Feature Extraction Method Research Articles

Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics

BackgroundThis study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals.MethodsA total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation.ResultsOn the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p < 0.05). The Area Under the Curve (AUC) values for the three models (Gradient Boosting Decision Tree, Gaussian Process, and Random Forest) are 0.90, 0.87, and 0.86, respectively. After testing with DeLong and McNemar methods, these three models all exhibit good performance in distinguishing CTN from normal individuals.ConclusionsRadiomics can aid in the clinical diagnosis of CTN, and it is a more objective approach. It serves as a reliable neurobiological indicator for the clinical diagnosis of CTN and the assessment of changes in the trigeminal nerve in patients with CTN.

A Framework for Prediction of Type II Diabetes through Ensemble Stacking Model

In order to prevent long term complications of diabetes its early diagnosis is crucial. With Increasing advances in Artifical Intelligence (AI) and Machine Learning(ML) researchers are increasingly focusing on using them for early diagnosis of diseases.AI and ML has significant potential for early prediction of type 2 diabetes. In this article we have described a ML based framework for prediction of type 2 diabetes -Improved Ensemble Learning with Dimensionality Reduction Model (IELDR) and discussed its result. An IELDR algorithm is an Auto encoder-based feature extraction method with ensemble learning. The experiments were carried out using the LS_diabetes dataset. LS_diabetes dataset containing 374 records with 35 features related to lifestyle and stress. Accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) were measured. After this results were tested and validated using Diabetes_2019 dataset and PIMA diabetes dataset. The IELDR model showed results in terms accuracy, precision, specificity, sensitivity, f1 score, roc and Mathew correlation coefficient (MCC) of 98.67%, 95.24%, 100%, 98.18%, 97.56%, 99.09% and 0.97 respectively. In comparison with PIMA diabetes dataset, LS_diabetes dataset showed an accuracy, precision, sensitivity, specificity, f1-score,roc and mcc value by 17.96%,13.15% 40.22%,5.59%,28.38%,22.09% and 0.4 respectively. The IELDR model achieved the best result on the LS_diabetes dataset showed an accuracy, sensitivity, roc and mcc value improved by 1.82%, 1.58%, 3.01%and 0.04 % compared to the Diabetes_2019 dataset .This proposed IELDR system predicts the risk of type 2 diabetes in a healthy person based on the person’s current lifestyle pattern. This system can be helpful for early prediction of type2 diabetes.

A Fast Multi-Scale of Distributed Batch-Learning Growing Neural Gas for Multi-Camera 3D Environmental Map Building.

Biologically inspired intelligent methods have been applied to various sensing systems in order to extract features from a huge size of raw sensing data. For example, point cloud data can be applied to human activity recognition, multi-person tracking, and suspicious person detection, but a single RGB-D camera is not enough to perform the above tasks. Therefore, this study propose a 3D environmental map-building method integrating point cloud data measured via multiple RGB-D cameras. First, a fast multi-scale of distributed batch-learning growing neural gas (Fast MS-DBL-GNG) is proposed as a topological feature extraction method in order to reduce computational costs because a single RGB-D camera may output 1 million data. Next, random sample consensus (RANSAC) is applied to integrate two sets of point cloud data using topological features. In order to show the effectiveness of the proposed method, Fast MS-DBL-GNG is applied to perform topological mapping from several point cloud data sets measured in different directions with some overlapping areas included in two images. The experimental results show that the proposed method can extract topological features enough to integrate point cloud data sets, and it runs 14 times faster than the previous GNG method with a 23% reduction in the quantization error. Finally, this paper discuss the advantage and disadvantage of the proposed method through numerical comparison with other methods, and explain future works to improve the proposed method.

1D and 2D Feature Extraction Based on AAC and DC Protein Descriptors for Classification of Acetylation in Lysine Proteins using Convolutional Neural Network

Post-Translational Modification (PTM) denotes a biochemical alteration observed in an amino acid, playing crucial roles in protein activity, functionality, and the regulation of protein structure. The recognition of associated PTMs serves as a fundamental basis for understanding biological processes, therapeutic interventions for diseases, and the development of pharmaceutical agents. Using computational approaches (in silico) offers an efficient and cost-effective means to identify PTM sites swiftly. The exploration of protein classification commences with extracting protein sequence features that are subsequently transformed into numerical features for utilization in classification algorithms. Feature extraction methodologies involve using protein descriptors like Amino Acid Composition (AAC) and Dipeptide Composition (DC). Yet, these approaches exhibit a limitation by neglecting crucial amino acid sequence details. Moreover, both descriptor techniques generate a limited number of 1-dimensional (1D) features, which may not be ideal for processing through the Convolutional Neural Network (CNN) classification method. This investigation presents a novel approach to enhance feature diversity through protein sequence segmentation techniques, employing adjacent and overlapping segment strategies. Furthermore, the study illustrates the organization of features into 1D and 2D formats to facilitate processing through 1D CNN and 2D CNN classification methodologies. The findings of this research endeavour highlight the potential for enhancing the accuracy of acetylation classification in lysine proteins through the multiplication of protein sequence segments in a 2D configuration. The highest accuracy achieved for AAC and DC-based feature extraction methods is 77.39% and 76.75%, respectively.

Advanced Multi-Label Fire Scene Image Classification via BiFormer, Domain-Adversarial Network and GCN

Detecting wildfires presents significant challenges due to the presence of various potential targets in fire imagery, such as smoke, vehicles, and people. To address these challenges, we propose a novel multi-label classification model based on BiFormer’s feature extraction method, which constructs sparse region-indexing relations and performs feature extraction only in key regions, thereby facilitating more effective capture of flame characteristics. Additionally, we introduce a feature screening method based on a domain-adversarial neural network (DANN) to minimize misclassification by accurately determining feature domains. Furthermore, a feature discrimination method utilizing a Graph Convolutional Network (GCN) is proposed, enabling the model to capture label correlations more effectively and improve performance by constructing a label correlation matrix. This model enhances cross-domain generalization capability and improves recognition performance in fire scenarios. In the experimental phase, we developed a comprehensive dataset by integrating multiple fire-related public datasets, and conducted detailed comparison and ablation experiments. Results from the tenfold cross-validation demonstrate that the proposed model significantly improves recognition of multi-labeled images in fire scenarios. Compared with the baseline model, the mAP increased by 4.426%, CP by 4.14% and CF1 by 7.04%.

Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding.

Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals

The N-body problem is a remarkable research topic in physics. We propose a new feature extraction model inspired by the N-body trajectory and test its feature extraction capability. In the first part of the research, an open-access electroencephalogram (EEG) dataset is used to test the proposed method. This dataset has three classes, namely (i) Alzheimer's Disorder (AD), (ii) frontal dementia (FD), and (iii) control groups. In the second step of the study, the EEG signals were divided into segments of 15 s in length, which resulted in 4,661 EEG signals. In the third part of the study, the proposed new self-organized feature engineering (SOFE) model is used to classify the EEG signals automatically. For this SOFE, two novel methods were presented: (i) a dynamic feature extraction function using a graph of the N-Body orbital, termed N-BodyPat, and (ii) an attention pooling function. A multileveled and combinational feature extraction method was proposed by deploying both methods. A feature selection function using ReliefF and Neighborhood Component Analysis (RFNCA) was used to choose the most informative features. An ensemble k-nearest neighbors (EkNN) classifier was employed in the classification phase. Our proposed N-BodyPat generates seven feature vectors for each channel, and the utilized EEG signal dataset contains 19 channels. In this aspect,133 (=19 × 7) EkNN-based outcomes were created. To attain higher classification performance by employing these 133 EkNN-based outcomes, an iterative majority voting (IMV)-based information fusion method was applied, and the most accurate outcomes were selected automatically. The recommended N-BodyPat-based SOFE achieved a classification accuracy of 99.64 %.

Object and spatial discrimination makes weakly supervised local feature better

Local feature extraction plays a crucial role in numerous critical visual tasks. However, there remains room for improvement in both descriptors and keypoints, particularly regarding the discriminative power of descriptors and the localization precision of keypoints. To address these challenges, this study introduces a novel local feature extraction pipeline named OSDFeat (Object and Spatial Discrimination Feature). OSDFeat employs a decoupling strategy, training descriptor and detection networks independently. Inspired by semantic correspondence, we propose an Object and Spatial Discrimination ResUNet (OSD-ResUNet). OSD-ResUNet captures features from the feature map that differentiate object appearance and spatial context, thus enhancing descriptor performance. To further improve the discriminative capability of descriptors, we propose a Discrimination Information Retained Normalization module (DIRN). DIRN complementarily integrates spatial-wise normalization and channel-wise normalization, yielding descriptors that are more distinguishable and informative. In the detection network, we propose a Cross Saliency Pooling module (CSP). CSP employs a cross-shaped kernel to aggregate long-range context in both vertical and horizontal dimensions. By enhancing the saliency of keypoints, CSP enables the detection network to effectively utilize descriptor information and achieve more precise localization of keypoints. Compared to the previous best local feature extraction methods, OSDFeat achieves Mean Matching Accuracy of 79.4% in local feature matching task, improving by 1.9% and achieving state-of-the-art results. Additionally, OSDFeat achieves competitive results in Visual Localization and 3D Reconstruction. The results of this study indicate that object and spatial discrimination can improve the accuracy and robustness of local feature, even in challenging environments. The code is available at https://github.com/pandaandyy/OSDFeat.

Improved Histogram of Oriented Gradient (HOG) Feature Extraction for Facial Expressions Classification

Facial expression classification system is one of the implementations of machine learning (ML) that takes facial expression datasets, undergoes training, and then utilizes the trained results to recognize facial expressions in new facial images. The recognized facial expressions include anger, contempt, disgust, fear, happy, sadness, and surprise expressions. The method employed for facial feature extraction utilizes histogram-oriented gradient (HOG). This study proposes an enhancement method for HOG feature extraction by reducing the feature dimension into multiple sub-features based on gradient orientation intervals, referred to as HOG channel (HOG-C). Classifier testing techniques are divided into two methods for comparison—support vector machines (SVM) with HOG features and SVM with HOG-C features. The testing results demonstrate that SVM with HOG achieves an accuracy of 99.9% with an average training time of 18.03 minutes, while SVM with HOG-C attains a 100% accuracy with an average training time of 18.09 minutes. The testing outcomes reveal that the implementation of SVM with HOG-C successfully enhances accuracy for facial expression classification.

An ultra-high-definition multi-exposure image fusion method based on multi-scale feature extraction

Multiple exposure image fusion is a technique used to obtain high dynamic range images. Due to its low cost and high efficiency, it has received a lot of attention from researchers in recent years. Currently, most deep learning-based multiple exposure image fusion methods extract features from different exposure images using a single feature extraction method. Some methods simply rely on two different modules to directly extract features. However, this approach inevitably leads to the loss of some feature information during the feature extraction process, thus further affecting the performance of the model. To minimize the loss of feature information as much as possible, we propose an ultra-high-definition (UHD) multiple exposure image fusion method based on multi-scale feature extraction. The method adopts a U-shaped structure to construct the overall network model, which can fully exploit the feature information at different levels. Additionally, we construct a novel hybrid stacking paradigm to combine convolutional neural networks and Transformer modules. This combined module can extract both local texture features and global color features simultaneously. To more efficiently fuse and extract features, we also design a cross-layer feature fusion module, which can adaptively learn the correlation between features at different layers. Numerous quantitative and qualitative results demonstrate that our proposed method performs well in UHD multiple exposure image fusion.

Dynamic convolutional time series forecasting based on adaptive temporal bilateral filtering

Time series data typically contain complex dynamic patterns, which not only include linear trends and seasonal variations but also significant nonlinear changes and complex dependencies. Currently, feature extraction methods for time series data primarily employ mixed-mode extraction within the temporal domain, neglecting the effective extraction and analysis of nonlinear characteristics between different observation points and the interdependencies between variables. To address these issues, we designed an adaptive temporal bilateral filtering module that effectively preserves and highlights the nonlinear features and patterns in time series while filtering out noise and redundant information. We also designed a nonlinear feature adaptive extraction module that integrates a gating mechanism and deformable convolutions. This design allows the model to adaptively adjust the shape of the convolutional kernels according to different time steps and conditions. This enables accurate capture and extraction of nonlinear features between various time observations and dependencies between different variables. Additionally, we use stacked convolutional layers to extract local contextual features, addressing fluctuations in local features caused by changes in data distribution in real-world scenarios. In summary, we propose DCNet, a dynamic convolutional network based on an adaptive temporal bilateral filter, and evaluated its performance on twelve real-world datasets. The results indicate that DCNet consistently achieves state-of-the-art performance in both short-term and long-term forecasting tasks, with favorable runtime efficiency.

Optimizing speaker identification: a comprehensive study with deep neural networks

This article investigates the development and evaluation of a speaker identification system using deep learning techniques, with a focus on Convolutional Neural Networks (CNNs) and the audioMNIST dataset. The study reveals significant advancements in speaker identification, demonstrating substantial improvements over state-of-the-art models. Our system achieves high accuracy and reliability in distinguishing speakers, showcasing its potential applications in forensic science, security, and privacy protection. The paper thoroughly examines audio signal representation, preprocessing techniques, and feature extraction methods, highlighting how these components contribute to the system's effectiveness. By leveraging CNNs, the proposed system provides highly accurate speaker identification and exhibits robustness in various conditions, including noise and varying speech patterns. The findings underscore the system’s capability to enhance security measures and forensic research, paving the way for future optimizations and broader applications. This contribution expands the knowledge base in speaker identification technology, offering scalable and efficient solutions for real-world scenarios. Future research directions include refining the dataset, exploring advanced optimization techniques, and addressing ethical considerations to ensure the system's robustness and practical utility in diverse applications.

Comparison of Feature Extraction Methods for Conducting Sentiment Classification in Ternate Malay Language using Machine Learning Approaches

Comparison of Feature Extraction Methods for Conducting Sentiment Classification in Ternate Malay Language using Machine Learning Approaches

Multi-Robot Collaborative Mapping with Integrated Point-Line Features for Visual SLAM.

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.

Clustered Automated Machine Learning (CAML) model for clinical coding multi-label classification

AbstractClinical coding is a time-consuming task that involves manually identifying and classifying patients’ diseases. This task becomes even more challenging when classifying across multiple diagnoses and performing multi-label classification. Automated Machine Learning (AutoML) techniques can improve this classification process. However, no previous study has developed an AutoML-based approach for multi-label clinical coding. To address this gap, a novel approach, called Clustered Automated Machine Learning (CAML), is introduced in this paper. CAML utilizes the AutoML library Auto-Sklearn and cTAKES feature extraction method. CAML clusters binary diagnosis labels using Hamming distance and employs the AutoML library to select the best algorithm for each cluster. The effectiveness of CAML is evaluated by comparing its performance with that of the Auto-Sklearn model on five different datasets from the Medical Information Mart for Intensive Care (MIMIC III) database of reports. These datasets vary in size, label set, and related diseases. The results demonstrate that CAML outperforms Auto-Sklearn in terms of Micro F1-score and Weighted F1-score, with an overall improvement ratio of 35.15% and 40.56%, respectively. The CAML approach offers the potential to improve healthcare quality by facilitating more accurate diagnoses and treatment decisions, ultimately enhancing patient outcomes.

Acoustic emission-based weld crack leakage monitoring via FGI and MCCF-CondenseNet convolutional neural network

Online monitoring of weld crack leakage in pressure pipelines of nuclear power ship based on acoustic emission (AE) technology is of great significance for maintaining the safe and stable operation of the system. However, most of the current leakage studies are conducted through artificially designed pipeline hole types, which deviate from the actual crack morphology and are weakly online, with low identification accuracy and slow monitoring speed. Therefore, a convolutional network of FGI and multi-scale channel information cross fusion based on AE technology is proposed in this paper. First, the FBank feature of the AE signal of pipeline weld leakage are extracted. On this basis, the Gini Index (GI) preference feature is used to filter the useless information in the FBank feature. Then, a multi-scale channel information cross fusion module is designed to improve the feature learning ability of the network through the interaction and fusion of different channel information. Finally, the superiority of the proposed FGI feature extraction method and the effectiveness of the proposed multi-scale channel information cross fusion CondenseNet (MCCF-CondenseNet) convolutional neural network are verified by the pipeline leakage AE monitoring experiments under three crack morphologies. The results show that the identification accuracy of the proposed method is as high as 96.42 %, and the identification speed is significantly faster than other state-of-the-art approaches under the premise of ensuring the identification accuracy. This work provides a new method for the online leakage monitoring of nuclear power pressure pipelines, and has important supporting significance for the online leakage monitoring of other large and complex equipment.

Leveraging tabular GANs for malicious address classification in ethereum network

The popularity of ethereum for cryptocurrency transactions attracts malicious actors to engage in illegal activities like phishing, ponzi, and gambling. Previous studies have focused mainly on phishing due to the large number of phishing addresses. However, there is no work done on ponzi or gambling classification due to the limited availability of these addresses, which makes their classification more challenging. In this paper, we propose a machine learning (ML) based method for classifying malicious addresses in ethereum, with a specific focus on phishing, ponzi, and gambling addresses. We use a selective upsampling technique through the tabular generative adversarial network (GAN) to solve limited data problems. We perform not only binary but also multiclass classification on various feature extraction methods, including Trans2Vec and Node2Vec, using Ethereum transactional data. We evaluate our method on F1 score, precision, recall, and accuracy. Our results show that the proposed method is effective in ponzi and gambling detection when compared with the state-of-the-art.

Composite Fixed-Length Ordered Features with Index-of-Max Transformation for High-Performing and Secure Palmprint Template Protection

Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints, in that it is larger in area, richer in information and able to work at a distance. However, the issue of palmprint privacy and security (especially palmprint template protection) remains under-studied. Among the very few research works, most of them only use orientational features of the palmprint with transformation processing, yielding unsatisfactory recognition and protection performance. Thus, this research work proposes a palmprint feature extraction method for palmprint template protection that is fixed-length and ordered in nature, by fusing point features and orientational features. Firstly, dual orientations are extracted and encoded with more accuracy based on the Modified Finite Radon Transform (MFRAT). Then, SURF feature points are extracted and converted to be fixed-length and ordered features. Finally, Composite Fixed-Length Ordered Features that fuse up the dual orientations and SURF points are transformed using the irreversible transformation of Index-of-Max (IOM) to generate the revocable palmprint templates. Experiments show that the matching accuracy of the proposed method of fixed-length and ordered point features are superior to all other feature extraction methods on the PolyU and CASIA datasets. It is also demonstrated that the EERs before and after IOM transformation are better than all other representative template protection methods. A thorough security and privacy analysis including brute-force attack, false accept attack, birthday attack, attack via record multiplicity, irreversibility, unlinkability and revocability is also given, which proves that our proposed method has both high performance and security.

Review on spiking neural network-based ECG classification methods for low-power environments.

This paper reviews arrhythmia classification studies using electrocardiogram (ECG) signals. Research on automatically diagnosing arrhythmia in daily life has been actively underway for early detection and treatment of heart disease. Development of automatic arrhythmia classification using ECG signal began based on handcrafted morphological feature extraction and machine learning-based classification methods. As deep neural networks (DNN) show excellent performance in the signal processing field, studies using various types of DNN are also being conducted in ECG classification. However, these DNN-based studies have extremely high computational complexity, making it challenging to perform real-time classification, and are unsuitable for low-power environments such as wearable devices due to high power consumption. Currently, research based on spiking neural network (SNN), which mimics the low-power operation of the human nervous system, is attracting attention as a method that can dramatically reduce complexity and power consumption. The classification accuracy of the SNN-based ECG classification studies is close to that of the DNN-based studies. When combined with neuromorphic hardware, it shows ultra-low-power performance, suggesting the possibility of use in lightweight devices. In this paper, the SNN-based ECG classification studies for low-power environments are mainly reviewed, and prior to this, conventional and DNN-based ECG classification studies are also reviewed. We hope that this review will be helpful to researchers and engineers interested in the field of ECG classification.