Weighted Extreme Learning Machine Research Articles

Optimizing Hadoop Data Locality: Performance Enhancement Strategies in Heterogeneous Computing Environments

As organizations increasingly harness big data for analytics and decision-making, the efficient processing of massive datasets becomes paramount. Hadoop, a widely adopted distributed computing framework, excels in processing large-scale data. However, its performance is contingent on effective data locality, which becomes challenging in heterogeneous computing environments comprising diverse hardware resources. This research addresses the imperative of enhancing Hadoop’s data locality performance in heterogeneous computing environments. The study explores strategies to optimize data placement and task scheduling, considering the diverse characteristics of nodes within the infrastructure. Through a comprehensive analysis of Hadoop’s data locality algorithms and their impact on performance, this work proposes novel approaches to mitigate challenges associated with disparate hardware capabilities. Weighted Extreme Learning Machine Technique (Weighted ELM) with the Firefly Algorithm (WELM-FF) is used in the proposed work. The integration of Weighted Extreme Learning Machine (WELM) with the Firefly Algorithm holds promise for enhancing machine learning models in the context of large-scale data processing. The research employs a combination of theoretical analysis and practical experiments to evaluate the effectiveness of the proposed enhancements. Factors such as network latency, disk I/O, and CPU capabilities are taken into account to develop a holistic framework for improving data locality and, consequently, overall Hadoop performance. The findings presented in this study contribute valuable insights to the field of distributed computing, offering practical recommendations for organizations seeking to maximize the efficiency of their Hadoop deployments in heterogeneous computing environments. By addressing the intricacies of data locality, this research strives to enhance the scalability and performance of Hadoop clusters, thereby facilitating more effective utilization of big data resources.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

Iron ore rock classification and mine remote sensing inversion based on spectroscopy and improved extreme learning machine

The classification of iron ore rock plays a crucial role in assisting the mine to develop scientifically sound mining strategies. Given the limitations of traditional manual identification and chemical techniques in terms of cost and detection time, it is crucial to develop an economical, high-precision, and efficient classification method for iron ore rocks. This paper takes an iron mine in Anshan as the research center and aims to establish an iron ore rock classification model by combining the improved extreme learning machine (ELM) with near-infrared spectra (NIRS). The NIRS of iron ore rock is denoised and feature extracted using the Savitzky–Golay filter and competitive adaptive reweighted sampling (CARS) methods, respectively. In this paper, we propose the IAdaBoost algorithm by introducing the sample similarity relationship into the weight-updating strategy of the AdaBoost integration algorithm. The weighted extreme learning machine (WELM) is adopted as the base learner for the IAdaBoost algorithm. Furthermore, we employ a multi-strategy improved gray wolf optimization (IGWO) algorithm to optimize the learning rate and the number of integrated base learners in the IAdaBoost algorithm. Consequently, we propose the IGWO-IAdaBoost-WELM model. Compared to other machine learning models, our proposed model performs better and can classify iron ore rocks consistently and efficiently. Meanwhile, we also used the model to invert the remote sensing images of the iron ore mining area, and the results show that the model can achieve fast, efficient, and large-scale iron ore rock classification, which provides an important reference for mining.

Intrusion Detection Using Whale Optimization Based Weighted Extreme Learning Machine in Applied Nonlinear Analysis

The development of computer networks and technology has led to a sharp rise in network assaults, making network intrusion a crucial area for research and the development of counter measures. The issue of network intrusion can be resolved with the advancement of artificial intelligence. We employ a Whale Optimization Algorithm (WOA) based Weighted Extreme Learning Machine (WELM) in this study to identify network intrusions. Prior to training the neural network and obtaining the output weight, ELM randomly allocates weight to the network. The ELM weights need to be optimized, thus we're utilising the whale optimisation technique to do this. To compare and evaluate the effectiveness of the model suggested in this study, NSL-KDD dataset is utilised. The experimental findings demonstrate that weighted ELM-based whale optimisation outperformed other approaches in detecting network intrusions and reducing false positive and false negative rates.

Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various performance metrics. The results show that SA-ELM achieves the highest accuracy with an R2 of 0.97, outperforming ELM and WELM by 4.6% and 15.4% respectively in terms of R2. SA-ELM also has the lowest MPE, RMSE and RRMSE values, indicating a higher accuracy in predicting global radiation. Furthermore, comparison with previously employed prediction techniques solidifies SA-ELM’s superiority, evident in its 0.275 RMSE.The study explores different input combinations for predicting global radiation in the study region, concluding that incorporating all relevant inputs yields optimal performance, although reduced input scenarios can still provide practical accuracy when data availability is limited. These results highlight the effectiveness of the SA-ELM model in accurately predicting global radiation, which is expected to have significant implications for renewable energy applications in the region. However, further testing and evaluation of the models in different regions and under different weather conditions is recommended to improve the generalizability and robustness of the results.

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

WEL-ODKC: weighted extreme learning optimal diagonal-kernels convolution model for accurate classification of skin lesions

ABSTRACT This paper presents a new model, the Weighted Extreme Learning Machine optimized Diagonal-Kernels Convolution (WELM-ODKC), for automatic skin cancer detection that addresses imbalanced data and eliminates inter-operator variability. The model combines the WELM and the Enhanced Remora Optimization Algorithm (EROA) with the Diagonal-Kernels Convolution Neural Network (DKCNN) to enhance the weight function and accurately predict skin lesion class. The model was evaluated on the MNIST HAM10000 and PAD-UFES-20 datasets and outperformed other existing skin cancer classification methods such as SVM BWO, CNN, DGC-NB, and GWO-CNN. The accuracy, recall, precision, and F1-score were used to evaluate the performance of WEL-ODKC, and the results show a high accuracy during training and validation. The proposed model efficiently classifies different types of skin cancer images from the two baseline datasets and provides promising results for automatic skin cancer detection.

RUE: A robust personalized cost assignment strategy for class imbalance cost-sensitive learning

Cost-sensitive learning is a popular paradigm to address class-imbalance learning (CIL) problem. Traditional cost-sensitive learning approaches always solve CIL problem by assigning a constant higher training error penalty for all minority instances than that of majority instances, but ignore the significance of location information. Therefore, several recent studies began to focus on the personalized cost assignment, i.e., designating different costs for different instances based on their location information. The emerging personalized cost-sensitive approaches always perform better than those traditional ones; however, the estimation for location information may be inaccurate as it is apt to be impacted by data density variation. To address this problem, we propose a novel location information estimation and cost assignment strategy called RUE. Unlike previous approaches, our proposed strategy explores location information by an indirect way: the error rate feed backed from a random undersampling ensemble. The strategy is robust towards data distribution, and is helpful for accurately estimating the significance of each instance regardless the complexity of data distribution. In context of Fuzzy Support Vector Machine (FSVM) and Weighted Extreme Learning Machine (WELM), the proposed cost assignment strategy is compared with several popular and state-of-the-art approaches, and the results show its effectiveness and superiority.

Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM.

Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem

Class imbalance problem occurs when the training dataset contains significantly fewer samples of one class in contrast to another class. Conventional extreme learning machine (ELM) gives the same importance to all the samples leading to the results, which favor the majority class. To solve this intrinsic deficiency, modifications of ELM have been developed such as weighted ELM (WELM) and WELM based on the overall distribution (ODW-ELM). Recently class-specific ELM (CS-ELM) was designed for class imbalance learning. It has been shown in this work that the derivation of the output weights, \(\varvec{\beta }\), is more efficient compared to class-specific cost regulation ELM (CCRELM) for handling the class imbalance problem. Motivated by CCRELM, X. Luo et al. have proposed the classifier ODW-ELM, which is also not efficient for imbalance learning. In this work, a novel class-specific ELM based on overall distribution (OD-CSELM) and the kernelized version of OD-CSELM (OD-CSKELM) is proposed to address the binary class imbalance problem more effectively. OD-CSELM and OD-CSKELM are motivated by CS-ELM. In addition, the computational complexity of OD-CSELM and OD-CSKELM is significantly lower than WELM and kernelized WELM, respectively. The proposed work is evaluated by using the benchmark real-world imbalanced datasets. The experimental results demonstrate that the proposed work gives good generalization performance in contrast to the rest of the classifiers for class imbalance learning.

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

A New Model for a Secure Social Media Application

The progress of data technology and wireless networks is generated by open online communication channels. Unfortunately, trolls are abusing the technology for executing cyberattacks and threats. An automated cybersecurity solution is vital for avoiding the threats and security issues from social media. This can be a requirement for tackling and considering cyberbullying in various aspects including prevention of such incidents and automated detection. This study introduces a novel Artificial Fish Swarm Algorithm with Weighted Extreme Learning Machine (AFSA-WELM) model for cybersecurity on social media. The proposed model is mostly intended to detect the existence of cyberbullying on social media. The proposed model starts by processing the dataset and making it ready for the next stages of the model. It then uses the TF-IDF vectorizer for word embedding. After that, it uses the WELM model for the identification and classification of cyberbullying. Finally, the optimal tunning parameters used in the WELM model are derived for the AFSA model. The experimental analysis has shown that the proposed model achieves maximum accuracy compared with existing algorithms. Moreover, our proposed model achieves maximum precision–recall performance with various datasets.

Cat Swarm Optimization-Based Computer-Aided Diagnosis Model for Lung Cancer Classification in Computed Tomography Images

Lung cancer is the most significant cancer that heavily contributes to cancer-related mortality rate, due to its violent nature and late diagnosis at advanced stages. Early identification of lung cancer is essential for improving the survival rate. Various imaging modalities, including X-rays and computed tomography (CT) scans, are employed to diagnose lung cancer. Computer-aided diagnosis (CAD) models are necessary for minimizing the burden upon radiologists and enhancing detection efficiency. Currently, computer vision (CV) and deep learning (DL) models are employed to detect and classify the lung cancer in a precise manner. In this background, the current study presents a cat swarm optimization-based computer-aided diagnosis model for lung cancer classification (CSO-CADLCC) model. The proposed CHO-CADLCC technique initially pre-process the data using the Gabor filtering-based noise removal technique. Furthermore, feature extraction of the pre-processed images is performed with the help of NASNetLarge model. This model is followed by the CSO algorithm with weighted extreme learning machine (WELM) model, which is exploited for lung nodule classification. Finally, the CSO algorithm is utilized for optimal parameter tuning of the WELM model, resulting in an improved classification performance. The experimental validation of the proposed CSO-CADLCC technique was conducted against a benchmark dataset, and the results were assessed under several aspects. The experimental outcomes established the promising performance of the CSO-CADLCC approach over recent approaches under different measures.

Multi-objective optimization-based adaptive class-specific cost extreme learning machine for imbalanced classification

Imbalanced classification is a challenging task in the fields of machine learning and data mining. Cost-sensitive learning can tackle this issue by considering different misclassification costs of classes. Weighted extreme learning machine (W-ELM) takes a cost-sensitive strategy to alleviate the learning bias towards the majority class to achieve better classification performance. However, W-ELM may not achieve the optimal weights for the samples from different classes due to the adoption of empirical costs. In order to solve this issue, multi-objective optimization-based adaptive class-specific cost extreme learning machine (MOAC-ELM) is presented in this paper. To be specific, the initial weights are first assigned depending on the class information. Based on that, the representation of the minority class could be enhanced by adding penalty factors. In addition, a multi-objective optimization with respect to penalty factors is formulated to automatically determine the class-specific costs, in which multiple performance criteria are constructed by comprehensively considering the misclassification rate and generalization gap. Finally, ensemble strategy is implemented to make decisions after optimization. Accordingly, the proposed MOAC-ELM is an adaptive method with good robustness and generalization performance for imbalanced classification problems. Comprehensive experiments have been performed on several benchmark datasets and a real-world application dataset. The statistical results demonstrate that MOAC-ELM can achieve competitive results on classification performance.

Hyperparameter optimization based deep convolution neural network model for automated bone age assessment and classification

Bone age assessment (BAA) is widely employed in therapeutic investigation of endocrinology issues in children. BAA is usually carried out by radiological investigation of the left hand. Since manual BAA is prone to observer variations, it is needed to design automated BAA approaches. Recently developed deep learning (DL) models have demonstrated fascinating outcomes in automatic BAA. This paper presents a novel hyperparameter optimization based deep learning model for automated bone age assessment and classification (HPTDL-BAAC). The proposed HPTDL-BAAC technique aims to predict the bone age and classify it into several stages using X-ray images. The proposed model involves data normalization for pre-processing. For feature extraction, a Regional Convolutional Neural Network (RCNN) based mask with SqueezeNet is employed as a baseline model. For boosting the performance of SqueezeNet method, swallow swarm optimization (SSO) algorithm is used for hyperparameter optimization. Finally, SoftMax classifier-based age prediction and weighted extreme learning machine (WELM) based stage classification models are applied to determine the proper bone age and class label. A wide range of simulations were performed on Digital Hand Atlas (DHA) Database and the outcomes are examined with respect to several measures. The experimental outcomes highlighted the supremacy of HPTDL-BAAC technique over the other existing techniques.

A novel multivariate signal processing-based fault diagnosis approach of rotating machinery under various operating conditions

Compared with signals collected by the single sensor, the collected multivariate signals contain more information to reflect the state of mechanical equipment, which has a positive effect on fault diagnosis. However, different acquisition channels and various operating conditions interfere with the extraction of fault features of rotating machinery. To solve this problem, taking rolling bearings as an example in this paper, a novel method is adopted to alleviate these interferences and combined with an improved extreme learning machine (ELM) to achieve intelligent fault diagnosis of rolling bearings under various operating conditions. First, adaptive projection intrinsically transformed multivariate empirical mode decomposition is used to decompose multivariate signals and obtain intrinsic mode functions of each channel to construct feature matrices. Then, nuisance attribute projection (NAP) is employed to alleviate the interference components in the feature matrix, which are originated from different channels and operating conditions. Finally, vectors belonging to the processed feature matrix as samples are input into the proposed weighted extreme learning machine (WELM) for intelligent fault classification. The weighted matrix of the WELM can compress the dimension of the sample and extract sensitive features, and the effectiveness of the proposed fault diagnosis model via the above methods is verified by experiments. Furthermore, comparative experiments show that the proposed fault diagnosis model has higher accuracy than the model combined with NAP and traditional single-hidden layer feedforward neural network or ELM. Therefore, the proposed fault diagnosis model may potentially aid experts on fault diagnosis of rotating machinery under various operating conditions.

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data preprocessing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial with Gradient Penalty Networks (WGAN-GP) to generate synthetic samples to populate the rare fault class and enrich the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks.

Detection of web service anti-patterns using weighted extreme learning machine

‘Anti-Pattern’ is a term often used by software engineers and practitioners nowadays. An anti-pattern is a supplement of the design pattern. Similar to design patterns, an anti-pattern is a template and a repeatable way of solving a specific problem, but in a non-optimal and ineffective way. Therefore, there is a requirement for the timely identification and modification of anti-patterns to increase software systems performance and efficiency. Anti-pattern detection using the source code metric can be used as an initial step in the software development life cycle, both to reduce the maintenance of the software system and to improve the quality of the software. The work in this paper empirically investigates the effectiveness of two machine learning algorithms, i.e., Extreme Learning Machine (ELM) and Weighted Extreme Learning Machine (WELM), with four different kernels in detecting web service anti-patterns. This work also investigates the application of different aggregation techniques and data sampling techniques to handle imbalanced data in predicting five different anti-patterns. The inference of this research is studied over 226 WSDL (Web Service Description Language) files. The experimental findings reveal that the model developed by WELM has superior prediction accuracy compared to the model developed by ELM.

Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

A slab quality prediction model based on machine learning plays an important role in improving final slab quality. However, the class imbalance of continuous casting datasets has a negative impact on the training of basic machine-learning models. In this study, weighted extreme learning machine (WELM) models are constructed to predict the slab quality of under different operation patterns by feeding millions of data. The results show that WELM models can achieve better prediction performance on the two types of continuous casting datasets than the basic algorithms. The superiority of WELM is demonstrated by the relatively high-precision identification of every kind of slab. The performance of WELM models with different weighting schemes is studied and the model with the golden section ratio weighting method is recommended for application as a quality prediction model. Meanwhile, WELM can still maintain a good predictive performance and generalization ability when training a large amount of data. This model can satisfy the demands for slab quality prediction and optimize the continuous casting process.