Quadratic Discriminant Classifier Research Articles

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Data augmentation using conditional generative adversarial network (cGAN): applications for sewer condition classification and testing using different machine learning techniques

ABSTRACT The increasing availability of condition assessment data highlights the challenge of managing data imbalance in the asset management of aging infrastructure. Aging sewer pipes pose significant threats to health and the environment, underscoring the importance of proactive management practices to enhance asset maintenance and mitigate associated risks. While machine learning (ML) models are widely employed to model the complex deterioration process of sewer pipes, they face performance limitations when trained on imbalanced condition grade data. This paper addresses this issue by proposing a novel approach using conditional generative adversarial network (cGAN) for data augmentation. By generating synthetic data for minority classes, the skewed distribution of the sewer dataset is balanced, facilitating more robust and accurate predictive models. The utility of the proposed method is evaluated by training different ML classifiers, including neural network (NN), decision tree, quadratic discriminant analysis, Naïve Bayes, support vector machine (SVM), and K-nearest neighbor. Quadratic discriminant, Naïve Bayes, NN, and SVM classifiers demonstrated improvement. The cGAN-based data augmentation method also outperformed two other data imbalance handling techniques, random under-sampling, and cost-sensitive NN. Consequently, data generated by cGAN can effectively aid asset management by developing proactive classifiers that accurately predict pipes at a high risk of failure.

The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning

This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.

An Intelligent Model for Predictions of PIWI-Interacting RNAs and Their Functions

A documented class of short non-coding RNA molecules is known as PIWI interacting RNA (PiRNA). The creation of new drugs and the identification of various tumor types are linked to the PiRNA molecules. Additionally, it is related to controlling transcription of genes, squelching transposons, and preserving genomic stability. The discovery of piRNAs and their functionality has grown to be a significant research topic in bioinformatics because of the crucial influence that piRNAs play in biological processes. The 2L-piRNA-ML predictor is a strong two-layer predictor that is suggested in this research to enhance the prediction of PiRNA and their functionality. The suggested model uses Quadratic Discriminant Analysis Classifier, Linear Discriminant Analysis, Passive Aggressive Classifier, Extra Tree Classifier, Logistic Regression, Random Forest, Ridge Classifier CV for classification. It also employs DNC and TNC for extraction of features. The suggested model is created using a two-layer construction strategy. The 1st layer makes a prediction about a given sequence whether it is PiRNA or not, and the 2nd layer makes a prediction about a given PiRNA sequence whether it is having the function of instructing target mRNA deadenylation or not. Proposed model achieved 95.65 % accuracy at the first layer and 92.30% accuracy at the second layer.

An Improved Binary Quadratic Discriminant Analysis Classifier by Using Robust Regularization

An Improved Binary Quadratic Discriminant Analysis Classifier by Using Robust Regularization

Event-related pupillary response-based authentication system using eye-tracker add-on augmented reality glasses for individual identification.

This study aimed at developing a noncontact authentication system using event-related pupillary response (ErPR) epochs in an augmented reality (AR) environment. Thirty participants were shown in a rapid serial visual presentation consisting of familiar and unknown human photographs. ErPR was compared with event-related potential (ERP). ERP and ErPR amplitudes for familiar faces were significantly larger compared with those for stranger faces. The ERP-based authentication system exhibited perfect accuracy using a linear support vector machine classifier. A quadratic discriminant analysis classifier trained using ErPR features achieved high accuracy (97%) and low false acceptance (0.03) and false rejection (0.03) rates. The correlation coefficients between ERP and ErPR amplitudes were 0.452-0.829, and the corresponding Bland-Altman plots showed a fairly good agreement between them. The ErPR-based authentication system allows noncontact authentication of persons without the burden of sensor attachment via low-cost, noninvasive, and easily implemented technology in an AR environment.

Harvest Forecasting Improvement Using Federated Learning and Ensemble Model

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

AbstractEpilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

Environmental Monitoring in Bus Transportation Using a Developed Measurement System

Air pollution and travel comfort in public transport is a significant problem as it can cause health problems for passengers. There are no sufficiently developed and researched monitoring systems for measuring the quality of the environment in public transport, which makes it difficult to assess the extent of the problem and implement effective solutions. This study presents the results of measurements made with a developed system for assessing air pollution and environmental quality in urban transport. Two time periods were defined based on air pollution and traffic levels, and informational features were selected for bus stops and buses. Noise was found not to be an informative feature, but vehicle acceleration should be considered in environmental quality monitoring. A quadratic discriminant classifier combined with principal components was shown to accurately classify high and low pollution time periods. Further research is planned to investigate the relationship between air pollution and travel discomfort and to develop effective strategies to improve air quality in urban public transport.

An efficient method for faults diagnosis in analog circuits based on machine learning classifiers

The presented paper introduces an accurate approach for detecting and classifying parametric or soft faults that affect analog integrated circuits. This technique is based on the use of machine learning algorithm to improve the accuracy and the performance of fault classification process. To achieve this, the real and imaginary frequency responses of output voltage and supply current of the circuits under test (CUT) are used to extract features for both normal and faulty cases. These features are then exploited to train machine learning classifiers, from which the selected one among its equivalents is the quadratic discriminant classifier since it allowed the highest average accuracy score. The faults to be investigated are parametric ones affecting resistors and capacitors values. The proposed approach is validated using three filters circuits that are Sallen-Key band-pass filter, four op-amp biquad high-pass filter, and a leapfrog filter circuit. Obtained results indicate a high classification average accuracy for all circuits that are undergone testing. The proposed approach has provided a highest classification accuracy level comparing to other research works.

Copper concentrate dual-band joint classification using reflectance hyperspectral images in the VIS-NIR and SWIR bands.

A study on the classification of copper concentrates relevant to the copper refining industry is performed by means of reflectance hyperspectral images in the visible and near infrared (VIS-NIR) bands (400-1000nm) and in the short-wave infrared (SWIR) (900-1700nm) band. A total of 82 copper concentrate samples were press compacted into 13-mm-diameter pellets, and their mineralogical composition was characterized via quantitative evaluation of minerals and scanning electron microscopy. The most representative minerals contained in these pellets are bornite, chalcopyrite, covelline, enargite, and pyrite. Three databases (VIS-NIR, SWIR, and VIS-NIR-SWIR) containing a collection of average reflectance spectra computed from 9×9p i x e l neighborhoods in each pellet hyperspectral image are compiled to train the classification models. The classification models tested in this work are a linear discriminant classifier and two non-linear classifiers, a quadratic discriminant classifier, and a fine K-nearest neighbor classifier (FKNNC). The results obtained show that the joint use of VIS-NIR and SWIR bands allows for the accurate classification of similar copper concentrates that contain only minor differences in their mineralogical composition. Specifically, among the three tested classification models, the FKNNC performs the best in terms of overall classification accuracy, achieving 93.4% accuracy in the test set when only VIS-NIR data are used to construct the classification model, up to 80.5% using only SWIR data, and up to 97.6% using both VIS-NIR and SWIR bands together.

Mining Wi-Fi Channel State Information for breathing and heart rate classification

Current vital signs monitoring systems require that the subject wears sensing devices. An alternative approach is using device-free technologies such as the Channel State Information (CSI) of a Wi-Fi signal. However, recent works using CSI for vital signs monitoring rely on complex signal processing techniques to improve its reliability. Considering that breathing and heart rate provide relevant information about the current health status of a subject, in this paper we develop an experimental system that combines signal processing techniques, such as filters and time and frequency domain analysis, with Data Mining techniques for breathing and heart rate monitoring. We also provide a thorough analysis for understanding CSI data as a technology for vital signs monitoring. Using K-Nearest Neighbors, Support Vector Machines, and Quadratic Discriminant Classifier models, our system achieves an accuracy of 99.18% for breathing rate classification while identifying heart rate monitoring challenges that are also stated in this paper.

OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration.

No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution). A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Statistical Classification Approach to Valve Condition Monitoring Using Pressure Features

This paper provides a novel health monitoring classification algorithm to estimate the level of valve leakage in reciprocating compressors. It is expected that the level of leakage will change the shape of the pressure-volume diagram. This method constructs a feature space using the polytropic exponent during the expansion and compression phases as well as the discharge and suction valve loss power. These features are extracted by measuring in-cylinder pressure, discharge pressure, suction pressure, and the crank angle. Linear and quadratic discriminant classifiers are used as machine learning approaches to classify the valve health of the compressor. This method is implemented on a single-stage double-acting industrial gas compressor operating on air. Faults are seeded by precisely machining valve poppets to simulate common valve leakage. The approach shows a high classification accuracy in determining the degree of leakage and shows promise for future work in prognostics.

Retinal Shape-Based Classification of Retinal Detachment and Posterior Vitreous Detachment Eyes.

Retinal detachment is a sight-threatening emergency, with more than half of those affected suffering permanent visual impairment. A diagnostic test to identify eyes at risk before vision is threatened would enable exploration of prophylactic treatment. This report presents the use of irregularities in retinal shape, quantified from optical coherence tomography (OCT) images, as a biomarker for retinal detachment. OCT images were taken from posterior and mid-peripheral retina of 264 individuals [97 after a posterior vitreous detachment (PVD), 99 after vitrectomy for retinal detachment and 68 after laser for a retinal tear]. Diagnoses were taken from history, examination and OCT. Retinal irregularity was quantified in the frequency domain, and the distribution of irregularity across the regions of the eye was explored to identify features exhibiting the greatest difference between retinal detachment and PVD eyes. Two of these features plus axial length were used to train a quadratic discriminant analysis classifier. Classifier performance was assessed by its sensitivity and specificity in identifying retinal detachment eyes and visualised with a receiver operating characteristic (ROC) curve. Validation set specificity was 84% (44/52 PVD eyes correctly labelled) and sensitivity 35% (23/64 retinal detachment eyes identified, p = 0.02). Area under the ROC curve was 0.75 (95% confidence intervals 0.58-0.85). Retinal detachment eyes were significantly more irregular than PVD eyes in the superior retina (0.70mm versus 0.49mm, p < 0.05) and supero-temporal retina (1.12mm versus 0.80mm, p < 0.05). Lower sensitivity (16/68, 24%) was seen for eyes with a retinal tear without detachment, that were intermediate in size between retinal detachment and PVD eyes. Axial length on its own was a poor classifier. Neither irregularity nor classification were affected by surgery for retinal detachment or the development of PVD. The classifier identified 1/3 of retinal detachment eyes in this sample. In future work, these features can be evaluated as a test for retinal detachment prior to PVD.

Obstructive Sleep Apnea Detection Using Speech Signals with High Frequency Components

In this study, the Obstructive Sleep Apnea (OSA) detection using speech signals during awake is considered. Traditional speech based OSA detection methods adopt traditional features (Formants, MFCC, etc.) on normal speech frequency range (&lt;6kHz). However, it ignores the signal components outside this range that usually appear in pathological voices. In this paper, higher order traditional speech features (with more high frequency components) are adopted for detection. To better characterize OSA patients’ speech, a high frequency feature set is proposed. It consists of the traditional speech features with optimized parameters and a new proposed feature: High frequency energy. Principal Component Analysis (PCA) based Sequence Forward Feature Selection (PCASFFS) are adopted as feature selection. In the simulation using 66 OSA patients’ speech signals, it achieves an accuracy of 84.85% for multi-class (4 levels) detection with the proposed high frequency feature set using quadratic discriminant analysis classifier (QDA).

A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization.

Quantitative ultrasound (QUS) offers a non-invasive and objective way to quantify tissue health. We recently presented a spatially adaptive regularization method for reconstruction of a single QUS parameter, limited to a two dimensional region. That proof-of-concept study showed that regularization using homogeneity prior improves the fundamental precision-resolution trade-off in QUS estimation. Based on the weighted regularization scheme, we now present a multiparametric 3D weighted QUS (3D QUS) method, involving the reconstruction of three QUS parameters: attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD). With the phantom studies, we demonstrate that our proposed method accurately reconstructs QUS parameters, resulting in high reconstruction contrast and therefore improved diagnostic utility. Additionally, the proposed method offers the ability to analyze the spatial distribution of QUS parameters in 3D, which allows for superior tissue characterization. We apply a three-dimensional total variation regularization method for the volumetric QUS reconstruction. The 3D regularization involving N planes results in a high QUS estimation precision, with an improvement of standard deviation over the theoretical 1/N rate achievable by compounding N independent realizations. In the in vivo liver study, we demonstrate the advantage of adopting a multiparametric approach over the single parametric counterpart, where a simple quadratic discriminant classifier using feature combination of three QUS parameters was able to attain a perfect classification performance to distinguish between normal and fatty liver cases.

Detection of confusion behavior using a facial expression based on different classification algorithms

Confusion detection systems (CDSs) that need Noninvasive, mobile, and cost-effective methods use facial expressions as a technique to detect confusion. In previous works, the technology that the system used represents a major gap between this proposed CDS and other systems. This CDS depends on the Facial Action Coding System (FACS) that is used to extract facial features. The FACS shows the motion of the facial muscles represented by Action Units (AUs); the movement is represented with one facial muscle or more. Seven AUs are used as possible markers for detecting confusion that has been implemented in the form of a single vector of facial action; the AUs that have been used in this work are AUs 4, 5, 6, 7, 10, 12, and 23. The database used to calculate the performance of the proposed CDS is gathered from 120 participants (91males, 29 females), between the ages of 18-45. Four types of classification algorithms are used as individuals; these classifiers are (VG-RAM), (SVM), Logistic Regression and Quadratic Discriminant classifiers. The best success rate was found when using Logistic Regression and Quadratic Discriminant. This work introduces different classification techniques to detect confusion by collecting an actual database that can be used to evaluate the performance for every CDS employing facial expressions and selecting appropriate facial features.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunotherapeutic drugs. In this work, we evaluated fifteen machine learning algorithms for the classification of tumor antigens. For this purpose, we build robust datasets, carefully selected from the TANTIGEN and IEDB databases. The feature computation of all antigens in this study was performed by developing a script written in Python 3.8, which allowed the calculation of 544 physicochemical and biochemical properties extracted from the AAindex database. All classifiers were subjected to the training, 10-fold cross-validation, and testing on an independent dataset. The results of this study showed that the quadratic discriminant classifier presented the best performance measures over the independent dataset, accuracy = 0.7384, AUC = 0.817, recall = 0.676, precision = 0.7857, F1 = 0.713, kappa = 0.4764, and Matthews correlation coefficient = 0.4834, outperforming common machine learning classifiers used in the bioinformatics area. We believe that our prediction model could be of great importance in the field of cancer immunotherapy for the search of potential tumor antigens. Taking all aspects mentioned before, we developed an immunoinformatic tool called TAP 1.0 with a friendly interface for tumor antigens prediction, available at https://tapredictor.herokuapp.com/.

WITHDRAWN: Bhattacharyya coefficient target feature matching based weighted emphasis adaptive boosting classification for predictive analytics with big data

Abstract Data warehousing is the most significant systems that hold large amounts of information. The data warehouse integrates the data from a large and inconsistent database that are located in different locations. Nowadays, data warehouse tools and technologies difficult to handle the analytic process of data since the dimensions of data volumes get increased rapidly. A novel technique Bhattacharyya Coefficient Target Matching Pursuit based Weighted Emphasis Adaptive Boosting Classification (BCTMP-WEABC) technique is introduced for predicting future outcomes with higher accuracy and lesser time consumption. The BCTMP-WEABC consists of two modules namely attribute (i.e., features) selection and classification for handling a large amount of data in the warehouse. In the BCTMP-WEABC technique, Bhattacharyya Coefficient Target Matching Pursuit is applied to select the relevant attributes for performing the classification process based on the similarity measure. After that, Weighted Emphasis Adaptive Boosting Classification (WEABC) process is carried out to perform predictive analytics with the selected attributes for minimizing the time consumption. The WEABC provides an accurate classification by implementing the set of weak learners as a quadratic discriminant classifier. The weak learner performs a likelihood ratio test for distinguishing the given input data into different classes based on mean and deviation. The WEABC combines the weak learners and provides accurate classification and also minimizes the quadratic error. Result evaluation is carried out with factors such as feature selection rate, classification accuracy, classification time, and false-positive rate with respect to the number of features. The evaluation and conferred statistical results notify the development of the proposed BCTMP-WEABC technique in terms of higher classification accuracy, feature selection rate, and minimum time consumption as well as false positives than the conventional methods.