With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances six machine learning methods were analyzed for prediction maximum ozone (O_3) concentration next-day. The models make using concentrations atmospheric components (PM2.5, PM10, Ozone (O3), S...

To My lovely wife, Tong Wang And my to-be-born baby girl, Lucia Wang i ABSTRACT This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks-NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is bas...

daptive equalization has been an active area of research for many years. Even in 1985 there were a A plethora of available solutions [ 17 whose properties were well understood. Many of the techniques are firmly based on linear adaptive filter algorithms and exhibit the same much-lauded ‘learning’ property as neural networks. Alternatively, maximum likelihood strategies, which are usually based ...

Train Operators can improve railway passengers’ service quality and traffic management by accurately predicting travel arrangements delays. Precise prediction of train delays is vital for creating feasible scheduled timetables. The import pruning stacked ensemble deep neural networks into delay helps model accuracy computational time. In this study, we propose a novel learning that uses pruned ...

This paper proposes a new method for the design, through simulated evolution, of biologically inspired receptive fields in feedforward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It proposes a combined neural architecture composed of two networks in ...

Automatic classification of electrocardiogram (ECG) arrhythmias is essential to timely and early diagnosis of conditions of the heart. In this paper, a new method for ECG arrhythmias classification using Wavelet Transform (WT) and neural networks (NN) is proposed. Here, we have used a discrete Wavelet Transform (DWT) for processing ECG recordings, and extracting some time-frequency features. In...

Accurate and computationally efficient means of classifying electrocardiography (ECG) arrhythmias has been the subject of considerable research effort in recent years. This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multi-layered perceptron (MLP) with backpropagation training algorithm, and a new fuzzy cl...

This study propose and demonstrates a novel technique incorporating multilayer perceptron (MLP) neural networks for feature extraction with Photometric stereo based image capture techniques for the analysis of complex and irregular 2D profiles and 3D surfaces. In order to develop the method and to ensure that it is capable of modelling non-axisymmetric and complex 2D/3D profiles, the network wa...

This paper investigates several approaches to address the acoustic scene classification (ASC) task. We start from low-level feature representation for segmented audio frames and investigate different time granularity for feature aggregation. We study the use of support vector machine (SVM), as a well-known classifier, together with two popular neural network (NN) architectures, namely multilaye...