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In milling processing, the wear state of the tool has an essential influence on the processing quality. The machining process is not continuous in the cycloid milling process, and the signal of the empty tool part increases the difficulty of identifying the tool wear state. At present, most of the researchers use experimental data and cut out the signal of the empty tool part in the signal by data processing. However, it will affect the original signal to a certain extent and destroy the confidential information in the original signal. A novel method using variational auto-encoder (VAE) for tool wear status identification is proposed. Due to VAE has structural characteristics that reduce the dimensionality of high-dimensional data to lower dimensionality. This requires VAE to find and learn the significant features, which are hidden in the complex raw data. In this paper, the signals of the empty tool do not need to be cut out; the effective value of the three-phase current signals obtained in the real processing is converted into the form of three-dimensional color images. VAE is applied to extract features from the image samples and then realize the classification of different wear states of the tool. A large number of comparison experiments are conducted, and the result shows that the presented method has a better recognition performance for the actual processing data. It is more suitable for the recognition of tool wear status in the actual milling process. [ABSTRACT FROM AUTHOR]

When the turning tool has worn and failed but the failure is not found, if it continues to be used for processing, it will break, and cause the workpiece to be scrapped, and even damage the machine tool. In order to avoid the loss caused by turning tool wear, the remaining useful life (RUL) prediction of turning tool wear has become a hot research topic in recent years. For RUL prediction in turning tools, the traditional machine is difficult to acquire sufficient degradation data and inconsistent data distribution among different turning tools in engineering, and they cannot provide better prediction accuracy to some extent. To solve the above problems, this paper proposes a multi-granularity feature extraction (MGFE) method based on the gray-level co-occurrence matrix (GLCM) and random forest (RF). Moreover, a health indicator (HI) of turning tools in the source domain was obtained. The common representative features in HI sequence of target domain was transferred to source domain and builds the condition monitoring and life prediction system of turning tools based on extreme learning machine and transfer learning. Finally, extreme vector machine (ELM) is used to construct the RUL prediction model. The research results show that the model constructed in this paper is effective in RUL prediction and can significantly improve the prediction accuracy of remaining useful life. [ABSTRACT FROM AUTHOR]

Hand gesture recognition system helps in development of interface system for entering text in human computer interaction. In this paper, we have presented a hand gesture recognition system designed for dataset consisting of numerals and alphabets in lower case. The proposed system detects the hand with the help of skin color and motion information. Hand tracking is done with the help of two-level tracking system using modified Kanade–Lucas–Tomasi (KLT) tracking algorithm. The existing KLT was not able to track the gesture trajectory once the skin detected becomes less in area resulting in decreased number of points. In this paper, traditional KLT has been modified with a new additional feature to overcome this difficulty. In feature extraction process, a feature matrix consisting of 30 features have been created. Among these 30 features, few features like density-1, density-2, and perimeter efficiency have been introduced and are used for calculating efficiency along with some existing features. Inclusion of new features helps in improving the performance and accuracy of the system. Recognition is done using six classifiers including SVM (Support Vector machine), Decision Tree, Naïve Bayes, k-NN (K nearest neighbor), ANN (Artificial neural Network) and ELM (Extreme learning Machine). The experimental results prove that 89.67% of accuracy is achieved for the recognition of dataset containing both numerals and alphabets. Our proposed system is also compared with two existing literatures and it has been observed that better accuracy is exhibited by the proposed system. [ABSTRACT FROM AUTHOR]

POI group recommendation is one of the hottest research topics in location-based social networks, which recommends the most agreeable places for a group of users. However, traditional POI group recommendation methods only generate a consensus function to aggregate individual preference into group preference and they do not consider all the factors that can determine the results of POI group recommendation, which leads to a low recommendation accuracy. What's more, these methods have a long running time. Therefore, in this paper, we propose a new POI group recommendation method with an extreme learning machine (ELM) called PGR-ELM. The PGR-ELM method regards POI group recommendation as a binary classification problem. First, three features are extracted from three factors: POI popularity, group members' distance to POI, members' interest preferences combined affinity between group members. These features simultaneously consider all the factors that can determine the results of recommendation and guarantee the effectiveness of POI group recommendation. Then, the extracted features are input to train an ELM classifier because of its fast learning speed, which guarantees the efficiency of POI group recommendation. Finally, extensive experiments verify the accuracy and efficiency of PGR-ELM method. [ABSTRACT FROM AUTHOR]

Almost all malwares running on web-server are php codes. Then, the present paper creates a next generation antivirus (NGAV) expert in auditing threats web-based, specifically from php files, in real time. In our methodology, the malicious behaviors, of the personal computer, serve as input attributes of the statistical learning machines. In all, our dynamic feature extraction monitors 11,777 behaviors that the web fileless attack can do when launched directly from a malicious web-server to a listening service in a personal computer. Our NGAV achieves an average 99.95% accuracy in the distinction between benign and malware web scripts. Distinct initial conditions and kernels of neural networks classifiers are investigated in order to maximize the accuracy of our NGAV. Our NGAV can supply the limitations of the commercial antiviruses as for the detection of Web fileless attack. In opposition of analysis of individual events, our engine employs authorial Web-server Sandbox, machine learning, and artificial intelligence in order to identify malicious Web-sites. [ABSTRACT FROM AUTHOR]

A rapid expansion in the world's population needs an enormous supply of food grains to satisfy agricultural needs. Unfortunately, crop diseases adversely impact food production and disrupt the supply chain. To circumvent the limitations of continuous human monitoring, machine learning techniques can automatically diagnose leaf diseases in their early stages based on image data. In this paper, an Interpretable Leaf Disease Detector (I-LDD) framework for image-based leaf disease detection using Extreme Learning Machine (ELM) is proposed. The choice of ELM for this work has been motivated by its quicker convergence, good generalization capability, and shorter learning time compared to the standard gradient-based learning algorithms. Experiments have been carried out using a publicly available PlantVillage dataset comprising healthy and diseased leaf images for 32 categories. In the first phase, the leaf images are preprocessed and segmented using the k-means clustering algorithm. In the second phase, textural and frequency-based features are extracted from the segmented images. In the third phase, several machine learning classifiers are trained using tenfold cross-validation. It is observed that I-LDD achieves an accuracy of 0.9322 ± 0.0088 at a 95% confidence level, outperforming the state-of-the-art methods. Moreover, a statistical significance test on the classification performance metrics also revealed the superiority of I-LDD over the state-of-the-art classifiers. Further, Local Interpretable Model-agnostic Explanations (LIME) is used to obtain the top 10 superpixels that contributed most to the class predicted by I-LDD. [ABSTRACT FROM AUTHOR]

Label-specific features learning is a multi-label learning framework that utilizes label feature extraction to solve a single example where multiple class labels exist simultaneously. As an essential multi-label learning method, label correlation learning has been widely used in multi-label classification learning. However, in the existing label-specific features learning, the label correlation measurement often assumes that the label correlations are a global structure or that the label correlations only have a local smoothness. In actual application scenarios, the two situations may occur together. This paper proposes a multi-label classification method by joint Label-Specific features and Global and Local label correlation learning, named LSGL. Firstly, we obtain the weight of the label-specific features of each class label utilizing the l 1 -norm and then learn high-order global label correlation and label local smoothness. By adding manifold regularization terms, we fully utilize the structural relationship between features and labels and mine global and local label association information. These processes are carried out in a unified optimization model, and each part learns and promotes each other. Finally, in the low-dimensional label-specific features representation learning is to carry out multi-label classification learning through the support vector machine and the extreme learning machine, respectively. A comparative study with state-of-the-art approaches and statistical hypothesis testing manifests the validity of the LSGL method and the features learned from label-specific features learning. [ABSTRACT FROM AUTHOR]

As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM). [ABSTRACT FROM AUTHOR]

In order to improve the classification of hyperspectral image, we propose a novel hyperspectral image classification model using ensemble extreme learning machine based on fuzzy entropy weights and auto-adapted spatial-spectral features (FEW-ASSELM). To be specific, the fuzzy entropy weight of the band is proposed as a new metric to measure the importance of each band, which can optimize the grouping strategy of the effective bands. With the change of grouping strategy and random training set, the method of auto-adapted spatial-spectral features extraction is proposed to extract the more effective features. According to the characteristics of the suboptimal grouping strategy and auto-adapted spatial-spectral features, the matching framework of ensemble extreme learning machine can be proposed to perform classification operation. Experimental results on the typical hyperspectral image datasets illustrate that the proposed FEW-ASSELM has few adjustable parameters to make the operation simple, and outperforms a variety of the image classification counterparts in terms of the calculation cost and classification accuracy. [ABSTRACT FROM AUTHOR]

Texture images classification plays an important role in machine vision that can be used to distinguish the surface and objects of an image from each other. Texture classification is a two-phases process consisting of feature extraction and classification. Feature extraction is a very important step in texture classification. Thus, we use improved local quinary pattern (ILQP) as descriptor to detect texture information of the images in feature extraction phase. Besides, in the classification phase, an innovative ensemble learning-based method is proposed which is named mixture of extreme learning machine-based experts with trainable gating network (MEETG). This method takes the advantages of extreme learning machine (ELM) for designing the structure of mixture of experts (ME) to overcome on some drawbacks of ME such as computation complexity and time-consuming learning process. The performance of texture features and MEETG is evaluated by applying five datasets: Brodatz album, ULUC dataset, KTH-TIPS dataset, Outex TC000012 dataset and ALOT dataset. Experimental results indicate that MEETG outperforms the other ensemble learning methods such as Bagging, Boosting and ME and also outperforms single classifiers such as nearest neighbor, decision tree, multi-layer perceptron and ELM on classification accuracy. [ABSTRACT FROM AUTHOR]

Diffuse lung diseases are a group of chronic disorders that affect the lungs. The highly prevalent lung patterns associated with diffuse lung diseases are emphysema, fibrosis, ground-glass opacity, and micro-nodules. For diffuse lung classification problem, TALISMAN (Texture Analysis of Lung ImageS for Medical diagnostic AssistaNce) is one of the widely studied dataset in the literature. It is observed in the dataset that there exists sample imbalance among different tissue patterns. To address the sample imbalance in the data weighted extreme learning machine classifier is employed in this work. To overcome the intra-class and inter-class variation among the diffuse lung patterns features are extracted using the modified intuitionistic local binary pattern along with Gabor filter bank and grey level co-occurrence matrix. These combined texture features are then used to train the weighted extreme learning machine to classify the diffuse lung patterns. The performance of the proposed approach is compared with the existing works in the literature. The comparison results indicate better performance of the proposed approach for diffuse lung classification with sample imbalance. [ABSTRACT FROM AUTHOR]