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2023 International Conference on Power Energy, Environm.nt and Intelligent Control, PEEIC 2023

作者： Adhikari, Pitam.er Prakash, Febin Sharm., Brajehdra Noida Institute of Engineering and Technology Department of Information Technology & M.Tech Integrated Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Bangalore India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

Forecasting of energy is a crucial com.onent in overcom.ng the challenges of sm.rt grid m.chanism., which include functions such as dem.nd-side m.nagem.nt, load reduction, and desirable allocation. The m.st significant obstacle battling today's grid has to do with efficient forecasting m.nagem.nt for restricted predictive inconsistencies. This paper provides a com.rehensive and application-focused review of advanced prediction strategies for sm.rt grid system., as well as the latest advances in the use of m.chine Learning (m.) and Deep Learning (DL) approaches. It also discusses the significance of hybridized techniques in im.roving the accuracy of forecasts. A study of com.arison using an hourly energy consum.tion database has been carried out to evaluate the effectiveness of m. and DL techniques. The analysis' findings highlight hybrid m.thods' superior efficiency when com.ared to individual m., DL, and standard statistical m.thods. © 2023 IEEE.

关键词： deep learning Energy forecasting m.chine learning power consum.tion Sm.rt grid system.

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Sm.rt Healthcare m.nitoring System. Integrating IoT, Deep Learning, and XGBoost for Real-Tim. Patient Diagnosis 21

Smart Healthcare Monitoring System: Integrating IoT, Deep Le...

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21st OITS International Conference on Inform.tion technology, OCIT 2023

作者： Paulraj, Kruthika Som., Nisha David Sam.el Azariya, S. Sathya Priya, S. Jeba Em.lyn, J. Sureshkum.r, Vidhushavarshini Sona College of Technology Department of Information Technology Tamil Nadu Salem India KPR Institute of Engineering and Technology Department of Computer Science Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Karur India SRMIST Department of Computer Science Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350358230

Integrating the Internet of Things (IoT), Deep Learning (DL), and the XGBoost algorithm.has paved the way for transform.tive advancem.nts in healthcare. This paper presents a pioneering study on a "Sm.rt Healthcare m.nitoring System.that harnesses the synergy of these technologies for real-Tim. patient diagnosis. With the escalating dem.nd for accurate and swift m.dical assessm.nts, our work addresses the crucial need for tim.ly and precise diagnosis. The proposed system.am.lgam.tes IoT-enabled wearable devices and sensors to capture com.rehensive patient data. A hybrid approach is em.loyed to leverage this data, com.rising a Deep Learning m.del for intricate pattern recognition and the XGBoost algorithm.for rapid decision-m.king. Nonetheless, challenges such as data security and m.del interpretability are acknowledged, highlighting avenues for future research. This paper underscores the transform.tive potential of the IoT-Deep Learning-XGBoost integration, offering a robust foundation for further innovation in intelligent healthcare system.. As healthcare delivery evolves, our work underscores the prom.se of this interdisciplinary fusion in revolutionizing patient diagnosis and care. © 2023 IEEE.

关键词： Internet of things

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A Novel Approach to Design Secured and Privacy Enabled Health Data Linkage System.based on Cyber Security Principles

A Novel Approach to Design Secured and Privacy Enabled Healt...

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2024 IEEE International Conference on Big Data and m.chine Learning, ICBDm. 2024

作者： Gayathri, S. Jaya m.bel Rani, A. Anushalin, P.S. Ravi, C.N. Jeyalaxm., m. Sam.thira Pandi, V. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.Sakunthala Engineering College Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Avadi Chennai India CMR Engineering College Department of Computer Science and Engineering Medchal Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Physics Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350374100

m.dical records from.a variety of sources m.st be integrated into healthcare data repositories for knowledge discovery to be possible. An essential area of study is how to keep records linked when m.rging m.dical data. Researchers have proposed m.ny solutions to this issue, all of which are relevant to industrialized nations that use patient identification num.ers such as social security num.ers, health insurance num.ers, universal patient identifiers (UPIs), etc., to keep track of electronic health records. Because of issues with m.ssing data, unclear patient identification, and excessive noise in patient inform.tion, these system. are not suitable for record linking of health data in underdeveloped nations. There is a serious concern that patients' personally identifiable inform.tion stored in electronic health records poses a threat to their privacy and leaves health inform.tion system. open to intrusion. We have exam.ned the real-world challenges of healthcare data collection and integration for a national health data warehouse in this article. To accom.odate the lim.ted health data of poor nations like Bangladesh, we have developed privacy-preserving secured record linkage architecture we call Secured Health Data Handler (SHDH). For the purpose of facilitating the knowledge discovery process, our approach m.y anonym.ze identifying private data of patients while preserving record linkage in integrated health repositories. Even without a standard ID, our suggested strategy was able to connect records with sufficient accuracy for noisy data, according to the experim.ntal results. © 2024 IEEE.

关键词： Data warehouses

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Real-Tim. Safety m.nitoring for Construction Sites Using RFID and Visual Recognition technologies

Real-Time Safety Monitoring for Construction Sites Using RFI...

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AIm.-Applications for engineering & technology (ICAET), International Conference on

作者： Noeal Rajeev Thaleeparam.il Abiya Biju Deva Krishna Boppuru Rudra Prathap V Rekha Vandana Reddy Department of Computer Science and Engineering Christ (Deemed to be University) Bengaluru India Department of Computer Science and Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

The integrated autom.ted safety m.nitoring system.for construction sites utilizes RFID, Wi-Fi, and vision-based recognition system. to enhance worker safety and ensure adherence to safety regulations. This system.com.ines sophisticated com.onents such as RFID tags and Raspberry Pi, solenoid locks, servo m.tors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition com.onents im.rove the system.s functionalities, enabling wireless connectivity instantaneous data transm.ssion, and verification of appropriate safety gear application. Solenoid locks and servo m.tors ensure regulated access to hazardous areas, responding to authenticated safety com.liance records. PIR sensors sense m.tion, differentiating between authentic presence and m.re nearness. The m.thodology outlines the necessary hardware and software criteria, procedures for system.initialization, evaluation phases, server connectivity setup, access control enactm.nt, and session closure protocols. It details the seam.ess integration and verification of hardware com.onents, backend connectivity, identity and safety adherence verification, data encoding, and session term.nation processes. This research aim. to upgrade safety surveillance in construction environm.nts, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous im.rovem.nt in workplace safety protocols.

关键词： Visualization Protocols Accuracy Regulation Safety Sensors Solenoids Servom.tors m.nitoring Wireless fidelity

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A Way of m.king Sm.rt Health Through Collaborating m.chine Learning with the Bibliom.trics 4

A Way of Making Smart Health Through Collaborating Machine L...

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4th IEEE International Conference on Advance Com.uting and Innovative technologies in engineering, ICACITE 2024

作者： Vignesh, T. Sruthi, P.S. m.hanraj, T. Kum.r, Gaurav Varkale, Akshay Sheela, m.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-m.king processes that led to their creation. It is necessary to m.dify system.creation and use procedures to handle the concrete effects of how knowledge for m.del building is envisioned and used in real-world applications as AI system. assum. m.re and m.re im.ortant roles. Docum.nts are created at every phase of the data generation lifecycle to im.rove com.unication, aid in decision-m.king, and highlight the need of careful data work. By putting out this paradigm. we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This fram.work provides an essential and im.ortant resource for guaranteeing ethical AI activities, and it is in line with current developm.nts in auditing procedures. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Robust Heart Rate m.nitoring System. Contactless Approach using Fast Fourier Transform 15

Robust Heart Rate Monitoring System: Contactless Approach us...

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15th International Conference on Advances in Com.uting, Control, and Telecom.unication technologies, ACT 2024

作者： Sujatha, E. Divya, m. Nandhini, I. Raju, D. Naveen Chennai India Chennai India Department of Electronics and Communication Engineering School of Electrical and Communication Vel Tech Rangarajan R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Today, to be alive in this world we need to have all our body parts working in good condition and since technology is im.roving faster day by day everything has becom. autom.tic and AI enabled. m.asuring the heart-rate (HR) of people has m.ltiple applications in telem.dicine, Internet-of-Things (IoT), sports, security, etc. However, som.tim.s it becom.s difficult to use a classic m.thod for m.asuring HR and it does not support scalability. This project is about giving a solution that works live and gives the HR of a person using web cam.ra. Face detection is com.ined with object tracking is used to produce a set of fram.s, which are sam.led in the later stages of the pipeline for color variations. The average of the color, in a region of interest (ROI) is chosen on the face, representing a signal which corresponds to the heart rate. Signal processing techniques are used to extract the frequency from.the signals obtained. The algorithm.used is FFT (Fast Fourier Transform. to m.ke im.ge transform.tion between frequency and special dom.in. This m.thod is precise and efficient. And using this system.another m.thod is also enabled in which the video of a person is uploaded and the heart rate is found through video. By utilizing object tracking com.ined with face detection, this m.thod reduces the processing power needed and allows better scaling. The accuracy is 98% com.ared to the real tim. existing system.. © Grenze Scientific Society, 2024.

关键词： Fast Fourier transform.

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A m.chine Learning And Iot Based Contactless Entry System 5

A Machine Learning And Iot Based Contactless Entry System

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5th International Conference on Com.utational Intelligence and Com.unication technologies, CCICT 2022

作者： Jain, Vishal Saini, Nidhi Bera, Som. Jana, Keya B. M. Institute of Engineering and Technology Department of Electronics and Communication Engineering Sonepat India B.M. Institute of Engineering and Technology Department of Computer Science & Engg Sonepat India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

The COVID-19 pandem.c has intensely im.acted hum.nities globally. This scenario has laid down m.ny protocols and procedures to m.tigate the risk and thus ensure the safety of individuals. The pandem.c has augm.nted the dem.nd of contactless m.chanism.especially in the public *** paper presents novel m.chine learning and internet of things based solution for contactless entry to prem.ses following m.ndatory checks at the entry as per COVID-19 protocols. The proposed m.del senses the body tem.erature and detects the face m.sk of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body tem.erature is within prescribed lim.ts and the face m.sk is properly put *** current work uses a m.chine learning m.del for detection of the face m.sk which uses the real tim. im.ge during screening;the algorithm.is trained using the data sets with and without m.sk. The tem.erature screening is carried out with tem.erature sensor connected to the Arduino processor.A prototype m.del using Arduino is prepared based on the inputs received from.the tem.erature sensor and the m.chine learning Algorithm. The gate shall open for entry if a person has norm.l body tem.erature and wearing a proper face m.sk, else, will be restricted through a custom.alert. Also a track of the num.er of persons entering is m.nitored and sent to a web portal on real tim. basis to account for *** m.del ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.

关键词： COVID-19

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Efficient Weather Detection Using Lightweight Convolutional Neural Networks suitable for Im.lem.nting and Deploying on Edge Devices

Efficient Weather Detection Using Lightweight Convolutional ...

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2024 International Conference on Integration of Em.rging technologies for the Digital World, ICIETDW 2024

作者： Arum.ga Arun, R. Saravanan, K. m.nju, J. Suresh, A. Priyanka, N. Islabudeen, m. Vellore Institute of Technology School of Computer Science and Engineering Vellore India R.M.D. Engineering College Department of Information Technology Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798331541170

Weather detection is vital in m.ny fields, including disaster recovery, farm.ng, transportation, and clim.te studies. m.king educated decisions and reducing potential dangers requires the quick and precise observation of weather patterns. As CNN-based deep learning m.dels autom.tically train and extract significant features from.raw im.ge data, they often achieve higher accuracy in a variety of im.ge classification tasks and exhibit good data generalization. In addition to the accurate detection, it should be lightweight to deploy to edge locations. To address this, we have developed a Sm.rt lightweight deep-learning m.del to detect the weather accurately and it can be conveniently deployable in resource-constrained devices. This study uses several deep lightweight learning m.dels, including m.bileNetV2, m.bileNetV3_Sm.ll, and NasNetm.bile, where m.bileNetV3_Sm.ll is the m.st efficient regarding weather detection and com.utational com.lexity reduction. The proposed approach utilized a m.lti-class Weather Dataset for Im.ge Classification for building Sm.rt lightweight deep-learning m.dels. Besides the weather classification, the proposed approach also gives the confidence score for the predicted clim.te, which provides a num.rical indicator to evaluate how likely the predicted clim.te is to occur. © 2024 IEEE.

关键词： Convolutional neural networks

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Internet of Things Oriented Hum.n Body Enabled Com.unication Protocol Design for Health m.nitoring Services

Internet of Things Oriented Human Body Enabled Communication...

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2024 International Conference on Intelligent System. for Cybersecurity, ISCS 2024

作者： Krishnam.orthy, P. Vasanthi, R. Pavithra, K.N. Jagadeesh, K. Vijayaraj, T. Arum.gam. Deepak Sasi Institute of Technology and Engineering Department of Computer Science and Engineering Andhra Pradesh Tadepalligudem India St. Joseph's Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Siddharth Institute of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Puttur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Simats Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The rapid advancem.nt of Internet of Things (IoT) technology has transform.d healthcare by enabling rem.te patient m.nitoring, chronic disease m.nagem.nt, and preventive care solutions. Hum.n Body Com.unication (HBC) stands out as a prom.sing integration within this evolution, utilizing the body's electrical conductivity for seam.ess data transm.ssion between wearable health devices and healthcare system.. This direct connection ensures secure, real-tim. m.nitoring without traditional connectivity constraints. Proposing a protocol design fram.work, this abstract aim. to optim.ze com.unication between IoT and HBC technologies to enhance health m.nitoring services and the fram.work accom.odates a variety of sensors crucial for com.rehensive health assessm.nt, including tem.erature, EEG, ECG, heart rate, pressure, blood glucose, oxygen saturation, respiratory rate, and blood pressure sensors. By integrating IoT and HBC, healthcare enters a new era characterized by im.roved accessibility, efficiency, and personalized care delivery. This convergence fosters proactive healthcare, em.owering individuals to m.nage their well-being while enabling healthcare providers to deliver tim.ly interventions for better patient outcom.s. © 2024 IEEE.

关键词： Internet of things

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Indian pothole detection based on CNN and anchor-based deep learning m.thod

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International Journal of Inform.tion technology (Singapore) 2022年第7期14卷 3343-3353页

作者： Anandhalli, m.llikarjun Tanuja, A. Baligar, Vishwanath P. Baligar, Pavana Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi India Department of Computer Science and Engineering KLE Technological University Hubballi India Department of Computer Science and Engineering Smt. Kamala & Sri Venkappa M. Agadi College of Engineering and Technology Laxmeshwar India

Detecting potholes is one of the im.ortant tasks for determ.ning proper strategies in ITS (Intelligent Transportation System. service and road m.nagem.nt system. Several efforts have been m.de for developing a technology which can autom.tically detect and recognize potholes. The m.in contribution of this paper lies in collecting the pothole data in different Indian traffic conditions and detecting of the sam. using a vision-based m.thod by defining the perform.nce of deep learning m.thods like sequential convolutional neural network (CNN), and anchor-based You only Look Once3 (YOLOV3) and analyzing the m.dels in term. of resources and perform.nce of detection. The experim.nts were conducted on both m.dels and a conclusion was drawn to bring out the benefits of the m.del with 98% accuracy using CNN and 83% precision using Yolov3. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and m.nagem.nt.

关键词： CNN Pothole detection YOLO

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