Hyperspectral image processing algorithms are computationally very costly, which makes them good candidates for parallel and, specifically, GPU processing. Extreme Learning Machine (ELM) is a recently proposed classification algorithm very suitable for its implementation on GPU platforms. In this paper we propose an efficient GPU implementation of an ELM-based classification strategy for hypers...

The Elaboration Likelihood Model (ELM) is a model of information processing and persuasion. Ever since its introduction in the 1980’s, ELM has been frequently cited by scholars from both cognitive/social psychology and consumer research. This paper reviews the application of ELM to consumer research over the past decade, with the focus on studies conducted in advertising contexts, effects on br...

A collocation method with the modified equilibrium on line method (ELM) forimposition of Neumann and Robin boundary conditions is presented for solving the two-dimensionalacoustical problems. In the modified ELM, the governing equations are integrated over the lines onthe Neumann (Robin) boundary instead of the Neumann (Robin) boundary condition equations. Inother words, integration domains are...

INTRODUCTION ..................................................................................... 16 I. THE TRANSFORMATIVE POWER OF STRUCTURED VIRAL COMMUNICATIONS .................................................................... 19 A. Viral Communications Overwhelms Centralized Communications .................................................................. 19 B. The Challenges and Advant...

In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson’s disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter γ in KELM are investigated in detail. With the obtained optimal parameters, ELM a...

Increasing the scalability of machine learning to handle big volume of data is a challenging task. The scale up approach has some limitations. In this paper, we proposed a scale out approach for CNN-ELM based on MapReduce on classifier level. Map process is the CNN-ELM training for certain partition of data. It involves many CNN-ELM models that can be trained asynchronously. Reduce process is t...

Density dynamics effect on the edge-localized-mode (ELM) behavior has been studied by using an integrated simulation code TOPICS-IB based on a 1.5 dimensional transport code with a stability code of peeling-ballooning modes and a transport model of scrape-off-layer (SOL) and divertor plasmas. Neutral models of core and SOL-divertor regions have been integrated with the TOPICS-IB. The TOPICS-IB ...

OBJECTIVE To evaluate macular morphological parameters measured by spectral domain optical coherence tomography (SD-OCT) as predictive factors of visual outcome in patients with exudative age-related macular degeneration (AMD) treated with ranibizumab. METHODS A retrospective study was conducted on 47 patients diagnosed with exudative AMD and treated with intravitreal ranibizumab for twelve m...

3D shape features play a crucial role in graphics applications, such as 3D shape matching, recognition, and retrieval. Various 3D shape descriptors have been developed over the last two decades; however, existing descriptors are handcrafted features that are labor-intensively designed and cannot extract discriminative information for a large set of data. In this paper, we propose a rapid 3D fea...

Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Extreme Learning Machine (ELM) is a singlehidden layer feedforward neural network with fast training rate and good generalization capability, but due to the existence of training error, ELM cannot be directly used to hand...