We present an experimental and theoretical study of the breakthrough performance of the flexible metal-organic framework Cu(bpy)2(BF4)2 (bpy = 4,4'-bipyridine), termed ELM-11. Pure CO2, He, CH4, and N2 gases, as well as binary gas mixtures of those species, were used to perform breakthrough experiments on ELM-11. ELM-11 exhibits a stepped breakthrough curve for CO2 not seen in rigid adsorbents....

The extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks (SLFNs) which need not be neuron alike and perform well in both regression and classification applications. An active topic in ELMs is how to automatically determine network architectures for given applications. In this paper, we propose an extreme learning machine with adaptive grow...

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme l...

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 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...

Although extreme learning machine (ELM) has been successfully applied to a number of pattern recognition problems, it fails to provide sufficient good results in hyperspectral image (HSI) classification due to two main drawbacks. The first is due to the random weights and bias of ELM, which may lead to ill-posed problems. The second is the lack of spatial information for classification. To tack...

Extreme Learning Machine (ELM) is a new learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based ones. However, ELM random generation may lead to nono...

Extreme learning machine (ELM) has shown its good performance in regression applications with a very fast speed. But there is still a difficulty to compromise between better generalization performance and smaller complexity of the ELM (number of hidden nodes). This paper proposes a method called Delta TestELM (DT-ELM), which operates in an incremental way to create less complex ELM structures a...

An emerging business model, known as Electronic Logistics Marketplace (ELM), is increasingly being recognised for its potential to address the issue of poor vehicle utilisation. This research investigates the feasibility of setting up a neutral government-supported regional ELM which will fully exploit the potential of such a collaborative network across industries and achieve transport optimis...

Extreme learning machine (ELM) as an emergent technology has shown its good performance in classification applications. However, ELM algorithm needs to find the inversion of matrix in nature, which will limit its application on many occasions. This paper proposes an ELM speedup algorithm based on the analysis of ELM algorithm. By applying randomized approximation method, the proposed algorithm ...