A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social robotics.It enhances systems’ability to interpret and respond to human behavior precisely.This research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse actions.The overall purpose of this study is to develop a robust and accurate system for human interaction recognition.This research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out scenes.Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method.Feature extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)techniques.The application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification process.It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities.The impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed technique.The proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM classification.This enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis.Moreover,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this paper.We have leveraged Linear Discriminant Analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia detection.Several ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected dataset.The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models.According to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component Analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

Moving-window bis-correlation coefficients method for visible and near-infrared spectral discriminant analysis with applications

The moving window bis corelation coefficients(MW BiCC)was proposed and employed for the discriminant analysis of transgenic sugarcane leaves and B-thalassemia with visible and near-infrared(Vis NIR)spectroscopy.The well-performed moving window principal component analysis linear discriminant analysis(MWPCA-LDA)was also conducted for comparison.A total of 306 transgenic(positive)and 150 nont ransgenic(negative)leave samples of sugarcane were collected and divided to calibration,prediction,and validation.The diffuse reflection spectra were corected using Savitzky-Golay(SG)smoothing with first-order derivative(d=1),third-degree polynomial(p=3)and 25 smpothing points(m=25).The selected waveband was 736-1054nm with MW-BiCC,and the positive and negative validation recognition rates(V_REC^(+),VREC^(-))were 100%,98.0%,which achieved the same effect as MWPCA-LDA.Another example,the 93 B-thalassemia(positive)and 148 nonthalassemia(negative)of human hemolytic samples were colloctod.The transmission spectra were corrected using SG smoothing withd=1,p=3 and m=53.Using M W-BiCC,many best wavebands were selected(e.g.,1116-1146,17941848 and 22842342nm).The V_REC^(+)and V_REC^(-)were both 100%,which achieved the same effect as MW-PCA-LDA.Importantly,the BICC only required ca lculating correlation cofficients between the spectrum of prediction sample and the average spectra of two types of calibration samples.Thus,BiCC was very simple in algorithm,and expected to obtain more applications.The results first confirmed the feasibility of distinguishing B-thalassemia and normal control samples by NIR spectroscopy,and provided a promising simple tool for large population thalassemia screening.

Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced Discriminant Analysis

Most face recognition techniques have been successful in dealing with high-resolution(HR) frontal face images. However, real-world face recognition systems are often confronted with the low-resolution(LR) face images with pose and illumination variations. This is a very challenging issue, especially under the constraint of using only a single gallery image per person.To address the problem, we propose a novel approach called coupled kernel-based enhanced discriminant analysis(CKEDA).CKEDA aims to simultaneously project the features from LR non-frontal probe images and HR frontal gallery ones into a common space where discrimination property is maximized.There are four advantages of the proposed approach: 1) by using the appropriate kernel function, the data becomes linearly separable, which is beneficial for recognition; 2) inspired by linear discriminant analysis(LDA), we integrate multiple discriminant factors into our objective function to enhance the discrimination property; 3) we use the gallery extended trick to improve the recognition performance for a single gallery image per person problem; 4) our approach can address the problem of matching LR non-frontal probe images with HR frontal gallery images,which is difficult for most existing face recognition techniques.Experimental evaluation on the multi-PIE dataset signifies highly competitive performance of our algorithm.

Surgical mortality in patients with malignant obstructive jaundice: a multivariate discriminant analysis

OBJECTIVE: To estimate the operative mortality in patients with malignant obstructive jaundice. METHODS: Twelve risk factors were analyzed using multivariate discriminant analysis in 90 patients who had been operated on. RESULTS: Operative mortality was significantly related to the following factors: age, duration of jaundice, packed RBC volume, white blood cell count and concentration of blood urine nitrogen; it was not significantly related to diseases and types of operation. The following formula was obtained: packed RBC volume×0.09954-age×0. 04018-blood urine nitrogen×0. 23693-duration of jaundice× 2. 07388-WBC count×0. 21118+5. 26593. With this formula, an operative mortality of 77. 8% was predicted. CONCLUSION: With a positive value from the formula, the patient should be operated on; otherwise non-operative treatment is advocated.

LOCAL CORRELATION DISCRIMINANT ANALYSIS AND ITS SEMI-SUPERVISED EXTENSION

Considering limitations of Linear Discriminant Analysis (LDA) and Marginal Fisher Analysis (MFA), a novel discriminant analysis called Local Correlation Discriminant Analysis (LCDA) is proposed in this paper. The main idea behind LCDA is to use more robust similarity measure, correlation metric, to measure the local similarity between image data. This results in better classifi-cation performance. In addition, to further improve the discriminant power of LCDA, we extend LCDA to semi-supervised case, which can make use of both labeled and unlabeled data to perform dis-criminant analysis. Extensive experimental results on ORL and AR face databases demonstrate that the proposed LCDA and its semi-supervised version are superior to Principal Component Analysis (PCA), LDA, CEA, and MFA.

Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

Linear Discriminant Analysis and Kernel Vector Quantization for Mandarin Digits Recognition

Linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase the class separability and optimize the clustering procedure. Speaker-dependent (SD) and speaker-independent (SI) experiments are performed to evaluate the performance of the proposed method. The experiment results show that the proposed method is capable of reaching the word error rate of 3.76% in SD case and 6.60 % in SI case. Such a system can be suitable for being embedded in personal digital assistant(PDA), mobile phone and so on to perform voice controlling such as digit dialing, calculating, etc.

Unsupervised Linear Discriminant Analysis

An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.

Incremental Linear Discriminant Analysis Dimensionality Reduction and 3D Dynamic Hierarchical Clustering WSNs

Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.

Infant and Under-five Mortality in Bangladesh: Discriminant Analysis

Bangladesh is on target for achieving the Millennium Development Goal 4 relating to infant and under-five mortality because of very rapid reduction in mortality in recent years. But this rate of reduction may be difficult to sustain and may hamper the achievement of Millennium Development Goal 4. Therefore, the main objective of this paper is to discuss and compare the dif- ferent covariates of infant and under-five mortality in the context of overall country, urban and rural levels of Bangladesh using discriminant analysis. For this, the data are taken from Bangladesh Demographic and Health Survey, 2004. In discriminant analysis, the stepwise procedure has been picked up and only the significant variables are ranked according to the rank of Wilk's Lambda val- ues. The canonical discriminant function coefficients (unstandard- ized and standardized) for the predictor variables have also been calculated. Both the results show that breastfeeding is the most important variable in discriminating the two groups of mothers, i.e., mothers experiencing to infant mortality or not and mothers experiencing to under-five mortality or not. The related results of discriminant function also indicate that the discriminant func- tion is statistically significant and discriminates well. Therefore, improvements in the health system are essential for promoting the breastfeeding practices (both inclusive and exclusive), which may be the effective strategies to reach families and communities with targeted messages and information.

Community Waste Classification Method Based on Discriminant Analysis

In response to the "compulsory era" of garbage classification in many places across the country,the garbage classification method has become a hot topic in social conferences.In this paper,the discriminant analysis was used to quantify and discriminate the garbage by using distance discrimination,Fisher discriminant and Bayesian discriminant method,and the specific method of garbage classification was given.The article first divided the domestic garbage into 3 categories,and then selected 5 indicators of calorific value,organic matter content,degradable time,water content and heavy metal content to determine the specific garbage category of the garbage,and convert the classification of the garbage into a discriminant function.The size of the comparison was a problem.The article found that the 2 indicators of degradable time and heavy metal content were the main factors to distinguish between organic waste and hazardous waste.Through the rational classification of waste,it would reduce environmental pressure and turn waste into treasure,which is an effective boost for China’s sustainable development.

A Comparison of Two Linear Discriminant Analysis Methods That Use Block Monotone Missing Training Data

We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.

OSS Project Assessment Based on Discriminant Analysis and Jump Diffusion Process Model for Fault Big Data

The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can apply to the system testing phase of software development. On the other hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.

Robust Classification through a Nonparametric Kernel Discriminant Analysis

The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discriminant classification function, which is able to address this challenge, has been developed and the misclassification rates computed for various bandwidth matrices. A comparison with existing parametric classification functions such as the linear discriminant and quadratic discriminant is conducted to evaluate the performance of this classification function using simulated datasets. The results presented in this paper show good performance in terms of misclassification rates for the kernel discriminant classifier when the correct bandwidth is selected as compared to other identified existing classifiers. In this regard, the study recommends the use of the proposed kernel discriminant classification rule when one wishes to classify units into one of several categories or population groups where parametric classifiers might not be applicable.

Nonlinear Discriminant Analysis Method Based on the Class Cover and Its Application

This paper introduces the related concepts of the hybrid spherical-shaped dataset and proposes a new discriminant analysis method based on the spherical-shaped dataset (SDAM), then SDAM is further improved by the idea of the class cover and presents the nonlinear discriminant analysis method (NDAM). To demonstrate the effectiveness of these two methods, this work constructs seven hybrid spherical-shaped datasets and uses nine UCI datasets. Numerical experiments on these examples indicate that SDAM can preferably solve the discriminant problem for the hybrid sphericalshaped dataset, but this method does not always work well for real datasets;NDAM overcomes the drawbacks of SDAM and better solves the discriminative problem of real datasets. Besides, it has better stability.

Clustering Seismic Activities Using Linear and Nonlinear Discriminant Analysis

Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

Discriminant analysis of terrestrial animal fat and oil adulteration in fish oil by infrared spectroscopy

In order to improve the effective utilization of animal fats and oils,and then ensure the feeding quality and safety in the field of animal husbandry engineering,the discriminant analysis of different species of terrestrial animal fats and oils in fish oil based on Fourier transform infrared spectroscopy was explored in this study.Twenty-seven different species of animal fat and oil materials including fish oil,lard,chicken oil,tallow and suet were studied.The experimental calibration and validation samples were prepared by adulterating different proportions of terrestrial fat and oil in fish oil.Results show that,it is easy to discriminate different species of raw material of fish oil,lard,chicken oil and ruminant fats based on the infrared spectral characteristics,while the distinction of tallow from suet samples is difficult.For the adulterated samples with percentage range of 1%-60%(w/w),ideal results were obtained to discriminate the terrestrial fat and oil ingredients(lard,chicken oil,tallow and suet)in fish oil,the correct discriminant rates for the independent validation set were all higher than 95%.It was proved by further study that the detection limits for the discriminant analysis of lard,chicken oil,tallow and suet in fish oil were 0.8%,0.6%,2%and 3%,respectively.

Rapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis

Rapid and sensitive recognition of herbal pieces according to different concocted processing is crucial to quality control and pharmaceutical effect. Near-infrared （NIR） and mid-infrared （MIR） technology combined with supervised pattern recognition based on partial least-squares discriminant analysis （PLSDA） was attempted to classify and recognize six different concocted processing pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. Recognition rates of 99.24%, 100% and 99.49% for original fingerprint, multiple scatter correct （MSC） fingerprint and second derivative （2nd derivative） fingerprint of NIR spectra were achieved by PLSDA models, respectively. Meanwhile, a perfect recognition rate of 100% was obtained for the above three fingerprint models of MIR spectra. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces ofA. catechu.