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Secure Deduplication with Dynamic Updates in Multi-Tenant Cloud Environment

Secure Deduplication with Dynamic Updates in Multi-Tenant Cl...

引用

2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Bharath Babu, S. Jothi, K.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781665495158

Cloud storage service providers accommodate customers by offering adequate storage based on customer requirements. Multi-Tenant architecture to provide low-cost resource provision. In a multitenant system single instance shares with multiple customers, similar applications data has been used multiple customers so Deduplication techniques eliminate redundant data to improve storage efficiency and Bandwidth. To improve the confidentiality of the user privacy data, convergent encryption with the deduplication techniques has been proposed to encrypt the user data before uploading it to the third-party CS Sometimes Confidentiality of the user data leads to issues with this deduplication method. To overcome these issues this paper proposed dynamic ownership management of server-side deduplication with the blowfish encryption algorithm. In this model to support tenant block-level deduplication. The proposed method computational storage efficiency and security level. © 2022 IEEE.

关键词： Cryptography

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Deep Learning Based Image Compression in DWT Domain 8

Deep Learning Based Image Compression in DWT Domain

引用

8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Agarwal, Harshit Santhi, V. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781665408165

We live in a digital era where the generation and transmission of data increases almost exponentially with the advance of time. A major chunk of this data is in the form of images owing to the catchy nature of visual graphics and the affinity of human mind towards the same. This has led to the evolution as well as invention of numerous applications that are centered around the concept of socializing through images and has indeed brought to life the idea that an image speaks more than a thousand words. With such enormous amounts of data being generated in the form of images and the need for fast transmission, it becomes necessary to minimize the storage space and the transmission cost of these images which is where compression is needed. Over the years, a lot of research has been carried out with the same objective and lot of techniques have been adopted to design an efficient model for performing this compression. Here, we have introduced a model which works in the wavelet transform domain. It makes use of the single Low Frequency sub band (LF) to predict the three High Frequency sub bands (HF) into which the image is split. This helps in the reduction of redundancy in the HF bands post which they are passed through the Auto Encoders for encoding which makes use of conditional probability models to predict the encoded codes for better compression. The model is a neural network and the training performed is unsupervised. The proposed model performs better than the traditional compression models such as JPEG, JPEG2000 and BPG. © 2022 IEEE.

关键词： Image compression

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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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Face-Crypt Messenger: Enhancing Security of Messaging Systems using AI based Facial Recognition and Encryption 6

Face-Crypt Messenger: Enhancing Security of Messaging System...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Jain, Niket Naik, Ojas Yalagoud, Adithya Bhuyan, Prithiraj Manikandan, K. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665410281

Information Security has always been a major discipline in the field of computer science. Many software has been developed around the world for fulfilling various tasks. However, it is important to ensure the security of the data involved in using that particular software. In the recent times, the issue of security related to chat systems has been trending. There have been instances where an unauthorized user has viewed the messages of a particular user. That is where this project presents a pragmatic solution. This project implements a chat system which authorizes a user into the application using facial authentication. The user messages are encoded using cryptographic algorithms. In case of sending a secret message, only the user, whose face matches with that at the time of sign-up can view the message. This feature is implemented using Machine Learning algorithms. © 2022 IEEE.

关键词： Cryptography

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Quantitative Trading Toolkit 3

Quantitative Trading Toolkit

引用

3rd IEEE Global Conference for Advancement in technology, GCAT 2022

作者： Sharma, Darsh Pradhan, Anirudh Garg, Yogek Victor, Akila Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Algorithmic trading is a branch of financial engineering in which the computer makes decisions about which transactions we can make. Algorithmic trading accounts for more than half of all trades. Because computers are significantly faster than humans, they can uncover profitable transactions in a fraction of a second and execute them in a way that a human could never. We construct our own trading algorithms in this project so that we may uncover profitable patterns and maximize our income. To assess the financial market, we enlist the support of artificial intelligence and other computer science techniques. The main objective of this project is to help out traders in analysing how volatile an asset is and also help them in making decisions regarding the buying and selling of those assets. Analysing the volatility of an asset is not an easy process and the methods used to achieve the same tend to be quite complex. Moreover, in-depth knowledge about the market is required for an individual to be able to make profitable transactions and more importantly, maximize his profits. Without proper knowledge about the trends going on in the market, individuals tend to make mistakes which ultimately result in them incurring huge losses. With this project, we aim to simplify the process of trading for them by not only providing them with the volatility of different assets but also giving them insights into the trends going on in the market. We use ARCH and GARCH models to provide the degree of volatility of different assets. Our work on detection of anomalies in the market using Autoencoders helps traders in maximizing their profits as anomalies in the financial markets a rare Reinforcement learning can be utilized in order to study the techniques and patterns used by successful traders in order to make profitable transactions. We deploy an Actor2Critic algorithm to achieve this goal. A complex equation known as Black-Scholes is required for the evaluations of Options in Trading

关键词： Reinforcement learning

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NFT based Green Energy and Assets Trading Application over Decentralized Marketplace 5

NFT based Green Energy and Assets Trading Application over D...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Naik, Madhusudan Singh, Akhilendra Pratap Pradhan, Nihar Ranjan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India Vellore Institute of Technology Vijayawada India

ISBN: (纸本)9798350313123

Digital decentralized marketplace is now becoming more popular since 2015 due to invention of NFT technology. NFT is most successfully implemented in digital contents like art and music and is evolving as a new market for the future. NFT and its underlying technology blockchain is now being diversified into various application areas to solve various requirement of human and its society. In the current global scenario green house effect and its solution though alternating energy sources like renewable energy generation is major challenge due to distributed nature of these sources. Moreover, establishment of such infrastructure need much investments. Distributed management using blockchain technology is the only solution to manage energy generation and exchange between prosumer and consumer. However, to achieve net zero emission (NZE) we need much more investment in the clean energy sector and is not possible unless more and more people join and participate in the investment. This study proposes a new solution for secure and intelligent trading of solar charging stations and electric vehicle equipment in a decentralized digital marketplace, utilizing NFT technology. The approach is designed to improve the reliability and transparency of transactions, providing a more secure and efficient system for trading these valuable assets. © 2023 IEEE.

关键词： Renewable energy resources

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A Novel Query Based Summerizer Model of Product Reviews Using Modified LDA 1

A Novel Query Based Summerizer Model of Product Reviews Usin...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Hazarika, Sangramjit Senthil Kumar, A.M. Vellore Institute of Technology Chennai School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

In this digital era, there must be a system which can summarize huge lot of data and categorize the documents under specific topic without its semantic meaning being detached. Some important information can be extracted out of these documents as and when it is needed. The system can ease out many cumbersome processes which in other times might require a lot of manual work. Additionally, it becomes easy to navigate through a summarized version of a document rather than investigating a huge lot. The efficiency gets increased and manual work gets decreased. The system is basically an integrated version of both topic modelling and question answering with suitable machine learning algorithms. So, in short, the system works out to ease out some traditional work and can also be a solution to some technical problems related to storage and processing since a summarized version of the document given as input is only stored and further processed to give specific answers to the queries raised by the users. © 2023 IEEE.

关键词： Semantics

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COVIDEffiNet: Pulmonary Diseases and COVID-19 Detection from Chest Radiographs Using EfficientNet Deep Learning Model

引用

International Virtual Conference on Industry 4.0, IVCI4.0 2021

作者： Magesh, Madhurima Menon, Hitarth S. Geetha, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789811999888

There have been attempts made previously to classify and determine the diagnosis of a disease of a patient based on the X-rays and computed tomography images of various parts of the body. In the field of lung disease diagnosis, there have been attempts to identify lungs infected with pneumonia, COVID-19, and tuberculosis, either individually classifying them into two groups of positive and negative of the given disease or in groups with multiple classes. These methods and approaches have used various deep learning models like CNNs, ResNet50, VGG19, Inception V3, MobileNet_V2, hybrid models, and ensemble learning methods. In this paper, we have proposed a model that takes an X-ray image of the lungs of the patients as input and classifies the result as one of the following classes: tuberculosis, pneumonia, COVID-19, or normal, that is, healthy lungs. What we have used here is transfer learning, with our base model being EfficientNet which gives an accuracy of 93%. For this, we have used different datasets of X-ray images of patients with different lung ailments, namely pneumonia, tuberculosis, and COVID. The dataset consists of images in four categories, the above-mentioned three diseases and a fourth category of normal healthy lungs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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CT Intensity Segmentation of Lungs 2

CT Intensity Segmentation of Lungs

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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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A Survey on Computational Aptitudes towards Precision Agriculture using Data Mining 3

A Survey on Computational Aptitudes towards Precision Agricu...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Murali, E. Anouncia, S.Margret Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Precision agriculture is a modern agriculture implementation technique in which analysis of numerous source data takes place for decision-making and operation in the management of crop production. The data for precision agriculture are collected through robots, sensors, satellites, and drones. The two approaches of precision agriculture are the predictive approach which is used for representing the static indicator during the crop cycle whereas the control approach is an updating of information Ontology is a demonstration of concepts and their shared association. It can be used in a wide range of contexts, including the classification of agricultural information and the development of knowledge bases. The basic steps involved in precision farming are assessing variation, managing variability, and evaluation. The various tools used in precision farming are the internet of things (IoT), a global positioning system (GPS), geographic information system (GPS), remote sensor, proximate sensor technology, grid sampling, etc. With the increase in information technology in the field of agriculture. Consequently, data mining become much essential for decision-making. This paper attempts to emphasize the coherence of data mining approaches toward helping precision agriculture as a valuable venture. © 2022 IEEE.

关键词： Data mining

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