Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature ...

The large number of users and the items offered in e-commerce make it difficult for buyers to choose right sellers offer their buyers. To overcome this problem, a system that can recommend goods automatically, namely recommendation is needed. One most popular methods used create collaborative filtering, recommendations are created based on similarities user behavior. Unfortunately, method has w...

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is first object method based on feature map enhancement Multi-Stage Refinement confidence map. The proposed was evaluated two datasets: tree car. For dataset, returned mean absolute error (MAE) 2.05, root-mean-squared (RMSE) 2.87 coeffic...

This study evaluated the performance of the newly developed forestry version of DRAINMOD-NII model using a long term (21-year) data set collected from an artificially drained loblolly pine (Pinus taeda L.) plantation in eastern North Carolina, U.S.A. The model simulates the main hydrological and biogeochemical processes in drained forested lands. The model was calibrated using observed data dur...

Cement stabilized soil is one of the commonly used as ground reinforcement solutions in geotechnical engineering. In this study, main object was to apply three machine learning (ML) methods namely gradient boosting (GB), artificial neural network (ANN) and support vector (SVM) predict unconfined compressive strength (UCS) cement soil. Soil samples were collected at Hai Duong city, Vietnam. A to...

This study aims to predict the shear strength of reinforced concrete (RC) deep beams based on artificial neural network (ANN) using four training algorithms, namely, Levenberg–Marquardt (ANN-LM), quasi-Newton method (ANN-QN), conjugate gradient (ANN-CG), and descent (ANN-GD). A database containing 106 results RC beam tests is collected used investigate performance proposed algorithms. The ANN p...

An experimental investigation is conducted to examine the effects of operating conditions and channel size on the liquid film thickness of vapor bubbles in adiabatic air-water flows within the slug flow regime. Acrylic test sections are fabricated to contain a single microchannel of square cross-section with hydraulic diameters of 510 μm and 1020 μm. High-speed visualizations are used to map th...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

Abstract The current paper deals with the performance evaluation of application three soft computing algorithms such as adaptive neuro-fuzzy inference system (ANFIS), backpropagation neural network (BPNN), and deep (DNN) in predicting oxygen aeration efficiency (OAE20) gabion spillways. Besides, classical equations, namely multivariate linear nonlinear regressions (MVLR MVNLR), including previo...