Short linear motifs (SLiMs) are protein binding modules that play major roles in almost all cellular processes. SLiMs are short, often highly degenerate, difficult to characterize and hard to detect. The eukaryotic linear motif (ELM) resource (elm.eu.org) is dedicated to SLiMs, consisting of a manually curated database of over 275 motif classes and over 3000 motif instances, and a pipeline to d...

Texture information is exploited for classification of HSI (Hyperspectral Imagery) at high spatial resolution. For this purpose, framework employs to LBP (Local Binary Pattern) to extract local image features such as edges, corners & spots. After the extraction of LBP feature two levels of fusions are applied along with Gabor feature & spectral feature, i.e. Feature level fusion & Decision leve...

Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. With the increase nodes in layers, computation cost greatly increased. In this paper, we propose novel algorithm, named constrained voting extreme (CV-ELM). Compared with traditional ELM,...

Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879–892], which randomly generates hidden nodes and then analytically determines the output weig...

Hepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consi...

Samples collected from most industrial processes have two challenges: one is contaminated by the non-Gaussian noise, and other gradually obsolesced. This feature can obviously reduce accuracy generalization of models. To handle these challenges, a novel method, named robust online extreme learning machine (RO-ELM), proposed in this paper, which least mean p-power criterion employed as cost func...

The primitive Extreme Learning Machine (ELM) [1, 2, 3] with additive neurons and RBF kernels was implemented in batch mode. In this paper, its sequential modification based on recursive least-squares (RLS) algorithm, which referred as Online Sequential Extreme Learning Machine (OS-ELM), is introduced. Based on OS-ELM, Online Sequential Fuzzy Extreme Learning Machine (Fuzzy-ELM) is also introduc...

recently, gasimov and yenilmez proposed an approach for solving two kinds of fuzzy linear programming (flp) problems. through the approach, each flp problem is first defuzzified into an equivalent crisp problem which is non-linear and even non-convex. then, the crisp problem is solved by the use of the modified subgradient method. in this paper we will have another look at the earlier defuzzifi...

Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational ...

The extreme learning machine (ELM) that is proposed by Huang is designed based on single-hidden layer feedforward neural networks (SLFNs), which can randomly choose the parameters of hidden nodes and the output weights gotten analytically. So it can get the solution fastly. However, the learning time of ELM is mainly spent on calculating the Moore-Penrose generalized inverse matrices of the hid...