Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends

With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.

Name Entity Recognition on Covid-19 Dataset using Machine Learning algorithms

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. So we are working on COVID-19 dataset on Omicron variant to recognise the name entity from a given text. We collect the COVID related data from newspaper or from tweets. This article covered the name entity like COVID variant name, organization name and location name, vaccine name. It include tokenisation, POS tagging, Chunking, levelling, editing and for run the program. It will help us to recognise the name entity like where the COVID spread (location) most, which variant spread most (variant name), which vaccine has been given (vaccine name) from huge dataset. In this work, we have identified the names. If we assume unemployment, economic downfall, death, recovery, depression, as a topic we can identify the topic names also, and in which phase it occurred. © 2022 IEEE.

COVID19: INTELLIGENT CHATBOT AND FINANCIAL AID APPLICATION

Covid19 has devastated all continents causing disasters not only on the health sector but also at social, economic, and at political levels. The world is still trying to eradicate the virus. One of the measures taken is to inform citizens about the virus in order to avoid contamination as much as possible. Several people lost their jobs, and found themselves without any income. The whole world is confined, and the poor can no longer endure this critical situation. Financial assistance is therefore necessary in order to reduce the impact. This paper aims to propose an intelligent financial support application that computes the eligibility for a citizen to get a support during the pandemic;and to explain steps for chatbot using DialogFlow. The training realized using a machine learning algorithm was chosen after making a comparison between some other algorithms. Gradient Boosting Classifier algorithm was the accurate and most efficient for the application. It is possible to train the system again using other data set to make any adaptive results or computations. Copyright © (2022) by International Association for Development of the Information Society (IADIS). All rights reserved.

Predicting the Level of Anxiety of Coronavirus Pandemic among College Students Using Different Classifier Algorithms

The Novel Coronavirus ravaged the entire world, and there was no guarantee that it would ever be wiped out. The purpose of this study is to clarify the factors causing anxiety. Anxiety may be brought on by a number of associated circumstances, such as covid.To create the model with the best fit for predicting anxiety and to prioritize the variables that affect anxiety. An online poll was used to gather data, and it asked about global issues, Covid-19 stresses, and (GAD-7). Anxiety levels are rated on this scale from none to severe. The impact of anxiety on these people's quality of life is significant. Since the challenges of college life may act as a trigger for these disorders, college students are particularly sensitive to anxiety. This is especially concerning during the Covid-19 pandemic because new information indicates that students of college age are experiencing increased anxiety. It could have an impact on their academic performance Here, we use classifier techniques to implement anxiety prediction. a series of survey questions, from which we collect information to ascertain whether or not a person would practice anxiety. Finally, we demonstrated the cure for individuals who are concerned and provided advice. © 2022 IEEE.

Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm

In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets. © 2022 IEEE.

Mini Medical Assistant: A Handheld Emergency Medical Support Program During COVID19 Pandemic

Android phones are one of the most common accessories used all over the world. Although once a luxury, it has now become a basic need for all generations. It is a multipurpose tool that can be used for all sorts of necessities and entertainment. Through our android app corona care, a mobile phone can be a helping hand for health care. This app can help prevent the deadly virus known as COVID-19 through plasma donation, consultation with doctors, setting up appointments, predicting corona risk assessment from symptoms using the Gaussian Naive Bayes method of predicting the risk percentage, providing emergency health services and updating users about the safety instructions about Covid-19. Our application consists of most features needed in a mHealth application that can provide necessary medical assistance to each and every household. © 2021 IEEE.

A Cloud Based Intelligent Model for Predicting the Pre-Testing Corona Virus Infection Outbreak

Corona virus disease (COVID) is a transmittable disease caused by a newly discovered corona virus. For this a system is require which trace the location and predict the health of the people. In the present study, a cloud based a model is proposed. The proposed model will be connect with a cloud computing system that will predict the corona virus infected patients using naïve bayes classifier and provides geographic based danger areas to prevent the spreading of corona virus. This way will provide the great help to the local administration and health care agencies to control the spreading of covid. © 2022 IEEE.

Twitter Sentiment Analysis on Approval of Covaxin for Children in India

Covid-19 and its different variants are still a big issue the whole world is facing right now. At present different SARS-CoV-2 vaccines are playing vital role to combat the coronavirus. The objective of this paper is to perform sentiment analysis on approval of Bharat Biotech covaxin for emergency use for children. The presented paper emphasizes on the sentiment analysis of tweets of the microblogging site Twitter. Python programming language with Natural Language processing toolkit (NLTK), TextBlob library and tweepy twitter API are used for the process. Machine learning algorithms are used for the classification of tweeets. Graphical representation has been used for the representation of the data after sentiment analysis based on hashtags. © 2022 IEEE.

Performance Comparison of Multiple Supervised Machine Learning Algorithms for COVID-19 Mortality Prediction

The coronavirus disease (COVID-19) has wreaked havoc on populations around the world. Every day, thousands of people are dying as a result of this lethal virus. Patients with pre- existing conditions, as well as the elderly, are more susceptible to the disease. Artificial intelligence can play a vital role to track patient health conditions using various parameters. It assists in determining how to best handle certain patients in order to save their lives. The various parameters of a patient's health condition may have a significant impact on the outcome. Various artificial intelligence strategies are a blessing in minimizing the loss from COVID-19. This paper focuses on predicting the potential outcome of a patient using the COVID-19 dataset obtained from John Hopkins University of infected patients, which will help minimizing the death toll of COVID-19 disease. In this study, the performance of various machine learning models is compared for predicting COVID-19-affected patient's mortality using Logistic Regression, Support Vector Machine, K Nearest Neighbor, Decision Tree and Gaussian Naive Bayes. Finally, the best model for hyper parameter tuning was chosen from the comparative section. After hyper parameter optimization, a maximum accuracy of 95 percent and an F1 score of 89 percent using the K Nearest Neighbor algorithm was achieved. © 2022 IEEE.

A Machine Learning Approach for Sentiment Analysis to Nurture Mental Health Amidst COVID-19

During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts news about COVID-19 on social media, the news may manifest itself to accommodate the said person's fear or negative propaganda which can potentially trigger a mass panic outbreak or can disrupt the mental health of a reader. This research discusses the application of Machine Learning in Sentiment Analysis to classify Tweets about Coronavirus as fear sentiment or panic sentiment. It proposes the idea of a web-based application that caters to filter out the fear-inducing sentiment from a user's daily Twitter feed, thus giving the user accurate and well-spirited information. Textual analysis is performed along with necessary textual data visualization. A substantial accuracy of 91% is achieved in the classification of brief Tweets using the Naïve Bayes method. An accuracy of 74% is achieved using the Logistic Regression classification method for brief tweets. This depicts the advancements in the field of sentimental analysis and sheds light on how it can be employed amidst a challenging situation like the pandemic to preserve mental health. © 2021 ACM.