lead time for reservoir inflow forecasting. The two well-knows ML: Support Vector Regression (SVR) and Random Forests (RF); were used to predict water inflow volume into the reservoir. Both methods will improve the efficiency of the reservoir inflow...The problem of reservoir inflow forecasting plays a critical role in reservoir management. However; reservoir inflow forecasting must be necessarily accurate and timely. This paper presents practical machine learning (ML) technique and the optimal

. The prediction model has been designed; trained; and tested with some empirical machine learning algorithms namely Decision Tree; Random Forest; K-Nearest Neighbor; and Support Vector Machine. Finally; the experimentation shows that Decision Tree and Random... the diagnosis complexity and treatment cost. In this study; a data mining and machine learning model is proposed to predict three types of heart blocks; such as 1st-degree A-V block; Left Bundle Branch Block (LBBB); and Right Bundle Branch Block (RBBB

method. For the ensemble method; the weighted average ensemble method is applied. For the stacked ensemble method; we use the support vector machine for the second-level classifier. In the experiments; firstly; we experimented with discovering

-learning based classification is exploited to develop a model in discriminate positive and negative risk of being MDD according to Thai screening questionnaire(2Q). From evaluation; the best machine-learning technique for the task was support vector machine... major depressive disorder (MDD) from Thai posts in social media. Unlike using questionnaire which requires a person to personally perform; the proposed method can help in an MDD screening task to cover in mass-screening on social media. The machine

local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform...The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different

of all original features; a feature optimisation process is performed. The optimal feature set is searched by a machine learning approach; like naïve Bayes and support vector machine classifier. Hand-drawn ground-truth images from expert ophthalmologists

together. This study presents a method of gathering reports of clinical trials from PubMed which describe diagnosis and treatment of child mental health issues. The main mechanism of the proposed method is a Support Vector Machine with a Multi Aspect TF