Search

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 study utilizes classification models to provide a robust algorithm for imbalanced data where the minority class is of the interest, that is, in the context of default payments. In developing an integrated predictive accuracy algorithm, this study proposes machine learning classifiers and applies DNN, SVM, KNN, and ANN. The proposed algorithm utilizes a 30,000 imbalanced dataset to improve the accuracy of the prediction of default payments by implementing oversampling and undersampling strategies, such as synthetic minority oversampling technique (SMOTE), SVM SMOTE, random undersampling, and ALL-KNN. The results indicate that the SVM under the ALL-KNN sampling technique is able to achieve an accuracy of 98.6%, with the lowest cross entropy loss measurement of 0.028. Through the accurate implementation of the neural networks and neurons used in the proposed algorithm, this paper presents better insights into the functioning of the neural networks when used in conjunction with the resampling techniques. Using the methodology and algorithm presented in this study, credit risk assessments can be more accurately predicted in practical applications where most of the clients are categorized as non-default payments. [ABSTRACT FROM AUTHOR]

This paper utilizes the cognitive radio (CR) spectrum to the fullest extent for extended applications requiring discretion. The CR technology provides various supports for cognitive radio networks (CRNs). The latter has CR nodes that sense free channels. Then, the CRN allocates the unused channels to secondary users (SUs) or unlicensed users. This allocation is termed the spectrum handoff. In this paper, by considering the identical channels in CR networks, a novel machine learning algorithm (the support vector machine—SVM) is employed. In addition, the queuing model of the preemptive resume priority M/M/1 is used. The proposed spectrum handoff algorithm selects the best possible CR network channel. The spectrum handoff algorithm uses the stated SVM algorithm scheme, which covers the transmitted and received power, the minimum service time, the data rate and the maximum vacancy time for the SU, to attain the maximum throughput. However, in multi-user greedy channel selection (GCS), only two parameters are considered. The proposed spectrum handoff algorithm based on the SVM scheme enhances the performance, and the SU throughput is improved to 68.7%. This approach is better than the GCS channel selection scheme. Additionally, this approach decreases the number of spectrum handoffs. As a result, the training accuracy of the SVM method is 97.6%, and it outperforms conventional methods. [ABSTRACT FROM AUTHOR]

The 'Brainstorming' approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures. [ABSTRACT FROM AUTHOR]