The small-scale linear microphone arrays that are widely found in smartphones could be used to locate a sound source for indoor environments. After the Time Differences Of Arrival (TDOAs) in microphone pairs are estimated, a TDOA-based hybrid localisation scheme is proposed for a small-scale linear microphone array. The scheme contains two stages: the initialisation stage using the Levenberg-Ma...

The purpose of this research is to develop a system that is able to recognize and classify a variety of vehicles using image processing and artificial neural network. In order to perform the recognition, first, all the images containing the vehicles are required to go through several images processing technique such as thresholding, histogram equalization and edge detection before obtaining the...

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due...

This paper proposes new approximate QR-based algorithms for recursive nonlinear least squares (NLS) estimation. Two QR decomposition-based recursive algorithms are introduced based on the classical Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms in nonlinear unconstrained optimization or least squares problems. Instead of using the matrix inversion formula, recursive QR decomposition ...

This paper provides an effective method for parameter extraction of microelectronic devices and elements. A novel method, memetic differential evolution (MDE) algorithm, is proposed in this paper. By combining differential evolution (DE) algorithm, mutations in immune algorithm (IA), and special operators for parameter extraction, MDE possesses characteristics of high accuracy, stability, gener...

Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data colle...

Abstract Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models become quite common these days. The training algorithm is most important part an ANN that affects performance controller. Training optimize weights biases according to input-output patterns. In this paper, different was evaluate...

Smart social systems are ones where a number of individuals share and interact with each other via various networking devices. There exist benefits to including smart-based in networks such as religions, economy, medicine, networks. However, the involvement several cyber threats leads adverse effects on society terms finance, business, liability, psychology etc. The aim this paper is present se...

The study was conducted to evaluate performance of artificial neural network (ANN) models for estimating reference evapotranspiration (ET0) semi-arid region Haryana state. Ten years (2011-2020) daily weather data maximum and minimum temperature, relative humidity, wind speed sun shine hours collected from the meteorological observatory at CCS HAU, Hisar. Multilayer perceptron feed forward back ...

The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. utilization real-time over-the-air digital frequency (RF) data the development a model based on classification (AMC) is presented in this study as step incorporating opportunistic onto NomadicBTS architecture. Some techniques were studied sec...