ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal最新文献_第2页

Factors of Blockchain Adoption for FinTech Sector: An Interpretive Structural Modelling Approach 金融科技行业采用区块链的因素:一种解释结构建模方法

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.28395

Somya Gupta, Ganesh Prasad Sahu

引用次数: 0

Analyzing Social Media Sentiment: Twitter as a Case Study 分析社交媒体情绪:以Twitter为例

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.28394

Y. Jasim, M. G. Saeed, M. Raewf

{"title":"Analyzing Social Media Sentiment: Twitter as a Case Study","authors":"Y. Jasim, M. G. Saeed, M. Raewf","doi":"10.14201/adcaij.28394","DOIUrl":"https://doi.org/10.14201/adcaij.28394","url":null,"abstract":"This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"9 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77415638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method 基于机器学习和Vader Lexicon方法的Covid-19疫苗推文情绪分析

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.27349

Vishakha Arya, A. Mishra, Alfonso González-Briones

{"title":"Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method","authors":"Vishakha Arya, A. Mishra, Alfonso González-Briones","doi":"10.14201/adcaij.27349","DOIUrl":"https://doi.org/10.14201/adcaij.27349","url":null,"abstract":"The novel Coronavirus disease of 2019 (COVID-19) has subsequently named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have tormented the lives of millions of people worldwide. Effective and safe vaccination might curtail the pandemic. This study aims to apply the VADER lexicon, TextBlob and machine learning approach: to analyze and detect the ongoing sentiments during the affliction of the Covid-19 pandemic on Twitter, to understand public reaction worldwide towards vaccine and concerns about the effectiveness of the vaccine. Over 200000 tweets vaccine-related using hashtags #CovidVaccine #Vaccines #CornavirusVaccine were retrieved from 18 August 2020 to 20 July 2021. Data analysis conducted by VADER lexicon method to predict sentiments polarity, counts and sentiment distribution, TextBlob to determine the subjectivity and polarity, and also compared with two other models such as Random Forest (RF) and Logistic Regression (LR). The results determine sentiments that public have a positive stance towards a vaccine follows by neutral and negative. Machine learning classification models performed better than the VADER lexicon method on vaccine Tweets. It is anticipated this study aims to help the government in long run, to make policies and a better environment for people suffering from negative thoughts during the ongoing pandemic.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"28 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74056826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contextual Urdu Text Emotion Detection Corpus and Experiments using Deep Learning Approaches 情境乌尔都语文本情感检测语料库及深度学习方法实验

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.30128

Muhammad Hamayon Khan Vardag, Ali Saeed, Umer Hayat, Muhammad Farhat Ullah, Naveed Hussain

{"title":"Contextual Urdu Text Emotion Detection Corpus and Experiments using Deep Learning Approaches","authors":"Muhammad Hamayon Khan Vardag, Ali Saeed, Umer Hayat, Muhammad Farhat Ullah, Naveed Hussain","doi":"10.14201/adcaij.30128","DOIUrl":"https://doi.org/10.14201/adcaij.30128","url":null,"abstract":"Textual emotion detection aims to discover human emotions from written text. Textual emotion detection is a significant challenge due to the unavailability of facial and voice expressions. Considerable research has been done to identify textual emotions in high-resource languages such as English, French, Chinese, and others. Despite having over 300 million speakers and large volumes of literature available online, Urdu has not been properly investigated for the textual emotion detection task. To address this gap, this study makes two contributions: (1) the creation of a novel dialog-based corpus for Urdu (Contextual Urdu Text Emotion Detection Corpus). CUTEC contains 30,160 training and 5,509 testing labelled dialogues, where each dialogue consists of three Urdu contextual sentences. In addition, all dialogues are labelled using four emotion classes, i.e., Happy, Sad, Angry, and Other. As a second contribution (2) five deep learning models, i.e., RNN, LSTM, Bi- LSTM, GRU, and Bi-GRU have been trained and tested using CUTEC with different parametric settings. The highest results (Accuracy = 87.28 and F1 = 0.87) are attained using a GRU-based architecture.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"82 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76107951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Containerization and its Architectures: A Study 集装箱化及其体系结构研究

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.28351

S. Verma, Brijesh Pandey, Bineet Kumar Gupta

引用次数: 0

Performance Evaluation of Efficient Low Power 1-bit Hybrid Full Adder 高效低功耗1位混合全加法器的性能评价

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI: 10.14201/adcaij.28558

R. Upadhyay, R. Chauhan, Manish Kumar

引用次数: 0

Efficient Implementation of Tanh: A Comparative Study of New Results Tanh的有效实施:新成果的比较研究

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-04-29 DOI: 10.5121/csit.2023.130701

Samira Sorayaasa, M. Ahmadi

引用次数: 0

Heart Disease Prediction using Chi-Square Test and Linear Regression 用卡方检验和线性回归预测心脏病

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-04-29 DOI: 10.5121/csit.2023.130712

Dinesh Kalla, Arvind Chandrasekaran

{"title":"Heart Disease Prediction using Chi-Square Test and Linear Regression","authors":"Dinesh Kalla, Arvind Chandrasekaran","doi":"10.5121/csit.2023.130712","DOIUrl":"https://doi.org/10.5121/csit.2023.130712","url":null,"abstract":"Heart disease is most common disease reported currently in the United States among both the genders and according to official statistics about fifty percent of the American population is suffering from some form of cardiovascular disease. This paper performs chi square tests and linear regression analysis to predict heart disease based on the symptoms like chest pain and dizziness. This paper will help healthcare sectors to provide better assistance for patients suffering from heart disease by predicting it in beginning stage of disease. Chi square test is conducted to identify whether there is a relation between chest pain and heart disease cases in the United States by analyzing heart disease dataset from IEEE Data Port. The test results and analysis show that males in the United States are most likely to develop heart disease with the symptoms like chest pain, dizziness, shortness of breath, fatigue, and nausea. This test also shows that there is a week corelation of 0.5 is identified which shows people with all ages including teens can face heart diseases and its prevalence increase with age. Also, the tests indicate that 90 percent of the participant who are facing severe chest pain is suffering from heart disease where majority of the successful heart disease identified is in males and only 10 percent participants are identified as healthy. The evaluated p-values are much greater than the statistical threshold of 0.05 which concludes factors like sex, Exercise angina, Cholesterol, old peak, ST_Slope, obesity, and blood sugar play significant role in onset of cardiovascular disease. We have tested the dataset with prediction model built on logistic regression and observed an accuracy of 85.12 percent.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"95 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91353305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting New Cigarette Launch Strategy based on Synthetic Control Method 基于综合控制方法的新型卷烟上市策略预测

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-04-29 DOI: 10.5121/csit.2023.130704

Yu-Hua Mo, Chao Deng, Feijie Huang, Qian Tan, Yuan-Kun Li

引用次数: 0

Psychological Lights: An Intelligent LED System to Relief Youth Stress Level using AI and Internet of the things 心理灯:利用人工智能和物联网缓解青少年压力水平的智能LED系统

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-04-29 DOI: 10.5121/csit.2023.130706

Zixuan Cheng, Zihang Cheng, Ang Li

引用次数: 0