scholarly journals Estimation of reference evapotranspiration using machine learning models with limited data

2021 ◽  
Vol 7 (3) ◽  
pp. 268-290
Author(s):  
Adeeba Ayaz ◽  
◽  
Maddu Rajesh ◽  
Shailesh Kumar Singh ◽  
Shaik Rehana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reference Evapotranspiration ◽  
Limited Data ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Online learning behavior analysis based on machine learning

2019 ◽  
Vol 14 (2) ◽  
pp. 97-106
Author(s):  
Ning Yan ◽  
Oliver Tat-Sheung Au
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Correlation Analysis ◽  
Prediction Accuracy ◽  
Classification Models ◽  
Limited Data ◽  
Learning Models ◽  
Learning Behavior ◽  
Content Type ◽  
Machine Learning Models

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics

2020 ◽  
pp. 259-270
Author(s):  
Didem Filiz ◽  
Ömer Özgür Tanrıöver
Keyword(s):  
Machine Learning ◽  
Mobile Phones ◽  
Machine Learning Algorithms ◽  
Biometric Identification ◽  
Classification Methods ◽  
Limited Data ◽  
Learning Models ◽  
Keystroke Dynamics ◽  
Touch Screen Devices ◽  
Machine Learning Models

In this chapter, authors explore keystroke dynamics as behavioral biometrics and effectiveness of state-of-the-art machine learning algorithms for identifying and authenticating users based on keystroke data. One of the motivations is to explore the use of classifiers to the field keystroke dynamics. In different settings, recent machine learning models have been effective with limited data and computationally relatively inexpensive. Therefore, authors conducted experiments with two different keystroke dynamics datasets with limited data. They demonstrated the effectiveness of models on dataset obtained from touch screen devices (mobile phones) and also on normal keyboard. Although there are similar recent studies which explore different classification algorithms, their main aim has been anomaly detection. But authors experimented with classification methods for user identification and authentication using two different keystroke datasets from touchscreens and keyboards.

scholarly journals Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data

2019 ◽  
Vol 50 (6) ◽  
pp. 1730-1750 ◽  
Author(s):  
Lifeng Wu ◽  
Youwen Peng ◽  
Junliang Fan ◽  
Yicheng Wang
Keyword(s):  
Machine Learning ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Temperature Data ◽  
The Other ◽  
Estimation Accuracy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

Abstract The estimation of reference evapotranspiration (ET0) is important in hydrology research, irrigation scheduling design and water resources management. This study explored the capability of eight machine learning models, i.e., Artificial Neuron Network (ANN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Spline (MARS), Support Vector Machine (SVM), Extreme Learning Machine and a novel Kernel-based Nonlinear Extension of Arps Decline (KNEA) Model, for modeling monthly mean daily ET0 using only temperature data from local or cross stations. These machine learning models were also compared with the temperature-based Hargreaves–Samani equation. The results indicated that the estimation accuracy of these machine learning models differed in various scenarios. The tree-based models (RF, GBDT and XGBoost) exhibited higher estimation accuracy than the other models in the local application. When the station has only temperature data, the MARS and SVM models were slightly superior to the other models, while the ANN and HS models performed worse than the others. When there was no temperature data at the target station and the data from adjacent stations were used instead, MARS, SVM and KNEA were the suitable models. The results can provide a solution for ET0 estimation in the absence of complete meteorological data.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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