Detection Of CT Kidney Disease Using GLCM Feature Extraction and Kernel Extreme Learning Machine (KELM) Classification Method

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis

Machine learning based approach for modeling and forecasting of GPS–TEC during diverse solar phase periods

Spectra data classification with kernel extreme learning machine

Research is still being done on the automated identification and classification of plant diseases. A quick and precise methodology for identifying plant diseases can improve both small-scale commercial crop protection and large-scale food security. Deep learning (DL) methods also enable the monitoring of plant health and the early diagnosis of illnesses. This study's DLEN-KLM model, which is built on EfficientNet and Kernel extreme Learning Machine (KLM) and is designed to diagnose plant diseases intelligently, addresses this issue. In contrast to limited adaptive histogram equalisation as a preprocessing method, the suggested DLEN-KLM model uses median filtering in its construction. A feature extractor built on EfficientNet B0 is also part of the DLEN-KLM model, which is used to create the best feature vectors before categorising them with the KLM model. Utilising a benchmark dataset, the effectiveness of the DLEN-KLM technique is validated. The experimental results showed that the technique outperformed more contemporary methods in terms of disease diagnosis.

In order to ensure the safety of coal mine production, a mine water source identification model is proposed to improve the accuracy of mine water inrush source identification and effectively prevent water inrush accidents based on kernel principal component analysis (KPCA) and improved sparrow search algorithm (ISSA) optimized kernel extreme learning machine (KELM). Taking Zhaogezhuang mine as the research object, firstly, Na+, Ca2+, Mg2+, Cl-, SO2- 4 and HCO- 3 were selected as evaluation indexes, and their correlation was analyzed by SPSS27 software, with reducing the dimension of the original data by KPCA. Secondly, the Sine Chaotic Mapping, dynamic adaptive weights, and Cauchy Variation and Reverse Learning were introduced to improve the Sparrow Search Algorithm (SSA) to strengthen global search ability and stability. Meanwhile, the ISSA was used to optimize the kernel parameters and regularization coefficients in the KELM to establish a mine water inrush source discrimination model based on the KPCA-ISSA-KELM. Then, the mine water source data are input into the model for discrimination in compared with discrimination results of KPCA-SSA-KELM, KPCA-KELM, ISSA-KELM, SSA-KELM and KELM models. The results of the study show as follows: The discrimination results of the KPCA-ISSA-KELM model are in agreement with the actual results. Compared with the other models, the accuracy of the KPCA-ISSA-KELM model is improved by 8.33%, 12.5%, 4.17%, 21.83%, and 25%, respectively. Finally, when these models were applied to discriminate water sources in a coal mine in Shanxi, and the misjudgment rates of each model were 28.57%, 19.05%, 14.29%, 23.81%, 9.52% and 4.76%, respectively. From this, the KPCA-ISSA-KLEM model is the most accurate about discrimination and significantly better than other models in other evaluation indicators, verifying the universality and stability of the model. It can be effectively applied to the discrimination of inrush water sources in mines, providing important guarantees for mine safety production.

Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection

In the context of graduate learning in China, mentors are the teachers with the highest frequency of contact and the closest relationships with postgraduate students. Nevertheless, a number of issues pertaining to the relationship between mentors and postgraduate students have emerged with increasing frequency in recent years, resulting in a notable decline in the quality of graduate education. In this paper, we investigate the influence of the relationship between mentors and postgraduate students on the postgraduate learning performance, with postgraduate students' admission motivation and learning pressure acting as moderating variables. In practice, outliers often appear during the data collection stage, and they have a significant impact on the convergence speed and prediction accuracy of machine learning models. In order to mitigate the impact of outliers, we propose a novel kernel extreme learning machine model that is robust to outliers and name it a robust kernel extreme learning machine (RK-ELM). The RK-ELM model can automatically detect any data that may be corrupted by uncertain disturbances, thereby enhancing the robustness and generalization ability of the model. We take 873 full-time postgraduate students from universities in Zhejiang Province, China as the research object, and then form a dataset based on the postgraduates' questionnaire results and their grade point averages in the current academic year. Experimental results show that: 1) RK-ELM is an effective model for predicting postgraduate learning performance; 2) The relationship between mentors and postgraduates has a significant impact on learning performance, but it cannot directly predict learning performance; 3) The combination of the relationship between mentors and postgraduates and enrollment motivation can be used to predict learning performance, where the former can predict learning performance by influencing learning pressure.

Aiming at solving the problems of low accuracy and high false alarm rate for network intrusion detection, an algorithm of intrusion detection based on PSO and kernel Extreme learning is proposed in this article. Particle Swarm Optimization (PSO) is a kind of swarm intelligent algorithm. Kernel extreme learning machine (KELM) is a kind of classical learning method of kernel machine with its fast learning rate and strong generalization ability, but the selection of kernel function and parameters of KELM directly affects its classification performance. In this article, PSO is used to optimize the kernel parameters of kernel limit learning machine. The global kernel function with strong generalization ability of linear combination and the local kernel function with strong learning ability are used to form multi-kernel limit learning machine, which can improve the performance of single kernel Extreme Learning Machine (ELM) classifier. Finally, the performance of the algorithm is compared and analyzed through experiments and the experimental results verify the validity of this algorithm.

Optical image provides a unique spectral and textural opportunity to acquire data free of charge in cloud-free weather conditions. However, textural information from recent Sentinel-2A data has not been sufficiently recognized. Several researches have shown that texture features are essential for extracting crop information. This study presents a new scheme that integrates spectral bands and textural features for mapping maize fields and other land cover types (i.e., build-up, trees, others, and water) and evaluates the effectiveness of random forest (RF) and support vector machine (SVM) classifiers with different kernel functions based on Sentinel 2A (S-2A) data. The textural features were derived from multiple bands of S-2A image using a grey-level co-occurrence matrix (GLCM) algorithm. To normalize the analysis of these features, Random Forest feature selection was employed. The outcomes show that S-2A images are appropriate for mapping maize fields and other land covers when the image is accessible during the growing season. The SVM classifier with a radial basis function (RBF) kernel is superior to a polynomial (P), linear (L), and sigmoid (S) kernel functions, and the RF classifiers. For the SVM_RBF, the highest overall accuracy was obtained from the integration of spectral bands and textural features at scenario 2 SVM_RBF (87.40%), followed by the combination of spectral and textural features at scenario 2 SVM_P kernel (86.56%). However, scenario 5 SVM_P kernel (85.54%), scenario 4 SVM_P kernel function (85.30%), the spectral features alone, and the SVM_P kernel function, have the highest O/A (83.90%) than that of the SVM_RBF (83.66%). The inclusion of textural features contributed significantly in mapping the smallholder’s maize field in the study area. This demonstrated the value of S-2A in agricultural research.

For transition of a supercritical flow into a subcritical flow in an open channel, a hydraulic jump phenomenon is used. Different shaped channels are used as useful tools in the extra energy dissipation of the hydraulic jump. Accurate prediction of relative energy dissipation is important in designing hydraulic structures. The aim of this paper is to assess the capability of a Kernel extreme Learning Machine (KELM) meta-model approach in predicting the energy dissipation in different shaped channels (i.e., rectangular and trapezoidal channels). Different experimental data series were used to develop the models. The obtained results approved the capability of the KELM model in predicting the energy dissipation. Results showed that the rectangular channel led to better outcomes. Based on the results obtained for the rectangular and trapezoidal channels, the combination of Fr1, (y2-y1)/y1, and W/Z parameters performed more successfully. Also, comparison between KELM and the Artificial Neural Networks (ANN) approach showed that KELM is more successful in the predicting process.

The increasing reliance on Automated Teller Machines (ATMs) has highlighted the urgent need for advanced authentication mechanisms to safeguard user transactions against fraud and unauthorized access. However, traditional methods such as Personal Identification Numbers (PINs) and passwords remain vulnerable to attacks, while unimodal biometric systems face challenges of inter-class variance, environmental interference, and non-universality. Specifically, single-modality approaches and conventional ATM cameras fall short in capturing reliable biometric features under varying conditions, while the effectiveness of bimodal approaches in such environments has not been adequately investigated. Therefore, this study developed a bimodal biometric authentication system integrating face and iris recognition with Gray Level Co-Occurrence Matrix (GLCM) for enhanced ATM security. The system leverages GLCM for powerful texture feature extraction from both modalities, capturing intricate spatial relationships that are difficult to spoof. The extracted feature vectors were used to train Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel as classifiers for both face and iris recognition. The final authentication decision was made using Boolean OR rule fusion. The system achieved a remarkable accuracy of 98.2%, with a False Acceptance Rate (FAR) of 1.8% and a False Rejection Rate (FRR) of 1.2%. These results demonstrably outperformed comparable unimodal systems and existing biometric ATMs, validating the proposed framework as a highly secure and efficient solution for financial authentication systems.

An Intelligent Fault Diagnosis Approach for PV Array Based on SA-RBF Kernel Extreme Learning Machine

Relationship of CT Radiomics and Dose Texture to Radiation-Induced Swallow Dysfunction in Head and Neck Cancer

This paper presents an evaluation of the Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM), and the Kernel Extreme Learning Machine optimized by the Whale Optimization Algorithm (WOA-KELM) in the context of fruit image classification. Fruits play a crucial role in everyday life, and the application of machine learning for fruit classification can significantly improve efficiency. The ELM, a single hidden layer feedforward neural network, is the foundation for this comparison. The KELM, an evolution of the ELM, incorporates a kernel function to map input data into a high-dimensional feature space, thereby enhancing performance. The development of WOA-KELM involves optimizing the regularization parameter in KELM and the width parameter of the radial basis function through the Whale Optimization Algorithm, further enhancing its capabilities. In classifying twelve types of fruit images, the ELM achieved a classification accuracy of 77%. In contrast, the WOA-KELM, as proposed in this paper, significantly outperforms with a remarkable classification accuracy of 92%.

The precision of person recognition using facial images in complex illumination conditions deteriorates severely. Various illumination normalization and compensation techniques alleviate the classification performance by linear and static modeling of illumination variations thus result in inappropriate illumination normalization. Here, the authors present novel and adaptive illumination normalization method with the help of kernel extreme learning machine (KELM). Few low frequency discrete-Cosine-transform (DCT) coefficients are used as input to KELM for learning the non-linear regression model for illumination variations. The trained KELM is used to find the illumination variations present in the input face image which is represented by the regression value. Depending upon the regression value corresponding to the illumination variations in the test image, the number of low frequency DCT coefficients is selected adaptively. The selected low frequency DCT coefficients are processed by a fuzzy filter. The processed face images are classified by KELM with polynomial kernel. The evaluation of the proposed illumination normalization method is conducted on Extended Yale B face database. Zero, 0.19 and 1.11 percent error rate on subset 3, 4 and 5, respectively, of this database proves the efficacy of the proposed method.