Surface quality and dimensional precision will greatly affect parts during their useful life especially in cases where the components will be in direct contact with other elements during their application. This paper deals with three soft computing techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS), Neural Networks (NN) and regression in predicting the surface roughness in turning ...

Modeling of non-linear behavior of a laser diode is carried out in this paper. A combined approach of fuzzy logic and neural network is used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) based model is trained using experimental data. Theoretical analysis of temperature dependent nonlinearity becomes complex as it requires simultaneous consideration of mechanisms like Auger recombination, I...

Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensib...

Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decisionmaking model combining fuzzy log...

Addressing crime detection, cyber security and multi-modal gaze estimation in biometric information recognition is challenging. Thus, trained artificial intelligence (AI) algorithms such as Support vector machine (SVM) adaptive neuro-fuzzy inference system (ANFIS) have been proposed to recognize distinct discriminant features of (intrinsic hand demographic cues) with good classification accurac...

Inertial navigation is a crucial part of vehicle systems in complex and covert surroundings. To address the low accuracy inertial multifaced surroundings, this study, we proposed an error estimation based on adaptive neuro fuzzy inference system (ANFIS) which can quickly accurately output position end-to-end. The new was tested using both single-sequence multi-sequence data collected from by KI...

Hydrogen is a new promising energy source. Three operating parameters, including inlet gas flow rate, pH and impeller speed, mainly determine the biohydrogen production from membrane bioreactor. The work aims to boost by determining optimal values of control parameters. proposed methodology contains two parts: modeling parameter estimation. A robust ANIFS model simulate bioreactor has been cons...

An adaptive neuro-fuzzy inference systems (ANFIS) model was used for predicting regional average wind speed and power values in the Central Anatolian region of Turkey. In model development, longitude, latitude and altitude of wind stations and wind speed measurement height were taken as input variables, while wind speed and power values were taken as output variables for 4 different surface rou...

In this work neural and neuro-fuzzy controllers are developed for the inverters of Uninterruptible Power Supplies (UPS) to improve their transient response and adaptability to various loads. Idealized load-currentfeedback controller is built to obtain example patterns for training the networks. Example patterns under various loading conditions are used in the off-line training of the selected n...

In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based...