Feature Extraction Research Articles

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech

Background and purposeDiagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. Materials and methodsMODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. ResultsThe HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. ConclusionsThe findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

DFCNet +: Cross-modal dynamic feature contrast net for continuous sign language recognition

In sign language communication, the combination of hand signs and facial expressions is used to convey messages in a fluid manner. Accurate interpretation relies heavily on understanding the context of these signs. Current methods, however, often focus on static images, missing the continuous flow and the story that unfolds through successive movements in sign language. To address this constraint, our research introduces the Dynamic Feature Contrast Net Plus (DFCNet+), a novel model that incorporates both dynamic feature extraction and cross-modal learning. The dynamic feature extraction module of DFCNet+ uses dynamic trajectory capture to monitor and record motion across frames and apply key features as an enhancement tool that highlights pixels that are critical for recognizing important sign language movements, allowing the model to follow the temporal variation of the signs. In the cross-modal learning module, we depart from the conventional approach of aligning video frames with textual descriptions. Instead, we adopt a gloss-level alignment, which provides a more detailed match between the visual signals and their corresponding text glosses, capturing the intricate relationship between what is seen and the associated text. The enhanced proficiency of DFCNet+ in discerning inter-frame details translates to heightened precision on benchmarks such as PHOENIX14, PHOENIX14-T and CSL-Daily. Such performance underscores its advantage in dynamic feature capture and inter-modal learning compared to conventional approaches to sign language interpretation. Our code is available at https://github.com/fyzjut/DFCNet_Plus.

A fast deployable model for crack identification with laser thermography testing

This paper presents a novel feature extraction method, enabling efficient training and deployment of neural networks for rapid identification of crack defects in Laser Array Spot Thermography (LAST). We trained the crack defect identification model based on pixel-level features, encoding each pixel as a feature vector using Frangi filter, and classifying them using a neural network. Experimental results demonstrate that Frangi features are an effective method for distinguishing cracks, speckles, and background noise interference in the experiment. Furthermore, the model only requires a small region of interest (ROI) as training samples to achieve effective training and efficient crack identification under the same detection conditions, allowing for rapid deployment in practical inspections.

Enhancing water quality prediction with advanced machine learning techniques: An extreme gradient boosting model based on long short-term memory and autoencoder

Achieving carbon neutrality in the pulp and paper industry necessitates effectively recycling pulp and papermaking wastewater, where continuous monitoring of effluent quality indices is crucial. This study suggests a novel machine learning-based model named LSTMAE-XGBOOST that integrates the feature extraction capabilities of autoencoder, the sequential feature learning capabilities of LSTM, and the high prediction accuracy of XGBOOST. This model is capable of extracting the complex relationships, non-Gaussian characteristics, and time series features from the papermaking wastewater data, and it demonstrates superior predictive performance. Compared to traditional machine learning models, the proposed model exhibits higher prediction accuracy. Specifically, when contrasted with partial least squares regression, LSTMAE-XGBOOST achieves a 40% increase in R2 and a 35% reduction in RMSE. Further comparative assessments against other machine learning-based hybrid models with similar structures confirm the superiority of integrating LSTM and XGBOOST within the hybrid model approach. This study contributes a compelling methodology for modeling effluent quality indices, offering significant implications for environmental management in the pulp and paper industry.

Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.

A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis

Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.

Toward Foundation Models for Inclusive Object Detection: Geometry- and Category-Aware Feature Extraction Across Road User Categories

Toward Foundation Models for Inclusive Object Detection: Geometry- and Category-Aware Feature Extraction Across Road User Categories

Convolutional neural network augmented soft-sensor for autonomous microfluidic production of uniform bubbles

Microfluidics has emerged as a foundational process for creating highly uniform emulsions and bubbles. To enable integration of microfluidic platforms into industrial processes, achieving precise control over the size uniformity of microfluidic-generated bubbles and emulsions is crucial. Even if the external variables such as flow rates or pressures are kept constant, microfluidic processes can be easily disturbed by unknown factors that would substantially compromise the uniformity of resulting emulsions and bubbles. In this study, we introduce a two-step soft-sensor approach that combines a convolutional neural network (CNN) and an image recognition algorithm for feature extraction to detect both the flow regime and the size and uniformity of resulting bubbles. By using a CNN to detect flow regimes, our controller is able to restore the bubble-producing flow regime in response to disturbances. Beyond self-recovery, our controller actively adjusts to minimize errors, maintain setpoints, mitigate disturbances, and ensure system stability over extended periods. 99.2% of bubbles produced during an 8-hour period remain within 5% of the setpoint with our controller acting. By leveraging the soft sensor and artificial intelligence-assisted feedback control, our work presents a widely applicable approach for precise and automated control of microfluidics in diverse applications.

Identification of apple watercore based on ConvNeXt and Vis/NIR spectra

This paper proposes a method for discriminating between normal apples and watercore apples using visible/near-infrared spectroscopy technology, which combines Gramian Angular Fields (GAF) encoding technology and the ConvNeXt deep learning network. The existing feature extraction methods for visible/near-infrared spectroscopy data do not perform deeper information mining on the extracted features, which results in the quality of the established model being entirely determined by the extracted features. Additionally, the process of building a visible/near-infrared spectroscopy data classification model is complex and time-consuming, and the accuracy of the established model is not high. To address these issues, the experimental visible/near-infrared spectroscopy data of apples was first transformed into two-dimensional images using Gramian Angular Summation Fields (GASF) and Gramian Angular Difference Fields (GADF) with sizes of 64, 128, 256, and 512. These images were then input into the ConvNeXt network, and the performance of different encoding methods and sizes was compared. The results showed that, under the conditions provided in this paper, the GADF encoding method with a size of 256 achieved the highest classification accuracy of 98.48%. Next, ResNet, EfficientNet, and RegNet deep learning networks were selected to classify the encoded images under the same conditions. The results above indicate that the apple variety discrimination method based on GAF encoding technology and ConvNeXt network combined with visible/near-infrared spectroscopy technology can achieve deep information mining of visible/near-infrared spectroscopy data and provide a relatively simple method for establishing qualitative classification models of visible/near-infrared spectroscopy. This method has a relatively excellent discrimination effect between normal apples and watercore apples.

PreAlgPro: Prediction of allergenic proteins with pre-trained protein language model and efficient neutral network

Allergy is a prevalent phenomenon, involving allergens such as nuts and milk. Avoiding exposure to allergens is the most effective preventive measure against allergic reactions. However, current homology-based methods for identifying allergenic proteins encounter challenges when dealing with non-homologous data. Traditional machine learning approaches rely on manually extracted features, which lack important protein functional characteristics, including evolutionary information. Consequently, there is still considerable room for improvement in existing methods. In this study, we present PreAlgPro, a method for identifying allergenic proteins based on pre-trained protein language models and deep learning techniques. Specifically, we employed the ProtT5 model to extract protein embedding features, replacing the manual feature extraction step. Furthermore, we devised an Attention-CNN neural network architecture to identify potential features that contribute to the classification of allergenic proteins. The performance of our model was evaluated on four independent test sets, and the experimental results demonstrate that PreAlgPro surpasses existing state-of-the-art methods. Additionally, we collected allergenic protein samples to validate the robustness of the model and conducted an analysis of model interpretability.

AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages

AI-Based Feature Extraction Approaches for Dual Modalities of Autism Spectrum Disorder Neuroimages

Disentangled feature fusion network for lightweight image super-resolution

Recently, the quality of generated images in image super-resolution (SR) has significantly improved due to the widespread application of convolutional neural networks. Existing super-resolution methods often overlook the importance of network width and instead prioritize designing deeper network structures to improve performance. Deeper networks, however, demand greater computational resources and are more challenging to train, making them less suitable for devices with limited performance. To address this issue, we propose a novel method called the disentangled feature fusion network (DFFN). Specifically, we construct dual feature extraction streams (DFES) using the strategies of feature disentanglement and parameter sharing, which increase the network's width without increasing the number of parameters. To facilitate the interaction of information between the dual feature extraction streams, we design a feature interaction module (FIM) that separately enhances high and low-frequency features. Furthermore, a feature fusion module (FFM) is presented to efficiently fuse different levels of high and low-frequency feature information. The proposed DFFN integrates DFES, FIM, and FFM, which not only widens the network without increasing parameters but also minimizes the loss of crucial feature information. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms other state-of-the-art lightweight super-resolution methods, achieving a superior balance between parameter quantity, reconstruction performance, and model complexity. The code is available at https://github.com/zhoujy-aust/DFFN.

Research on Digital Technology of Mechanical Structure Fault Diagnosis of Power Transformer Based on Comprehensive Feature Extraction and SABO-PNN

Research on Digital Technology of Mechanical Structure Fault Diagnosis of Power Transformer Based on Comprehensive Feature Extraction and SABO-PNN

AI-Based Malware Detection and Classification for Wireless Networks

Abstract: This study highlights the shortcomings of conventional detection strategies like signature-based and anomaly-based approaches by examining the application of artificial intelligence (AI) techniques for malware detection and classification in wireless networks. Using machine learning and deep learning algorithms, the study shows notable gains in false positive rates, detection accuracy, and adaptation to changing threats. The acquisition of data from many sources, the extraction of features from malware samples, and the thorough training and assessment of models are important techniques. The results demonstrate how successful AI-based systems are in detecting and reducing malware threats, even in the face of issues like dataset variety and interpretability. The potential of AI technology to improve cybersecurity measures in wireless environments is highlighted by this research.

Enhanced Breast Cancer Diagnosis Through an Integration of MSVM and Genetic Algorithms

Breast cancer remains a major health concern among women, often spreading from the breast to other parts of the body, and is the second leading cause of death in women. While early detection significantly increases the chances of successful treatment, current methods face challenges in accuracy and efficiency. This paper presents an approach for early-stage breast cancer detection using a combination of genetic algorithms and decision trees. The genetic algorithm, an iterative technique, enhances the speed of results, while the decision tree aids in classification. Additionally, a multi-support vector machine (MSVM) is employed for feature extraction and comparison with trained images. The proposed model is evaluated based on classification accuracy, precision, F-score, and recall. Simulation results demonstrate improved performance over existing systems

Imperial Study on Convolutional Layer in CNN for Data Mining

Convolutional Neural Networks (CNNs) have become a cornerstone in the field of data mining, particularly for tasks involving large-scale image and pattern recognition. This paper presents an imperial study on the efficacy of convolutional layers within CNN architectures in data mining applications. We analyze the impact of varying convolutional layer configurations, such as kernel size, stride, and depth, on performance metrics including accuracy, computational cost, and feature extraction quality. By conducting a series of experiments across different datasets, we explore how these configurations influence the model's ability to generalize and detect complex patterns in structured and unstructured data. Our findings indicate that optimizing the convolutional layers significantly improves the efficiency of CNNs for data mining tasks, offering insights into best practices for architectural design in such applications. This study provides valuable guidelines for leveraging CNNs in the realm of data mining, pushing the boundaries of automated data analysis and knowledge discovery

Lightweight concrete crack detection based on spiking neural networks

PurposeMost existing methods for concrete crack detection are based on deep learning techniques such as convolutional neural networks. However, these models, due to their large memory footprint, high power consumption and insufficient feature extraction capabilities, face challenges in mobile applications. To address these issues, this paper proposes a lightweight spiking neural network detection model.Design/methodology/approachThis model achieves fast and accurate crack detection. Firstly, the Gabor-Spiking (GS) module preprocesses input images, extracting texture features and edge features of crack images through Gabor filter convolution modules and spiking convolution modules, respectively. Next, the multiscale residual (MR) module is designed, composed of convolutional layers and residual modules of various scales, to process the fused features and perform crack detection.FindingsExperimental results demonstrate that the model’s size can be reduced to 4.6 MB, achieving accuracy improvements to 87.3 and 96.4% on the SDNET and OCD datasets, respectively.Originality/valueThis paper proposes a lightweight spiking neural network detection model based on the GS module for edge texture feature fusion and the MR module for crack detection.

Review of Convolutional Neural Network

Over the past decade, the domain of deep learning has emerged as a swiftly expanding area of interest among researchers globally. It offers substantial benefits over traditional shallow networks, particularly in the realms of feature extraction and model fitting. Deep learning excels at uncovering intricate, distributed features from raw data, which exhibit robust generalization capabilities. It has triumphantly addressed challenges that were once deemed intractable within the field of artificial intelligence. With the exponential growth in the volume of training data and a marked increase in computational power, deep learning has achieved remarkable strides and found applications across a spectrum of domains, including but not limited to object detection, computer vision, natural language processing, speech recognition, and semantic parsing, thereby accelerating the evolution of artificial intelligence. Deep learning encompasses a series of non-linear transformations organized hierarchically, with deep neural networks representing the predominant form of contemporary deep learning methodologies. Inspired by the neural connections found in the animal visual cortex, these networks are characterized by local connectivity, shared weights, and pooling operations. Such attributes not only simplify the network model and curtail the number of trainable parameters but also engender invariance to shifts, distortions, and scaling, thereby endowing the model with enhanced robustness and fault tolerance. This makes the training and optimization of the network structure more manageable. This paper commences with a retrospective on the evolution of convolutional neural networks (CNNs). Subsequently, it delineates the architectures of neuron models and multilayer perceptron. It proceeds to dissect the CNN architecture, which typically encompasses a sequence of convolutional and pooling layers, culminating in fully connected layers, each serving distinct functions. The paper also elaborates on various enhanced algorithms for CNNs, such as the “Network in Network” and “Spatial Transformer Networks,” while also introducing supervised and unsupervised learning methodologies pertinent to CNNs and highlighting several prevalent open-source tools. Further, the paper scrutinizes the application of CNNs in various fields, including image classification, facial recognition, audio retrieval, electrocardiogram analysis, and object detection. It posits the amalgamation of CNNs with recurrent neural networks as a potential alternative for training datasets. The research culminates in the design of CNN structures with varying parameters and depths, supported by experimental analysis that elucidates the interplay among these parameters and the ramifications of their configuration. Ultimately, the paper synthesizes the merits and lingering challenges associated with CNNs and their practical applications.

Efficient Semantic Segmentation for Large-Scale Agricultural Nursery Managements via Point Cloud-Based Neural Network

Remote sensing technology has found extensive application in agriculture, providing critical data for analysis. The advancement of semantic segmentation models significantly enhances the utilization of point cloud data, offering innovative technical support for modern horticulture in nursery environments, particularly in the area of plant cultivation. Semantic segmentation results aid in obtaining tree components, like canopies and trunks, and detailed data on tree growth environments. However, obtaining precise semantic segmentation results from large-scale areas can be challenging due to the vast number of points involved. Therefore, this paper introduces an improved model aimed at achieving superior performance for large-scale points. The model incorporates direction angles between points to improve local feature extraction and ensure rotational invariance. It also uses geometric and relative distance information for better adjustment of different neighboring point features. An external attention module extracts global spatial features, and an upsampling feature adjustment strategy integrates features from the encoder and decoder. A specialized dataset was created from real nursery environments for experiments. Results show that the improved model surpasses several point-based models, achieving a Mean Intersection over Union (mIoU) of 87.18%. This enhances the precision of nursery environment analysis and supports the advancement of autonomous nursery managements.