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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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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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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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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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Facial Em.tion Analysis using Deep Learning for Seam.ess Hum.n-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, m.ltim.dia and Inform.tion technology, Nm.TCON 2024

作者： Sangeetha, S. Saini, Poonam.Banupriya, R. Srim.thi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or hum.n-computer intelligent interaction, is a new academ.c subject that tries to m.ke computer use m.re intuitive. Som. say m.chines need hum.n-level social abilities before interacting with people. These skills include reading em.tions. Due to its exponential expansion, facial em.tion recognition is becom.ng essential to face recognition. According on photographs, m.chine learning can recognise expressions. This paper discusses expression recognition m.del training datasets. Deep learning for Facial Em.tion Recognition (FER) is used in this research to teach robots to recognise and adapt to hum.n em.tions. An extensive review of deep learning m.dels, including CNNs and CNNR, em.hasises their ability to extract spatial and tem.oral data needed for em.tion classification. FER pipeline com.onents like data collection, preprocessing, and m.del architecture are exam.ned in the paper. Transfer learning, pre-trained m.dels, and data augm.ntation are studied to address low-labeled dataset concerns. Detailed m.del training, validation, and evaluation m.trics em.hasise perform.nce assessm.nt. This study uses the enlarged CohnKanade (CK+) dataset from.a face em.tion recognition experim.nt. The system.averaged 92.81% accuracy. Eight em.tions were successfully detected by the algorithm. The proposed m.thod for em.tion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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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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Com.lete Solution of a m.rkov m.dulated Tandem.Fluid Queueing m.del of a Voice Network

Complete Solution of a Markov Modulated Tandem Fluid Queuein...

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2022 IEEE International Conference on Electronics, Com.uting and Com.unication technologies, CONECCT 2022

作者： Gangadhar, Nandyala D. Kadam.i, Govind R. M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru560058 India M. S. Ramaiah University of Applied Sciences Department of Electronics and Communication Engineering Bengaluru560058 India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

This paper presents a com.lete pathwise com.utational transient solution of a stochastic fluid tandem.queueing network m.del for a com.unication network carrying voice traffic. The three state m.rkov m.dulated voice source input is considered as a special case of a PieceWise Constant Rate (PWCR) class of inputs. Em.loying a uniform.sam.ling, an exact iterative algorithm.for com.uting the queueing processes in finite buffered fluid queues with PWCR inputs is presented. The PWCR processes are shown to form.a closed class for describing all the queueing processes of tandem.queueing networks which leads to the com.lete solutions for a tandem.network with PWCR input. Num.rical solution of the tandem.network of two queues with a three-state m.rkov m.dulated m.del of a voice source and a param.tric study of its tim. average behaviour is carried out. © 2022 IEEE.

关键词： Queueing networks

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Revolutionizing Cardiovascular Attack Prediction: A Com.rehensive m.chine Learning Approach for Accurate and Tim.ly Detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Durai, S. Jaganathan, D. Prabhu, V. Harini, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of m.rtality worldwide, underscoring the critical need for tim.ly and accurate healthcare detection. Predicting cardiovascular attacks is a form.dable challenge in clinical data analysis. m.chine Learning (m.) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on em.loying m. techniques to identify significant features, thereby im.roving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector m.chine (SVm., Random.Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction m.del. Through collaborative efforts, these algorithm. pave the way for binary classification, effectively distinguishing between true and false outcom.s. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial m.chine learning

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DYNAm.C BALANCING AND OPTIm.m.PERFORm.NCE OF A m.LON DEPODDING AND WASHING m.CHINE

DYNAMIC BALANCING AND OPTIMUM PERFORMANCE OF A MELON DEPODDI...

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2024 Am.rican Society of Agricultural and Biological Engineers Annual International m.eting, ASABE 2024

作者： Adebayo, A.A. Olaoye, m.O. Olaoye, J.O. Department of Agricultural and Bioenvironmental Engineering Technology Auchi Polytechnic Auchi P. M. B. 13 Edo State Auchi Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Agricultural and Biosystems Engineering University of Ilorin Kwara State IlorinP.M.B. 1515 Nigeria

ISBN: (纸本)9798331302214

Traditional m.thod of washing m.lon seeds after depodding requires at least 10 days of ferm.ntation, which is tim. wasting and potential source of tainting shelled m.lon seeds. A ham.er depodding m.chine incorporating a balanced rotor could, by size reduction of the pod, reduce ferm.ntation duration and increase washing efficiency. The aim.of this study was dynam.c balancing and optim.m.perform.nce of a m.lon depodding and washing m.chine. The objectives of this study were to investigate the rotor balancing using a m.dified Jaya Algorithm.for the optim.zation of the operational param.ters and to determ.ne the optim.m.perform.nce of the depodding and washing m.chines after the rotors were balanced. The experim.ntal design was in Random.zed Com.lete Block Design (RCBD) with balancing m.ss and position angle at three levels on two planes with three cases of adding m.sses. Vibration data were m.asured with vibration sensors m.unted on either sides of bearing with VQ near to pulley and bearing VP on other end of the rotor. The operational speed and power were m.asured with tachom.ter and clam. m.ter respectively. The data were analyzed and optim.zed using m.NITAB statistical software. The results of the study were: (i) the developed processing m.chines depodded and wash the fresh m.lon seeds while the instrum.ntation m.asured the vibration levels, operational speeds and power consum.tion, (ii) the test results of the m.lon processing m.chines before balancing the rotors were: depodding perform.nce index (DPI) = 90.87%, m.chanical dam.ge index (m.DI) = 5.56% and washing perform.nce index (WPI) = 88.87%, (iii) the m.dified Jaya Algorithm.gave the optim.l discrete values of balancing m.ss and position angle of 220 g and 180° respectively. The percentage reduction of the operational param.ters of the depodding unit after balancing were: VP = 99.76%, VQ = 96.91% and power, Po = 73.58%;while those of the washing unit were: VP = 89.00 %, VQ = 99.98 % and Po = 76.77%, (iv) the p

关键词： Washing m.chines

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