A Hybrid Machine Learning Model for Predicting USA NBA All-Stars

Throughout the modern age, sports have been a very important part of human existence. As our documentation of sports has become more advanced, so have the prediction capabilities. Presently, analysts keep track of a massive amount of information about each team, player, coach, and matchup. This collection has led to the development of unparalleled prediction systems with high levels of accuracy. The issue with these prediction systems is that they are proprietary and very costly to maintain. In other words, they are unusable by the average person. Sports, being one of the most heavily analyzed activities on the planet, should be accessible to everyone. In this paper, a preliminary system for using publicly available statistics and open-source methods for predicting NBA All-Stars is introduced and modified to improve the accuracy of the predictions, which reaches values close to 0.9 in raw accuracy, and higher than 0.9 in specificity.

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Uncertainty quantification of ultimate compressive strength of CCFST columns using hybrid machine learning model

Engineering With Computers ◽

10.1007/s00366-021-01339-1 ◽

2021 ◽

Author(s):

Mai-Suong T. Nguyen ◽

Minh-Chien Trinh ◽

Seung-Eock Kim

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Uncertainty Quantification ◽

Learning Model ◽

Ultimate Compressive Strength ◽

Machine Learning Model ◽

Hybrid Machine

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Heart Disease Prediction using Hybrid machine Learning Model

2021 6th International Conference on Inventive Computation Technologies (ICICT) ◽

10.1109/icict50816.2021.9358597 ◽

2021 ◽

Author(s):

M. Kavitha ◽

G. Gnaneswar ◽

R. Dinesh ◽

Y. Rohith Sai ◽

R. Sai Suraj

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Learning Model ◽

Disease Prediction ◽

Machine Learning Model ◽

Hybrid Machine

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Predicting heart disease using hybrid machine learning model

Journal of Physics Conference Series ◽

10.1088/1742-6596/1916/1/012208 ◽

2021 ◽

Vol 1916 (1) ◽

pp. 012208

Author(s):

G Renugadevi ◽

G Asha Priya ◽

B Dhivyaa Sankari ◽

R Gowthamani

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Learning Model ◽

Machine Learning Model ◽

Hybrid Machine

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Corrigendum to “A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA” [Sci. Total Environ. (2017) 1160–1172]

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.06.215 ◽

2019 ◽

Vol 685 ◽

pp. 1306

Author(s):

Katherine M. Ransom ◽

Bernard T. Nolan ◽

Jonathan A. Traum ◽

Claudia C. Faunt ◽

Andrew M. Bell ◽

...

Keyword(s):

Machine Learning ◽

Nitrate Concentration ◽

Central Valley ◽

Learning Model ◽

Machine Learning Model ◽

Hybrid Machine

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Improving Logging Prediction on Imbalanced Datasets

International Journal of Open Source Software and Processes ◽

10.4018/ijossp.2016040103 ◽

2016 ◽

Vol 7 (2) ◽

pp. 43-71 ◽

Cited By ~ 3

Author(s):

Sangeeta Lal ◽

Neetu Sardana ◽

Ashish Sureka

Keyword(s):

Machine Learning ◽

Open Source ◽

Class Imbalance ◽

Learning Model ◽

Learning Models ◽

Class Imbalance Problem ◽

Imbalanced Datasets ◽

Imbalance Problem ◽

Machine Learning Model ◽

Machine Learning Models

Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.

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