In the present work, two different types of artificial neural network (ANN) architectures viz. back propagation neural network (BPNN) and radial basis function network (RBFN) have been used in an attempt to predict flank wear in drills. Flank wear in drill depends upon speed, feed rate, drill diameter and hence these parameters along with other derived parameters such as thrust force, torque an...

This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removi...

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired durin...

The distributed measured data in large regions and remote locations, along with a need to estimate climatic for point sites where no have been recorded, has encouraged the implementation of spatial interpolation techniques. Recently, increasing use artificial intelligence become promising alternative conventional deterministic algorithms interpolation. present study aims evaluate some machine l...

Nonlinearities inherent in soft-tissue interactions create roadblocks to realization of high-fidelity real-time haptics-based medical simulations. While finite element (FE) formulations offer greater accuracy over conventional spring-mass-network models, computational-complexity limits achievable simulation-update rates. Direct interaction with sensorized physical surrogates, in offline or onli...

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting...

In this paper we present an extensive comparison between several feedforward neural network types in the context of a clinical diagnostic task, namely the detection of coronary artery disease (CAD) using planar thallium-201 dipyridamole stress-redistribution scintigrams. We introduce results from well-known (e.g. multilayer perceptrons or MLPs, and radial basis function networks or RBFNs) as we...

This paper presents a method for detecting abnormal motion in real time using a computer vision system. The method is based on the modeling of human body image, which takes into account both orientation and velocity of prominent body parts. A comparative study is made of this method with other existing algorithms based on optical flow and the use of accelerometer body sensors. From the real tim...

Efficient interference cancellation often requires nonlinear processing of a reference signal. In this paper, hyper radial basis function (HRBF) neural networks for adaptive interference cancellation is developed. We show that the HRBF networks, with an appropriate learning algorithm, is able to approximate the interference signal more efficiently than standard radial basis function (RBF) netwo...