Improved Measures of Redundancy and Relevance for mRMR Feature Selection

Cognitive Workload Detection of Air Traffic Controllers Based on mRMR and Fewer EEG Channels

For air traffic controllers, the extent of their cognitive workload can significantly impact their cognitive function and response time, consequently influencing their operational efficiency or even resulting in safety incidents. In order to enhance the accuracy and efficiency in determining the cognitive workload of air traffic controllers, a cognitive workload detection method for air traffic controllers based on mRMR and fewer EEG channels was proposed in this study. First of all, a set of features related to gamma waves was initially proposed; subsequently, an EEG feature evaluation method based on the mRMR algorithm was employed to pinpoint the most relevant indicators for the detection of the cognitive workload. Consequently, a model for the detection of the cognitive workload of controllers was developed, and it was optimized by filtering out channel combinations that exhibited higher sensitivity to the workload using the mRMR algorithm. The results demonstrate that the enhanced model achieves the accuracy and stability required for practical applications. Notably, in this study, only three EEG channels were employed to achieve the highly precise detection of the cognitive workload of controllers. This approach markedly increases the practicality of employing EEG equipment for the detection of the cognitive workload and streamlines the detection process.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

A new improved maximal relevance and minimal redundancy method based on feature subset

Feature selection plays a very significant role for the success of pattern recognition and data mining. Based on the maximal relevance and minimal redundancy (mRMR) method, combined with feature subset, this paper proposes an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subset. In ImRMR, the Pearson correlation coefficient and mutual information are first used to measure the relevance of a single feature to the sample category, and a factor is introduced to adjust the weights of the two measurement criteria. And an equal grouping method is exploited to generate candidate feature subsets according to the ranking features. Then, the relevance and redundancy of candidate feature subsets are calculated and the ordered sequence of these feature subsets is gained by incremental search method. Finally, the final optimal feature subset is obtained from these feature subsets by combining the sequence forward search method and the classification learning algorithm. Experiments are conducted on seven datasets. The results show that ImRMR can effectively remove irrelevant and redundant features, which can not only reduce the dimension of sample features and time of model training and prediction, but also improve the classification performance.

Automatic Assessment of Abdominal Exercises for the Treatment of Diastasis Recti Abdominis Using Electromyography and Machine Learning

Diastasis Recti Abdominis (DRA) is a medical condition in which the two sides of the rectus abdominis muscle are separated by at least 2.7 cm. This happens when the collagen sheath that exists between the rectus muscles stretches beyond a certain limit. The recti muscles generally separate and move apart in pregnant women due to the development of fetus in the womb. In some cases, this intramuscular gap will not be closed on its own, leading to DRA. The primary treatment procedures of DRA involve different therapeutic exercises to reduce the inter-recti distance. However, it is tedious for the physiotherapists to constantly monitor the patients and ensure that the exercises are being done correctly. The objective of this research is to analyze the correctness of such performed exercises using electromyogram (EMG) signals and machine learning. To the best of our knowledge, this is the first work reporting the objective evaluation of rehabilitation exercises for DRA. Experimental studies indicate that the surface EMG signals were effective in classifying the correctly and incorrectly performed movements. An extensive analysis was carried out with different machine learning models for classification. It was inferred that the RUSBoosted Ensembled classifier was effective in differentiating these movements with an accuracy of 92.3%.

Grab'Em: A Novel Graph-Based Method for Combining Feature Subset Selectors

Feature selection is one of the most frequent tasks in data mining applications. Its ability to remove useless and redundant features improves the classification performance and gains knowledge about a given problem makes feature selection a common first step in data mining. In many feature selection applications, we need to combine the results of different feature selection processes. The two most common scenarios are the ensembles of feature selectors and the scaling up of feature selection methods using a data division approach. The standard procedure is to store the number of times every feature has been selected as a vote for the feature and then evaluate different selection thresholds with a certain criterion to obtain the final subset of selected features. However, this method is suboptimal as the relationships of the features are not considered in the voting process. Two redundant features may be selected a similar number of times due to the different sets of instances used each time. Thus, a voting scheme would tend to select both of them. In this article, we present a new approach: instead of using only the number of times a feature has been selected, the approach considers how many times the features have been selected together by a feature selection algorithm. The proposal is based on constructing an undirected graph where the vertices are the features, and the edges count the number of times every pair of instances has been selected together. This graph is used to select the best subset of features, avoiding the redundancy introduced by the voting scheme. The proposal improves the results of the standard voting scheme in both ensembles of feature selectors and data division methods for scaling up feature selection.

AI for Health-Related Data Modeling, DCN Application Analysis

Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.

Graph-Based Feature Selection Approach for Molecular Activity Prediction

In the construction of QSAR models for the prediction of molecular activity, feature selection is a common task aimed at improving the results and understanding of the problem. The selection of features allows elimination of irrelevant and redundant features, reduces the effect of dimensionality problems, and improves the generalization and interpretability of the models. In many feature selection applications, such as those based on ensembles of feature selectors, it is necessary to combine different selection processes. In this work, we evaluate the application of a new feature selection approach to the prediction of molecular activity, based on the construction of an undirected graph to combine base feature selectors. The experimental results demonstrate the efficiency of the graph-based method in terms of the classification performance, reduction, and redundancy compared to the standard voting method. The graph-based method can be extended to different feature selection algorithms and applied to other cheminformatics problems.