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Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

This paper designs a maritime anomaly detection algorithm based on a support vector machine (SVM) that considers the spatiotemporal and motion features of trajectories. Since trajectories are two-dimensional, it is difficult to present their motion features. To accurately describe trajectory features, a novel trajectory feature extraction method based on statistical theory is proposed in this paper. This method maps trajectories onto a high-dimensional space, which can account for both the spatiotemporal features and motion features of the trajectories. With the proposed feature extraction method, the density-based spatial clustering of applications with noise algorithm is employed to recognize vessel traffic patterns by simultaneously considering the spatiotemporal and motion features. Then, an improved SVM is designed by employing a weighted hybrid kernel function and differential operator to detect anomalous behaviours from recognized vessel traffic patterns that include the spatiotemporal and motion characteristics. Compared with standard SVM, it can adaptively determine the optimal kernel function according to sample set. Finally, a numerical example based on automatic identification system data from the waters off Chengshan Jiao is fulfilled to verify the proposed algorithm effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those are illumination variant. The paper uses feature extraction using LBP (Local Binary Pattern) descriptor, which is illumination invariant. The Viola-Jones algorithm is used for face detection and SVM (support vector machine) is considered for classification along with the LBP descriptor to make a complete model for depression level detection. The proposed method captures frontal face from the videos of subjects and their facial features are extracted from each frame. Subsequently, the facial features are analyzed to detect depression levels with the post-processing model. The performance of the proposed system is evaluated using machine learning algorithms in MATLAB. For the real-time system design, it is necessary to test it on the hardware platform. The LBP descriptor has been implemented on FPGA using Xilinx VIVADO 16.4. The results of the proposed method show satisfactory performance and accuracy for depression detection comparison with similar previous work. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid intelligent technique is presented based on the improvement in the feature selection method for multi-fault classification. The bearing conditions used in this study include healthy condition, defective inner ring, defective outer ring, and the faulty rolling element at different rotating motor speeds. To form the feature matrix, at first, the vibration signals are decomposed using empirical mode decomposition and wavelet packet decomposition. Then, the time and frequency domain features are extracted from the raw signals and the components are obtained from the signal decomposition. The high-dimensional feature matrix leads to increasing the computational complexity and reducing the efficiency in the classification accuracy of faults. Therefore, in the first stage of the feature selection process, the redundant and unnecessary features are eliminated by the FDAF-score feature selection method and the preselected feature set is formed. The FDAF-score technique is a combination of both F-score and Fisher discriminate analysis (FDA) algorithms. Since there may exist the features that are not susceptible to the presence of faults, the binary particle swarm optimization (BPSO) algorithm and the support vector machine (SVM) are used to select the optimal features from the preselected features. The BPSO algorithm is used to determine the optimal feature set and SVM classifier parameters so that the predictive error of the bearing conditions and the number of selected features are minimized. The results obtained in this paper demonstrate that the selected features are able to differentiate the different bearing conditions at various speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method. [ABSTRACT FROM AUTHOR]

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure. [ABSTRACT FROM AUTHOR]

In the bioinformatics field, the classification of unknown biological sequences is a key task that is fundamental for simplifying the consistency, aggregation, and survey of organisms and their evolution. We can view biological sequences as data components of higher non-fixed dimensions, corresponding to the length of the sequences. Numerical encoding performs an important function in DNA sequence evaluation via computational procedures such as one-hot encoding (OHE). However, the OHE method has drawbacks: 1) it does not add any details that may produce the additional predictive variable, and 2) if the variable has many classes, then OHE increases the feature space significantly. To overcome these drawbacks, this paper presents a computationally effective framework for classifying DNA sequences of living organisms in the image domain. The proposed strategy relies upon multilayer perceptron trained by a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process does not have to set the learning control parameters or indicate the number of hidden layers. Therefore, the PILAE classifier can accomplish better performance contrasting with other deep neural network (DNNs) strategies such as VGG-16 and Xception models. Experimental results have demonstrated that this proposed strategy achieves high prediction accuracy as well as to a significant degree high computational efficiency over different datasets. [ABSTRACT FROM AUTHOR]