scholarly journals A Comparative Approach To Predict Corona Virus Using Machine Learning

2021 ◽  
Author(s):  
M Rohini ◽  
K.R Naveena ◽  
G Jothipriya ◽  
S Kameshwaran ◽  
M Jagadeeswari
Keyword(s):  
Machine Learning ◽  
Comparative Approach ◽  
Corona Virus

scholarly journals Sentiment Analysis on Twitter Data: A Comparative Approach

2021 ◽  
Vol 9 (10) ◽  
pp. 1-12
Author(s):  
Subhadip Chandra ◽  
Randrita Sarkar ◽  
Sayon Islam ◽  
Soham Nandi ◽  
Avishto Banerjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Comparative Approach ◽  
Support Vector ◽  
Affective States ◽  
Twitter Data ◽  
Social Media Platforms

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.

scholarly journals Detection of Novel Corona Virus Using Machine Learning and Image Recognition

2020 ◽  
Vol 6 (12) ◽  
pp. 394-397
Author(s):  
Dhruv Garg and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recent Past ◽  
Great Precision ◽  
The Past ◽  
Corona Virus ◽  
The World ◽  
Affected Person ◽  
Precise Machine ◽  
Self Learning

In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne nature of this disease has made it highly contagious which has led to a great number of people being infected very fast. This requires a new method of testing that is faster and more precise. Machine Learning has allowed us to develop sophisticated self-learning models that can learn from data being fed and decide on entirely new options. In the past we have used different Machine Learning algorithm to make models on different biomedical dataset to detect various kind of acute or chronic diseases. Here we have developed a model that successfully detects severe cases of Novel corona virus affected person with great precision.

scholarly journals Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

2019 ◽  
Author(s):  
Charles F Rowlands ◽  
Diana Baralle ◽  
Jamie M Ellingford
Keyword(s):  
Machine Learning ◽  
In Silico Analysis ◽  
Mrna Splicing ◽  
Comparative Approach ◽  
Mendelian Disease ◽  
Learning Approaches ◽  
Basic Principles ◽  
Prediction Tools ◽  
Splicing Defects ◽  
Made In

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient’s variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this Review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

scholarly journals Using Machine Learning Methods to Estimate the Cost of Housing

2021 ◽  
pp. 67-73
Author(s):  
V. Tretynyk ◽  
А. Voznyak ◽  
V. Domrachev
Keyword(s):  
Machine Learning ◽  
Fuzzy Logic ◽  
Real Estate ◽  
The State ◽  
Automated System ◽  
Comparative Approach ◽  
Fuzzy Decision Making ◽  
Correct Result ◽  
The Cost ◽  
Python Programming

Introduction. Nowadays, the state has enshrined at the legislative level the definition of appraised value for tax purposes in sales of real estate as mandatory. The comparative approach most often used by appraisers has disadvantages such as the inability to find analogues in some cases and the need to make corrections, which affects the reliability of the results. The module of electronic determination of appraisal value (Module) similar to the object of property appraisal of the Unified database of appraisal reports works on the same approach and quite often overestimates appraisal value that leads to increase in the size of the tax during sales as the real estate cannot be sold for the price less than the estimated cost. Today to determine the price of an automated system correctly, it is necessary to fill the Unified Valuation Database in the State Property Fund with large knowledge bases - a huge IT system. So far, the thoughtless machine still determines the price by the average value. Currently there are often situations when the appraised value of real estate, determined by the Module, exceeds its real market value. Given that the approach used by the Valuation Module does not always give the correct result, there is a need to find a better method to determine the value of housing that could be used by the Module. The purpose of the paper. In this paper, an approach based on fuzzy logic was used to estimate the cost of housing in Kyiv. Fuzzy methods allow to apply a linguistic description of complex processes, to establish fuzzy relationships between concepts, to predict the behavior of the system, to create a set of alternative actions, to formally describe fuzzy decision-making rules. Results. The software implementation of the model in Python programming language was performed. Data for modeling were taken for the period July – October 2020 from a single database of property valuation reports. The sample contained 2133 records, it was filtered, divided into training and testing in the proportion of 85 : 15. To assess the quality of the program, the average relative error of the developed model was calculated. Keywords: fuzzy logic, machine learning, Python programming, linguistic variables, predictive model.

scholarly journals Predicting the intention to use google glass: A comparative approach using machine learning models and PLS-SEM

2021 ◽  
pp. 311-320 ◽  
Author(s):  
MAhmad Qasim Mohammad AlHamad ◽  
Iman Akour ◽  
Muhammad Alshurideh ◽  
Asma Qassem Al-Hamad ◽  
Barween Al Kurdi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
Comparative Approach ◽  
Perceived Ease Of Use ◽  
Learning Models ◽  
Google Glass ◽  
Exchange Information ◽  
Machine Learning Models

Technology-based education is the modern-day medium that is widely being used by teachers and their students to exchange information over applications based on Information and Communication Technology (ICT) such as Google Glass. There is still resistance shown by a few universities around the globe when it comes to shifting to the online mode of education. While few have shifted to Google Glass, others are yet to do so. We base this study to explore Google Glass Adoption in the Gulf area. We thought that introducing the teachers and students to all the pros that Google Glass presents on the table might get their attention in considering using it as the medium to exchange information in their respective institutes. This paper presents the structure of a framework depicting the association between TAM and other Influential factors. All in all, this investigation analyzes the incorporation of the Technology Acceptance Model (TAM) with the major features associated with the method such as instructing and learning facilitator, functionality, and trust and information privacy to improve correspondence among facilitators and students during the learning process. A total of 420 questionnaires were collected from various universities. The data that was gathered through the surveys was employed for the analysis of the research model using the Partial least squares-structural equation modeling (PLS-SEM) and machine learning models. The outcome showed that the factor of functionality and trust and privacy goes hand in hand with perceived usefulness and perceived ease of use associated with Google Glass. Both the Factors, Perceived usefulness and perceived ease of use have a significant impact on Google Glass adoption. This implies the significant impact of Perceived ease of use and Trust and privacy on the adoption of Google Glass The study also offers practical implications of outcomes for future research.

scholarly journals CoviDecode : Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks

2020 ◽  
Vol 6 (12) ◽  
pp. 436-439
Author(s):  
Rishabh Raj
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Virus Disease ◽  
X Ray ◽  
Corona Virus ◽  
Life Saving ◽  
Chest X Ray

ommand, product recommendation and medical diagnosis. The detection of severe acute respiratory syndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images were used in the experiments, which involved the training of deep learning and machine learning classifiers. Experiments were performed using convolutional neural networks and machine learning models. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.

scholarly journals A Study of Real World Data Visualization of COVID-19 dataset using Python

2020 ◽  
Vol 4 (8) ◽  
pp. 104-107
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Real World ◽  
Data Science ◽  
Real World Data ◽  
World Data ◽  
Corona Virus ◽  
To Come

The importance of data science and machine learning is evident in all the domains where any kind of data is generated. The multi aspect analysis and visualizations help the society to come up with useful solutions and formulate policies. This paper takes the live data of current pandemic of Corona Virus and presents multi-faceted views of the data as to help the authorities and Governments to take appropriate decisions to takle this unprecedented problem. Python and its libraries along with Google Colab platform is used to get the results. The best possible techniques and combinations of modules/libraries are used to present the information related to COVID-19..

Sign in / Sign up

Export Citation Format

Share Document