Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.

Thermophysical Properties and Supercritical Heat Transfer Characteristics of R515A

The heat transfer of supercritical fluids is a vastly growing field, specifically to find suitable alternative to replace conventional R134a, which can be beneficial for climate change. A considerable suggestion is R515A which possesses considerably lower global warming potential. The present simulations are designed to study supercritical fluid R515A under cooling conditions in horizontal position. The effect of pressure, mass flux, heat flux and tube diameter were considered for horizontal tube in the vicinity of pseudo critical temperature. Numerical investigations on heat transfer characteristics of supercritical fluid R515A were performed using widely used shear-stress transport (SST) model. Moreover, heat transfer correlations were developed and suggested to accurately predict Nusselt number within 10% accuracy. The simulation results showed about 3.98% average absolute deviation.

Numerical Investigations on Supercritical Heat Transfer Characteristics of Environmental Friendly Refrigerants

The heat transfer of supercritical fluids is a vastly growing field, specifically to find suitable alternatives to replace conventional R134a, which can be beneficial for climate change. Most of the experimental and numerical investigations have been conducted to explore supercritical water, carbon dioxide and R134a as heat transfer working fluids. Hydrofluoroolefin (HFO) and refrigerants blends have been considered the most environment-friendly refrigerants to replace Chlorofluorocarbons (CFCs), Hydrochlorofluoro-carbons (HCFCs) and Hydrofluorocarbons (HFCs). Their main advantage of zero Ozone Depletion Potential (ODP) and comparatively lower Global Warming Potential (GWP) have attracted growing amount of attention to mitigate environmental issues. This work adopts the computational method and takes the environmentally friendly refrigerants to investigate the heat transfer characteristics under widely used shear-stress transport (SST) model. A comprehensive comparison was performed at reduced pressure of 1.10 for supercritical fluids R515A, R1234ze(E) and R134a. The peaks of heat transfer coefficient occurred in the vicinity of pseudo critical temperature for all of these considered fluids;however, R134a resulted in higher heat transfer coefficient, Reynolds number and Prandtl number in comparison with R515A and R1234ze(E). The higher heat transfer coefficient of supercritical fluid R134a is owing to its thermophysical properties and the specific heat plays crucial role in the heat transfer of supercritical fluids. Owing to environmental issues, R515A can be a considerable replacement of R134a. R1234ze(E) is also promising alternative to R134a;however, safety issues should thoroughly concern its mild flammable characteristics.

Impact Assessment of COVID-19 Pandemic Through Machine Learning Models

Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.

A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before.Medical experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection.Further,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable.The research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive reference.To solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)technique.The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and professionals.The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives.We also perform robustness analysis to validate the conclusions of our analysis.As a result,we obtained highly corroborative results that can be used as a reference.The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion.Peepli also has the lowest priority in the estimation.

Context Based Adoption of Ranking and Indexing Measures for Cricket Team Ranks

There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winnings and defeats of the teams.The model used by the ICC to implement rankings is deficient in certain key respects.It ignores key factors like winning margin and strength of the opposition.Various measures of the ranking concept are presented in this research.The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics.The proposed approaches not only rank the teams on their losing/winning stats but also take into consideration the margin of winning and the quality of the opposition.Three cricket team ranking techniques are presented i.e.,(1)Cricket Team-Index(ct-index),(2)Cricket Team Rank(CTR)and(3)Weighted Cricket Team Rank(WCTR).The proposed metrics are validated through the collection of cricket dataset,extracted from Cricinfo,having instances for all the three formats of the game i.e.,T20 International(T20i),One Day International(ODI)and Test matches.The comparative analysis between the proposed and existing techniques,for all the three formats,is presented as well.