scholarly journals Predicting Performance of Non-contiguous I/O with Machine Learning

2015 ◽  
pp. 257-273 ◽  
Author(s):  
Julian Kunkel ◽  
Michaela Zimmer ◽  
Eugen Betke
Keyword(s):  
Machine Learning ◽  
Predicting Performance

scholarly journals Predicting performance in 4 x 200-m freestyle swimming relay events

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254538
Author(s):  
Paul Pao-Yen Wu ◽  
Toktam Babaei ◽  
Michael O’Shea ◽  
Kerrie Mengersen ◽  
Christopher Drovandi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Staff ◽  
Individual Event ◽  
Team Members ◽  
Team Selection ◽  
Predicting Performance ◽  
Gold Silver ◽  
Forest Models ◽  
Random Forest Models ◽  
The Individual

Aim The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. Data and methods Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010–2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. Results Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [−0.94, −0.30]) and American swimmers (−0.48 s [−0.89, −0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. Discussion Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer’s season’s best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.

scholarly journals Forecasting INR Exchange Rate Against USD, GBP, JPY, SGD, EUR, AED Using Machine Learning

2021 ◽  
pp. 684-689
Author(s):  
Sumith Pevekar
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Forest ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Random Forest Algorithm ◽  
Currency Exchange ◽  
Predicting Performance ◽  
Machine Learning Model

The price of a native currency expressed in terms of another currency is known as a foreign exchange rate. In other terms, a foreign exchange rate compares the value of one currency to that of another. The value of standardized currencies varies with demand, supply, and consumer confidence around the world due to which their values fluctuate over time. To forecast the exchange rate of INR, I have developed a machine learning model. The model was trained to estimate six foreign currency exchange rates against the Indian Rupee using historical data. This model uses Random Forest algorithm to train and predict the values. The suggested system’s predicting performance is assessed and contrasted using statistical metrics. According to the findings, the Random Forest algorithm-based model predicts well and achieves an accuracy of 93.61%. KEYWORDS: Regression, Random Forest, Exchange Rate, INR

scholarly journals The Machines Aren’t Taking Over (Yet): An Empirical Comparison of Traditional, Profiling, and Machine Learning Approaches to Criterion-Related Validation

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Kristin Allen ◽  
Mathijs Affourtit ◽  
Craig Reddock
Keyword(s):  
Machine Learning ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Group Differences ◽  
Learning Approaches ◽  
New Techniques ◽  
Selection Procedures ◽  
Predicting Performance ◽  
Learning Techniques ◽  
Efficient Alternative

Criterion-related validation (CRV) studies are used to demonstrate the effectiveness of selection procedures. However, traditional CRV studies require significant investment of time and resources, as well as large sample sizes, which often create practical challenges. New techniques, which use machine learning to develop classification models from limited amounts of data, have emerged as a more efficient alternative. This study empirically investigates the effectiveness of traditional CRV with a variety of profiling approaches and machine learning techniques using repeated cross-validation. Results show that the traditional approach generally performs best both in terms of predicting performance and larger group differences between candidates identified as top or non-top performers. In addition to empirical effectiveness, other practical implications are discussed.

scholarly journals A New Stacking Based Ensemble Predictive Model to Predict the Mortality after Heart Surgery on a Highly Imbalanced Dataset

2021 ◽  
Author(s):  
Arman Ghavidel ◽  
Rouzbeh Ghousi ◽  
Alireza Atashi
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Heart Surgery ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Sampling Algorithm ◽  
Predicting Performance ◽  
Unbalanced Dataset ◽  
Under Sampling ◽  
Fold Cross Validation

Nowadays, according to spectacular improvement in health care and biomedical level, a tremendous amount of data is recorded by hospitals. In addition, the most effective approach to reduce disease mortality is to diagnose it as soon as possible. As a result, data mining by applying machine learning in the field of diseases provides good opportunities to examine the hidden patterns of this collection. An exact forecast of the mortality after heart surgery will cause Successful medical treatment and fewer costs. This research wants to recommend a new stacking predictive model after utilizing the random forest feature importance method to foresee the mortality after heart surgery on a highly unbalanced dataset by using the most practical features. To solve the unbalanced data problem, a combination of the SVM-SMOTE over-sampling algorithm and the Edited-Nearest-Neighbor under-sampling algorithm is used. This research compares the introduced model with some other machine learning classifiers to ensure efficiency through shuffle hold-out and 10-fold cross-validation strategies. In order to validate the performance of the implemented machine learning methods in this research, both shuffle hold-out, and 10-fold cross-validation results indicated that our model had the highest efficiency compared to the other models. Furthermore, the Friedman statistical test is applied to survey the differences between models. The result demonstrates that the introduced stacking model reached the most accurate predicting performance after Logistic Regression.

scholarly journals SSL-VFC4.5: An approach to adapt Very Fast C4.5 classification algorithm to deal with semi-supervised learning

2021 ◽  
Author(s):  
Carlos Eduardo Nass ◽  
Agustín Alejandro Ortíz Díaz ◽  
Fabiano Baldo
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Training Time ◽  
Predicting Performance ◽  
Learning Techniques

The growing popularity of audio and video streaming, industry 4.0 and IoT (Internet of Things) technologies contribute to the fast augment of the generation of various types of data. Therefore, to analyze these data for decision-making, supervised machine learning techniques need to be fast while keeping a suitable predicting performance even in many real-life scenarios where labeled data are expensive and hard to be gotten. To overcome this problem, this work proposes an adaptation to the Very Fast C4.5 (VFC4.5) algorithm implementing on it a semi-supervised impurity metric presented in the literature. The results pointed out that this adaptation can slightly increase the accuracy of the VFC4.5 when the datasets have the presence of a very few amount of labeled instances, but it increases the training time, especially when the number of labeled instances in the datasets increase.

scholarly journals Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 952
Author(s):  
Talko B. Dijkhuis ◽  
Matthias Kempe ◽  
Koen A. P. M. Lemmink
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Energy Expenditure ◽  
Physical Performance ◽  
Early Phase ◽  
Additional Information ◽  
Predicting Performance ◽  
Machine Learning Approach ◽  
Bayes Algorithm ◽  
The Individual

Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer.

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