Kernel Linear Discriminant Analysis Research Articles

Adam Adadelta Optimization based bidirectional encoder representations from transformers model for fake news detection on social media

Social platform have disseminated the news in rapid speed and has been considered an important news resource for many people over worldwide because of easy access and less cost benefits when compared with the traditional news organizations. Fake news is the news deliberately written by bad writers that manipulates the original contents and this rapid dissemination of fake news may mislead the people in the society. As a result, it is critical to investigate the veracity of the data leaked via social media platforms. Even so, the reliability of information reported via this platform is still doubtful and remains a significant obstacle. As a result, this study proposes a promising technique for identifying fake information in social media called Adam Adadelta Optimization based Deep Long Short-Term Memory (Deep LSTM). The tokenization operation in this case is carried out with the Bidirectional Encoder Representations from Transformers (BERT) approach. The measurement of the features is reduced with the assistance of Kernel Linear Discriminant Analysis (LDA), and Singular Value Decomposition (SVD) and the top-N attributes are chosen by employing Renyi joint entropy. Furthermore, the LSTM is applied to identify false information in social media, with Adam Adadelta Optimization, which comprises a combo of Adam Optimization and Adadelta Optimization . The Deep LSTM based on Adam Adadelta Optimization achieved maximum accuracy, sensitivity, specificity of 0.936, 0.942, and 0.925.

A Novel Event-Driven Spiking Convolutional Neural Network for Electromyography Pattern Recognition.

Electromyography (EMG) pattern recognition is an important technology for prosthesis control and human-computer interaction etc. However, the practical application of EMG pattern recognition is hampered by poor accuracy and robustness due to electrode shift caused by repeated wearing of the signal acquisition device. Moreover, the user's acceptability is low due to the heavy training burden, which is caused by the need for a large amount of training data by traditional methods. In order to explore the advantage of spiking neural network (SNN) in solving the poor robustness and heavy training burden problems in EMG pattern recognition, a spiking convolutional neural network (SCNN) composed of cyclic convolutional neural network (CNN) and fully connected modules is proposed and implemented in this study. High density surface electromyography (HD-sEMG) signals collected from 6 gestures of 10 subjects at 6 electrode positions are taken as the research object. Compared to CNN with the same structure, CNN-Long Short Term Memory (CNN-LSTM), linear kernel linear discriminant analysis classifier (LDA) and spiking multilayer perceptron (Spiking MLP), the accuracy of SCNN is 50.69%, 33.92%, 32.94% and 9.41% higher in the small sample training experiment, 6.50%, 4.23%, 28.73%, and 2.57% higher in the electrode shifts experiment respectively. In addition, the power consumption of SCNN is about 1/93 of CNN. The advantages of the proposed framework in alleviating user training burden, mitigating the adverse effect of electrode shifts and reducing power consumption make it very meaningful for promoting the development of user-friendly real-time myoelectric control system.

Feature Selection Model-based Intrusion Detection System for Cyberattacks on the Internet of Vehicles Using Cat and Mouse Optimizer

The Internet of Vehicles (IoV) is an environment that is changing quickly. To protect user data and car safety, cyber security is very important. Choosing the right features is important for making intruder detection systems (IDS) more useful and efficient in Internet of Things (IoT) situations. This paper shows a new way to use optimization methods for feature selection-based intruder detection in IoV. Optimization methods are used in the proposed framework for selecting the most important attributes. This makes detection more accurate and reduces the cost of computing. The population-based optimization technique formula is a linked, important, and helpful way to resolve optimization issues. The Cat and Mouse Optimizer (CMO) is a new way to optimize based on how mice and cats naturally act. The proposed CMO acts like cats attacking mice and mice running away to safe places. Popular methods like Kernel Linear Discriminant Analysis (KDA) and the Cuckoo technique are used to compare the CMBO's results. The research showed that the CMO-Bi-LSTM model was very accurate on the CICIDS-2018 and Car hacking datasets, with scores of 99.36% and 99.80%, respectively. Testing and analysis show that the proposed approach successfully finds attacks while reducing false positives. The flexible and expandable nature of the system makes it perfect for securing IoV settings from new hacking attacks.

Classification of cardiac diseases from ECG signals through bio inspired classifiers with Adam and R-Adam approaches for hyperparameters updation

This study emphasizes the classification of different cardiac diseases through bio-inspired classifiers with and without hyperparameters selection. Principal Component Analysis (PCA), Linearity Preserving Projection (LPP), Kernel-Linear Discriminant Analysis (K-LDA), and Variational Bayesian Matrix Factorization (VBMF) are used for the dimensionality reduction of ECG Signals. After the dimensionality reduction, the ECG features were classified using several classifiers such as Particle Swarm Optimization (PSO), Fish Swarm Optimization (FSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Support Vector Machine with (RBF (kernel)), K Nearest Neighbor (KNN) and Naïve Bayesian Classifier (NBC). The average accuracy for the classifiers without hyperparameters selection is 64.99%. Further, the classifiers (PSO, FSO, WOA, and GWO) performance is enhanced through the selection of hyperparameters by the Adam and Randomized Adam (R-Adam) approaches. The average accuracy for the classifiers with Adam and R-Adam is at 72.32% and 85.63%. In this research, the MIT-BIH datasets of Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), ST Change (ST), Supra Ventricular Tachycardia (SVT), Ventricular Tachycardia (VT) and Normal Sinus Rhythm (NSR) are utilized from the physionet archived database. The Overall Accuracy (OA), Mean Square Error (MSE), F1-Score (F1), Fowlkes Mallows Index (FM), Mathew Correlation Coefficient (MCC) and Error Rate (ER) of the different classifiers with and without Adam and R-Adam methods are evaluated. The result demonstrates that the GWO-R-Adam classifier with the LPP dimensionality reduction technique with 98.90% overall accuracy in classifying {ST}-{NSR} classes outperforms all other classifiers in terms of classification performance measures.

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals.

In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

An hierarchical approach for automatic segmentation of leaf images with similar background using kernel smoothing based Gaussian process regression

Real-time automation of leaf image segmentation is a difficult task when there are similar leaves in the background, particularly in leaf images captured in the cultivation fields. These leaf images play a key role in monitoring the growth and health of the plants. An hierarchical approach based on Kernel Linear Discriminant Analysis (KLDA) and Gaussian process regression is proposed in this paper for automating the segmentation process. In the first level, KLDA is used to discriminate the target leaf from its similar leaf background in two steps - (i) detecting the surfaces of the target leaf and (ii) detecting the edges of the target leaf. The resulting coarsely segmented image is further subjected to the second level consisting of the edge detection and morphological operations necessary for obtaining the fine segmented image. To fully automate the segmentation process, it is proposed to use the Gaussian process based regression technique for estimating the tuning parameters required for morphological processing. The proposed method is tested on a Sunflower leaf dataset and the ImageCLEF (Pl@ntleaves) dataset. The experimental results reveal the potential of the proposed method in automating the leaf segmentation process.

Classification from only positive and unlabeled functional data

In various fields, data recorded continuously during a time interval and curve data, such as spectral data, become common. These kinds of data can be interpreted as functional data. In this paper we have studied binary classification from only positive and unlabeled functional data (PU classification for functional data). Our first contribution is to present a simple classification algorithm for this problem. The key feature of the algorithm is that it is not required an estimation of the unknown class prior (or the constant probability that a positive object is labeled). It is worth noting that the idea of our method can be applied to kernel linear discriminant analysis for general data. Our second contribution is to prove that, under mild regularity conditions similar to those in a supervised context, the proposed algorithm can achieve perfect asymptotic classification in the context of PU classification. In fact, we show that the proposed algorithm works well not only in numerical experiments but also for real data examples. Moreover, as an important practical application, we have used the proposed algorithm to identify handball players at risk for anterior cruciate ligament (ACL) injury based on ground reaction force data.

Kernal based speaker specific feature extraction and its applications in iTaukei cross language speaker recognition

Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.

Protein Subnuclear Localization Based on Radius-SMOTE and Kernel Linear Discriminant Analysis Combined with Random Forest

Protein subnuclear localization plays an important role in proteomics, and can help researchers to understand the biologic functions of nucleus. To date, most protein datasets used by studies are unbalanced, which reduces the prediction accuracy of protein subnuclear localization—especially for the minority classes. In this work, a novel method is therefore proposed to predict the protein subnuclear localization of unbalanced datasets. First, the position-specific score matrix is used to extract the feature vectors of two benchmark datasets and then the useful features are selected by kernel linear discriminant analysis. Second, the Radius-SMOTE is used to expand the samples of minority classes to deal with the problem of imbalance in datasets. Finally, the optimal feature vectors of the expanded datasets are classified by random forest. In order to evaluate the performance of the proposed method, four index evolutions are calculated by Jackknife test. The results indicate that the proposed method can achieve better effect compared with other conventional methods, and it can also improve the accuracy for both majority and minority classes effectively.

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal.

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

Randomized independent component analysis and linear discriminant analysis dimensionality reduction methods for hyperspectral image classification

Dimensionality reduction is an important step in processing the hyperspectral images (HSI) to overcome the curse of dimensionality problem. Linear dimensionality reduction methods such as Independent component analysis (ICA) and Linear discriminant analysis (LDA) are commonly employed to reduce the dimensionality of HSI. These methods fail to capture non-linear dependency in the HSI data, as data lies in the nonlinear manifold. To handle this, nonlinear transformation techniques based on kernel methods were introduced for dimensionality reduction of HSI. However, the kernel methods involve cubic computational complexity while computing the kernel matrix, and thus its potential cannot be explored when the number of pixels (samples) are large. In literature a fewer number of pixels are randomly selected to partial to overcome this issue, however this sub-optimal strategy might neglect important information in the HSI. In this paper, we propose randomized solutions to the ICA and LDA dimensionality reduction methods using Random Fourier features, and we label them as RFFICA and RFFLDA. Our proposed method overcomes the scalability issue and to handle the non-linearities present in the data more efficiently. Experiments conducted with two real-world hyperspectral datasets demonstrates that our proposed randomized methods outperform the conventional kernel ICA and kernel LDA in terms overall, per-class accuracies and computational time.

A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications

Several methods utilizing common spatial pattern (CSP) algorithm have been presented for improving the identification of imagery movement patterns for brain computer interface applications. The present study focuses on improving a CSP-based algorithm for detecting the motor imagery movement patterns. A discriminative filter bank of CSP method using a discriminative sensitive learning vector quantization (DFBCSP-DSLVQ) system is implemented. Four algorithms are then combined to form three methods for improving the efficiency of the DFBCSP-DSLVQ method, namely the kernel linear discriminant analysis (KLDA), the kernel principal component analysis (KPCA), the soft margin support vector machine (SSVM) classifier and the generalized radial bases functions (GRBF) kernel. The GRBF is used as a kernel for the KLDA, the KPCA feature selection algorithms and the SSVM classifier. In addition, three types of classifiers, namely K-nearest neighbor (K-NN), neural network (NN) and traditional support vector machine (SVM), are employed to evaluate the efficiency of the classifiers. Results show that the best algorithm is the combination of the DFBCSP-DSLVQ method using the SSVM classifier with GRBF kernel (SSVM-GRBF), in which the best average accuracy, attained are 92.70% and 83.21%, respectively. Results of the Repeated Measures ANOVA shows the statistically significant dominance of this method at p < 0.05. The presented algorithms are then compared with the base algorithm of this study i.e. the DFBCSP-DSLVQ with the SVM-RBF classifier. It is concluded that the algorithms, which are based on the SSVM-GRBF classifier and the KLDA with the SSVM-GRBF classifiers give sufficient accuracy and reliable results.

A hybrid improved kernel LDA and PNN algorithm for efficient face recognition

This paper proposes a hybrid approach to face recognition based on a combination of probabilistic neural networks (PNNs) and improved kernel linear discriminant analysis (IKLDA). The dimensions of a sample’s features are first of all reduced, whilst retaining its relevant information, A PNN method is then adopted to solve face recognition problems. The proposed IKLDA+PNN method not only improves the overall computing efficiency, but also its precision. Face recognition experiments conducted on the ORL, YALE and AR datasets, which contain a wide variety of facial expressions, facial details, and degrees of scale, were used to validate the feasibility of the IKLDA+PNN method. The results showed that it can obtain an average recognition accuracy of 97.22%, 83.8% and 99.12%, across the three datasets, respectively.

SAX Breakpoints for Random Forest Based Real-Time Contrast Control Chart

In the manufacturing process, process monitoring is very important. Real-time contrast (RTC) control chart outperforms existing monitoring methods. However, the performance of RTC control chart depends on the classifier. The existing RTC charts use random forest (RF), support vector machine (SVM), or kernel linear discriminant analysis (KLDA) as a classifier. RF classifier can find cause of faults but the performance is lower than others. Therefore, we suggest the data representation method to improve the RF based RTC control chart. Symbolic aggregate approximation (SAX) is famous method to improve the performance of classification and clustering. We convert the input data by using SAX. We change the parameters of SAX such as alphabet size and breakpoints to improve the performance. Experiment shows that represented data is efficient method to improve the performance of RTC control chart.

Performance Evaluation of Kernel-Based Feature Extraction Techniques for Face Recognition System

Face recognition is considered to be one of the most reliable biometrics where security issues are of concerned. Feature extraction which is a functional block of a face recognition system becomes a critical problem when there is need to obtain the best feature with minimum classification error and low running time. Most existing face recognition systems have adopted different non-linear feature extraction techniques for face recognition but identification of the most suitable non-linear kernel variants for these systems remain an open problem. Hence, this research work analyzed the performance of three kernel feature extraction technique (Kernel Principal Component Analysis, Kernel Linear Discriminant Analysis and Kernel Independent Component Analysis) for face recognition system. A database of 360 face images was created by obtaining facial images from LAUTECH Biometric Research Group consisting of six facial expressions of 60 persons. Images were preprocessed (gray scaling, cropping and histogram equalization) and the kernel variants were used to extract distinctive features and reduce the dimensionality of each of the images from 600x800 pixels to four smaller dimensions: 50x50, 100x100, 150x150 and 200x200 pixels. Euclidean Distance similarity measure was used for classification. The performance of the three kernel variants was evaluated for face recognition system using 180 images for training and 180 images for testing using the following metrics: Recognition Accuracy (RA) and Recognition Time (RT). Empirical result indicate that KLDA performs best for face recognition system with an average accuracy of 94.52%. The larger image dimension also results in better recognition performance. We intend to experiment on other classifiers for face recognition system in our future work. Keywords— Biometrics, Face, Feature extraction, Kernel, KICA, KPCA, KLDA, Linear, Non-linear;

Performance Evaluation of Feature Extraction Techniques in Multi-Layer Based Fingerprint Ethnicity Recognition System

This paper is set out to evaluate the performance of feature extraction techniques that can determine ethnicity of an individual using fingerprint biometric technique and deep learning approach. Hence, fingerprint images of one thousand and fifty-four (1054) persons of three different ethnic groups (Yoruba, Igbo and Middle-Belt) in Nigeria were captured. Kernel Principal Component Analysis (K-PCA) and Kernel Linear Discriminant Analysis (KLDA) were used independently for feature extraction while Convolutional Neural Network (CNN) was used for supervised learning of the features and classification.&#x0D; The results showed that out of sixty (60) individual fingerprints tested, eight (8) were classified as Yoruba, forty-eight (48) as Igbo and four (4) as Hausa. The Recognition Accuracy for K-PCA was 93.97% and KLDA was 97.26%. For Average Recognition time, K-PCA used 9.98seconds while KLDA used 10.02seconds. The memory space utilized by K-PCA was 94.57KB while KLDA utilized 52.17KB.&#x0D; T-Test paired sample statistics was carried out on the result obtained; the outcome presented reveal that KLDA outperformed the K-PCA technique in terms of Recognition Accuracy. The relationship between the average recognition time () and threshold value () was found to be polynomial of order four (4) with a high correlation coefficient for KPCA and polynomial of order three (3) with a high correlation coefficient for KLDA. In terms of computation time analysis, KLDA is computationally more expensive than KPCA by reason of processing speed.

IoT Assisted Kernel Linear Discriminant Analysis Based Gait Phase Detection Algorithm for Walking With Cognitive Tasks

Gait phase detection is an important procedure in the application of many lower limb auxiliary robots. The gait phase detection algorithms that utilize surface electromyography (sEMG) signals have been developed to overcome unnatural walking. However, traditional studies mostly consider precise gait walking events when the subject is focusing only on walking, and the accuracy of traditional algorithms is susceptible to more realistic gait scenarios, such as walking with cognitive tasks. In this paper, the gait phase detection is considered in more realistic and challenging scenarios, in which the subject is walking while performing cognitive tasks. A kernel linear discriminant analysis (LDA)-based nonlinear fusion model is proposed for gait recognition, which can effectively reduce the error caused by cognitive tasks, making it an ideal model for gait phase detection while cognitive tasks are performed in the process of walking. Furthermore, the Internet of Things (IoT) framework is incorporated to reduce the gait phase detection algorithm's process time by offloading the data from local sEMG sensors to the IoT server with powerful computation capability. The experiments have been conducted to validate our proposed algorithms, demonstrating that the boundaries between the stance period and swing period are more blurred when walking with cognitive tasks.

Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm.

A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate. Specifically, based on the position-specific scoring matrix (PSSM), we proposed a new expression, correlation position-specific scoring matrix (CoPSSM) as the protein feature representation. Based on the classic nonlinear dimension reduction algorithm, kernel linear discriminant analysis (KLDA), we added a new discriminant criterion and proposed a dichotomous greedy genetic algorithm (DGGA) to intelligently select its kernel bandwidth parameter. Two public datasets with Jackknife test and KNN classifier were used for the numerical experiments. The results showed that the overall success rate (OSR) with single representation CoPSSM is larger than that with many relevant representations. The OSR of the proposed method can reach as high as 87.444% and 90.3361% for these two datasets, respectively, outperforming many current methods. To show the generalization of the proposed algorithm, two extra standard datasets of protein subcellular were chosen to conduct the expending experiment, and the prediction accuracy by Jackknife test and Independent test is still considerable.

Ant Colony Technique for Optimizing the Order of Cascaded SVM Classifier for Sunflower Seed Classification

In Seed Testing Laboratory (STL), test for other distinguishable variety (ODV) is carried out for the seed sample received from the seed producer after harvesting. This is done to obtain foundation/certified tag from the seed certification department before marketing the product. This is currently done manually by observing the morphological characteristics of the seeds through the naked eye. This is a time-consuming process for the STL and there is a chance for human error in identifying ODV. Hence, there is the need for the machine vision technique to automate the process of identifying ODV in the seed testing laboratory. Various techniques for varietal identification of sunflower seeds are explored in this paper. Experiments are performed on a dataset that contains ten varieties of sunflower seeds and the success rate achieved using various techniques is reported in this paper. In cascaded support vector machine (SVM), the order in which the classifier blocks are arranged plays an important role in improving the classification rate. The main contribution of this paper lies in the manipulation of ant colony optimization technique for obtaining the order of cascaded SVM by maximizing the total probability of correct decision. When the SVM is cascaded in optimum order, classification rate has been increased from $88.32\%$ (obtained using the actual order) to $98.82\%$ for kernel linear discriminant analysis based boundary descriptors. The closed form expression for computing the total probability of correct detection of the constructed cascaded SVM classifier is also reported in this paper.

Comparison of feature extraction techniques for classification of hardwood species

The texture of an image plays an important role in identification and classification of images. The hardwood species of an image contains four key elements namely: vessels (popularly known as pores in cross-section view), fibres, parenchyma's and rays, useful in its identification and classification. Further, the arrangements of all these elements posses texture rich features. Thus, in this work investigation of existing texture feature extraction techniques for the classification of hardwood species have been done. The texture features are extracted from greyscale images of hardwood species to reduce the computational complexity. Further, linear support vector machine (SVM), radial basis function (RBF) kernel SVM, random forest (RF) and linear discriminant analysis (LDA) have been employed as classifiers to investigate the efficacy of the texture feature extraction techniques. The classification accuracy of the existing texture descriptors has been compared. Further, principal component analysis (PCA) and minimal-redundancy-maximal-relevance (mRMR) feature selection method is employed to select the best subset of feature vector data. The PCA reduced feature vector data of co-occurrence of adjacent local binary pattern (CoALBP24) texture feature extraction technique has attained maximum classification accuracy of 96.33 ± 1.14% with the help of LDA classifier.