Feature Extraction Techniques Research Articles

Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.

To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.

Semantic Based Image Indexing using Deep Learning

This paper introduces a semantic-based image indexing system utilizing a custom Convolutional Neural Network (CNN) for feature extraction and semantic embedding techniques for understanding image content. Traditional image indexing methods rely heavily on low-level visual features, often resulting in inaccurate or irrelevant results. By leveraging deep learning, our proposed system bridges this gap, allowing high-level semantic features to guide indexing and retrieval. Tested on the CIFAR-10 dataset, our approach demonstrates a significant improvement in precision, recall, and overall retrieval performance, showcasing its potential in real-world applications

Developing Innovative Feature Extraction Techniques from the Emotion Recognition Field on Motor Imagery Using Brain–Computer Interface EEG Signals

Research on brain–computer interfaces (BCIs) advances the way scientists understand how the human brain functions. The BCI system, which is based on the use of electroencephalography (EEG) signals to detect motor imagery (MI) tasks, enables opportunities for various applications in stroke rehabilitation, neuroprosthetic devices, and communication tools. BCIs can also be used in emotion recognition (ER) research to depict the sophistication of human emotions by improving mental health monitoring, human–computer interactions, and neuromarketing. To address the low accuracy of MI-BCI, which is a key issue faced by researchers, this study employs a new approach that has been proven to have the potential to enhance motor imagery classification accuracy. The basic idea behind the approach is to apply feature extraction methods from the field of emotion recognition to the field of motor imagery. Six feature sets and four classifiers were explored using four MI classes (left and right hands, both feet, and tongue) from the BCI Competition IV 2a dataset. Statistical, wavelet analysis, Hjorth parameters, higher-order spectra, fractal dimensions (Katz, Higuchi, and Petrosian), and a five-dimensional combination of all five feature sets were implemented. GSVM, CART, LinearSVM, and SVM with polynomial kernel classifiers were considered. Our findings show that 3D fractal dimensions predominantly outperform all other feature sets, specifically during LinearSVM classification, accomplishing nearly 79.1% mean accuracy, superior to the state-of-the-art results obtained from the referenced MI paper, where CSP reached 73.7% and Riemannian methods reached 75.5%. It even performs as well as the latest TWSB method, which also reached approximately 79.1%. These outcomes emphasize that the new hybrid approach in the motor imagery/emotion recognition field improves classification accuracy when applied to motor imagery EEG signals, thus enhancing MI-BCI performance.

Enhancing Email Security: A Real‐Time Machine Learning‐Based Spam Detection System

ABSTRACTSpam emails pose a persistent threat to online security and productivity. To combat this menace, we propose a robust machine learning‐based spam email detection system, leveraging algorithms like Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF). Our system integrates Python's Natural Language Processing (NLP) library and cloud sourcing to achieve unparalleled accuracy and adaptability. Advanced feature extraction techniques, including Term Frequency‐Inverse Document Frequency (TF‐IDF) and N‐grams, enable comprehensive analysis of email content. Real‐time scanning and detection capabilities ensure immediate protection, while continuous learning allows the system to evolve and counter emerging spam tactics. Our system demonstrates exceptional performance, achieving accuracy rates of up to 97.58% with SVM. Comparative analysis of 11 machine learning algorithms reveals notable performance variations. By minimizing false positives and negatives, our system provides reliable protection against unwanted and harmful emails. This research contributes to the development of effective spam detection solutions, offering insights into optimal algorithms, time complexity‐accuracy trade‐offs, and scalable system design. Our system has significant implications for enhancing email security, safeguarding users from security breaches, and mitigating the economic impacts of spam.

Thermal imaging-based identification of facial features in noisy environment

<p class="Abstract">Face identification is amongst the most efficacious and extensive applications in biometrics involving extraction and locating facial features. With identification being monotonous task attributable to reliance on parameters like varied cameras, fluctuating backgrounds, and exposure to the environment in which an individual is present. Thermal imaging is endeavoring to resolve the accuracy issue of apparent imaging, such as lighting and brightness intensity, among all biometric variables. This paper presents a study of thermal imaging and effective methods involved in the feature extraction process for facial features with thermal imaging under the influence of varied noise. A novel face dataset is created TID comprising 27 thermal images and its corresponding visual band image using Fluke 480 Ti Pro camera. The study analyses detection efficiency of six feature extraction techniques in visible and thermal bands in facial features identification. Also, the influence of noise in the thermal band within the region of interest using feature points FIN, FOUT has been estimated. Throughout TID dataset, ORB extraction technique has been able to identify strongest inlier features FIN to a maximum extent with detection around the nose, eyes, and mouth. Further, results indicate feature detection in thermal images being invariant to effect of noise for detecting facial features.</p>

Sentiment analysis of student’s comments using long short-term memory with multi head attention

<a name="_Hlk156758974"></a><span lang="EN-US">Classroom teaching is a viable and effective approach for enhancing student learning and promoting engagement in the educational process. The opinions of students play a vital role in the evaluation of teachers. This paper presents a comprehensive overview of sentiment analysis techniques based on recent research and subsequently explores machine learning, i.e., ensemble classifiers, deep learning, long short-term memory (LSTM), convolutional neural network (CNN), LSTM with single attention, LSTM with multi-head attention, and feature extraction techniques (TFidfVector and Word2Vec), in the context of sentiment analysis over student opinion datasets, i.e., the Vietnamese student feedback corpus, as well as data collected from a final-year student's comment in 2023. Further, the Vietnamese student feedback corpus is translated to English and pre-processed with the proposed framework, which yields interesting facts about the capabilities and deficiencies of different methods. In this paper, we conducted experiments with ensemble classifiers, LSTM and CNN, LSTM with single attention, and LSTM with multi-head attention. We conclude that LSTM with multi-head attention produces an accuracy result of 95.57%, which outperform as compare to other three baseline methods and earlier studies.</span>

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Machine Learning for Automated Oil Palm Fruit Grading: The Role of Fuzzy C-Means Segmentation and Textural Features

Oil palm fruit, a high-demand and economically valuable crop, faces grading challenges in Malaysia due to labour-intensive methods, resulting in unharvested ripe fruits and yield losses. Extensive research has been conducted on outdoor palm fruit classifiers, but most high-accuracy research in this field relies heavily on large image datasets. This creates a trade-off between employing more sophisticated deep-learning approaches that require extensive data and utilising traditional techniques that have lower data requirements. Therefore, the aim is to propose an outdoor image-based fresh fruit bunch (FFB) classification system, emphasising pre-processing technique and feature extraction techniques suitable for limited datasets. To achieve this, the FFB was localised within the original images using the YOLOv4-Tiny algorithm. Subsequently, a salient segmentation method utilising superpixel-based Fast Fuzzy C-means (FFCM) clustering is employed to remove the background from the images. Next, colour moment and opposite colour local binary pattern (OCLBP) features are extracted from the segmented images to capture important information for classification. Finally, the extracted features are fed into a Multilayer Perceptron (MLP) classifier, which enables the system to predict five classes: damaged, empty, unripe, ripe, and overripe. The developed system demonstrated a commendable performance in accurately classifying fruit bunches, achieving an accuracy of 93.68%. In conclusion, the proposed system effectively addresses the issue of unripe fruit harvesting and contributes to the advancement of state-of-the-art methods in classifying outdoor FFB images.

Data-Driven Fault Detection and Positioning of Eccentric Rolls in Roll-to-Roll Systems Using Wrap Angle and Sensor Proximity

This paper presents a novel approach for detecting and positioning eccentricity defects in roll-to-roll (R2R) slot-die coating systems, which is critical for maintaining high production quality and efficiency. It introduces the feature combination matrix (FCM) method, which improves fault detection and precise positioning of eccentric rolls through focused feature engineering. The study employs the FCM method to enhance defect detection accuracy, using Support Vector Machine (SVM) as the classifier to consistently evaluate the effectiveness of selected feature sets in identifying and positioning eccentricity in R2R systems. By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. The FCM methodology achieved a notable improvement in eccentric roll detection and positioning accuracy, reaching 97.3%, while also reducing data capacity requirements and processing time. These outcomes highlight the effectiveness of selective feature optimization and strategic combinations, demonstrating that high-impact features enhance both detection accuracy and efficiency. Conclusively, the FCM is positioned as a pivotal tool for advancing industrial diagnostic processes, with future work suggested in the areas of adaptive feature extraction techniques and real-time model integration to improve the reliability and adaptability of R2R manufacturing.

Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.

Predicting Electricity Consumption using an Innovative Deep Energy Predictor Model for Enhanced Accuracy and Efficiency

Accurate forecasting of electricity consumption is crucial for efficient energy management and planning. Traditional models often struggle to capture the complex, nonlinear relationships inherent in energy consumption data, leading to less reliable predictions. In the quest to improve the accuracy and efficiency of electricity consumption predictions, we introduce the Deep Energy Predictor Model (DEPM). This innovative hybrid model combines the strengths of XGBoost for classification and Deep Neural Networks (DNN) for deep learning (DL) capabilities. The model leverages advanced feature extraction techniques using Cascaded ResNet, ensuring the capture of intricate patterns within the data. Implemented in Python, the DEPM highlights an impressive accuracy of 98%, with a precision of 0.97, recall of 0.99, and an F1-score of 0.98, setting a new standard for predictive models in the energy sector. This study elaborates on the model architecture, implementation details, and the evaluation metrics that underscore the model's superior performance. Our results demonstrate the potential of DEPM to significantly enhance the reliability of electricity consumption forecasts, providing a robust tool for energy management and planning.

Suspicious action recognition in surveillance based on handcrafted and deep learning methods: A survey of the state of the art

Suspicious action recognition is a captivating and testing task in the realm of surveillance. An anomaly recognition framework recognizes abnormal happenings uniquely in contrast to existing examples because any anomaly is an example that is not the same as a bunch of standard examples. Security is a fundamental need in each space, whether it is public or private. The utilization of feature extraction techniques, both from hand-crafted and deep learning methods, significantly influences the comprehensive methodology discussed in detail within this paper. This survey paper comprehensively covers multiple areas of advancements in surveillance. Starting with the importance and application of anomaly recognition in surveillance which leads to a comparison of different survey papers is also presented for reference which also includes the areas that are covered in this survey paper. Available datasets in the realm of surveillance are also explored in this survey paper leading to feature extraction methods of both handcrafted and deep learning. This paper also summarizes different methods available for suspicious action recognition in surveillance. The paper delves into the challenges faced when addressing this vital issue, presents valuable findings, and outlines limitations associated with the topic. It provides extensive analysis and ends by outlining potential future trends.

Detecting Conjunctival Hyperemia Using an Effective Machine Learning based Method

Among the many complex sensory organs, the human eye stands out. Prevention of eye illnesses is of the utmost importance since irreversible vision loss might result from postponed treatment. Therefore, to manage the continuous course of eye illnesses, early identification and monitoring are vital. Hyperemia, or redness, from increased blood flow, is one sign of conjunctivitis, an eye disorder defined by inflammation of the conjunctiva. Many illnesses and problems are often curable or significantly reducible with the help of the greatest medicines, sophisticated procedures, and early, accurate diagnosis by medical experts. Because there is a severe lack of diagnostic specialists, patients with vision difficulties are often not given the care they need because their conditions are not properly diagnosed. Segmentation approaches are crucial for detecting and quantifying hyperemic areas, which are necessary for diagnosing and evaluating conjunctivitis. This paper presents a very efficient machine learning framework for the detection of eye problems. The system does this by integrating segmentation approaches with feature extraction techniques. The use of the discrete cosine transform (DCT) generates feature vectors from the segmented regions of interest. Random forests and neural networks are two machine learning classifiers that are learned using feature vectors. This approach has a 96% success rate and has potential to assist ophthalmologists in assessing the severity of illnesses in objective and accurate manner. This strategy might be advantageous for adopting ICT-based solutions in public health systems in remote regions.

User Interface Bug Classification Model Using ML and NLP Techniques: A Comparative Performance Analysis of ML Models

Analyzing user interface (UI) bugs is an important step taken by testers and developers to assess the usability of the software product. UI bug classification helps in understanding the nature and cause of software failures. Manually classifying thousands of bugs is an inefficient and tedious job for both testers and developers. Objective of this research is to develop a classification model for the User Interface (UI) related bugs using supervised Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques. Also, to assess the effect of different sampling and feature vectorization techniques on the performance of ML algorithms. Classification is based upon ‘Summary’ feature of the bug report and utilizes six classifiers i.e., Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosting (GB). Dataset obtained is vectored using two vectorization techniques of NLP i.e., Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). ML models are trained after vectorization and data balancing. The models ' hyperparameter tuning (HT) has also been done using the grid search approach to improve their efficacy. This work provides a comparative performance analysis of ML techniques using Accuracy, Precision, Recall and F1 Score. Performance results showed that a UI bug classification model can be built by training a tuned SVM classifier using TF-IDF and SMOTE (Synthetic Minority Oversampling Techniques). SVM classifier provided the highest performance measure with Accuracy: 0.88, Precision: 0.86, Recall: 0.85 and F1: 0.85. Result also inferred that the performance of ML algorithms with TF-IDF is better than BoW in most cases. This work provides classification of bugs that are related to only the user interface. Also, the effect of two different feature extraction techniques and sampling techniques on algorithms were analyzed, adding novelty to the research work.

A Review on Advances in Indian Sign Language Recognition: Techniques, Models, and Applications

Abstract: Sign language serves as a crucial medium of communication for individuals with hearing and speech impairments, yet it presents barriers for those unfamiliar with its nuances. Recent advancements in artificial intelligence, computer vision, and deep learning have paved the way for innovative sign language recognition (SLR) systems. This paper provides a comprehensive survey of cutting-edge approaches to real-time sign language recognition, with a specific focus on Indian Sign Language (ISL). Various methodologies, including Convolutional Neural Networks (CNNs), Hidden Markov Models (HMMs), and transfer learning, are explored to evaluate their performance in recognizing static hand poses and dynamic gestures. Systems leveraging grid-based features, skin color segmentation, and feature extraction techniques such as Histogram of Oriented Gradients (HOG) are examined for their accuracy and efficiency. Furthermore, the integration of advanced frameworks, including pre-trained models like ResNet-50 and novel pipelines combining gesture reclassification with text-to-gesture synthesis, is discussed. This survey emphasizes the role of machine learning models, from Random Forests to state-of-the-art Large Language Models (LLMs), in addressing linguistic variability and enabling cross-linguistic translations between sign languages. By consolidating these diverse techniques, this study aims to provide valuable insights into the development of robust, real-time SLR systems that enhance accessibility, social integration, and inclusivity for the hearing-impaired community.

Cybersecurity on social media platforms: The use of deep learning methodologies for detecting anomalous user behavior

The exponential growth of social media platforms has brought unprecedented opportunities for global communication, networking, and information exchange. However, this expansion has also given rise to significant challenges, particularly in identifying and mitigating anomalous or malicious user behaviors such as spamming, cyberbullying, and misinformation dissemination. Traditional anomaly detection methods, which often rely on rule-based systems or basic statistical models, have proven inadequate in addressing the complex, dynamic, and evolving nature of user behavior on these platforms. In response, this paper explores the application of deep learning methodologies—specifically Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Autoencoders, and Generative Adversarial Networks (GANs)—for detecting anomalous behavior in social media environments. We present a novel deep learning architecture that integrates these models to enhance the detection accuracy of behavioral anomalies. By leveraging large-scale datasets and advanced feature extraction techniques, our approach demonstrates significant improvements over traditional methods, with higher precision, recall, and overall detection rates. The proposed model's ability to adapt to new and emerging patterns of behavior underscores its potential for real-world application in monitoring social media platforms. This research contributes to the growing body of literature on deep learning for cybersecurity and digital trust, offering a robust solution for maintaining the integrity of online social spaces. Our findings suggest that the implementation of these advanced methodologies can provide a more secure and reliable social media environment, benefiting both users and platform providers alike.

Gait Analysis through Animated GIFs: A Novel Approach for Visualizing Step Dynamics

The field of prosthetic knee development has made great progress in recent years, notably with the use of machine learning (ML) approaches. Researchers made significant progress in enhancing the functionality, flexibility, and user experience of prosthetic knees using revolutionary machine learning techniques.Since gait analysis delivers part of the most important information about human motion patterns, it becomes critical in the development of a prosthetic knee. In this paper, some new techniques for gait analysis with feature extraction are proposed that include visualization using animated GIF images. This methodology extracts key parameters like step length and step width from the frames of a GIF image and can completely represent the dynamics of gait with respect to time.This methodology is particularly significant to design an efficient prosthetic knee since it shows how combining computer analysis and visual data may enhance gait analysis methods. For this study, brief walking footage of both healthy individuals and amputees are recorded, then turned into GIFs. Subsequently, these GIF data points are retrieved, making it possible to quantify step length and other essential metrics required to analyses gait patterns.Gait analysis is incorporated as part of the development process for these prosthetic knee joints due to the fact that it gives information on human motion patterns. The methods used in this paper provide novel ways of carrying out gait analysis through feature extraction and visualization using animated GIF images. This method would help in depicting the full picture of gait dynamics with time and assist in extracting important parameters such as step length and width from the frames of the GIF. The work, as described, falls squarely into the use of computer vision techniques for feature extraction and into time-series data analysis for trend identification within the broader application of machine learning in the development of the prosthetic knee. These techniques could be generalized toward designing better prosthetic knee design for more natural and fluent motion.

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method

 Despite their vulnerability to competent forgers, signatures are one of the most widely used user verification methods. Recent research has revealed that EEG signals are harder to reproduce and give superior biometric information. This study aims to improve the effectiveness of person authentication by using deep learning techniques on electroencephalogram (EEG) signals. The broad implementation of EEG-based authentication systems has been hindered by problems such as noise, variability, and inter-subject variances despite the potential distinctiveness of EEG signals. We propose a multiscale convolutional neural network (CNN) and a Bidirectional LSTM (BiLSTM) model called CNN-BiLSTM to extract features and classify raw EEG data. This methodology involves acquiring raw EEG data, preprocessing for noise reduction, standardization, normalization, and employing deep learning techniques for feature extraction and classification. Experimental results exhibit a notable improvement in accuracy and reliability compared to existing EEG authentication methods such as LOF, CNN, FCN, EfficientNet-B0, and BiLSTM. The results showcase the performance of the proposed deep learning model utilizing established metrics such as precision, sensitivity, specificity, and accuracy. The proposed methodology outperforms existing methods and achieves a training and validation accuracy of 98.9% and 92.2%, respectively. The findings of the research demonstrate that the proposed approach has been successful in achieving highly effective results by using EEG signals for the purpose of resolving issues related to person identification. 

Retraction Note: Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique

Retraction Note: Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique