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Harnessing the Power of web3: A Blockchain Approach to Crowdfunding 15

Harnessing the Power of web3: A Blockchain Approach to Crowd...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Savant, Richa Vivek Sunder, S. Navin Spoorthi, M. Rajagopal, Shinu M. Balakrishnan, Roshni M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Conventional crowdfunding methods frequently involve intermediaries, lack transparency, encounter regulatory challenges, and are not highly secure. Instead, a blockchain-based agreement can be formed, carried out, and enforced between members without the assistance of a third party. To achieve this, creating smart contracts, with blockchain-compatible executable code, is crucial. Additionally, cryptocurrency can be used, for which crypto wallets can securely store private keys, giving users access, control, and ownership over their digital assets while also encouraging decentralization and interoperability. Crypto wallets protect users' assets against cyber threats and unauthorized access by utilizing technologies like encryption, two-factor authentication, and multi-signature support to provide strong authentication and protection. Using a web3 platform like Thirdweb eliminates the need for an administrator by providing automated tools and infrastructure that streamline the creation, management, and deployment of decentralized applications. This study proposes the use of web3 technology to create a multi-user cross-platform blockchain-based crowdfunding DApp which employs Ethereum, smart contracts via solidity and crypto wallets for decentralized fundraising. © 2024 IEEE.

关键词： Authentication Blockchain Crowdfunding Crypto Wallets Cryptocurrency DApp Digital Assets Ethereum Private Keys Smart Contracts Solidity Thirdweb web3 Technology

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Cryptographically Secured Data Transmission Over a Network with NetCAT 1

Cryptographically Secured Data Transmission Over a Network w...

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1st IEEE International Conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Rao, Subhiksha Suresh Mishra, Sahil Akhilesh, S. Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350371802

This study focuses on cryptically secure data trans-mission on a network with NetCAT and emphasizes the improvement of the Advanced Encryption Standard (AES) algorithm to improve data security. It specifically addresses file encryption using the modified AES and uses the flexibility of NetCAT utility for safe transfer. In particular, the deliberate transformation of intercepted data into incomprehensible gibberish emphasizes the robustness of modified AES against potential surveillance and unauthorized access. This document presents an improved and practical approach to improving data protection during trans-mission, especially in the context of encrypted data transmission through a network with NetCAT. Through the discussion of this specific topic, it contributes significantly to the evolving field of cryptography and emphasizes the need for accurate specifications and a clear focus on the treatment of data security challenges. © 2024 IEEE.

关键词： Cryptography

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Comparative Image Analysis of Chest X-RAY Image Encryption using Symmetric and Asymmetric Key Encryption Algorithms 8

Comparative Image Analysis of Chest X-RAY Image Encryption u...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Pavani, Bhupathi Vishva Bhumika, Challagulla Sai Jagadeesh, Dumpa Venkata Sai Kavitha, C.R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350367904

Assuring the medicinal data is very important for protecting the patient's privacy and their trust in the team. Prohibited access to confidential medicinal data gives rise to privacy breaches, healthcare scams, and improper health treatment. The technique that is suggested offers an opportunity for the encryption of chest scan images. The approach makes use of a multilayer encryption mechanism that contains a hashing layer, a digital signature layer, and the final encryption layer. The utilization of multiple algorithms for the layers mentioned above is analyzed and contrasted. For the encryption of an image, two algorithms are used which are AES and DES. For creating a digital signature, RSA and DSS are utilized. For all the three methods mentioned, RSA Key Encryption and SHA-256 hash algorithm are been frequently used. As for the evaluation metrics for the three approaches, time and space complexities are computed. The methods mentioned above prove the CIA triad which is confidentiality, Integrity, and authentication of the chest X-ray images. The most effective approach proves to be AES image encryption with RSA digital signature as it takes a short amount of time to decrypt. © 2024 IEEE.

关键词： Anonymity

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An Empirical Analysis on ARIMA and Regression Models for Time Series Forecasting on Bitcoin Dataset

An Empirical Analysis on ARIMA and Regression Models for Tim...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Ganesh, K. Anbazhagan, M. Visweshwaran, Shreyas Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350382693

n the rapidly evolving world of cryptocurrency markets, the precise forecasting of Bitcoin's value against the US Dollar acquires paramount importance, catering to the interests of diverse stakeholders including investors, regulatory agencies, and academia. This study ventures into a comprehensive assessment of various time series forecasting methodologies, including but not limited to Random Forest Regression, ARIMA, Linear Regression, and XGBoost. Notably, our investigation unveils a pivotal revelation: the foundational models like Linear Regression and Random Forest Regression, traditionally con-sidered less complex, not only contend but also surpass the forecast accuracy of ARIMA models in the realm of Bitcoin. This paper aims to demystify the underpinnings of this superior performance, especially in mitigating the inherent volatility and unpredictability characteristic of Bitcoin. Our findings herald a transformative perspective in financial time series forecasting, potentially reshaping investment strategies and predictive analytics in the digital currency landscape.n the rapidly evolving world of cryptocurrency markets, the precise forecasting of Bitcoin's value against the US Dollar acquires paramount importance, catering to the interests of diverse stakeholders including investors, regulatory agencies, and academia. This study ventures into a comprehensive assessment of various time series forecasting methodologies, including but not limited to Random Forest Regression, ARIMA, Linear Regression, and XGBoost. Notably, our investigation unveils a pivotal revelation: the foundational models like Linear Regression and Random Forest Regression, traditionally considered less complex, not only contend but also surpass the forecast accuracy of ARIMA models in the realm of Bitcoin. This paper aims to demystify the underpinnings of this superior performance, especially in mitigating the inherent volatility and unpredictability characteristic of Bitcoin. Our findings

关键词： Predictive analytics

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Deep Learning Approach for Automated Data Augmentation and Multi-class Classification of Pap Smear Images 2

Deep Learning Approach for Automated Data Augmentation and M...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nayar, Sanjana Priya, Devi Vinitha Panicker, J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

AI is now used to analyze large datasets, which can subsequently be used to predict outcomes and provide patient insights. In this paper, we aim to address the challenge of insufficient medical data, which in turn hinders ML and AI from being used in medicine. By producing additional and related medical data, data augmentation will make it easier to develop machine learning or deep learning models to automate diagnosis. This study aims to utilize Generative Adversarial Networks to create synthetic images and to develop a model to effectively classify the dataset, including the newly generated images, into NILM, HSIL, LSIL, or SCC, which were the four types of cancer cells provided in the dataset. The LBC dataset for multi-class classification has been classified with a state-of-the-art accuracy of 99.1% in our study. The testing accuracy achieved using traditional methods is 98.8%, whereas using GAN is 99.1%, demonstrating that employing synthetic images created using GANs can be a better augmentation method than conventional ways. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Homomorphic Encryption for Secure Data Analysis: A Hybrid Approach using PKCS1_OAEP Padding 2

Homomorphic Encryption for Secure Data Analysis: A Hybrid Ap...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Shanmukha Aditya, G. Kruthika, B. Rajagopal, Shinu M. Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

In today's data-driven world, striking a balance between data utility and privacy is paramount. Homomorphic encryption, a revolutionary cryptographic technique, facilitates computations on encrypted data without decryption. This study addresses the growing demand for secure data analysis, emphasizing the seamless integration of homomorphic encryption in connecting robust data analysis with essential privacy safeguards. Serving as a vital component in this context, this cutting-edge technique proves pivotal in fostering a secure, privacy-centric approach to data analytics. The outcomes underscore its significance, representing a substantial stride towards the evolution of a conscientious and responsible data-driven ecosystem. The achieved results signal progress in harmonizing data utility and privacy, enhancing overall security and ethical data practices without compromising analytical capabilities. © 2024 IEEE.

关键词： AES Encryption Cloud Security Homomorphic Encryption Privacy Preservation RSA Encryption Secure Data Analytics

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Sentiment Analysis of Airline Tweets Using Word Embeddings and Deep Learning Techniques 15

Sentiment Analysis of Airline Tweets Using Word Embeddings a...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Nikhith, Surapaneni Pavan Kumar, Popuri Varun Udaybhasker, Aira Reddy, Tangaturu Devendranath Reddy, Parlapalli Bhargav Singh, Tripty Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This project deals with the testing of different word embedding, deep learning techniques, and XAI tools for text classification tasks. It talks about the effect of Word2Vec embeddings in CNN models on text context and shows their capability of grasping it. Furthermore, this research sees the application of BERT and RoBERTa embedding and shows their influence on CNN and BiLSTM models and their comparable performance. Besides, this project also proves the significance of transformer-based embeddings in the information extraction from texts with various contextual nuances and points to their possible to increase the text classification accuracy on different deep learning architectures. We lastly, look into the function of eXplainable AI tools in the interpretation of model decisions, which ultimately helps to understand and support the model prediction outcomes in the field of text classification. © 2024 IEEE.

关键词： Bidirectional long short-term memory Convolutional neural network Text classification Word embeddings

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Airfoil Self Noise Prediction Using Machine Learning and Explainable AI 6

Airfoil Self Noise Prediction Using Machine Learning and Exp...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Pandey, Honey Pandey, Prateek Gupta, Deepa Amrita School of Computing Department Of Computer Science And Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350381689

A major environmental challenge in the aerospace sector is presented by aircraft noise, particularly airfoil noise. Traditional methods for predicting airfoil noise involve complex and computationally intensive numerical simulations. This research explores the use of machine learning to enhance the efficiency and accuracy of airfoil noise prediction. By utilizing extensive datasets from wind tunnel experiments, encompassing a variety of airfoil geometries and flow conditions, supervised learning algorithms are employed to construct a predictive model. Diverse regression models, including random forests, support vector regression, CatBoost, XgBoost, Gradient Boosting and decision trees, are trained on the NACA 0012 airfoil dataset to uncover correlations between airfoil characteristics and noise emissions. Initial findings underscore the potential of machine learning, with Gradient Boosting regression achieving a noteworthy R2 score of 0.96625 and RMSE as 1.2622. This study not only equips engineers with a valuable tool for optimizing airfoil shapes to reduce noise but also establishes the groundwork for real-time noise prediction systems in aircraft operations. © 2024 IEEE.

关键词： Random forests

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An Ensemble Approach to Tuberculosis Prediction using Shenzhen and Montgomery Datasets 15

An Ensemble Approach to Tuberculosis Prediction using Shenzh...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Varshith, Tummala Koneri, Thanush S. Reddy, Tetali Syam Kumar Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Chest X-rays are critical for detecting a variety of lung disorders, with tuberculosis (TB) being a particular priority due to its global impact on public health. TB must be identified quickly and accurately using medical imaging to begin treatment and stop the spread of the illness. This work examines the importance of chest X -ray analysis and its role in TB identification. A novel convolutional neural network (CNN) based ensemble approach is proposed, thoroughly investigating the performance of state-of-the-art sophisticated deep learning models, such as Xception, InceptionV3, DenseNet201, ResNet50, EfficientNetB2, ResNet50-InceptionV3, EfficientNetB2-Xception, DenseNet201-Xception, and DenseNet201-InceptionV3, for differentiating between normal and tuberculosis-affected chest X-rays. The standard Shenzhen and Montgomery datasets were used for training and testing the efficacy of the proposed model. The findings of this study promote the development of accurate and effective TB detection technologies, ultimately enhancing global efforts to combat this deadly illness. Notably, the DenseNet201InceptionV3 ensemble produced the best results for TB detection, achieving a precision of 0.91, a recall of 0.85, an F score of 0.89, and an accuracy of 95.14%. © 2024 IEEE.

关键词： Lung cancer

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Explainable Screening and Classification of Cervical Cancer Cells with Enhanced ResNet-50 and LIME 3

Explainable Screening and Classification of Cervical Cancer ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Chandana, Baddireddi Sree Kommana, Chakrika Madhav, G. Sethu Basa Pati, Peeta Singh, Tripty Afnaan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350381931

High levels of recovery rates are proven to be possible at early detection of cancerous cells through various tests, meticulous monitoring, and previous findings. Pap-smear tests are widely used to obtain cervical cells as they are of low cost and due to their painless diagnosis. The use of Deep Learning techniques to create an automated screening and classification system for cervical cancer cells is becoming more and more popular. Our proposed method uses the SIPAKMED pap-smear imagine dataset and Deep Learning classification algorithms to screen for cervical cancer using ResNet-50. The experiment's findings showed that the suggested classification strategy for the ResNet50 architectural model had the highest accuracy (91.04%), outperforming both the Inception-V3 and MobileNet models. In addition, our experiment is advancing further by integrating Explainable Artificial Intelligence (XAI) into the analysis of test images generated by various models. The incorporation of LIME Explainability aims to enhance the interpretability and transparency of the diagnostic process. © 2024 IEEE.

关键词： Classification (of information)

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