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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Chelliah, Balika J. Arunkumar, S. Prabavathi, R. Aarthi, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Assistant Professor Sri Sairam Institute of Technology West Tambaram Chennai India

ISBN: (纸本)9789811612435

The world today battles the new coronavirus (COVID-19) spread that can lead to a major endemic or pandemic outbreak with devastating outcomes which has overturned all our usual calculus seemingly. Because of the unavailability of a vaccine and the serious extent of infectiousness, social distancing is perhaps the only way left to fight the infection. The tracking system is still a challenge for monitoring the COVID-19 affected person and the community spread. This leads to an epidemic situation, where the number of secondary infections keeps going higher and higher which intern leads to the difficulty of tracking and treating each infected person. An AI-based mathematical modelling is proposed for tracking and monitoring the pandemic outbreak. A GPS tracking system, geolocation using Fit bit and Wi-Fi tracking modules are developed to locate the secondary infections and the carriers. Calculation of real-time coordinates and the conditional probabilities with respect to time is done followed by the classification based on risk using CNN connectionist temporal classification (CTC) model, a type of neural network which is used to train the recurrent process. Monitoring the affected person using sensors like thermal sensors and a tracking chip inbuilt in a wristband for real-time updates. A comparative study is done on various classification models. Therefore, the system provides a robust model with 96.5% accuracy to combat this pandemic situation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security

IoT Based Human Biometric System Using Alcohol Level Detecti...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Anurag Sinha Nitish Kumar Amrita Ticku Vyom Modi Meghal Shah Kapil Shrivastava School of Computing and Information Sciences IGNOU New Delhi India Department of Computer Science Lovely Professional University Phagwara Punjab India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Rajkot Gujarat India Department of Computer Science & Engineering Charotar University of Science & Technology Gujarat India Assistant professor Department of computer engineering and applications GLA University mathura India

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver; they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol.

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Machine Learning in K–12 Educational Settings

Machine Learning in K–12 Educational Settings

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model

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computers, Materials & Continua 2021年第5期67卷 1881-1897页

作者： Thavavel Vaiyapuri Sachi Nandan Mohanty M.Sivaram Irina V.Pustokhina Denis A.Pustokhin K.Shankar College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science&Engineering IcfaiTechICFAI Foundation for Higher EducationHyderabadIndia Assistant professor Research Research CenterLebanese French UniversityErbil44001Iraq Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the *** VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing *** current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN *** presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and *** Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in *** SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN *** HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge *** experimentation outcome verified that the presented method was better under several *** projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

关键词： Deep learning license plate recognition intelligent transportation segmentation

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions

Optimizing Battery Management Systems For Electric Vehicles:...

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Global Conference for Advancement in Technology (GCAT)

作者： V.S.Prakash Annamalai Muthu B. Anandan V. Velmurugan T. Alex Stanley Raja Purushothaman V Department of Computer Science Kristu Jayanti College Bengaluru Electrical Section Engineering Department University of Technology and Applied Science Nizwa Oman Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur Andhrapradesh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Assistant Professor Level III Bannari Amman Institute of Technology Sathyamangalam Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide.

关键词： Technological innovation Electric potential Battery management systems Estimation Electric vehicles Thermal management Market research Sustainable development Thermal stresses Optimization

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

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Materials Today: Proceedings 2022年 51卷 756-763页

作者： Ayan Banik Anurag Shrivastava Ravindra Manohar Potdar Sanjiv Kumar Jain Santosh Gopal Nagpure Mukesh Soni National Institute of Technical Teachers' Training & Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Associate Professor Department of E&TC Engineering Bhilai Institute of Technology Chhattisgarh India Assistant Professor Department of Electrical Engineering Medi-Caps University Indore India Assistant Professor Department of E&TC Engineering D. Y. Patil School of Engineering Academy Pune India Research Scholar Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

In order to meet the world's future energy demand, alternative renewable green energy supplies must be widely promoted. Among all DERs, the most prevalent is Solar PV for a number of important reasons. In this study, the authors analyze several elements of the Single-Phase Solar Photovoltaic Rooftop System with MATLAB. In order to analyze the characteristics of the output current, a new grid synchronization methodology has been devised. For maximum power tracking, the INC control technique has been adopted. The output of the solar DC power system has been synchronized with the grid through hysteresis current loop control. Extensive testing verifies the practicality of the proposed power grid model and promising outcomes and results.

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Cloud-Based Smart Sensing System for Real-Time Health Management of Dementia Patients

Cloud-Based Smart Sensing System for Real-Time Health Manage...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： R. Latha R. Ramya U. Arul Nilamadhab Mishra S. Dhanalakshmi Professor Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India Department of Mathematics S.A Engineering College Chennai Tamil Nadu India Professor Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India

The paper describes an innovative technique of monitoring updates on the health of people with dementia by integrating smart sensing technologies with a cloud-based platform. Because dementia is a degenerative neuropsychiatric disease that develops with time, constant patient monitoring is essential for ensuring the health and safety of those living with the condition. The proposed system uses smart sensing devices, including wearable and ambient sensors, to monitor dementia patients' health in real-time. Data like heart rates, movements used, and ambient conditions are collected and sent to a server in the cloud for analysis. The camera can monitor and analyze visual data, capture crucial information, and help evaluate the patient's well-being. Multiple patients can be monitored effectively in real time because of the cloud-based system's scalability, accessibility, and storage capacities. For healthcare providers to recognize irregular movements, identify possible risks to health, and implement preventative measures, doctors utilize the moment data analytics devices to gather valuable insights from the acquired data. In addition, the cloud-based solution improves collaborative care and enhances patient support by allowing for continuous communication and information sent among caregivers, doctors, nurses, and family members. Predefined thresholds or anomalous occurrences may trigger alerts and messages, allowing for quick action in reaction to emergencies. Modern technology and data analytics increase the health and happiness of people with dementia.

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Optimal Dietary Management via Data Analytics, ANN Based Health Insights, and Ecosystem for Continuous Nutrient Analysis

Optimal Dietary Management via Data Analytics, ANN Based Hea...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Chitra Sabapathy Ranganathan Arshadh Ariff Mohamed Abuthahir Riazulhameed G. Swathi Pavithra M.R S. Senthil C. Srinivasan Associate Vice President Mphasis Corporation Chennai Tamil Nadu India Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Assistant Professor Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Assistant Professor Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. Real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

关键词： Data analysis Ecosystems Transforms Propulsion Nutrients Real-time systems Internet of Things

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Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第3期48卷 1-9页

作者： Desiani, Anita Erwin, Member Suprihatin, Bambang Yahdin, Sugandi Putri, Ajeng I. Husein, Fathur R. Assistant Professor of Mathematics Department and Phd candidate of Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Computer Engineering Department Computer Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Head of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia An undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia

This paper is concern about segmentation on pap smear images to use for classification cervical cancer. A cervical cancer screening by recognizing the cell shape pattern on the Pap-smear image can provide information about the presence of cervical cancer Pap-smear. The manual screening process for classifying cells is a challenging endeavor prone to the risk of error. There are several studies on the classification of cervical cell images which are sometimes not segmented first. Segmentation is very important to get the features contained in the image of cervical cells including the cell nucleus and cytoplasm. However, the segmentation results are also determined by the quality of images. This study proposed 2 paths that had a combination of image segmentation and classification. Before segmentation, image enhancement was carried out to improve image quality using Normalization, CLAHE and Adaptive Gamma Correction. The first path, the segmentation based on the CNN architecture. The second path is a classification process to test the segmentation results used by applying the KNN and ANN methods. The accuracy (ACC) result at the segmentation path was used to measure the match between the location of the segmented pixels and the results on ground truth, while the accuracy at the classification path was the success of the machine in classifying or predicting the segmentation results based on the class label for each group according normal and abnormal Pap-smear's label. The ACC result obtained in segmentation was only 0.77. However, the results of the segmentation when used in classification to classify which Pap-smears were normal and which are abnormal could provide excellent accuracy results, which were above 0.9. The results of the performance of Sensitivity (SN), Specificity (SP) and F1-score on the segmentation path also only gave results below 0.72, but the performance on the classification path that used the results on the segmentation path gave well results, wh

关键词： Image enhancement

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AI Framework for Keys Instruction and Music Notation

AI Framework for Keys Instruction and Music Notation

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Krishnaraj Rao N S Smitha GL P. S. Ramesh Nandhini T J P. Nagaveni A Balaji Assistant Professor Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of IT Panimalar Engineering College

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The study of artificial intelligence focuses on the principles that guide all human intellectual endeavors. The integration of several academic disciplines, including as computer science, philosophy, politics, linguistics, systems engineering, neurophysiology, and behavioral science, has allowed it to grow. This article will describe how to build an intelligent machine, consisting of both hardware and software, that can replicate and exceed the abilities of the human brain in order to handle complicated issues that need specialized knowledge. It will specifically examine how to more effectively use artificial intelligence support systems into piano instruction. An Introduction, an oral language and music-assisted education system based on the ARM and SA algorithm, is proposed by this study in order to ascertain the implementation principle guiding the functioning of the piano automation recording system. It also delves deeply into the theory and operation of automated music recording systems, compiling and evaluating data that these systems generate via internal databases. is an effort to improve music education via the use of artificial intelligence support systems. The results of the experiment show that the training system for the automated notation algorithms for playing the piano and songs has a 56.81 and fast growing machine learning auxiliary technology utilization rate. Thus, we may infer that using an AI support system is necessary while teaching performance piano and automated notation.

关键词： Training Machine learning algorithms Education Music Machine learning Software Recording Multiple signal classification Telecommunication computing Artificial intelligence

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