Multiclass Extreme Learning Machine Research Articles

A two‐stream deep neural network‐based intelligent system for complex skin cancer types classification

Medical imaging systems installed in different hospitals and labs generate images in bulk, which could support medics to analyze infections or injuries. Manual inspection becomes difficult when there exist more images, therefore, intelligent systems are usually required for real-time diagnosis. Melanoma is one of the most common and severe forms of skin cancer that begins from the cells beneath the skin. Through dermoscopic images, it is possible to diagnose the infection at the early stages. In this regard, different approaches have been exploited for improved results. In this study, we propose a two-stream deep neural network information fusion framework for multiclass skin cancer classification. The proposed technique follows two streams: initially, a fusion-based contrast enhancement technique is proposed, which feeds enhanced images to the pretrained DenseNet201 architecture. The extracted features are later optimized using a skewness-controlled moth–flame optimization algorithm. In the second stream, deep features from the fine-tuned MobileNetV2 pretrained network are extracted and down-sampled using the proposed feature selection framework. Finally, most discriminant features from both networks are fused using a new parallel multimax coefficient correlation method. A multiclass extreme learning machine classifier is used to classify lesion images. The testing process is initiated on three imbalanced skin data sets—HAM10000, ISBI2018, and ISIC2019. The simulations are performed without performing any data augmentation step in achieving an accuracy of 96.5%, 98%, and 89%, respectively. A fair comparison with the existing techniques reveals the improved performance of our proposed algorithm.

Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition

Power quality events are usually associated with more than one disturbance and their recognition is typically based on multilabel learning. In this study, we propose a new method for recognizing multiple power quality disturbances (MPQDs) based on variational mode decomposition (VMD) and a random discriminative projection extreme learning machine for multilabel learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multilabel classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using MATLAB synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with the several recent state-of-the-art multilabel learning algorithms, RDPEML achieved better classification performance with superior computational speed.

Multi-Class Learning from Label Proportions for Bank Customer Classification

In this paper, we study multi-class learning from label proportions, and apply it to bank customer classification, in order to provide advice for banks to better manage customer relationships. We attempt to apply the multi-class extreme learning machine (ELM) to learning from label proportions (LLP). With a structure similar to neural network, ELM possesses higher computational speed and better generalization ability. As a result, it is suitable to deal with large-scale and multi-class problems. LLP is a learning problem, which classifies training data into bags where only the label proportions of each class in each bag is available. Furthermore, in order to maintain the stable model accuracy when the bag sizes increasing, we manage to add small number of labeled samples in our model, called LLP-ELM with in a semi-supervised learning framework. The experiments prove that our improvement has advantages in the case of large bag sizes. In practical, it is worth to consider to apply the proposed algorithm to multi-class learning from label proportions for bank customer classification and other multi-class scenarios.

Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach.

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.

Selection of Most Relevant Input Parameters Using Principle Component Analysis for Extreme Learning Machine Based Power Transformer Fault Diagnosis Model

The dissolved gas-in-oil analysis is a prevailing methodology being extensively utilized to diagnose incipient faults in oil-immersed power transformers. However distinct approaches have been implemented to find out dissolved gas analysis (DGA) results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI)-based methodology is assorted with change of input parameters. Thus, selection of input variable to an AI model is major research area. In this paper, principle component analysis algorithm using RapidMiner is applied to 360 experimental datasets, imitated in lab to identify most pertinent input variables for incipient fault classification. Thereafter, multi-class Extreme Learning Machine (ELM) technique is implemented to classify the incipient faults of power transformer and its performance is compared with artificial neural network, gene expression programming, fuzzy-logic, and support vector machine. The compared result shows that ELM provides better diagnosis results up to 100% accuracy at proposed input variable in short of time period which is helpful in on-line condition monitoring.

A facial expression emotion recognition based human-robot interaction system

A facial expression emotion recognition based human-robot interaction U+0028 FEER-HRI U+0029 system is proposed, for which a four-layer system framework is designed. The FEER-HRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on 2D-Gabor, uniform local binary pattern U+0028 LBP U+0029 operator, and multiclass extreme learning machine U+0028 ELM U+0029 classifier is presented, which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios, i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEER-HRI system can be applied in home service, smart home, safe driving, and so on.

External Fault Classification Experienced by Three-Phase Induction Motor Based on Multi-Class ELM

Fault diagnosis and condition assessment (FDCA) of rotating machines becomes important due to the age of machine in service. Proper FDCA enhance the machine's operational life, efficiency and reducing catastrophic failure. This paper describes a realistic FDCA method for three phase induction motors (IMs) using readily available data. External faults experienced by IM are monitored by Multi-class Extreme Learning Machine (ELM) and compared its performance with multilayer perceptron (MLP) neural network which revealed that ELM algorithm is quite faster in investigations leading to reduction in computational load. RMS value of 3-phase voltages and currents are utilized as input variable in ELM model to identify six types of external faults experienced by IM and normal operating (NF) condition. Testing analysis of 160 instances has been performed to represent the robustness of the investigated seven status conditions of IM for wide changes in operating and loading condition perturbation.

A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line

This paper presents a new method for fault classification in series-compensated transmission line using multiclass support vector machine (MCSVM) and multi class extreme learning machine (MCELM). These methods use the information obtained from the wavelet decomposition of fault current signals for fault classification. Using MATLAB simulink, data set has been generated with different types of fault and system variables, which include the fault resistance, fault distance, load angle and fault inception angle. The proposed method has been tested on a 400-kV, 300-km transmission line under variety of fault conditions. The performance of MCSVM and MCELM is compared in terms of training time and classification accuracy. The comparisons have been made for both One-Versus-One and One-Versus-Rest methods of SVMs and ELMs. Results show that MCELMs need less training time compared to MCSVMs, and the classification accuracy of MCELMs is more or less similar to MCSVMs. The feasibility of the proposed methods is also tested on a practical 220-kV series-compensated transmission line, and the results obtained are quite promising.