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 precise...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.展开更多
This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of ...This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples.To test the effect of this method,this work uses seven UCI data sets.Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model.展开更多
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 dysph...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.展开更多
Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are ...Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are not only undocumented but also often overlooked and not included in National Administrative Registers. Accordingly, find it hard to participate in Social and Economic Affairs. There has been a major push by UNHCR and international partners to “map” the size of stateless populations and their demographic profile, as well as causes, potential solutions and human rights situation. One of the requirements by the UNHCR in their push is for countries to find a potential solution to statelessness which starts with classifying/associating a person from these communities to a particular local community that is recognized in Kenya. This paper addresses this problem by adopting a Robust Nonparametric Kernel Discriminant function to correctly classify the stateless communities in Kenya and compare the performance of this method with the existing techniques through their classification rates. This is because Non-parametric functions have proven to be more robust and useful especially when there exists auxiliary information which can be used to increase precision. The findings from this paper indicate that Nonparametric discriminant classifiers provide a good classification method for classifying the stateless communities in Kenya. This is because they exhibit lower classification rates compared to the parametric methods such as Linear and Quadratic discriminant functions. In addition, the finding shows that based on certain similarities in characteristics that exist in these communities that surround the Pemba Community, the Pemba community can be classified as Giriama or Rabai in which they seem to have a strong link. In this regard, the study recommends the use of the Kernel discriminant classifiers in classifying the stateless persons and that the Government of Kenya consider integrating/recognizing the Pemba community into Giriama or Rabai so that they can be issued with the National Identification Cards and be recognized as Kenyans.展开更多
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 discrimin...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.展开更多
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 whohad been operated on...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 whohad been operated on.RESULTS: Operative mortality was significantly related to the following factors: age, duration ofjaundice, packed RBC volume, white blood cell count and concentration of blood urine nitrogen; it wasnot significantly related to diseases and types of operation. The following formula was obtained: packedRBC 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% waspredicted.CONCLUSION: With a positive value from the formula, the patient should be operated on; otherwisenon-operative treatment is advocated.展开更多
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 ...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.展开更多
As the basal group of Polypodiales, the specific taxonomy of Dicksoniaceae is still being debated. As aquantitative analysis method, numerical taxonomy has been applied to the taxonomic study of many plant families an...As the basal group of Polypodiales, the specific taxonomy of Dicksoniaceae is still being debated. As aquantitative analysis method, numerical taxonomy has been applied to the taxonomic study of many plant families andgenera in recent years due to its simplicity and high accuracy. However, the numerical analysis of the Dicksoniaceae fossilshas not been reported at present. In the present study, the pinnule morphological data of 42 Mesozoic fossil species of theDicksoniaceae were analyzed using cluster analysis, principal component analysis and correlation analysis. The resultsrevealed that 42 taxonomic units could be divided into six representative groups, which are consistent with the traditionaltaxonomy. After screening, an identification key on 28 fossil species of four genera with a definite taxonomic position wasestablished. According to the quantitative analysis, a Bayes discriminant model was established for the selected species.Lastly, the model was tested using the morphological data of the fossil pinnules in Dicksoniaceae from the YaojieFormation, suggesting that the discriminant model is accurate to a certain extent. As a result, the numerical taxonomy canbe applied to the classification of the Dicksoniaceae fossils.展开更多
Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen. Cardiotocography (CTG) is the...Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen. Cardiotocography (CTG) is the most widely used technique to monitor the fetal health and fetal heart rate (FHR) is an important index to identify occurs of fetal distress. This study is to propose discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) to evaluate fetal distress. The results show that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78%, respectively.展开更多
The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for t...The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.展开更多
Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojia...Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.展开更多
High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand id...High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.展开更多
Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition...Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.展开更多
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...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.展开更多
GIS and remote sensing were utilized for prioritizing the W. Mujib catchment. Fifty three fourth-order sub-watersheds were prioritized based on morphometric analysis of linear and shape parameters. ASTER DEM (v.2), to...GIS and remote sensing were utilized for prioritizing the W. Mujib catchment. Fifty three fourth-order sub-watersheds were prioritized based on morphometric analysis of linear and shape parameters. ASTER DEM (v.2), topographical maps, and Arc GIS (10.1) software, have been employed to delineate the 53 sub-basins, to extract the drainage networks, and to compute the required basic, linear, and shape parameters, and to compile the necessary thematic maps such as elevation and slope categories. The land use/land cover map was generated using ERDAS Imagine (2015), LANDSAT 8 image, and supervised classification (Maximum Likelihood Method). Soil map was digitized using the Arc GIS tool. Each sub-basin is prioritized by assigning ranks based on the calculated compound parameter (Cp). The final score for each sub-basin is ascribed as per erosion threat. The 53 sub-watersheds were grouped into four categories of priority: very high (15 sub-basins, 28.3% of the total), high (17 sub-basins, 32% of the total), moderate (16 sub-basins, 30.2% of the total), and low (5 sub-basins, 9.5% of the total). Sub-basins categorized as very high and high priority (60.3% of the total) are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. The validity of the prioritized four groups was tested statistically by means of Discriminant Analysis (DA), and a significant difference was found between the four priority classes. A relatively complete separation exists between the recognized priority classes;thus, they are statistically valid, distinct, and different from each other. The present results intend to help decision makers pay sufficient attention to soil and water conservation programs, and to encourage tree plantation over the government-owned sloping land. Such procedures are essential in order to minimize soil erosion loss, and to increase soil moisture on farms, thus, reducing the impact of recurrent droughts and the possibility of flooding downstream.展开更多
In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor anal...In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.展开更多
An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wa...An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.展开更多
This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the clas...This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.展开更多
基金funding this work under the Research Group Funding Program Grant Code(NU/RG/SERC/12/6)supported via funding from Prince Satam bin Abdulaziz University Project Number(PSAU/2023/R/1444)+1 种基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R348)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,and this work was also supported by the Ministry of Science and ICT(MSIT),South Korea,through the ICT Creative Consilience Program supervised by the Institute for Information and Communications Technology Planning and Evaluation(IITP)under Grant IITP-2023-2020-0-01821.
文摘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.
基金Supported by the National Natural Science Foundation of China(52070119)Key Laboratory of Financial Mathematics of Fujian Province University(Putian University)(JR201801).
文摘This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples.To test the effect of this method,this work uses seven UCI data sets.Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model.
文摘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.
文摘Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are not only undocumented but also often overlooked and not included in National Administrative Registers. Accordingly, find it hard to participate in Social and Economic Affairs. There has been a major push by UNHCR and international partners to “map” the size of stateless populations and their demographic profile, as well as causes, potential solutions and human rights situation. One of the requirements by the UNHCR in their push is for countries to find a potential solution to statelessness which starts with classifying/associating a person from these communities to a particular local community that is recognized in Kenya. This paper addresses this problem by adopting a Robust Nonparametric Kernel Discriminant function to correctly classify the stateless communities in Kenya and compare the performance of this method with the existing techniques through their classification rates. This is because Non-parametric functions have proven to be more robust and useful especially when there exists auxiliary information which can be used to increase precision. The findings from this paper indicate that Nonparametric discriminant classifiers provide a good classification method for classifying the stateless communities in Kenya. This is because they exhibit lower classification rates compared to the parametric methods such as Linear and Quadratic discriminant functions. In addition, the finding shows that based on certain similarities in characteristics that exist in these communities that surround the Pemba Community, the Pemba community can be classified as Giriama or Rabai in which they seem to have a strong link. In this regard, the study recommends the use of the Kernel discriminant classifiers in classifying the stateless persons and that the Government of Kenya consider integrating/recognizing the Pemba community into Giriama or Rabai so that they can be issued with the National Identification Cards and be recognized as Kenyans.
文摘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.
文摘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 whohad been operated on.RESULTS: Operative mortality was significantly related to the following factors: age, duration ofjaundice, packed RBC volume, white blood cell count and concentration of blood urine nitrogen; it wasnot significantly related to diseases and types of operation. The following formula was obtained: packedRBC 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% waspredicted.CONCLUSION: With a positive value from the formula, the patient should be operated on; otherwisenon-operative treatment is advocated.
基金supported by National Natural Science Foundation of China(60802069,61273270)the Fundamental Research Funds for the Central Universities of China+1 种基金Natural Science Foundation of Guangdong Province(2014A030313173)Science and Technology Program of Guangzhou(2014Y2-00165,2014J4100114,2014J4100095)
文摘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.
基金support from the National Natural Science Foundation of China(Grant No41262001)the Science and Technology Support Fund of Gansu Province(Grant No.1104FKCA116)
文摘As the basal group of Polypodiales, the specific taxonomy of Dicksoniaceae is still being debated. As aquantitative analysis method, numerical taxonomy has been applied to the taxonomic study of many plant families andgenera in recent years due to its simplicity and high accuracy. However, the numerical analysis of the Dicksoniaceae fossilshas not been reported at present. In the present study, the pinnule morphological data of 42 Mesozoic fossil species of theDicksoniaceae were analyzed using cluster analysis, principal component analysis and correlation analysis. The resultsrevealed that 42 taxonomic units could be divided into six representative groups, which are consistent with the traditionaltaxonomy. After screening, an identification key on 28 fossil species of four genera with a definite taxonomic position wasestablished. According to the quantitative analysis, a Bayes discriminant model was established for the selected species.Lastly, the model was tested using the morphological data of the fossil pinnules in Dicksoniaceae from the YaojieFormation, suggesting that the discriminant model is accurate to a certain extent. As a result, the numerical taxonomy canbe applied to the classification of the Dicksoniaceae fossils.
文摘Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen. Cardiotocography (CTG) is the most widely used technique to monitor the fetal health and fetal heart rate (FHR) is an important index to identify occurs of fetal distress. This study is to propose discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) to evaluate fetal distress. The results show that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78%, respectively.
文摘The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.
文摘Partial least squares discriminant analysis (PLS-DA) with integrated moving-window (MW) waveband screening was applied to the discriminant analysis of liquor brands with near-infrared (NIR) spectroscopy. Luzhou Laojiao, a popular liquor with strong fragrant flavor, was used as the identified liquor brand (160 samples, negative, 52 vol alcoholicity). Liquors of 10 other brands with strong fragrant flavor were used as the interferential brands (200 samples, positive, 52 vol alcoholicity). The Kennard-Stone algorithm was used for the division of modeling samples to achieve uniformity and representativeness. Based on the MW-PLS-DA, a simplified optimal model set with 157 wavebands was further proposed. This set contained five types of wavebands corresponding to the NIR absorption bands of water, ethanol, and other micronutrients (i.e., acids, aldehydes, phenols, and aromatic compounds) in liquor for practical choice. Using five selected simple models with 4775 - 4239, 7804 - 6569, 6264 - 5844, 9435 - 7896, and 12066 - 10373 cm-1, the validation recognition rates were obtained as 99.3% or higher. Results show good prediction performance and low model complexity, and also provided a valuable reference for designing small dedicated instruments. The proposed method is a promising tool for large-scale inspection of liquor food safety.
文摘High-end wine brand is made through the use of high-quality grape variety and yeast strain, and through a unique process. Not only is it rich in nutrients, but also it has a unique taste and a fragrant scent. Brand identification of wine is difficult and complex because of high similarity. In this paper, visible and near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to explore the feasibility of wine brand identification. Chilean Aoyo wine (2016 vintage) was selected as the identification brand (negative, 100 samples), and various other brands of wine were used as interference brands (positive, 373 samples). Samples of each type were randomly divided into the calibration, prediction and validation sets. For comparison, the PLS-DA models were established in three independent and two complex wavebands of visible (400 - 780 nm), short-NIR (780 - 1100 nm), long-NIR (1100 - 2498 nm), whole NIR (780 - 2498 nm) and whole scanning (400 - 2498 nm). In independent validation, the five models all achieved good discriminant effects. Among them, the visible region model achieved the best effect. The recognition-accuracy rates in validation of negative, positive and total samples achieved 100%, 95.6% and 97.5%, respectively. The results indicated the feasibility of wine brand identification with Vis-NIR spectroscopy.
文摘Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.
基金Supproted by the National Natural Science Foundation of China(No.60875004)the Natural Science Foundation of Jiangsu Province of China(No.BK2009184)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.07KJB520133)
文摘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.
文摘GIS and remote sensing were utilized for prioritizing the W. Mujib catchment. Fifty three fourth-order sub-watersheds were prioritized based on morphometric analysis of linear and shape parameters. ASTER DEM (v.2), topographical maps, and Arc GIS (10.1) software, have been employed to delineate the 53 sub-basins, to extract the drainage networks, and to compute the required basic, linear, and shape parameters, and to compile the necessary thematic maps such as elevation and slope categories. The land use/land cover map was generated using ERDAS Imagine (2015), LANDSAT 8 image, and supervised classification (Maximum Likelihood Method). Soil map was digitized using the Arc GIS tool. Each sub-basin is prioritized by assigning ranks based on the calculated compound parameter (Cp). The final score for each sub-basin is ascribed as per erosion threat. The 53 sub-watersheds were grouped into four categories of priority: very high (15 sub-basins, 28.3% of the total), high (17 sub-basins, 32% of the total), moderate (16 sub-basins, 30.2% of the total), and low (5 sub-basins, 9.5% of the total). Sub-basins categorized as very high and high priority (60.3% of the total) are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. The validity of the prioritized four groups was tested statistically by means of Discriminant Analysis (DA), and a significant difference was found between the four priority classes. A relatively complete separation exists between the recognized priority classes;thus, they are statistically valid, distinct, and different from each other. The present results intend to help decision makers pay sufficient attention to soil and water conservation programs, and to encourage tree plantation over the government-owned sloping land. Such procedures are essential in order to minimize soil erosion loss, and to increase soil moisture on farms, thus, reducing the impact of recurrent droughts and the possibility of flooding downstream.
文摘In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.
文摘An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.
文摘This paper discusses the problem of classifying a multivariate Gaussian random field observation into one of the several categories specified by different parametric mean models. Investigation is conducted on the classifier based on plug-in Bayes classification rule (PBCR) formed by replacing unknown parameters in Bayes classification rule (BCR) with category parameters estimators. This is the extension of the previous one from the two category cases to the multi-category case. The novel closed-form expressions for the Bayes classification probability and actual correct classification rate associated with PBCR are derived. These correct classification rates are suggested as performance measures for the classifications procedure. An empirical study has been carried out to analyze the dependence of derived classification rates on category parameters.
