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Zero-Shot Sentiment Analysis Exploring BART Models 8

Zero-Shot Sentiment Analysis Exploring BART Models

引用

8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350373615

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique. © 2023 IEEE.

关键词： Sentiment analysis

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Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm

Smart Grid Solar Tracking Optimization using Deep Reinforcem...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Kavitha, H.S. Anu, H. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Sharmila, N. Naveen, K.B. Sheela, S. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Rns Institute of Technology Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagar India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of solar power into smart grids necessitates efficient solar tracking to maximize energy capture. Conventional methods like Single-Axis Trackers (SAT) and Dual-Axis Trackers (DAT) have limitations, including high maintenance costs, limited adaptability, and inefficiency under variable weather conditions. These drawbacks result in suboptimal energy generation, hindering the performance of smart grids. The proposed Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm (SGST-DRLA) addresses these issues by utilizing a Deep Reinforcement Learning (DRL) approach to dynamically optimize solar panel orientation. Unlike static control strategies of SAT and DAT, SGST-DRLA continuously learns from realtime data, adjusting the tracker's position to maximize sunlight exposure and energy capture. This adaptive method enhances tracking precision and reduces operational costs. SGST-DRLA significantly improves performance metrics, achieving a 0.25% increase in energy capture efficiency, a 0.20% reduction in operational costs, and a 0.30% improvement in adaptability to varying weather conditions compared to traditional methods. This approach not only optimizes solar tracking in smart grids but also supports sustainable and efficient renewable energy integration, making it a viable solution for overcoming the limitations of existing tracking systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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Detection of Cardiovascular Activities of Multiple Patients using MIMO mmWave Radar

Detection of Cardiovascular Activities of Multiple Patients ...

引用

2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Jena, Paramananda Sahu, Kedar Nath Behera, Sagarika Electronics and Radar Development Establishment DRDO Bengaluru India Dept. of Electronics and Communication Engineering Stanley College of Engineering and Technology for Women Hyderabad India Dept. of Computer Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350379693

This paper presents the detection and analysis of cardiovascular activities for localisation of multiple patients using Doppler signals associated with their chest wall motion, heart beat and sleep disorder. The modeling of the vital signs has been carried out considering the vibrating surface of human body illuminated by a radar. The main challenge of a medical radar is the noncontact diagnosis of multiple people. MIMO radar has enormous potential in two areas: micro Doppler detection and fine angular resolution. In this work, MIMO radar is designed and analysis of micro Doppler due to (i) chest movement, (ii) heartbeat and (iii) tidal volume of patients in their sleeping conditions has been carried out using MATLAB models. © 2024 IEEE.

关键词： Doppler radar

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Proposed Model for Prediction of Stock Market Price of Netflix

Proposed Model for Prediction of Stock Market Price of Netfl...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance technology Conference, ATCON-1 2023

作者： Patwal, Pratap Singh Srivastava, Anoop Kumar Laxmi Devi Institute of Engineering & Technology Dept. of Computer Science & Engineering Rajasthan Alwar India Aced Alliance University Dept. of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665456272

Accurate prediction of stock market price is highly challenging. This paper presents a proposed model for prediction of stock market price of Netflix. We have considered a five-year data set (April, 2017 - April, 2022) of Netflix. An Exploratory Data Analysis (EDA) of Netflix's stock price data for predicting its stock market prices using time series is done. The implementation of the model is done using Python language. We imported five-years data and applied several techniques: importing libraries, calculating stock return, line plot, plot all, plot return year wise, plot histogram, plot kernel density, plot box plot, differencing method, resample daily to monthly data etc. EDA proved that using time series technique achieved better results in prediction of stock price and visualizing. © 2023 IEEE.

关键词： Forecasting

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Pseudonymous Decentralised Reputation System 9

Pseudonymous Decentralised Reputation System

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Nalini, N. Kumar, Anshul Sharma, Megha Sil, Aniruddha Khan, Womika Nitte Meenakshi Institute of Technology Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350314090

In this paper, we propose a novel pseudonymous decentralized reputation system that aims to establish trust within decentralized networks. The system allows users to maintain pseudonymous identities while ensuring their reputation scores are transparent, trustworthy, and resistant to manipulation. We review existing reputation systems and discuss their limitations. Our proposed system incorporates cryptographic techniques, a consensus algorithm, and distributed data storage to ensure data integrity, privacy, and scalability, specifically designed for the Decentralized Finance (DeFi) ecosystem. We present an implementation of our system, focusing on its application in DeFi platforms, and evaluate its performance against various attack vectors. Finally, we discuss the implications of our system on decentralized financial applications, such as lending and borrowing protocols, and future research directions. Lastly, we explore the broader implications of our decentralized reputation system for the DeFi ecosystem, identifying potential applications in areas such as decentralized exchanges, insurance, and asset management. © 2023 IEEE.

关键词： Blockchain

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Deep Learning Approach for Hate and Non Hate Speech Detection in Online Social Media 3

Deep Learning Approach for Hate and Non Hate Speech Detectio...

引用

3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Sharma, Parshuram Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

People's ability to freely and anonymously share their thoughts and feelings online on social media platforms is contributing to a rising issue of hate speech. Hate speech has the potential to hurt both people and groups, contribute to the polarisation of society, and even provoke acts of physical violence. Therefore, identifying and removing hate speech from online social media is an important task for the purpose of maintaining an online environment that is healthy and respectful. A method based on deep learning is proposed in this study for differentiating between hate speech and other types of communication that may be found in online social media. The solution that has been developed makes use of a natural language processing (NLP) and long term short memory (LSTM) model that is trained on a huge dataset consisting of tweets that have been annotated. Tweets that include hate speech and tweets that do not contain hate speech were both included in the dataset and were labelled by human annotators. Python software with IDE of Spyder version 3.7 is used to carry out the simulation work. The accuracy level reached overall is 91.14%. © 2023 IEEE.

关键词： Hate LSTM Machine Learning NLP Social Media Speech Twitter

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Drug Authentication and Counterfeit Drug Detection Using Blockchain 7

Drug Authentication and Counterfeit Drug Detection Using Blo...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chandras, Atharva Agrawal, Satyam Chaudhary, Mithil Deshmukh, Anushka Shinde, Shilpa Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350304268

Drug counterfeiting is a worldwide phenomenon which affects practically every industry involved with the healthcare sector in both developed and developing countries. This widespread smuggling and usage of counterfeit drugs has serious economic, health, and safety impacts on any country. In addition to this, the credibility of the healthcare system can be questioned and harmed if the counterfeit drugs find a way into the market and harm the public opinion about the pharmaceutical chain. An annual analysis done by the US Trade Representative office on rights protection in 2019 stated that up to 20% of the pharmaceutical supply sold in India were counterfeit and this has only increased since the pandemic. In an estimate done between 2020 to 2021, it was found that incidents of counterfeit and fake drugs had shot up to about 47 percent. Traditional methods of drug authentication have proven to be ineffective in detecting counterfeit drugs. Blockchain technology provides a promising solution to the problem of counterfeit drugs. The proposed system for detecting counterfeit drugs using blockchain technology consists of a blockchain network, a web application, and a database. The proposed system aims to provide an efficient and secure method of detecting counterfeit drugs using blockchain technology, which can enhance trust in the healthcare system and reduce the cost of drug authentication. © 2023 IEEE.

关键词： Blockchain Ganache Node js Solidity Truffle

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A Comprehensive Study on Routing Approach in Wireless Sensor Networks

A Comprehensive Study on Routing Approach in Wireless Sensor...

引用

2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Kumar, Mritunjay Dwivedi, Anshu Kumar Buddha Institute of Technology Dept. of Computer Science and Engineering Gida Gorakhpur India

ISBN: (纸本)9798350302820

Environmental monitoring, healthcare, smart agriculture and industrial automation are just some areas where wireless sensor networks (WSNs) hold promise. In WSN, routing is one of the most critical functions as it determines how data is collected and sent from sensor nodes to sink nodes. However, planning to direct conferences for WSNs has been stifling due to limitations such as limited energy resources, unreliable correspondence, versatility, portability, and security concerns. Various design issues related to routing in WSN are discussed in depth in this research article. We discuss the different routing conventions for WSNs, including their benefits and limitations. The article lays out the preliminary plan that emerges in WSN steering and provides knowledge into the rise and fall of research patterns and future titles. © 2023 IEEE.

关键词： Routing protocols

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Deep Learning with Encoders for Intrusion Detection Systems (IDS) 10

Deep Learning with Encoders for Intrusion Detection Systems ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Budania, Vijay Ahmed, Mushtaq Verma, Anshita Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9789380544472

An Intrusion Detection System (IDS) is often used to keep safe and efficiently utilise communication network. And generally this is achieved by monitoring the network regularly for possible intrusions. Nowadays in networks, zero-day attacks are common type of intrusions. So it became necessary to use IDS, which is capable of detecting zero-day attacks. Machine Learning (ML) algorithms are suitable in designing such IDS. Deep Learning (DL), being part of ML, have promising approaches that can be used in designing IDS, which is capable of detecting intrusions of new and old types. This paper proposes DL based IDS which create model of normal network traffic. This model classifies attacks based on the various features present in the dataset. The proposed model uses a Convolution Neural Network, bi-directional Long Short Term Memory (LSTM), and a stack of encoders to handle spatial and temporal features more effectively. The model achieves a low False Positive Rate and high Detection Rate compared to existing models. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Zero-day attack

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