Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.

Predicting Corynebacterium glutamicum promoters based on novel feature descriptor and feature selection technique

The promoter is an important noncoding DNA regulatory element, which combines with RNA polymerase to activate the expression of downstream genes. In industry, artificial arginine is mainly synthesized by Corynebacterium glutamicum. Replication of specific promoter regions can increase arginine production. Therefore, it is necessary to accurately locate the promoter in C. glutamicum. In the wet experiment, promoter identification depends on sigma factors and DNA splicing technology, this is a laborious job. To quickly and conveniently identify the promoters in C. glutamicum, we have developed a method based on novel feature representation and feature selection to complete this task, describing the DNA sequences through statistical parameters of multiple physicochemical properties, filtering redundant features by combining analysis of variance and hierarchical clustering, the prediction accuracy of the which is as high as 91.6%, the sensitivity of 91.9% can effectively identify promoters, and the specificity of 91.2% can accurately identify non-promoters. In addition, our model can correctly identify 181 promoters and 174 non-promoters among 400 independent samples, which proves that the developed prediction model has excellent robustness.

Analyzing Public Opinions Regarding Virtual Tourism in the Context of COVID-19: Unidirectional vs. 360-Degree Videos

Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers’ comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold.

Artificial Intelligence and 3D Scanning Laser Combination for Supervision and Fault Diagnostics

In this work, we combine some of the most relevant artificial intelligence (AI) techniques with a range-resolved interferometry (RRI) instrument applied to the maintenance of a wind turbine. This method of automatic and autonomous learning can identify, monitor, and detect the electrical and mechanical components of wind turbines to predict, detect, and anticipate their degeneration. A scanner laser is used to detect vibrations in two different failure states. Following each working cycle, RRI in-process measurements agree with in-process hand measurements of on-machine micrometers, as well as laser scanning in-process measurements. As a result, the proposed method should be very useful for supervising and diagnosing wind turbine faults in harsh environments. In addition, it will be able to perform in-process measurements at low costs.

Fuzzy Information Discrimination Measures and Their Application to Low Dimensional Embedding Construction in the UMAP Algorithm

Dimensionality reduction techniques are often used by researchers in order to make high dimensional data easier to interpret visually, as data visualization is only possible in low dimensional spaces. Recent research in nonlinear dimensionality reduction introduced many effective algorithms, including t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), dimensionality reduction technique based on triplet constraints (TriMAP), and pairwise controlled manifold approximation (PaCMAP), aimed to preserve both the local and global structure of high dimensional data while reducing the dimensionality. The UMAP algorithm has found its application in bioinformatics, genetics, genomics, and has been widely used to improve the accuracy of other machine learning algorithms. In this research, we compare the performance of different fuzzy information discrimination measures used as loss functions in the UMAP algorithm while constructing low dimensional embeddings. In order to achieve this, we derive the gradients of the considered losses analytically and employ the Adam algorithm during the loss function optimization process. From the conducted experimental studies we conclude that the use of either the logarithmic fuzzy cross entropy loss without reduced repulsion or the symmetric logarithmic fuzzy cross entropy loss with sufficiently large neighbor count leads to better global structure preservation of the original multidimensional data when compared to the loss function used in the original UMAP algorithm implementation.