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2023 IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2023

作者： Nijaguna, S.G. Kumar, Sharan Sv, Devika Lal, Bechoo Pradeep Kumar, S. S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India H. K. E. Society's S. L. N. College of Engineering Dept of Electronics & Communication Engg Raichur India Hyderabad Institute of Technology and Management Dept of Electronics and Communication Engineering Telangana India K L University Vadeeshwaram Dept of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Sleep is essential for human survival since it helps to restore and maintain our bodies' immune systems and other essential processes. One-third of a person's life is devoted to sleeping, although few are aware of the many positive aspects of this activity. Two distinct types of sleep, REM and NREM, have been identified. A good night's rest is achieved when REM and NREM sleep alternate in a regular pattern. Disruptions to this cycle, whether they originate physiologically or psychologically, have been linked to a variety of health problems. Polysomnography (PSG) equipment is often used in sleep labs inside hospitals to perform sleep studies. A polysomnogram is an in-depth medical technique that records a patient's vital signs while they sleep and necessitates a hospital stay. Clinically, sleep apnea is defined as a breathing disease in which there are periodic pauses in breathing lasting 10 seconds or more that occur more than five times during the night. Sleep apnea may be classified as either Obstructive, Central, or Mixed. The prevalent sleep problem known as obstructive sleep apnea (OSA) is caused by the relaxation of muscles in the upper airway during sleep. The purpose of this study is to provide a technique for screening for Obstructive Sleep Apnea by analysing Heart Rate Variability of Electrocardiogram (ECG) data while the subject is asleep. The goals of this study are to create computational approaches for identifying OSA based on characteristics extracted from Heart Rate Variability (HRV) signals derived from sleep electrocardiograms (ECGs). Physio Net's Apnea-ECG recordings serve as the source for the ECG data. © 2023 IEEE.

关键词： Electrocardiograms

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures 5

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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5th International Conference on electronics and Sustainable communication Systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT Based Alcohol Detection and Vehicle Control System 5

IoT Based Alcohol Detection and Vehicle Control System

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sapthami, I. Raju, V. V. Narasimha Vaithianathan, V. Chinnasamy, P. Kumaran, G. Ayyasamy, Ramesh Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad500043 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Kalavakkam Chennai603110 India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering TamilNadu Srivilliputtur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350389609

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates. © 2024 IEEE.

关键词： Highway accidents

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Securing IoT Devices and Data: An Analysis of Blockchain Technology and Mechanisms 15

Securing IoT Devices and Data: An Analysis of Blockchain Tec...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Thakur, Sujata Negi Sidana, Sagar Sinha, Anurag Akthar, Haniya Kumar, Biresh Kumar, Nitish Raj, Petheru Maheswaran, S. Al-Khayyat, Ahmed Graphic Era Hill University Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India School of Computing and information Science IGNOU New Delhi India Amity University Jharkhand Department of computer science and engineering Ranchi India Lovely Professional University Department of computer science and engineering Phagwara India Reliance Jio Platforms Ltd Bangalore India Kongu Engineering College Department of Electronics and Communication Engineering Tamilnadu 638060 India Islamic University Department of computer science and engineering Iraq

ISBN: (纸本)9798350370249

The Internet of Things, or IoT, has expanded substantially in recent years. Combining IoT and the Internet allows heterogeneous intelligent devices to process real-time data and conduct transactions. Thanks to the Internet of Things (IoT), almost everything and everything can be connected to the internet and covered. Smart housing, smart healthcare, smart irrigation, and many other uses for IoT are all common [2]. However, the IoT's dependability in terms of privacy and security presents significant development challenges. Consensus mechanisms, decentralization, smart contracts, and data encryption are all blockchain elements that are well suited for building IoT systems since they assist designers in preventing potential attacks and lowering transaction costs. Blockchain serves as a decentralized and open database platform, which can improve the performance of IoT security. We must consider the security challenges and concerns that come with blockchain technology in addition to its ability to provide us with more dependable and useful services. Blockchain technology creates a decentralised, tamper-proof ledger that can securely record and confirm transactions, offering a viable solution for improving the security of IoT devices. Using an explanation of the underlying blockchain technology, a discussion of the security issues that IoT devices must deal with, and a description of the possible advantages of using blockchain for IoT security, we investigate the utilization of blockchain innovation for safeguarding IoT gadgets in this paper. © 2024 IEEE.

关键词： Blockchain Consensus mechanism IoT

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An Intelligent IoT-Driven Smart Shopping Cart with Reinforcement Learning for Optimized Store Navigation 1

An Intelligent IoT-Driven Smart Shopping Cart with Reinforce...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Vadivel, P. Sundara Karthika, B. Robinson, Y. Harold Krishnan, R. Santhana Rachel, L. Sundararajan, S. Saveetha Engineering College Department of Artificial Intelligence & Machine Learning Chennai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Francis Xavier Engineering College Department of Computer Science and Engineering Tirunelveli India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Latha Mathavan Engineering College Department of Electronics and Communication Engineering Madurai India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India

ISBN: (纸本)9798350359756

In the retail commercial sector, various advancements in IoT and AI have industrially renovated the domain. Due to the introduction of boosted connectivity and availability of intelligent systems pave way for redefined shopping experience. The current research work highlights a pioneering tactic to improve shopping experiences. It integrates an intelligent IoT-driven Perceptive shopping cart and reinforcement learning techniques for optimized store navigation. In the research work, the salient features of the two existing deep learning algorithms viz. Reinforcement Learning and Q Network with Long Short-Term Memory are integrated and an innovative algorithm called Reinforced Q-Network with Long Short-Term Memory (ReQL-Net) is proposed. The ReQL-Net algorithm authorizes the perceptive cart to unconventionally acquire effectual navigation strategies. Extensive simulation results show that The ReQL-Net algorithm outperforms other reinforcement learning or Q-Network methods in terms of Navigation Efficiency, Exploitation Trade-off and Convergence Rate. Using the proposed algorithm, the customer can personalize his shopping experience. Also the shopkeeper can designed for individual customer experiences. This information can be used to create targeted marketing strategies. Also, such researches on smart shopping cart opens up new possibilities for the future of intelligent shopping systems which can adapt to changing retail environments. © 2023 IEEE.

关键词： Edge computing

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Artificial Robotic Material (ARM) - A Supporting Gadget for the Disabled

Artificial Robotic Material (ARM) - A Supporting Gadget for ...

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2024 International Conference on communication, Computing and Internet of Things, IC3IoT 2024

作者： Umamaheswaran, S.K. Chidambara Raja, G. Aravintharaj, S. Srinivas, S. Hasan Sultan, S. Edal Queen, C. Sri Sairam Engineering College Dept Of Mathematics Chennai India Sri Sairam Engineering College Dept Of Information Technology Chennai India Sri Sairam Engineering College Dept Of Computer Science Engineering Chennai India Sri Sairam Engineering College Dept Of Mechanical Engineering Chennai India Sri Sairam Engineering College Dept Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Dysgraphia and cerebral palsy are neurological conditions that significantly impact an individual's ability to perform precise and controlled hand movements, affecting their quality of life and ability to communicate effectively through writing or drawing. This paper presents a novel approach to addressing these challenges by introducing a robotic arm-assisted intervention system designed to enhance motor skills and aid in the rehabilitation process for individuals with dysgraphia and cerebral palsy. Our proposed robotic arm system is equipped with advanced sensors and adaptive control algorithms that enable precise and customizable support for users with varying degrees of motor impairment. Through a user-friendly interface, therapists can tailor the system to individual needs and track progress over time. The robotic arm's assistance ranges from gentle guidance to active compensation, thereby promoting skill development while maintaining user engagement. In addition to its therapeutic benefits, our robotic arm system also holds potential for remote therapy, allowing individuals to access therapy resources from the comfort of their homes. The system's intuitive interface and data-sharing capabilities facilitate collaboration between therapists, caregivers, and patients, enhancing the overall quality of care. © 2024 IEEE.

关键词： Robotic arms

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Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

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computers, Materials & Continua 2022年第8期72卷 2429-2444页

作者： Thavavel Vaiyapuri K.Priyadarshini A.Hemlathadhevi M.Dhamodaran Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Electronics and Communication Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any *** the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely *** article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or *** addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature ***,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial ***,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN *** enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

关键词： Financial risks intelligent models financial crisis prediction deep learning feature selection metaheuristics

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Assistive Technology: An Alternative for Locomotion Disability and Resultant Social Participation

Assistive Technology: An Alternative for Locomotion Disabili...

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2023 IEEE World Conference on communication and Computing, WCONF 2023

作者： Biradar, Vijayalaxmi Mohanty, Sarat Chandra Jana, Anup Kumar Goswami, Bigit Krishna Marturkar, Rakesh Kalinga University Elelctrical Engineering Department Raipur India Kalinga University Mechanical Engineering Department Raipur India Kalinga University Computer Science & Information Technology Raipur India Guru Nanak Dev Engineering College Electronics and Communication Engineering Department Bidar India

ISBN: (纸本)9798350311204

Having effective mobility is crucial to carry out daily activities and participate in social roles. This paper presents a robot that can detect hand gestures and provide assistance to people with mobility impairments, including the elderly and those with disabilities. The proposed technology utilizes image processing with the Raspberry Pi Arm - 11 board (700 MHz) to control the dc motors, which are connected to motor driver circuits (L298). The robot's actions are based on the data received from a webcam interfaced by USB ports. The processors used are compatible with Linux and GUI operating systems, making the technology both high-performance and economical. © 2023 IEEE.

关键词： Python

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Integration of Secure Data communication with Wireless Sensor Network Using Cryptographic Technique 4th

Integration of Secure Data Communication with Wireless Senso...

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4th International Conference on Mobile Radio communications and 5G Networks, MRCN 2023

作者： William, P. Chinthamu, Narender Saxena, Aditi Lakshmi, T. R. Vijaya Tiwari, Mohit Department of Information Technology Sanjivani College of Engineering Kopargaon India CTO Candidate DallasTX United States Department of Electronics and Communication Engineering GLA University Mathura India Mahatma Gandhi Institute of Technology Hyderabad Gandipet500075 India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (纸本)9789819706990

A WSN is a wireless network comprising small sensor nodes to monitor environmental or physical parameters. Since wireless sensor networks have limited computational power, memory, throughput, and energy, traditional security solutions designed for resource-rich systems are inappropriate. Given these limitations, providing basic security methods for data transfer in wireless sensor networks is vital. Our work is divided in three phases. Phase 1 focuses on development of a pairwise key management technique. We suggested broadcast tree construction for a wireless sensor network in phase 2. In phase 3, we proposed an enhanced watchdog strategy as a practical way of detecting rogue nodes. The main objective of this model’s depiction is to emphasize how important it is to reduce network power consumption to prolong network lifespan and identify and terminate rogue nodes before they broadcast packets. Experimental analysis shows that our model provides better results than state of art systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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