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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm

Procedia computer science 2024年 235卷 3397-3407页

作者： B. Valarmathi N. Srinivasa Gupta V. Karthick T. Chellatamilan K. Santhi Dhanush Chalicheemala Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Assistant Systems Engineer Tata Consultancy Services Chennai Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today’s digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved.

关键词： COVID-19 Natural Language Processing Text Mining Long Short-Term Memory WordCloud Opinion mining Sentiment analysis Opinion extraction

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BlindAid-Android-based Mobile Application Guide for Visually Challenged People 12

BlindAid-Android-based Mobile Application Guide for Visually...

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12th IEEE Annual information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Senarathne, Gagani Punchihewa, Dilpri Liyanage, Dilini Ishara Wimalaratne, Geethanjali De Silva, Hansi Sri Lanka Institute of Information Technology Department of Information Technology Colombo Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Colombo Sri Lanka

ISBN: (纸本)9781665400664

Millions of people across the world are affected by visual impairments. The proposed application is designed for visually impaired people consists of three components which are face recognition with emotion, obstacle identification through distance measurement, extract and convey critical information like text, labels, and currency details. Most works reported in the literature depends on the availability of specific hardware. The proposed proof of concept prototype aims to make visually impaired people more independent and carry out tasks conveniently and safely. © 2021 IEEE.

关键词： Face recognition

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Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

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computers, Materials & Continua 2021年第2期66卷 1613-1629页

作者： G.Arun Sampaul Thomas Y.Harold Robinson E.Golden Julie Vimal Shanmuganathan Seungmin Rho Yunyoung Nam CSE Department J.B.Institute of Engineering and TechnologyHyderabad500075India School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering Anna University Regional CampusTirunelveli627007India Department of Information Technology National Engineering CollegeKovilpatti628503India Department of Software Sejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision *** of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked *** this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate *** CNN model is trained by different images of eyes that have retinopathy and those which do not have *** fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent *** feature loss factor increases the label value to identify the patterns with the kernel-based *** performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and *** results show that the proposed CNN performs better.

关键词： Convolutional neural networks dental diagnosis image recognition diabetic retinopathy detection

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Correction: Harnessing Federated Generative Learning for Green and Sustainable Internet of Things

arXiv

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arXiv 2024年

作者： Qi, Yuanhang Shamim Hossain, M. School of Computer Science University of Electronic Science and Technology of China Zhongshan Institute Zhongshan528402 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh13272 Saudi Arabia

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future. © 2024, CC0.

关键词： Internet of things

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Adapting MaryTTS for Synthesizing Sinhalese Speech to Communicate with Children 6

Adapting MaryTTS for Synthesizing Sinhalese Speech to Commun...

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6th International Conference on information Technology Research, ICITR 2021

作者： Lakmal, M.A.J.A. Methmini, K.A.D.G. Rupasinghe, D.M.H.M. Hettiarachchi, D.I. Piyawardana, Vijani Senarathna, Manuri Reyal, Shyam Pulasinghe, Koliya Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665420006

The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1-6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM-based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented. © 2021 IEEE.

关键词： Speech

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract)

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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International Conference on Data engineering

作者： Tsz Nam Chan Zhe Li Leong Hou U Reynold Cheng Department of Computer Science and Software Engineering Shenzhen University Alibaba Cloud Department of Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Department of Computer Science and Musketeers Foundation Institute of Data Science The University of Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods.

关键词： Training Support vector machines Additives Pedestrians Accuracy Data engineering Kernel

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Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students 3

Academic Depression Detection Using Behavioral Aspects for S...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Gamage, Madhuransi Agrahere Arachchi, Ranruwini Matara Naotunna, Sawandi Rubasinghe, Tharushi Silva, Chamari Siriwardana, Samanthi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works. © 2021 IEEE.

关键词： Students

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Virtual Dressing Room: Smart Approach to Select and Buy Clothes 3

Virtual Dressing Room: Smart Approach to Select and Buy Clot...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Weerasinghe, S.W.P.N.M. Rajapaksha, R.M.D.D. Sathsara, L.G.I. Gunasekara, H.S.D.N. Wijendra, Dinuka R. De Silva, Dilshan I. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

The clothing industry portrays a major part of a respective country's economy. Due to the predilection for clothing items of the people have led to the increasing of physical and online clothing stores in all around the world. Most of the people are used to go to the physical shopping and purchase their desired clothing items. But, as a consequence of the current pandemic situation, most of the people are unable to step out from their homes. This application is intended to cater an opportunity to the customers, who are not able to reach the physical clothing stores due to a pandemic situation and mobility difficulties. In addition, this application diminishes the time wastage, clothing size mismatches and the lesser user satisfaction ratio inside a physical clothing store. A customized 3D model has featured in the application to cater the virtual fitting experience to the customer. And the AI chatbot assistant in the application interacts with the user while catering virtual assistance for a better cloth selection process. In addition to that, this application has concentrated on the clothing shop by providing a future sales prediction component utilizing the K-Nearest Neighbors algorithm to provide an aid to their business commitments. © 2021 IEEE.

关键词： Machine learning

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Deep Transfer Learning Approach for Facial and Verbal Disease Detection 3

Deep Transfer Learning Approach for Facial and Verbal Diseas...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Manage, Dilki Madara Alahakoon, A. M. Ishan Shavinda Weerathunga, Kavindi Weeratunga, Thenuka Lunugalage, Dilani De Silva, Hansi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Millions of people have been subjected to different kind of acute diseases, some of them are eye diseases, facial skin diseases, tongue diseases and voice abnormalities. Most of eye diseases cause fully or partial blindness. Skin and tongue complications can be signs of cancers. Voice abnormalities can be cured at initial stages. Well-practiced medical practitioners have the ability of diagnose these diseases, but due to the pandemic situations and high consultation costs people do not tend to consult doctors. This research is predominantly focused on development of an application for automatic detection of eye, skin, tongue and verbal diseases using transfer learning (TL) based deep learning (DL) approach. Deep learning is a part of machine learning (ML) which has been used in most computer vision approaches. Transfer learning has been used to rebuild the existing convolutional neural network (CNN) models and used in disease detection. DenseNet121, MobileNetV2, RestNet152V2, models have been used to detect eye, skin and tongue diseases respectively and a new model has been used to detect voice abnormalities. CNN models are capable of automatically extracting features from the given images and voice data. All the trained models have been given accuracy rate of 80%-95%. © 2021 IEEE.

关键词： Convolutional neural networks

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Design and Implementation of an Iot-Based Monitoring System for Early Detection of Lumpy Skin Disease in Cattle

SSRN

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SSRN 2024年

作者： Shahab, Hammad Iqbal, Muhammad Sohaib, Ahmed ur Rehman, Atiq Bermak, Amine Munir, Kashif Institute of Computer and Software Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Institute of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan

Livestock farming serves a crucial role in global food security and sustainability, yet infections like Lumpy Skin Disease (LSD) present huge difficulties. LSD reduces the marketability and productivity of cattle, but it is not fatal. Current preventive measures that rely on routine veterinary testing can be time-consuming and miss early signs of infection, requiring a more effective management strategy first works This article presents an IoT-enabled intelligent system to prevent LSD in animals. The system uses a smart wearable sensor device to collect real-time health information including body temperature, heart rate and 3-axis motion data. Advanced analysis of data is considered to identify early diagnosis of potential LSD infection results based on deviations from normal parameters, this data prevents further infection within livestock by supporting immediate isolation. On a cloud-based online platform, and integrated into the mobile application provides a comprehensive experience and timely alerts to provide potential health issues. Furthermore, the IoT features of the system enable farmers and veterinarians to quickly guide and optimize disease control strategies, improving biosecurity protocols and overall livestock health management. This creative approach represents a significant improvement over traditional methods, providing a cost-effective, accurate and real-time solution for animal health monitoring and management. © 2024, The Authors. All rights reserved.

关键词： Smart agriculture

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