Machine learning model development for quantitative analysis of CT heterogeneity in canine hepatic masses may predict histologic malignancy

2020 ◽

Author(s):

Diana Gabrielyan ◽

Jaan Masso ◽

Lenno Uusküla

Keyword(s):

Machine Learning ◽

Quantitative Analysis ◽

Text Mining ◽

High Frequency ◽

Learning Model ◽

Online News ◽

Inflation Expectations ◽

Forecasting Accuracy ◽

Machine Learning Model ◽

Forecasting Performance

In this paper we use high frequency multidimensional textual news data andpropose an index of inflation news. We utilize the power of text mining and itsability to convert large collections of text from unstructured to structured formfor in-depth quantitative analysis of online news data. The significantrelationship between the household’s inflation expectations and news topics isdocumented and the forecasting performance of news-based indices isevaluated for different horizons and model variations. Results suggest that withoptimal number of topics a machine learning model is able to forecast theinflation expectations with greater accuracy than the simple autoregressivemodels. Additional results from forecasting headline inflation indicate that theoverall forecasting accuracy is at a good level. Findings in this paper supportthe view in the literature that the news are good indicators of inflation and areable to capture inflation expecta-tions well.

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Machine Learning Model Development for Screening Potential Entrepreneurs in the B40 (Bottom 40%) for Targeting Assistance

International Journal of Academic Research in Business and Social Sciences ◽

10.6007/ijarbss/v11-i12/11896 ◽

2021 ◽

Vol 11 (12) ◽

Author(s):

Sagaran Gopal ◽

Sulochana Nair

Keyword(s):

Machine Learning ◽

Model Development ◽

Learning Model ◽

Machine Learning Model

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Machine Learning Model to Predict Automated Testing Adoption

International Journal of Software Innovation ◽

10.4018/ijsi.293268 ◽

2022 ◽

Vol 10 (1) ◽

pp. 0-0

Keyword(s):

Machine Learning ◽

Software Development ◽

Software Testing ◽

Model Development ◽

Learning Model ◽

Development Project ◽

Automated Testing ◽

Machine Learning Model ◽

Manual Testing ◽

Automation Testing

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.

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