ELM Method Research Articles

Application of DE-ELM Algorithm in SF6 Circuit Breaker Contact Ablation Evaluation

When assessing the ablation status of circuit breaker contacts, the average dynamic contact resistance of the arc contacts of high-voltage SF6 circuit breakers is crucial. The average dynamic contact resistance of arc contacts on circuit breakers under various current levels may be accurately predicted using a method based on a differential evolution algorithm and extreme learning machine (DE-ELM), which is suggested in this research. Combining optimization algorithms yields the ideal input weight and hidden layer bias of the ELM method. The DE-ELM approach has outstanding anticipation performance for anticipation data under various current levels when compared to other anticipation methods. Finally, an expert system for assessing the ablation state of contacts based on the DE-ELM algorithm is created using the average dynamic contact resistance data of arc contacts predicted by DE-ELM. As a guide for the upkeep of high-voltage SF6 circuit breakers, the ablation status of contacts is categorized into four grades: A-level ablation, B-level ablation, C-level ablation, and D-level ablation.

Turkish sign language recognition using fuzzy logic asisted ELM and CNN methods

This work aimed to develop a data glove for the real-time translation of Turkish sign language. In addition, a novel Fuzzy Logic Assisted ELM method (FLA-ELM) for hand gesture classification is proposed. In order to acquire motion information from the gloves, 12 flexibility sensors, two inertial sensors, and 10 Hall sensors were employed. The NVIDIA Jetson Nano, a small pocketable minicomputer, was used to run the recognition software. A total of 34 signal information was gathered from the sensors, and feature matrices were generated in the form of time series for each word. In addition, an algorithm based on Euclidean distance has been developed to detect end-points between adjacent words in a sentence. In addition to the proposed method, CNN and classical ANN methods, whose model was created by us, were used in sign language recognition experiments, and the results were compared. For each classified word, samples were collected from 25 different signers, and 3000 sample data were obtained for 120 words. Furthermore, the dataset’s size was reduced using PCA, and the results of the newly created datasets were compared to the reference results. In the performance tests, single words and three-word sentences were translated with an accuracy of up to 96.8% and a minimum 2.4 ms processing time.

Short-Term Electrical Load Forecasting of 150 kV Ternate System Using Optimally Pruned Extreme Learning Machine (OPELM)

Short-term electrical loads forecasting is one of the most important factors in the design and operation of electrical systems. The purpose of electric load forecasting is to balance electricity demand and electricity supply. The load characteristics of Ternate City vary, so this study uses the Optimally Pruned Extreme Learning Machine (OPELM) method to predict electrical loads. The advantages of OPELM are the fast-learning speed and the selection of the right model, even though the data has a non-linear pattern. The accuracy of the OPELM method can be determined using a comparison method, namely the ELM method. Mean Absolute Percentage Error (MAPE) is used as the accuracy criterion. The results of the comparison of accuracy criteria show that the predictive performance of OPELM is better than that of ELM. The minimum error average of the OPELM forecast test results shows a MAPE of 5,2557%, for Saturday's forecast, while the ELM method gives a MAPE of 6.4278% on the same day.

Machine Learning for predicting climate change in the environment: Review

Climate changes currently occur abruptly and immediately being unpredictable by the population, causing damage and material losses, but with the support of current technologies, such as artificial intelligence: machine learning, will help us to anticipate these events. Therefore, this review aims to analyze the effectiveness of machine learning for the prediction of climate changes in the environment, to provide the validity of its performance and improvement. The methodology employed in this systematic review consisted of using PICO to establish eligibility criteria by grouping them into components that were finally reduced to PIOC, with which the following question was established, to what extent does Machine Learning improve the prediction of climate changes in the environment? which gave way to the development of the keywords for the creation of the search equation. Subsequently, the PRISMA methodology was used to discard articles by exclusion and inclusion, starting with a base of 2020 articles and after applying all the filters, 22 articles were included in the SLR. The results showed that machine learning showed superior performance in unraveling complex and interactive associations between environment and plant diversity, furthermore the ELM method generally provided superior accuracy to the other methods in predicting monthly soil temperatures at various depths. It was concluded that machine learning is an effective method that stands out among the other types of artificial intelligence showing a positive relationship to predict temperature changes in the environment, according to the approach presented, the most effective model that suits the research should be applied to obtain better results.

Domain adaptive extreme learning machine for epileptic EEG classification

Epilepsy is a common brain disease, caused by abnormal discharge of human brain neurons, resulting in brain dysfunction syndrome. Although epilepsy does not have much impact on patients in the short term, but long-term frequent seizures can lead to physical and mental impact of patients. At present, the method used to detect epilepsy is to make a comprehensive judgment by EEG examination combined with clinical symptoms. With the application of AI technology, some advanced algorithms have been used to assist medical diagnosis. In this trend, we use extreme learning machine to observe and detect patients with epilepsy. ELM has the characteristics of high efficiency and high precision, so it is often used in regression and classification problems. However, in the face of different data sets, ELM structure is not enough to achieve good performance. This is caused by the uneven distribution of data in different data sets. To solve this problem, we add the transfer learning module to the basic ELM structure. The purpose of adding transfer learning is to divide the disordered data in the domain space and construct a data set suitable for ELM learning. Specifically, the raw data are mapped to high-dimensional space by kernel method through domain adaptive method. Secondly, in high-dimensional space, the distance between different domains should be reduced appropriately. Finally, ELM method is used to analyze and predict the changed data set. In the whole algorithm process, due to the characteristics of ELM updating weight, only a certain amount of hidden nodes are needed, and the training process is very fast. At the same time, after adding the transfer learning function module, the accuracy of ELM is also satisfactory. In this paper, the epilepsy data of patients were used for comparative experiments. The experimental results show that the method can maintain high efficiency and satisfactory accuracy.

Optimization of extraction parameters of cationic dye using emulsified liquid membrane process. Application of Box-Behnken design

Cationic dye (Methylene blue) was extract from waste water by using emulsified liquid membrane process. The membrane composed of two phases: organic and aqueous. D2EHPA, SPAN 80 and acid are the composition of aqueous phase, however the fuel oil represent the organic phase. The effect of different parameters such as the initial concentration of cationic dye (MB), the addition of salt (KCl/ NaCl/ Na2SO4), acid (HCl/ H2SO4/H3PO4) and the extractant concentration (10, 30 and 50 ppm) was examined using Box-Behnken design. 
 The optimization of the extraction has been showed that the extraction efficiency reach100% for the following optimum operating conditions: 30 ppm for the initial concentration of [BM]0, 10%w for the extracting (D2EHPA) with the presence of Na2SO4 and H2SO4. It can be said that the ELM method is efficient for the removal of methylene bleu.

Quality Analysis of Extreme Learning Machine based on Cuckoo Search and Invasive Weed Optimization

This paper explicates hybrid optimization driven Extreme Machine Learning (ELM) strategy is developed with feed forward neural network (FFNN) for the classification of data and improving ELM. The pre-processing of input data is carried for the missing value imputation and transformation of data into numerical value using exponential kernel transform. The significant feature is determined using the Jaro–Winkler distance. The classification of data is done using the FFNN classifier, which is trained with the help of the hybrid optimization algorithm, namely developed modified Cuckoo Search and Invasive Weed Optimization (CSIWO) ELM. The modified CSIWO is devised by integrating the modified Cuckoo search (CS) algorithm and Invasive Weed Optimization (IWO) algorithm. The experimental results proposed in this paper show the feasibility and effectiveness of the developed CSIWO ELM method with encouraging performance compared with other ELM methods.

Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM Method

Automated Large-Scale Mapping of the Jahazpur Mineralised Belt by a MapReduce Model with an Integrated ELM Method

SOCIAL MEDIA ADVERTISING ASSISTANCE WITH PERSUASION CONCEPT – ELABORATION LIKELIHOOD MODEL (ELM) FOR SMES IN SURABAYA

Background: Along with the development of digital media tools, the direction of marketing shifts from conventional to social media-oriented. However, this is not easily conducted by SME business circles. Many of them do not have high literacy, do not have IT coding skills. In addition, capitalizing as well as maintaining a website are costly for them. This fact encourages the SMEs to look at another opportunity, namely the use of social media. However, it proves that these SMEs have not utilized the use of social media with persuasive advertisements. Objective: This community service activity purposes to assist the SMEs in Surabaya creating their social media advertisements using the persuasion concept-elaboration likelihood model (ELM) to solve the problems in marketing. Methods: The methods used are (1) discussion; (2) training and assisting in making advertisements; (4) evaluating/monitoring. As a result of this community service activity, the SMEs that have been assisted are able to create persuasive social media advertisements and can improve their marketing performance. Results: These MSME owners are able to create advertisements using the ELM method and determine the performance of these advertisements. Conclusion: SME owners can create advertisements using the ELM method during the training period, conduct independent evaluations of advertisements, the sales performance of these SMEs tends to increase compared to the previous by creating advertisements using the ELM method, practical modules are provided so that making advertisements using same method can be practiced for current and future needs.

Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification

Kernel Risk-Sensitive Loss (KRSL) is a nonlinear similarity measure defined in kernel space, which enable the gradient based method to achieve higher accuracy while effectively weakening the negative effects caused by noise and outliers. Defined as a kernel function expectation between two random variables, KRSL has been successfully applied in robust machine learning and signal processing. Extreme Learning Machine, as one of the most popular methods of machine learning, has attracted great attention in supervised learning and semi-supervised learning. However, when the data contains noise and outliers, the manifold structure of the data is not considered or the neural network structure is too complex, the performance of traditional ELM methods will decline. Therefore, based on KRSL, hyper-graph regularization and L2,1-norm, we first propose a more robust ELM method named Kernel Risk-Sensitive Loss Based Hyper-graph Regularized Robust Extreme Learning Machine (KRSL-HRELM). In KRSL-HRELM, KRSL is introduced into ELM to enhance its ability to handle noise and outliers. Moreover, the hyper-graph regularization is integrated into the method to learn the higher-order geometric structure information between the data. In addition, the L2,1-norm is introduced to constrain the output weight matrix to obtain a sparse network model. Inspired by other semi-supervised ELM methods, we extend KRSL-HRELM to semi-supervised learning and propose its semi-supervised version semi-supervised KRSL-HRELM (SS- KRSL-HRELM). Empirical studies on a large number of real-world datasets show that the proposed methods are competitive with other advanced supervised or semi-supervised learning methods in terms of robustness and efficiency.

EVALUATION OF DYNAMIC CHANGES OF CONSUMERS' ONLINE REVIEWS FROM THE ELABORATION LIKELIHOOD MODEL METHOD

One of the essential models that explain the concept of persuasion is the Elaboration Probability Model, which argues that there are two processes in the persuasion process, namely central and environmental persuasion. İt is thought that the article, including a detailed explanation of the model and information about the model, filled a gap in this regard. The research findings show that the model is mainly used in marketing and advertising studies. In terms of the level of affecting the probability of elaboration, the feature subject to the most significant number of studies is interesting. It was revealed that the factors involved in the elaboration process and whose effect has been handled the most in the number of studies are the power of assertion and the source credibility. The review provides valuable information in terms of allowing the model's use and the variables in the model to be seen as a holistic example in Amazon. Key words: ELM method, Amazon, main factors, reviews

Improved Crow Search Algorithm Optimized Extreme Learning Machine Based on Classification Algorithm and Application

In view of the problems of the connection weights and thresholds of the extreme learning machine are randomly generated before training and remain unchanged during the training process, the number of hidden layer nodes is pre-allocated, and the hidden layer parameters are randomly selected. Too many hidden layer nodes not only make the network more complex but also reduce the generalization ability of the algorithm. Aiming at this problem, an improved crow search algorithm is proposed to optimize the extreme learning machine. Based on the analysis of the limitations of the original crow search algorithm, a particle swarm algorithm search strategy is proposed to enhance the global search capability. In the latter part of the algorithm iteration, Gaussian function is added, and the penalty coefficient of the function is used for local disturbance, gradually reducing the amplitude of the search trajectory, and then adaptively adjusting the parameters to avoid being attracted by local extremum. Finally, the improved crow search algorithm is used to optimize the hidden layer neurons and connection weights of the extreme learning machine neural network, so as to obtain accurate prediction results. Through function fitting, regression data set fitting and classification data set for classification experiment verification, the proposed algorithm has higher training speed and efficiency. At the same time, this method is not only significantly higher than the traditional ELM method, but also obtains a more compact network structure, which is an effective neural network optimization algorithm.

Two-Stage Game Strategy for Multiclass Imbalanced Data Online Prediction

For multiclass imbalanced data online prediction, how to design a self-adapted model is a challenging problem. To address this issue, a novel dynamic multi-classification algorithm which uses two-stage game strategy has been put forward. Different from typical imbalanced classification methods, the proposed approach provided a self-updating model quantificationally, which can match the changes of arriving sample chunk automatically. In data generation phase, two dynamic ELMs with game theory are utilized for generating the lifelike minority class to equilibrate the distribution of different samples. In model update phase, both the current prediction performance and the cost sensitivity are taken into consideration simultaneously. According to the suffer loss and the shifty imbalance ratio, the proposed method develops the relationship between new weight and individual model, and an aggregate model of game theory is adopted to calculate the combination weight. These strategies help the algorithm reduce fitting error of sequence fragments. Also, alterative hidden-layer output matrix can be calculated according to the current fragment, thus building the steady network architecture in the next chunk. Numerical experiments are conducted on eight multiclass UCI datasets. The results demonstrate that the proposed algorithm not only has better generalization performance, but also improves the predictive ability of ELM method for minority samples.

Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM). Graphical abstract This paper proposes a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. It is aim to solve the safety problem of SS-ELM method that SS-ELM yields worse performance than ELM. With the help of safety mechanism, the performance of our method is still better than supervised ELM method.

Detecting bi-level false data injection attack based on time series analysis method in smart grid

Smart grid is a crucial Cyber-Physical system and is prone to False Data Injection Attack (FDIA). In this paper, we propose a novel detection mechanism for a new-type FDIA which targets at inducing generation rescheduling and load shedding. We exploit a signal processing method to recognize the behavior features of the estimated states under this FDIA and employ the captured features to train a time-series-analysis based detector. Before training the detector, an improved ELM method is proposed to eliminate the redundancies of the feature vectors. By doing so, our proposed detection mechanism can effectively detect the new-type FDIA by analysing the deviations between the feature vectors in both the spatial and temporal aspects. We assess the performance of the proposed mechanism with comprehensive simulations on IEEE 14- and 118-bus systems. The results indicate that the proposed mechanism can be performed in a real-time way with satisfactory detection accuracy.

Temperature detection based transient load/boundary condition calculations for spindle thermal simulation

Calculations of transient thermal loads/boundary conditions are crucial for accurate thermal simulations of motorized spindle unit with time-varying working parameters (such as rotation speed). By the ELM theory, this paper proposes a modeling and calculation method onto transient thermal loads/boundary conditions for spindle simulation, according to spindle temperature detections. Firstly, heat-fluid-solid coupling finite element model is established to simulate spindle thermal behaviors. Secondly, the relationship between the real-time simulated spindle temperatures and the corresponding transient thermal loads/boundary conditions are constructed by ELM method. With real-time experimental temperatures being the input vector, transient thermal loads/boundary conditions for simulation are calculated by the established ELM model. Ultimately, with the spindle operation with progressive rotation being an example, the reliability of the proposed method is verified by the agreement between simulated spindle temperatures and their detections. Furthermore, obtained transient thermal loads/boundary conditions and simulation results are utilized to analyze spindle pure heat absorption, for the guidance onto spindle coolant strategy optimization.

The performance comparison of the soft computing methods on the prediction of soil compaction parameters

The compaction parameters of soils known as the optimum moisture content (OMC) and maximum dry density (MDD) are necessary for the geotechnical engineering applications such as the fills, embankments, and dams. However, it takes a long time to determine the compaction parameters due to the laboratory test procedure. It was aimed to estimate the compaction parameters of soils with four soft computing methods and also to compare the performance of the methods in this study. For this purpose, a wide database consisting the index and standard proctor (SP) test results were used. Although all AI methods used in this study are successful on estimation of the MDD and OMC parameters, it was seen that the ELM method was the most successful method on the prediction of compaction parameters.

Virtual design of urban planning based on GIS big data and machine learning

In order to build a virtual urban planning model and improve the effect of urban planning, this paper builds a virtual urban planning design model based on GIS big data technology and machine learning algorithms, and proposes a solution that combines multiple features. With the development of polarized SAR in the direction of high resolution, a single feature often cannot fully express the detailed information of ground objects, resulting in poor classification results and low accuracy. The combination of multiple features can express feature information well. In addition, this paper uses the ELM method to plan SAR ground object classification, uses an extreme learning machine classification algorithm with fast learning speed and good classification effect, and uses ELM as a classifier. Finally, this paper designs experiments to explore the performance of the model constructed in this paper from two aspects: detection accuracy and planning score. The research results show that the model constructed in this paper meets the expected goals.

Exploring the Extreme Learning Machine for Classification of Brain MRIs

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper.

Multilayer Incremental Hybrid Cost-Sensitive Extreme Learning Machine With Multiple Hidden Output Matrix and Subnetwork Hidden Nodes

Recently, multilayer extreme learning machine with subnetwork nodes and cost sensitive learning was applied to representation learning and classification. However, the existing ML-ELM methods suffer from several drawbacks: 1) manual tuning for the number of hidden nodes in each layer will influent the training speed of the model, the best optimal determine approach for the number of hidden nodes is still for probing; 2) random projection of the input weight and bias in each layer still suboptimal and the network structure hard to reach best generalization performance. Inspired by the Subnetwork nodes and cost sensitive methods, using multiple hidden output matrix, a novel ELM method which called Multilayer Incremental Hybrid Cost Sensitive Extreme Learning Machine with Multiple Hidden Output Matrix and Subnetwork Hidden Nodes is proposed in this paper. Whose contributions are 1) a hybrid hidden nodes optimization methods is given which based on ant clone method and multiple grey wolf optimization method; 2) a multiple hidden layer output matrices are being using through weighted calculation of different output matrices which can optimize the network architecture effectively; 3) a novel ELM structure is proposed by merging the merits of Cost Sensitive Extreme Learning Machine and Subnetwork Hidden Nodes, while the regularized parameter $C$ is not sensitive to the generalization performance. Extensive experiments results show that the proposed method achieves better performance than existing state-of-art ELM methods.