In this work, an attempt has been made to analyze human femur radiographic bone images using sharpness features and learning models. The sharpness features are derived for the neck of the femur bone images to characterize the trabecular structure. The significant parameters are found using Independent component analysis (ICA) and Principal Component Analysis (PCA). The first three most signific...

Sensor array with pattern recognition method is often used for gas detection and classification. Processing time and accuracy have become matters of widespread concern in using data analysis with semiconductor gas sensor array for volatile organic compound gas mixture classification. In this paper, a sensor array consisting of four nanostruc‐ tured semiconductor gas sensors was used to generate...

Extreme learning machine is a new learning algorithm for the single hidden layer feedforward neural networks (SLFNs). ELM has been widely used in various fields and applications to overcome the slow training speed and over-fitting problems of the conventional neural network learning algorithms. ELM algorithm is based on the empirical risk minimization, without considering the structural risk an...

In this paper, a novel learning algorithm termed Hybrid Online Sequential Extreme Learning Machine (HOSELM) is proposed. The proposed HOS-ELM algorithm is a fusion of the Online Sequential Extreme Learning Machine (OS-ELM) and the Minimal Resource Allocation Network (MRAN). It is capable of reducing the number of hidden nodes in Single-hidden Layer Feed-forward Neural Networks (SLFNs) with Radi...

Cyber bullying detection that are prevailing commonly in social networks like Twitter is one of the focussed research area. Text mining and detecting cyber bullying has several research challenges and lot of research scope to work with. This research work makes use of supervised feature selection by ranking method in order to choose the features from the tweets. After that extreme learning mach...

In this article, a stochastic gradient based online learning algorithm for Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion based on Lyapunov approach is used to prove both asymptotic stability of estimation error and stability in the estimated parameters suitable for identification of nonlinear dynamic systems. The developed algorithm not only guarantees stability, ...

Compared with conventional weighted voting methods, class-specific soft voting (CSSV) system has several advantages. On one hand, it not only deals with the soft class probability outputs but also refines the weights from classifiers to classes. On the other hand, the class-specific weights can be used to improve the combinative performance without increasing much computational load. This paper...

Profiting from the great progress of information technology, a huge number multi-label samples are available in our daily life. As result, classification has aroused widespread concern. Different traditional machine learning methods which time-consuming during training phase, ELM-RBF (extreme machine-radial basis function) is more efficient and become research hotspot classification. However, b...

With the development of technology and the widespread use of machine learning, more and more models need to be trained to mine useful knowledge from large scale data. It has become a challenging problem to train multiple models accurately and efficiently so as to make full use of limited computing resources. As one of ELM variants, online sequential extreme learning machine (OS-ELM) provides a ...

Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other tr...