Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the lineari...
In this paper, we focus on the challenges of training efficiency, the designation of reward functions, and generalization in reinforcement learning for visual navigation and propose a regularized extreme learning machine-based inverse reinforcement l...
Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to co...
Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers. ELM provides...
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machi...
The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of...
The recent advancements in sensor, big data, and artificial intelligence (AI) have introduced digital transformation in the manufacturing industry. Machine maintenance has been one of the central subjects in digital transformation in the manufacturin...
There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which mak...
This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine...
Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features...
We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the lo...
Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system ar...
Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine le...
Hysteresis is a non−unique phenomenon known as a multi−valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a no...
This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for...
Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications. It has advantages such as...
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common dr...
Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the...
Today, numerous methods have been developed to address various problems, each with its own advantages and limitations. To overcome these limitations, hybrid structures that integrate multiple techniques have emerged as effective computational methods...
Machine learning, as an increasingly prominent method in recent years, has introduced new methodologies and perspectives for extracting geological alteration information. To enhance the accuracy of remote-sensing-alteration mineral information, this...
An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression...
Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-p...
The stability number of a breakwater can determine the armor unit’s weight, which is an important parameter in the breakwater design process. In this paper, a novel and simple machine learning approach is proposed to evaluate the stability of rubble-...
In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed...
Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techni...
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this pr...
Power disaggregation is aimed at determining appliance-by-appliance electricity consumption, leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models (FHMMs) have prod...
This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by mac...
This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning m...
This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH...
A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of m...
Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essent...
The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state ca...
A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electr...
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emergi...
Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diese...
This paper addresses a second-order sliding mode control method for the formation problem of multirobot systems. The formation patterns are usually symmetrical. This sliding mode control is based on the super-twisting law. In many real-world applicat...
This paper addresses the automatic detection of Armillaria disease in cherry trees, a high-impact phytosanitary threat to agriculture. As a solution, a computer application is developed based on RGB images of cherry trees and the exploitation of mach...
Extreme learning machines (ELMs) have recently attracted significant attention due to their fast training speeds and good prediction effect. However, ELMs ignore the inherent distribution of the original samples, and they are prone to overfitting, wh...
Melody extraction is an important task in music information retrieval community and it is unresolved due to the complex nature of real-world recordings. In this paper, the melody extraction problem is addressed in the extreme learning machine (ELM) f...
A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequen...
In this study, an improved version of the outlier robust extreme learning machine (IORELM) is introduced as a new method for multi-step-ahead hourly air temperature forecasting. The proposed method was calibrated and used to estimate the hourly air t...
Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. Howev...
Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic and accurate discrimination of good and bad borrowers. Notably, en...
Classification of diseases is of great importance for early diagnosis and effective treatment processes. However, etiological factors of some common diseases complicate the classification process. Therefore, classification of health datasets by proce...
Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for spa...
24 January 2017
With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some lo...
This study examines the erosion potential of earthen spillways under the growing risks posed by changing climate and extreme flood events, which threaten the stability and safety of dam infrastructure. Specifically, it employs a machine learning appr...
Sentinel-2 satellite images allow high separability for mapping burned and unburned areas. This problem has been extensively addressed using machine-learning algorithms. However, these need a suitable dataset and entail considerable training time. Re...
The analytical model (AM) of suspension force in a bearingless flywheel machine has model mismatch problems due to magnetic saturation and rotor eccentricity. A numerical modeling method based on the differential evolution (DE) extreme learning machi...
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