The main aim of this work is the determination aromaticity in biochar from easier accessible parameters (e.g., elemental composition). To end, two machine learning models, including adaptive neurofuzzy inference system (ANFIS) and least-squares support vector (LSSVM), were used to predict constant form 98 dataset gathered earlier reported sources. outputs statistical showed that LSSVM model has...

Abstract. Flood causes several threats with outcomes which include peril to human and animal life, damage property, adversity agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal environmental damage. The aim of this study was assessing performance different machine learning methods in predicting Karkheh basin. To this, we used Support Vector Machine (...

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector (LSSVM), group method data handling neural network (GMDHNN) classification regression trees (CART) air forecasting using monthly Ast...

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure s...

In this study, an efficient local online identification method based on the evolving Takagi–Sugeno least square support vector machine (eTS-LS-SVM) for nonlinear time series prediction is introduced. As an innovation, this paper has applied the nonlinear models, i.e. local LSSVM models, as the consequence parts of the fuzzy rules, instead of the linear models used in the conventional evolving T...

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. ...

Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop software or product. Many approaches have been tried this probabilistic accurately, but no single technique has consistently successful. There many studies on effort estimation using Fuzzy Machine Learning. For reason, study aims combine Learning get better results.Methods: Various methods ...

This paper presents a comparison of different data imputation approaches used in filling missing data and proposes a combined approach to estimate accurately missing attribute values in a patient database. The present study suggests a more robust technique that is likely to supply a value closer to the one that is missing for effective classification and diagnosis. Initially data is clustered a...