

Stress detection is a complex challenge with implications for health and well-being. It often relies on sensors recording biomarkers and biosignals, which can be uncomfortable and alter behaviour. Video-based facial feature analysis offers a noninvasive alternative. This study explores video-level stress detection using top-k Multiple Instance Learning applied to medical videos. The approach is motivated by the assumption that subjects partly show normal behaviour while performing stressful experimental tasks. Our contributions include a tailored temporal feature network and optimised data utilisation by additionally incorporating bottom-k snippets. Leave-five-subjects-out stress detection results of 95.46% accuracy and 95.49% F1 score demonstrate the potential of our approach, outperforming the baseline methods. Additionally, through multiple instance learning, it is possible to show which temporal video segments the network pays particular attention to. © 2025 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Adversarial machine learning

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internet of Things Enabled Smart Hand Gesture Virtual Mouse System

Internet of Things Enabled Smart Hand Gesture Virtual Mouse ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Danish Muneer Chelekkodan Jayapandian N S Abhinav Aaron Shyjan Department of Computer Science and Engineering Christ University Bangalore India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This research is aim to focus on IoT based hand gesture model. Mouse is one of the most important input devices of a computer. It works as a pointing device and allows the user to move the pointer as needed by the user. In the early days, a wired mechanical mouse was used for this purpose. In mechanical mouse a ball is fixed underneath the mouse, which rotates as the user moves the mouse. This movement of the ball is used to move the mouse pointer on the screen. Now mostly we use optical mouse which can be wired or wireless. An optical mouse has a high-power laser below it, which takes more than thousand pictures of the surface below the mouse. An image comparator compares the images and sends the signal to move the mouse pointer as the texture of the image changes. Both the types of mouse works based on old technology. As technology leaps to greater heights, the need for simplicity also increases. With the invention of different kind of sensors, microcontrollers and other electronics, we can eliminate the mouse as an input device and instead use our hands to do the work of a mouse. This prototype is an embedded system which runs with the help of an arduino microcontroller. Flex sensors are used to capture the hand gestures. The proposed IoT based hand gesture model is providing high accuracy rate compare to the regular model. The proposed model is analyzed with accuracy level, the average accuracy level of proposed model is more than 90%.

关键词： Hands Resistance Technological innovation Flexible printed circuits Accuracy Input devices Sensor systems Sensors Optical sensors Intelligent sensors

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Patrol: An Intelligent Surveillance System for Mobile-Free Environments

Patrol: An Intelligent Surveillance System for Mobile-Free E...

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international Communication systems and Networks and Workshops, COMSNETS

作者： Aritra Dutta Banik Trisagni Mandal Anubhab Mukherjee Sharnabho Chatterjee Raja Karmakar Department of CSE (IoT & Cyber Security Including Block Chain Technology) Heritage Institute of Technology Kolkata India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

The widespread and unauthorized use of mobile phones in no-mobile zones such as examination halls, government offices, and hospitals presents significant security, confidentiality, and productivity challenges. Traditional signal-based detection methods fail to detect phones in airplane mode or disconnected from networks, leaving critical gaps in enforcement. To address this issue, we propose "Phone Patrol," a deep learning-based system designed to detect and report unauthorized mobile phone usage. Phone Patrol uses You Only Look Once version 5 (YOLOv5) for detecting individuals with mobile phones and You Only Look Once version 9 (YOLOv9) for face recognition, enabling identification of violators. Deployed on a Raspberry Pi 5, the system processes real-time video feeds and sends automated SMS alerts to offenders, offering a compact, standalone internet of Things (IoT) solution. Experimental results demonstrate that YOLOv5 achieved a mean Average Precision at 50% Intersection over Union (mAP50) score of 0.992, showcasing its accuracy in detecting mobile phones. Although the current prototype has limitations in scalability and optimization for larger environments, it presents a robust proof-of-concept for image-based detection independent of network signals, providing a more comprehensive solution to enforce no-mobile zones effectively.

关键词： YOLO Productivity Surveillance Scalability Prototypes Streaming media Mobile handsets Real-time systems internet of Things Security

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Automated Blood Group Detection System Using image Processing for Non- Invasive Antigen Feature Extraction and Classification

Automated Blood Group Detection System Using Image Processin...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： Gopu Chandrasekhar Reddy Gajavada SaiKrishna A. Annie Micheal Department of CSE with AIML Sathyabama Institute of Science and Technology Chennai India Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Blood group detection is essential in medical diagnostics and transfusion medicine but often relies on invasive methods requiring specialized infrastructure. This research introduces an automated, non-invasive blood group detection system using YOLOv8 object detection and advanced image processing. The system addresses challenges such as human error, lack of accessibility in resource-limited settings, and delays in traditional methods. By analyzing high-resolution blood sample images, the system extracts antigen features and classifies agglutination patterns for A, B, and D blood groups. With a mean average precision (mAP) of 97.4% and an inference time of 15 milliseconds per image, the system delivers scalable, real-time, and robust diagnostics. This research advances healthcare accessibility and precision medicine objectives, offering an efficient and practical alternative to traditional laboratory-based methods.”

关键词： Antigens Precision medicine Object detection Feature extraction Real-time systems Delays Medical diagnosis internet of Things Blood Medical diagnostic imaging

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Face Recognition based Vehicle Theft Detection

Face Recognition Based Vehicle Theft Detection

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), international conference on

作者： J. Prasad Chakravarthi Ctr Daniel K Deepak R Dheshnamoorthi M Department of ECE KPR Institute of Engineering & Technology Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Adding a vehicle theft security system and improving vehicle technology systems are becoming more and more popular study topics in an effort to prevent car theft in parking lots and occasionally when driving in unsafe locations. This work objective is to develop a prototype model to detect vehicle theft using a facial recognition algorithm. The image is initially captured using a camera that is angled differently, and it is subsequently trained using the cascade Haar classifier technique. Furthermore, a linear binary pattern algorithm in a Python environment is used to extract the captured images. If the extracted image and the training image differ, theft is detected, and the vehicle owner is contacted via the mobile app. The developed prototype model offers a superior security alarm for vehicle theft and is tested with a real-time application.

关键词： Training Face recognition Prototypes Real-time systems Classification algorithms Mobile applications internet of Things Protection Driver behavior Collaborative intelligence

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Smart Traffic Management for Emergency Response through IoT Integration

Smart Traffic Management for Emergency Response through IoT ...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Karthik N Mahiban Lindsay N Venkata Ramana Rao S Santhosh G Sreeganasan A Stutti Jain Shaik Sana Ranjith S Department of Electrical and Electronics Engineering HITS Chennai India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Traffic congestion is one of the most pressing issues faced by people in urban areas today. Every day, countless individuals spend valuable time stuck in long traffic jams, contributing to frustration and inefficiency. Gridlock not only wastes time but also increases the risk of traffic accidents. Emergency services such as ambulances, fire trucks, and police cars are often delayed due to heavy traffic, making it harder for them to reach emergencies in time, which can result in tragic consequences. To address these challenges, we propose an innovative solution: an IoT-based automated traffic signal monitoring and control system. This system will allow for real-time management of traffic signals and offer manual override capabilities via the internet. By streamlining traffic flow and providing dedicated green corridors for emergency vehicles, this system will reduce congestion and potentially save lives by ensuring that help reaches critical situations more quickly.

关键词： Law enforcement image processing Urban areas Pressing Manuals Emergency services Real-time systems internet of Things Monitoring Traffic congestion

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Generative AI Meets IoT: A Bibliometric Mapping of Research and Development

Generative AI Meets IoT: A Bibliometric Mapping of Research ...

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international conference on signal Processing and Communication (ICSC)

作者： Parneet Kaur Deepali Gupta Mudita Uppal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Computer technology has transformed the way people interact online within society. The integration of (GIoT) combines Artificial Intelligence (AI) and internet of Things (IoT) technology, offering numerous benefits in many fields for innovation and growth. GIoT helps with real-time applications for data prediction and alerts. based on the authors' understanding and searches in scholarly databases, less bibliometric analysis has yet been performed on Generative Artificial Intelligence (GAI) for the IoT, even though such analyses are crucial for research development in this field. This study conducted a thorough bibliometric examination of GAI applications in the IoT over the period from 2014 to 2024. The analysis is based on a dataset of 296 documents sourced from the Scopus database. The bibliometric review was conducted using the Biblioshiny application, a tool available within the Bibliometric package in the R programming language. Top literature sources, major fields of study, nations, well-known authors, popular subjects, authorship, citations, author-keywords, and co-keywords were all subjected to bibliometric analysis.

关键词： Technological innovation Computer languages Generative AI Databases Reviews Bibliometrics signal processing Real-time systems internet of Things Research and development

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