Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. Howeve...

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields’ performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptivenetwork-based fuzzy inference ...

Despite the importance of dams for water distribution various uses, adequate forecasting on a day-to-day scale is still in great need intensive study worldwide. Machine learning models have had wide application resource studies and shown satisfactory results, including time series levels dam flows. In this study, neural network (NN) adaptive neuro-fuzzy inference systems (ANFIS) were generated ...

The dynamic Young’s modulus (E dyn ) is a parameter needed for optimizing different aspects related to oil well designing. Currently, E determined from the knowledge of formation bulk density, in addition shear and compressional velocities, which are not always available. This study introduces three machine learning (ML) models, namely, random forest (RF), adaptive neuro-fuzzy inference system ...

Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel; design variables are the width and effective depth of the continuous beam and steel ratios for positive and nega...

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...

Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to curse of dimensionality. To effectively handle ensure optimal performance, this paper presents a deep neural fuzzy (DNFS) based on subtractive clustering-based ANFIS (SC-ANFIS). Inspi...

Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...