This paper presents a learning algorithm based on AdaBoost for solving two-class classification problem. The concept of boosting is to combine several weak learners to form a highly accurate strong classifier. AdaBoost is fast and simple because it focuses on finding weak learning algorithms that only need to be better than random, instead of designing an algorithm that learns deliberately over...

We have implemented a systematic method that can improve cancer classification. By extracting significant samples (which we refer to as support vector samples because they are located only on support vectors), we can let the back propagation neural networking (BPNN) to learn more tasks. We call this approach the multi-task support vector sample learning (MTSVSL) technique. We demonstrate experi...

Data collected from the supervisory control and data acquisition (SCADA) system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs), is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN) for au...

In order to improve the accuracy of solar radiation prediction and optimize energy management system. This study proposes a forecasting model based on empirical mode decomposition (EMD) Back Propagation Neural Network (BPNN). Empirical (EMD)-based ensemble methods with powerful predictive abilities have become relatively common in study. First, existing datasets are decomposed into an intrinsic...

Crack identification is a very important issue in mechanical systems, because it is a damage that if develops may cause catastrophic failure. In the first part of this research, modal analysis of a multi-cracked variable cross-section beam is done using finite element method. Then, the obtained results are validated usingthe results of experimental modal analysis tests. In the next part, a nove...

This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive in Shanghai, China. A total of 35 design parameters relating form, envelope properties, thermostat settings, green roof configurations were considered. First, Latin hypercube sampling (LHSM) was used generate set samples, samples obtained through computer simulation calcula...

The purpose of this paper is present probabilistic neural networks (PNN) as an alternative quantitative technique to both linear discriminant analysis (LDA) and backpropagated neural networks (BPNN) for forecasting corporate solvency. Although traditionally this task has been approached with rather simpler linear techniques such as LDA, there is increasing empirical evidence of the superiority ...

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), ...

Time series forecasting plays a significant role in numerous applications, including but not limited to, industrial planning, water consumption, medical domains, exchange rates and consumer price index. The main problem is insufficient accuracy. present study proposes hybrid methods to address this need. proposed method includes three models. first model based on the autoregressive integrated m...

In ubiquitous environments, analysis and classification of sound plays a critical role in various acoustic-based recognition systems. This work aims to contribute towards building an automatic sound recognition system that can understand the surrounding environment by the audio information. In this paper, an acoustic signal based context awareness system is proposed for detecting sound events i...