Explore the vast range of titles available.

Over the last two decades, automatic speaker recognition has been an interesting and challenging problem to speech researchers. It can be classified into two different categories, speaker identification and speaker verification. In this paper, a new classifier, extreme learning machine, is examined on the text-independent speaker verification task and compared with SVM classifier. Extreme learning machine (ELM) classifiers have been proposed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. They are extremely fast in learning and perform well on many artificial and real regression and classification applications. The database used to evaluate the ELM and SVM classifiers is ELSDSR corpus, and the Mel-frequency Cepstral Coefficients were extracted and used as the input to the classifiers. Empirical studies have shown that ELM classifiers and its variants could perform better than SVM classifiers on the dataset provided with less training time.

With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, owing to the noise of observation systems and the ever-changing external observation environments, the observed asteroid spectral data always contain noise and outliers exhibiting indivisible pattern characteristics, which will bring great challenges to the precise classification of asteroids. In order to alleviate the problem and to improve the separability and classification accuracy for different kinds of asteroids, this paper presents a novel Neighboring Discriminant Component Analysis (NDCA) model for asteroid spectrum feature learning. The key motivation is to transform the asteroid spectral data from the observation space into a feature subspace wherein the negative effects of outliers and noise will be minimized while the key category-related valuable knowledge in asteroid spectral data can be well explored. The effectiveness of the proposed NDCA model is verified on real-world asteroid reflectance spectra measured over the wavelength range from 0.45 to 2.45 μm, and promising classification performance has been achieved by the NDCA model in combination with different classifier models, such as the nearest neighbor (NN), support vector machine (SVM) and extreme learning machine (ELM).

As connections from the brain to an external device, Brain-Computer Interface (BCI) systems are a crucial aspect of assisted communication and control. When equipped with well-designed feature extraction and classification approaches, information can be accurately acquired from the brain using such systems. The Hierarchical Extreme Learning Machine (HELM) has been developed as an effective and accurate classification approach due to its deep structure and extreme learning mechanism. A classification system for motor imagery EEG signals is proposed based on the HELM combined with a kernel, herein called the Kernel Hierarchical Extreme Learning Machine (KHELM). Principle Component Analysis (PCA) is used to reduce the dimensionality of the data, and Linear Discriminant Analysis (LDA) is introduced to push the features away from different classes. To demonstrate the performance, the proposed system is applied to the BCI competition 2003 Dataset Ia, and the results are compared with those from state-of-the-art methods; we find that the accuracy is up to 94.54%.

Interval data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy, or variability must be taken into account. Considered in this paper is the learning of interval neural networks, of which the input and output are vectors with interval components, and the weights are real numbers. The back-propagation (BP) learning algorithm is very slow for interval neural networks, just as for usual real-valued neural networks. Extreme learning machine (ELM) has faster learning speed than the BP algorithm. In this paper, ELM is applied for learning of interval neural networks, resulting in an interval extreme learning machine (IELM). There are two steps in the ELM for usual feedforward neural networks. The first step is to randomly generate the weights connecting the input and the hidden layers, and the second step is to use the Moore–Penrose generalized inversely to determine the weights connecting the hidden and output layers. The first step can be directly applied for interval neural networks. But the second step cannot, due to the involvement of nonlinear constraint conditions for IELM. Instead, we use the same idea as that of the BP algorithm to form a nonlinear optimization problem to determine the weights connecting the hidden and output layers of IELM. Numerical experiments show that IELM is much faster than the usual BP algorithm. And the generalization performance of IELM is much better than that of BP, while the training error of IELM is a little bit worse than that of BP, implying that there might be an over-fitting for BP.

Big Data (BD) is participating in the current computing revolution in a big way. Industries and organizations are utilizing their insights for Business Intelligence using Machine Learning Models (ML-Models). Deep Learning Models (DL-Models) have been proven to be a better selection than Shallow Learning Models (SL-Models). However, the dynamic characteristics of BD introduce many critical issues for DL-Models, Concept Drift (CD) is one of them. CD issue frequently appears in Online Supervised Learning environments in which data trends change over time. The problem may even worsen in the BD environment due to veracity and variability factors. Due to the CD issue, the accuracy of classification results degrades in ML-Models, which may make ML-Models not applicable. Therefore, ML-Models need to adapt quickly to changes to maintain the accuracy level of the results. In current solutions, a substantial improvement in accuracy and adaptability is needed to make ML-Models robust in a non-stationary environment. In the existing literature, the consolidated information on this issue is not available. Therefore, in this study, we have carried out a systematic critical literature review to discuss the Concept Drift taxonomy and identify the adverse effects and existing approaches to mitigate CD.

Birth complications, particularly jaundice, are one of the leading causes of adolescent death and disease all over the globe. The main severity of these illnesses may diminish if scholars study more about their sources and progress toward effective treatment. Assured developments were prepared, but they are inadequate. Newborns repeatedly have jaundice as their primary medical concern. A raised level of bilirubin is a symbol of jaundice. Generally, in newborns, hyperbilirubinemia peaks in the initial post-delivery week. The inability to perceive issues early is sufficient for quick treatment, and the resemblance of indications might lead to misdiagnosis. Therefore, appropriate technologies are instantly required. Nowadays, researchers have begun to implement an image-processing model for analyzing jaundice. Paediatricians can detect and classify neonatal jaundice with machine learning (ML) and deep learning (DL) techniques. This study proposes an Early Diagnosis of Neonatal Jaundice Image Classification Using Kernel Extreme Learning Machine (EDNJIC-KELM) approach in the Healthcare Sector. The main intention of the EDNJIC-KELM approach is to build an effective system for diagnosing neonatal jaundice based on advanced methods. Initially, the image pre-processing stage applies the Wiener filtering (WF) method to improve the quality of an image and make it more suitable for analysis by removing the noise. In addition, the vision transformer (ViT) method is employed for the feature extraction process. Furthermore, the EDNJIC-KELM method employs the kernel extreme learning machine (KELM) method for the jaundice image classification. Finally, the enhanced coati optimization algorithm (ECOA) method is implemented for the hyperparameter tuning of the KELM method, which results in a higher classification process. The experimental analysis of the EDNJIC-KELM technique is examined using the Jaundice Image data. The performance validation of the EDNJIC-KELM technique portrayed a superior accuracy value of 96.97% over existing models.

Nowadays with the rapid development of network-based services and users of the internet in everyday life, intrusion detection becomes a promising area of research in the domain of security. Intrusion detection system (IDS) can detect the intrusions of someone who is not authorized to the present computer system automatically, so intrusion detection system has emerged as an essential component and an important technique for network security. Extreme learning machine (ELM) is an interested area of research for detecting possible intrusions and attacks. In this paper, we propose an improved learning algorithm named self- adaptive differential evolution extreme learning machine (SADE-ELM) for classifying and detecting the intrusions. We compare our methods with commonly used ELM, DE-ELM techniques in classifications. Simulation results show that the proposed SADE-ELM approach achieves higher detection accuracy in classification case.

In this paper, Self-adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) was proposed as a new class of learning algorithm for single-hidden layer feed forward neural network (SLFN). In order to achieve good generalization performance, SaDE-ELM calculates the error on a subset of testing data for parameter optimization. Since SaDE-ELM employs extra data for validation to avoid the over fitting problem, more samples are needed for model training. In this paper, the cross-validation strategy is proposed to be embedded into the training phase so as to solve the overtraining problem. Experimental results demonstrate that the proposed algorithms are efficient for Facial Age Estimation.

Chronic obstructive pulmonary disease (COPD) is considered to be one of the most commonly occurring respiratory disorders and is proliferating at an extremely high rate in the recent years. The proposed system aims to classify the various stages of COPD using a COPD patient dataset comprising 101 patients and 24 varied factors related to the disease. In addition, a self-acquired dataset containing 560 lung CT images was also used. The obtained hybrid database is normalized, augmented, and preprocessed using bilateral filter and contrast enhanced using dynamic histogram equalization. Segmentation is then performed using SuperCut algorithm. Feature extraction is done by binary feature fusion technique involving UNet and AlexNet. Kernel extreme learning machine-based classification is conducted further, and the results produced are optimized using dragon fly optimization algorithm. The proposed system produced an enhanced accuracy of 98.82%, precision of 99.01%, recall of 94.98%, F1 score of 96.11%, specificity of 98.09%, MCC value of 94.33%, and AUC value of 0.996 which are far better when compared with other existing systems.

Accurately predicting solar power to ensure the economical operation of microgrids and smart grids is a key challenge for integrating the large scale photovoltaic (PV) generation into conventional power systems. This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. The proposed method utilizes temperature, irradiance, and solar power output at instant i as input parameters, while the output parameters are temperature, irradiance, and solar power output at instant i+1, enabling next-day solar power output forecasting. The performance of the P-ELM algorithm is evaluated using mean absolute error (MAE) and root mean square error (RMSE), and it is compared with the extreme learning machine (ELM) algorithm. The results indicate that the P-ELM algorithm achieves higher accuracy in short-term prediction, demonstrating its suitability for ensuring accuracy and reliability in real-time solar power forecasting.