Software fault classification using extreme learning machine: a cognitive approach

The bakery sector is experiencing growth, with enterprises like XY Bakery & Cake Shop providing their products directly to consumers. The company's bread sales have exhibited irregular pattern, rendering the task of establishing precise sales forecasts difficult. This inconsistency has resulted in financial losses, such as the spoilage of bread within three days, the squandering of raw materials, labor, and other forms of inefficiency. The objective of this study is to forecast bread sales and evaluate the accuracy of the predictions using the Extreme Learning Machine (ELM) approach. The accuracy will be assessed using the Mean Square Error (MSE) measure at AR Bakery. The ELM approach is utilized to predict bread sales, using normalized data divided into 80% for training and 20% for testing. The experimental tests conducted with 8 neurons produced mean squared error (MSE) value of 0.27402, whereas utilizing 5 neurons resulted in an MSE of 0.28761. The minimum error value was achieved using 5 neurons. 10 predictions were made using a dataset consisting of 101 data points for each type of bread. In December, the sales reached their peak, with a total of 198,850 units sold.

Heart disease (HD) is frequently considered the most problematic human disease. Designing a more precise heart disease prediction technique is a challenging task. Recently, several health disease detection problems have been addressed using the extreme learning machine (ELM) approach. The ELM has become increasingly generally employed in various learning domains for prediction and control applications due to its rapid learning rate, straightforward structure, and excessive generalizability. Finding the perfect input weights and hidden bias parameters is the foremost challenge to enhancing the ELM's performance. Randomly selecting these parameters wants more hidden neurons than the traditional gradient learning technique, delaying the prediction response and decreasing the likelihood of finding the ideal output weight. In this study, bacterial colony optimization (BCO) is used to adjust the connection weights and bias of ELM (BCO+ELM) to address the drawbacks above. Additionally, the missing value of the heart dataset is filled with multivariate imputation by chained equation (MICE) and relevant features are selected by using recursive feature elimination (RFE) for obtaining more efficient solution accuracy and enhancing the performance of proposed BCO+ELM. According to the performance measures, BCO+ELM shows excellent prediction performances. The experimental effects indicate that the BCO+ELM creates better outcomes and low computation time with fast convergence time when compared to other approaches. The best-performing BCO+ELM shows consistent gains, although at a somewhat smaller rate (0.4% after imputation, 2.76% after feature selection). This implies that BCO+ELM still gains from the improvements provided by both imputation and feature selection, further optimizing its performance even if it is already highly optimized.

Stacked extreme learning machine (SELM) has emerged as an effective industrial process fault classification method. However, complex industrial processes usually involve a large number of variables, which may have different effects on fault classification performance. To distinguish process variables' effects on fault classification result, we propose a partial F value based weighted stacked extreme learning machine (FSELM). Firstly, the partial F value method is utilized to analyze the importance of process variables. Then, the weight of each process variable is determined based on contribution analysis. Lastly, SELM is utilized to learn the projection from the weighted process variables to fault types. Simulation results on the Tennessee Eastman process show that, the classification accuracy of the proposed method is higher, compared to the extreme learning machine and the SELM.

The learning-based methods have been widely applied to design a fault diagnosis model for rolling element bearing. However, the mainstream methods can only deal with the large training dataset, which is always violated in practical application. In this paper, we propose a physical knowledge-based hierarchical extreme learning machine(H-ELM) approach to adapt the problem of fault diagnosis for bearing with the small and imbalanced dataset. First, the proposed method uses the simple feature extraction algorithm to build a knowledge base for sample selection from the historical database, and the given training dataset is augmented with knowledge base. Second, a modified H-ELM algorithm is developed to identify fault location and recognize fault severity ranking based on the augmented dataset. Third, we design a self-optimizing module to optimize the sample selection and improve the performance of the H-ELM network. To evaluate the effectiveness of the proposed approach, the H-ELM without knowledge base and data augmentation-based support vector machine(SVM), back propagation neuron networks(BPNN) and deep belief networks(DBN) are tested in the numerical experiments to present a comprehensive comparison. The experimental results demonstrate that our approach outperforms in accuracy than other counterparts when dealing with the small and imbalanced datasets.

Recently, there has been an ever-increasing interest in the study of data-driven analytics to predict hidden dangers in the cloud service-based coal mine production, with the purpose of the prevention of possible accidents. In this paper, to achieve the above prediction, a machine learning algorithm based on the single-hidden layer feed forward network (SLFN) using timeliness managing extreme learning machine (TMELM) is utilized. Compared with those traditional learning algorithms, extreme learning machine (ELM) has its unique feature of a higher generalization capability at a much faster learning speed. In addition, the timeliness managing ELM has been proposed by incorporating timeliness management scheme into ELM approach. Under the timeliness managing ELM scheme used to predict the hidden dangers, the newly incremental data could be prior to the historical data while maximizing the contribution of the newly increasing training data, since it may be more feasible that the incremental data can contribute reasonable weights to represent the current production situation in accordance with the practical analysis for accidents in coal mine production. The experimental results on the coal mines of Beijing show that by using timeliness managing ELM, the prediction accuracy of hidden danger can be improved with better stability compared with other similar machine learning methods.

The smart grid (SG) idea was created to give the power lattice the elements and abilities it requirements to effectively integrate environmentally renewable energy sources (RES) and achieve versatility. Since energy sources like sun oriented and wind power are innately unstable and unusual, controlling in shrewd organizations can be troublesome. This study proposes an original answer for this issue by combining the upsides of particle swarm optimisation (PSO) and extreme learning machine (ELM) approaches. The proposed approach models and conjectures sustainable power age utilizing ELM, taking into account more exact preparation and determining. PSO guarantees maximized execution and effectiveness by improving the ELM calculation's boundaries in the meantime. Using a dataset of sun based energy yield, this study surveyed the proposed procedure and stood out its outcomes from those of other improvement techniques. The findings demonstrate that our ELM-PSO method lowers energy costs in smart grids and greatly increases the accuracy of predictions for renewable energy. Because the proposed approach can be employed to a range of RS sources, including hydroelectric power plants, wind turbines and solar panels, this investigation has wide-ranging implications. It can build a more robust and sustainable energy future by increasing the effectiveness and dependability of using renewable energy.

This paper provides an analysis of the combining effect of novel activation function and loss function based on M-estimation in application to extreme learning machine (ELM), a feed-forward neural network. Due to the computational efficiency and classification/prediction accuracy of ELM and its variants, they have been widely exploited in the development of new technologies and applications. However, in real applications, the performance of classical ELMs deteriorates in the presence of outliers, thus, negatively impacting the precision and accuracy of the system. To further enhance the performance of ELM and its variants, we proposed novel activation functions based on the psi function of M and redescend the M-estimation method along with the smooth l 2-norm weight-loss functions to reduce the negative impact of the outliers. The proposed psi functions of several M and redescending M-estimation methods are more flexible to make more distinct features space. For the first time, the idea of the psi function as an activation function in the neural network is introduced in the literature to ensure accurate prediction. In addition, new robust l 2 norm-loss functions based on M and redescending M-estimation are proposed to deal with outliers efficiently in ELM. To evaluate the performance of the proposed methodology against other state-of-the-art techniques, experiments have been performed in diverse environments, which show promising improvements in application to regression and classification problems.

Distance protection has been a challenging task due to the complexity of the power system. An effective protection scheme must perform fault detection, classification and location that help in quick restoration of the power supply. Although significant hybrid methods are available for fault detection, classification, and location, separately; there is always a need for an improved integrated approach performing fault detection, classification, and location. This chapter presents an integrated approach using autoregressive modeling and extreme learning machine (ELM). The performance of the scheme is investigated on 11 kV, 6 km medium voltage underground cable. The test system is simulated using PSCAD/EMTDC and the integrated protection scheme is implemented in MATLAB. The scheme uses half-cycle duration current samples. The data preprocessing is done using autoregressive (AR) modeling. The scheme detects the fault using the power of the critical pole. ELM binary classifier uses the AR coefficients to classify the type of fault. ELM in regression mode locates the fault distance. A total of 1260 data set is used to train the ELM fault classifier and ELM fault locator. The integrated protection scheme is tested with 10,800 test cases covering wide variation in operating conditions. The classification accuracy of the ELM fault classifier is 99.85% and the fault location error is less than 0.12% for the ELM fault locator, which is better than the other hybrid methods. Hence this integrated approach can be considered as quite suitable for protection of medium voltage underground cables.

This paper presents a novel feature selection method which is named filtered and supported sequential forward search (FS/spl I.bar/SFS) in the context of support vector machines (SVM). In comparison with conventional wrapper methods employing the sequential forward search (SFS) strategy, it has two important properties that reduce the computation time of SVM training during the feature selection process. First, instead of utilizing all the training samples to obtain the classifier, FS/spl I.bar/SFS, by taking advantage of the existence of support vectors in SVM, dynamically maintains an active data set for each SVM to be trained on. In this way, the computational demand of a single SVM training decreases. Secondly, a new criterion, in which the discriminant ability of individual features and the correlation between them are both taken into consideration, is proposed to effectively filter out non-essential features before every SFS iteration begins. As a result, the total amount of training is significantly reduced. The proposed approach is tested on both synthetic and real data to demonstrate its effectiveness and efficiency.

Surface roughness prediction by extreme learning machine constructed with abrasive water jet

This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m × 1.5 m × 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.

Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.

Extreme learning machine (ELM) is an easy-to use and effective learning algorithm of single-hidden layer feed-forward neural networks (SLFNs). The classical learning algorithm in neural network, e. g. backpropagation, requires setting several user-defined parameters and may get into local minimum. However, ELM only requires setting the number of hidden neurons and the activation function. It does not require adjusting the input weights and hidden layer biases during the implementation of the algorithm, and it produces only one optimal solution. Therefore, ELM has the advantages of fast learning speed and good generalization performance. In this paper, ELM is introduced in predicting reservoir permeability. By comparing to SVM, we analyze its feasibility and advantages in reservoir permeability prediction. The experimental results show that ELM has similar accuracy compared to SVR, but it has obvious advantages in parameter selection and learning speed.

Bayesian optimization of a hybrid system for robust ocean wave features prediction

Software-defined networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element in the SDN paradigm is the controller, which manages the flows of each correspondence forwarding element (switch or router). Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system. Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system. For the first level, we employ the k-nearest neighbor approach (kNN); for the second level, we use the extreme learning machine (ELM); and for the remaining levels, we utilize the hierarchical extreme learning machine (HELM) approach. In comparison with conventional supervised machine learning algorithms and other state-of-the-art methodologies based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves a good accuracy (84.29%), with an ability to detect new attacks that reaches 77.18%. Therefore, our approach presents an efficient approach for intrusion detection in SDNs.