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Multiclass Image Classification of COVID-19 Chest X-ray Scans Using Deep Learning 4th

Multiclass Image Classification of COVID-19 Chest X-ray Scan...

引用

4th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

作者： Rathod, Laya Jain, Harsh Kaliappan, Jayakumar Suganthan, C. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789811950360

The novel coronavirus is a family of animal transferred viruses that can cause illness in humans. This virus took over the world in 2019 and WHO deemed it as an epidemic naming it as COVID-19. A lot of research has gone in the prediction of the trends and classification of cases using various machine learning and deep learning techniques. With the outbreak of this pandemic, efficient detection of the disease at a faster rate has become very crucial. This study proposes a convolutional neural network (CNN)-based deep learning approach for classification of COVID-19 positive cases from normal cases using X-Ray radiology scans of the patients. The model consists of a large custom dataset of images extracted from an open source dataset and is then trained using our proposed model. Different optimizer algorithms are also compared in order to check which one of them gives the most accuracy. The model is further tested using the categorical accuracy metrics and then a graphical analysis of the results is provided. A comparative study is also conducted with an already existing support vector machines (SVM) model. The images were trained according to three classifications: normal, COVID infected, and viral pneumonia infected patients. The main objective of our research is to help further the research in early diagnosis of COVID-19 using modern deep learning techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Data Transmission and Optimization of Energy in Smart Campus Using LoRaWAN Industrial IoT technology 1st

Data Transmission and Optimization of Energy in Smart Campus...

引用

1st International Conference on Sci/Tech and engineering, ICSTE 2023

作者： Mariappan, Ramasamy Amulya, Ch. S. V. N. S. L. Vasanthi, M. Yogisri Aditya, Pothapu Manohar, Ch. Sai School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Dept of ECE VR Siddhartha Engg College Vijayawada India

ISBN: (纸本)9789819947126

Our idea is to build a Long-Range Wide-Area Network (LoRaWAN)-based smart campus monitoring system, in order to replace the current technology of short-range communication devices. This paper mainly focuses on transmitting the data collected from end nodes or wireless sensors like Digital Humidity Temperature Sensor–11 (DHT11), Ultrasonic Sensor, Passive Infra-Red Sensor (PIR), and Ambient Light Sensor (ALS), etc., to the network server using gateway, end nodes. The end users make use of this data for monitoring and controlling several appliances. The network interface is implemented by LoRa which solves communication failure problems and saves energy during data transmission with 100 percent accuracy. This LoRa supports moderate data rates which reduces energy consumption in turn optimizes the energy utilization in the campus. This paper explains how the sensor data is transferred from the sensor using the LoRa technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wide area networks

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Developing a Self-Driving Autonomous Car using Artificial Intelligence Algorithm 6

Developing a Self-Driving Autonomous Car using Artificial In...

引用

6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Nuhel, Ahsan Kabir Sazid, Mir Mohibullah Mahmud Bhuiyan, Md. Nafim Arif, Ariful Islam Roy, Priyadarshini Hriddhi Islam, Md Riazul Bangladesh Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9781665482714

A fully automated driving system allows an autonomous vehicle to adapt to external conditions that a human driver would typically handle. Using deep learning (DL), machine learning (ML), computer vision (CV), and conventional neural networks (CNN), a self-driving automobile will be developed in this study by modeling the design of a car body and the implementation of various sensors on the car chassis in order to automatically run the cars. In addition, the car will undergo real-time obstetrical on the roads and self-training in order to learn itself appropriately. In contrast, the fundamental principles of artificial intelligence and their relationship to the autonomous car are examined. © 2022 IEEE.

关键词： Autonomous vehicles

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Digital Learning Trends Post Covid-19 2

Digital Learning Trends Post Covid-19

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Madhani, Nishil Shah, Eshika Swarnalatha, P. Sivaraman, Arjun Furniturewala, Mahera School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781665493963

In the current society, mobile devices have become common in modern culture along with which the Internet transcends time and location constraints to become a widespread learning tool. This gave rise to digital learning which reached a new peak due to the recent COVID-19 pandemic. Not every student has the same learning opportunities. In order to make education more egalitarian, effective policies and programs must be implemented-And perhaps your unique data analysis could assist disclose the solution. Current research indicates that educational outcomes are far from egalitarian. The COVID-19 epidemic aggravated the imbalance. There is an immediate necessity for a higher standard of acknowledgement and quantify the range and impact of COVID-19 on the mentioned partisanship. This paper aims to understand the digital learning trends in the current times and visualize the inclination towards the use of different online learning tools. © 2022 IEEE.

关键词： COVID-19

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Comparative Correlational Study of Music Composition using Different Neural Networks 2

Comparative Correlational Study of Music Composition using D...

引用

2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Bhakat, Akash Vijayasherly, V. School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Music has always been considered as one of the wonders of human innovation. Artificial music composition yet being challenging, is an inspiring task. It involves many considerations like assigning pitches, determining rhythm, arranging accompaniment, and. estimating the next note. This paper attempts to compare the performance of different neural networks, namely, markov chains, single-LSTM network, stacked LSTM network, Restricted Boltzmann Machine, on music composition with focus only on correlation of pitches and notes produced in output music as compared to train set. First, different traditional neural network techniques are used to compose music, followed by hybrid stack architectures - RBM+Stacked LSTM network,RBM+Single LSTM network. Lastly it compares the correlations of produced output to train set for the architectures. © 2022 IEEE.

关键词： Long short-term memory

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A Survey Analysis on Decentralized Land Registry System Using Blockchain and Smart Contracts 1

A Survey Analysis on Decentralized Land Registry System Usin...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Narayanaprakash, K. Malarchelvi, P. D. Sheba Kezia Ranjithkumar, S. Sridevi, R. Anitha, P. Kumar, N.Suresh Alliance University Anekal Department of Computer Science and Engineering Bengaluru562 106 India Saranathan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Trichy India Oasys Institute of Technology Department of Computer Science and Engineering Pulivalam Trichy India Bangalore India

ISBN: (纸本)9798350341737

This study investigates the potential use of blockchain technology and smart contracts to construct a decentralised land registry system. It looks at the problems with traditional land registry systems and provides a technical breakdown of how to create a blockchain-based land registry system. The research also evaluates the countries that have already adopted such systems and discusses the possible adoption of a blockchain-based land register system in India. The implementation of a blockchain-based land registration system, per the research, can increase the security and efficacy of land transactions, reduce fraud and mistakes, and promote economic growth. © 2023 IEEE.

关键词： Smart contract

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Six Convolutional Layered Deep Convolutional Neural Network Based Real Time Single Image Super Resolution 4

Six Convolutional Layered Deep Convolutional Neural Network ...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Devi, M. Shyamala Pandian, J. Arun Thakur, Rahul Kumar Vemuri, Vinod Babu Bharath, Javvaji Naga Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350300888

The super-resolving images investigation has advanced in recent years through the use of cutting-edge Deep Learning-based architectures. Numerous previously documented super-resolution-based solutions need the most advanced and top-tier Graphics Processing Units (GPUs) to execute picture super-resolution. With the growing advancement of technology, this research focuses on suggesting the needed quantity of convolutional layers for creating the real time super resolution of single image using Convolutional Neural Network. The proposed Six Convolutional Layered Deep Convolutional Neural Network (6CL-DCNN) to predict the super resolution of images with high accuracy and ideal Peak Signal-to-Noise Ratio. The dataset extracted from ***(http://***/Research/Projects/CS/vision/grouping/BSR/BSR_***). The dataset contains 800 images with the combination of low resolution and high-resolution images having the image resolution of 300 ∗ 300 pixels. The proposed 6CL-DCNN built with single input layer followed by six convolutional layers followed by single lambda optimized output layer that predicts the super resolution of both high resolution images and low resolution images. Python was used through 500 training iterations and a 64-bit block size on a NVidia Geforce Tesla V100 GPU workstation. The processed low resolution images and high-resolution images are applied with proposed 6CL-DCNN model and also the performance is compared using Peak Signal to Noise Ratio with other optimized output layers. Experimental results shows that the proposed model 6CL-DCNN shows the maximum Peak Signal to Noise Ratio of 48 dB when compared to other optimized output layers. © 2023 IEEE.

关键词： Graphics processing unit

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Novel Approach for Group Communications using various Cybersecurity Techniques

Novel Approach for Group Communications using various Cybers...

引用

2022 International Conference on Advances in Computing, Communication and Materials, ICACCM 2022

作者： Anala, Jayanth Pothala, Dhanush Reddy Ruthvik Reddy, T. Manikandan, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781665474399

Internet surfers and smartphone owners frequently utilize chatting software, although these software applications are built with security encryption, cyber-attacks are created to break this encryption. This paper is concerned with the finding of the best cryptography algorithm, which is better safe for assaults, simple to encrypt and has more security features. When a message is being transmitted, message security is a crucial factor to take into account. Many cryptographic methods are used to do this. We will be comparing different types of cryptographic algorithms and coming up with a better and most secure application. We opted to compare Ron Rivest, Adi Shamir and Leonard Adleman algorithm (RSA), the Triple Data Encryption Standard (3DES) algorithm and the Elliptic curve cryptography(ECC) since these three algorithms are the ones that messaging platforms utilize the most. As previously said, we'll use a few metrics to compare different cryptographic methods. © 2022 IEEE.

关键词： Cybersecurity

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CT Intensity Segmentation of Lungs 2

CT Intensity Segmentation of Lungs

引用

2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Jayakumar, K. Deendayal, Namdev Parth Dhindsa, Gurnehmat Kaur Nagrani, Agrim Bali, Vinay Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

The early diagnosis and treatment of lung diseases is a very critical procedure and it requires the use of Computed Tomography (CT) imaging for the segmentation of lungs. Segmentation of the lung helps in the analysis of the lesions. The project proposes a CT lung and vessel segmentation model without any labels which is based on medical image processing using Python. This would assist the medical practitioners and scientists who are working in the field of CT intensity segmentation of lungs. It would make the diagnosis process easier and more convenient for patients, especially in pandemic situations like COVID. © 2022 IEEE.

关键词： Python

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Federated Learning for Improved Air Pollution Prediction: A Combined LSTM-SVR Approach 4

Federated Learning for Improved Air Pollution Prediction: A ...

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4th IEEE Annual Flagship India Council International Subsections Conference, INDISCON 2023

作者： Abimannan, Satheesh John, A. Shukla, Saurabh Satheesh, Dhivyadharsini Amity University Maharashtra Department of Computer Science and Engineering Mumbai India Galgotias University Department of Computer Science and Engineering UP Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350333558

In this research paper, we introduce a unique approach to predict air pollutant concentrations. The method involves combining two pre-existing models, LSTM and SVR, by utilizing federated learning. Our proposed model is evaluated based on air quality data gathered by the Taiwan Environmental Protection Agency (EPA). By using federated learning, we can create a global model that taps into the collective knowledge of all 70 data centers while safeguarding data privacy and security. This novel method has significant implications for addressing joint air pollution mitigation and environmental protection. Additionally, the research paper presents performance comparisons of various models across three locations in Taiwan. We demonstrate the efficiency of our FL-ALSTM and FL-SVR models in predicting air pollutant concentrations. Our findings show that the FL-ALSTM model outperforms other models, improving predictions of air pollution levels by more than 95%. Overall, our study underscores the potential of federated learning in advancing air pollution prediction and environmental sustain ability. © 2023 IEEE.

关键词： Environmental Protection Agency

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