Feature Classification Techniques Research Articles

Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer Interface System

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain–computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model’s strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.

Permutation conditional mutual information to quantify TMS-evoked cortical connectivity in disorders of consciousness

Objective. To improve the understanding and diagnostic accuracy of disorders of consciousness (DOC) by quantifying transcranial magnetic stimulation (TMS) evoked electroencephalography connectivity using permutation conditional mutual information (PCMI). Approach. PCMI can characterize the functional connectivity between different brain regions. This study employed PCMI to analyze TMS-evoked cortical connectivity (TEC) in 154 DOC patients and 16 normal controls, focusing on optimizing parameter selection for PCMI (Data length, Order length, Time delay). We compared short-range and long-range PCMI values across different consciousness states—unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and normal (NOR)—and assessed various feature selection and classification techniques to distinguish these states. Main results. (1) PCMI can quantify TEC. We found optimal parameters to be Data length: 500 ms; Order: 3; Time delay: 6 ms. (2) TMS evoked potentials (TEPs) for NOR showed a rich response, while MCS patients showed only a few components, and UWS patients had almost no significant components. The values of PCMI connectivity metrics demonstrated its usefulness for measuring cortical connectivity evoked by TMS. From NOR to MCS to UWS, the number and strength of TEC decreased. Quantitative analysis revealed significant differences in the strength and number of TEC in the entire brain, local regions and inter-regions among different consciousness states. (3) A decision tree with feature selection by mutual information performed the best (balanced accuracy: 87.0% and accuracy: 83.5%). This model could accurately identify NOR (100.0%), but had lower identification accuracy for UWS (86.5%) and MCS (74.1%). Significance. The application of PCMI in measuring TMS-evoked connectivity provides a robust metric that enhances our ability to differentiate between various states of consciousness in DOC patients. This approach not only aids in clinical diagnosis but also contributes to the broader understanding of cortical connectivity and consciousness.

X-ray Sources Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

Abstract X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on sys tematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reli able classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder - Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed random forest classifier, built on selected features derived from light curves, en ergy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classifi cation during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

Permutation conditional mutual information to quantify TMS-evoked cortical connectivity in disorders of consciousness.

To improve the understanding and diagnostic accuracy of disorders of consciousness (DOC) by quantifying transcranial magnetic stimulation evoked electroencephalography (TMS-EEG) connectivity using Permutation Conditional Mutual Information (PCMI).
Approach: PCMI can characterize the functional connectivity between different brain regions. This study employed PCMI to analyze TMS-evoked cortical connectivity (TEC) in 154 DOC patients and 16 normal controls, focusing on optimizing parameter selection for PCMI (Data length, Order length, Time delay). We compared short-range and long-range PCMI values across different consciousness states-unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and normal (NOR)-and assessed various feature selection and classification techniques to distinguish these states. 
Main Results: 1) PCMI can quantify TEC. We found optimal parameters to be Data length: 500ms; Order: 3; Time delay: 6ms. 2) TMS evoked potentials (TEP) for NOR showed a rich response, while MCS patients showed only a few components, and UWS patients had almost no significant components. The values of PCMI connectivity metrics demonstrated its usefulness for measuring cortical connectivity evoked by TMS. From NOR to MCS to UWS, the number and strength of TEC decreased. Quantitative analysis revealed significant differences in the strength and number of TEC in the entire brain, local regions and inter-regions among different consciousness states. 3) A decision tree with feature selection by mutual information performed the best (balanced accuracy: 87.0% and accuracy: 83.5%). This model could accurately identify NOR (100.0%), but had lower identification accuracy for UWS (86.5%) and MCS (74.1%). 
Significance: The application of PCMI in measuring TMS-evoked connectivity provides a robust metric that enhances our ability to differentiate between various states of consciousness in DOC patients. This approach not only aids in clinical diagnosis but also contributes to the broader understanding of cortical connectivity and consciousness.&#xD.

Machine Learning Driven Feature Extraction and Dimensionality Reduction for Image Classification

Cancer is the leading cause of death globally, affecting various organs in the human body. Early diagnosis of gastric cancer is essential for improving survival rates. However, traditional diagnosis methods are time-consuming, require multiple tests, and rely on specialist availability. This motivates the development of automated techniques for diagnosing gastric cancer using image analysis. While existing computerized techniques have been proposed, challenges remain. These include difficulty distinguishing healthy from cancerous regions in images and extracting irrelevant features during analysis. This research addresses these challenges by proposing a novel deep learning-based method for gastric cancer classification. The method utilizes deep feature extraction, dimensionality reduction, and classification techniques applied to a gastric cancer image dataset. This approach achieves high accuracy (99.32%), sensitivity (99.13%), and specificity (99.64%) in classifying gastric cancer.

Boosting the Performance of Deep Ear Recognition Systems Using Generative Adversarial Networks and Mean Class Activation Maps

Ear recognition is a complex research domain within biometrics, aiming to identify individuals using their ears in uncontrolled conditions. Despite the exceptional performance of convolutional neural networks (CNNs) in various applications, the efficacy of deep ear recognition systems is nascent. This paper proposes a two-step ear recognition approach. The initial step employs deep convolutional generative adversarial networks (DCGANs) to enhance ear images. This involves the colorization of grayscale images and the enhancement of dark shades, addressing visual imperfections. Subsequently, a feature extraction and classification technique, referred to as Mean-CAM-CNN, is introduced. This technique leverages mean-class activation maps in conjunction with CNNs. The Mean-CAM approach directs the CNN to focus specifically on relevant information, extracting and assessing only significant regions within the entire image. The process involves the implementation of a mask to selectively crop the pertinent area of the image. The cropped region is then utilized to train a CNN for discriminative classification. Extensive evaluations were conducted using two ear recognition datasets: mathematical analysis of images (MAI) and annotated web ears (AWEs). The experimental results indicate that the proposed approach shows notable improvements and competitive performance: the Rank-1 recognition rates are 100.00% and 76.25% for MAI and AWE datasets, respectively.

Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques

In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

RETRACTED ARTICLE: A review on emotion recognition from dialect speech using feature optimization and classification techniques

RETRACTED ARTICLE: A review on emotion recognition from dialect speech using feature optimization and classification techniques

Recent Advances in PCG Signal Analysis using AI: A Review

Abstract The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.

Hybrid ML-MDKL feature subset selection and classification technique accompanied with rat swarm optimizer to classify the multidimensional breast cancer mammogram image

Breast cancer is a pathological condition characterized by the abnormal proliferation of cells inside the breast tissue. The biggest challenge at the moment is determining the most optimal subset from large datasets. Numerous methodologies have been presented for the purpose of selecting the most suitable subset from datasets with high dimensions. However, the outcomes have proven to be insufficient in effectively addressing extensive quantities of multidimensional datasets. This publication presents a proposal for an efficient approach of selecting numerous feature subsets and performing classification on Multidimensional Datasets (MDD). In this study, the Moment Invariant Wavelet Feature Extraction (MI-WFE) approach is employed for the purpose of feature extraction. The New Adaptive Hybrid Levy Flight based Cuckoo Search Optimization Algorithm is employed to identify the most pertinent subset of features. This study presents a novel approach, referred to as H-RS-LVCSO, which aims to enhance the local search capability and optimization speed of the algorithm. This is achieved through the hybridization of the Rat Swarm Optimizer with the levy flight based cuckoo search optimization technique. In this study, it is suggested a hybridized approach for classifying breast cancer, utilizing a Multilayer Multiple Deep Kernel Learning (ML-MDKL) Classifier in conjunction with the standard Support Vector Machine (SVM) Classifier. The simulation of the suggested method is conducted using the MATLAB software. The approach under consideration is evaluated in comparison to two pre-existing methods. The suggested method demonstrates an accuracy improvement of 10.58% compared to existing methods. Furthermore, it outperforms these methods by 17.5% in terms of accuracy. The proposed method exhibits a precision improvement of 6.52% and 14.23% compared to the existing methods.

A Survey of Motion Data Processing and Classification Techniques Based on Wearable Sensors

The rapid development of wearable technology provides new opportunities for action data processing and classification techniques. Wearable sensors can monitor the physiological and motion signals of the human body in real-time, providing rich data sources for health monitoring, sports analysis, and human-computer interaction. This paper provides a comprehensive review of motion data processing and classification techniques based on wearable sensors, mainly including feature extraction techniques, classification techniques, and future development and challenges. First, this paper introduces the research background of wearable sensors, emphasizing their important applications in health monitoring, sports analysis, and human-computer interaction. Then, it elaborates on the work content of action data processing and classification techniques, including feature extraction, model construction, and activity recognition. In feature extraction techniques, this paper focuses on the content of shallow feature extraction and deep feature extraction; in classification techniques, it mainly studies traditional machine learning models and deep learning models. Finally, this paper points out the current challenges and prospects for future research directions. Through in-depth discussions of feature extraction techniques and classification techniques for sensor time series data in wearable technology, this paper helps promote the application and development of wearable technology in health monitoring, sports analysis, and human-computer interaction.

Drug-Protein Interactions Prediction Models Using Feature Selection and Classification Techniques.

Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base. To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction. This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder

The increasing prevalence of unknown-type attacks on the Internet highlights the importance of developing efficient intrusion detection systems. While machine learning-based techniques can detect unknown types of attacks, the need for innovative approaches becomes evident, as traditional methods may not be sufficient. In this research, we propose a deep learning-based solution called the log-cosh variational autoencoder (LVAE) to address this challenge. The LVAE inherits the strong modeling abilities of the variational autoencoder (VAE), enabling it to understand complex data distributions and generate reconstructed data. To better simulate discrete features of real attacks and generate unknown types of attacks, we introduce an effective reconstruction loss term utilizing the logarithmic hyperbolic cosine (log-cosh) function in the LVAE. Compared to conventional VAEs, the LVAE shows promising potential in generating data that closely resemble unknown attacks, which is a critical capability for improving the detection rate of unknown attacks. In order to classify the generated unknown data, we employed eight feature extraction and classification techniques. Numerous experiments were conducted using the latest CICIDS2017 dataset, training with varying amounts of real and unknown-type attacks. Our optimal experimental results surpassed several state-of-the-art techniques, achieving accuracy and average F1 scores of 99.89% and 99.83%, respectively. The suggested LVAE strategy also demonstrated outstanding performance in generating unknown attack data. Overall, our work establishes a solid foundation for accurately and efficiently identifying unknown types of attacks, contributing to the advancement of intrusion detection techniques.

Modeling online customer purchase intention behavior applying different feature engineering and classification techniques

In the evolution of digital technology, e-commerce sectors are gradually changing to realize customers’ demands and supply required things with low cost and due time. Recently, various machine learning techniques have been used to investigate different activities of customers and estimate different characteristics and requirements of customers. The goal of this work is to propose a machine-learning model that employs multiple data analytics and machine learning techniques to manipulate customer records and predict their buying intention more precisely. In this study, we collected an online shoppers’ purchasing intention dataset from a public data repository. Different feature transformation methods were employed in the primary dataset and generated its transformed datasets. Besides, we balanced the transformed datasets and detected outliers from them. Then, we applied different feature selection methods into primary and transformed-balanced datasets and again generated several feature subsets. Finally, various state-of-the-art classifiers were employed in primary, transformed, and all of their generated subsets. Then, different outcomes of the proposed model were analyzed and Random Forest was found as the stable classifier that produces more feasible results for any online shoppers’ buying instances. In this work, this classifier provided the best accuracy of 92.39% and f-score of 0.924 for the Z-Score and Gain Ratio transformed subset. In addition, it gave the highest AUROC of 0.975 for the Square Root and Information Gain subset. We also found Z-Score transformation and Information Gain more reliable methods to convert online shoppers’ customer intention dataset and get more feasible results from different classifiers.

Automated Detection of Autism Spectrum Disorder Using Bio-Inspired Swarm Intelligence Based Feature Selection and Classification Techniques

Autism spectrum disorders, or ASDs, are neurological conditions that affect humans. ASDs typically come with sensory issues like sensitivity to touch or soundor odour. Though genetics are the main causes, their early discovery and treatments are imperative. In recent years, intelligent diagnosis using MLTs (Machine Learning Techniques) have been developed to support conventional clinical methods in the domain of healthcare. Feature selections from healthcare databases consume nondeterministic polynomial timesand are hard tasks where again MLTs have been of great use. AGWOs (Adaptive Grey Wolf Optimizations) were used in this study to determine most significant features and efficient classification strategies in datasets of ASDs. Initially, pre-processing strategies based on SMOTEs (Synthetic Minority Oversampling Techniques) removed extraneous data from ASD datasets and subsequently AGWOs repeat this procedure to find smallest features with maximum classifications values for recall and accuracy. Finally, KVSMs (Kernel Support Vector Machines) classify instances of ASDs from the input datasets. The experimental results of suggested method are evaluated for classifying ASDs from datasets instances of Toddlers, Children, Adolescents, and Adults in terms of recalls, precisions, F-measures, and classification errors.

An end-to-end framework based on acoustic emission for welding penetration prediction

The adverse effects of inadequate welding penetration on the service performance of welded assemblies have raised significant concerns. In this study, we explore the benefits of acoustic emission (AE) sensors for monitoring the laser-arc hybrid weld penetration state during welding processes. The underlying mechanisms of AE signal generation under different penetration states are elaborated, and the feasibility of using time-frequency domain features for monitoring penetration state is validated. Building upon these findings, we propose an end-to-end framework named WAENet. This framework incorporates an automated optimization module for signal processing and time-frequency domain feature extraction, as well as an improved modular convolutional neural network (CNN) for recognition. To enhance the CNN's performance, techniques such as grouped convolution, depth-wise separable convolution, and global average pooling are employed. Furthermore, the training process of the CNN also considers the involvement of hyperparameters related to signal processing and feature extraction, as proposed in this article. WAENet achieves an average accuracy of 99.62 % in identifying the penetration states, which outperforms other models that use alternative feature extraction or classification techniques for comparison. These studies expand the application scope of AE and CNN in the field of intelligent welding.

A Review of Feature Extraction and Classification Techniques in Speech Recognition

A Review of Feature Extraction and Classification Techniques in Speech Recognition

A Review of Methods and Applications for a Heart Rate Variability Analysis

Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.

Comprehensive Study of Automatic Speech Emotion Recognition Systems

Speech emotion recognition (SER) is the technology that recognizes psychological characteristics and feelings from the speech signals through techniques and methodologies. SER is challenging because of more considerable variations in different languages arousal and valence levels. Various technical developments in artificial intelligence and signal processing methods have encouraged and made it possible to interpret emotions.SER plays a vital role in remote communication. This paper offers a recent survey of SER using machine learning (ML) and deep learning (DL)-based techniques. It focuses on the various feature representation and classification techniques used for SER. Further, it describes details about databases and evaluation metrics used for speech emotion recognition.

Analysis of Brain Tumor Detection Techniques

Abstract: The bio-medical image processing is the application of image processing to process medical data. The techniques of feature extraction and machine learning are efficient techniques brain tumor detection. Various network architectures are utilized, ordinarily with hundreds or thousands of adjustable parameters. The resulting condition forms are sufficiently general to unravel a large class of nonlinear classification and estimation problems and sufficiently complex to hide a multitude of sins. In this paper, various brain tumor detection techniques are reviewed analyzed in terms of certain parameters. It is analyzed that textual feature extraction and classification techniques give maximum accuracy for the brain tumor detection