Gotowa bibliografia na temat „PCA ALGORITHM”

In modern biological research, OMICs techniques, such as genomics, proteomics or metabolomics, are often employed to gain deep insights into metabolic regulations and biochemical perturbations in response to a specific research question. To gain complementary biologically relevant information, multiOMICs, i.e., several different OMICs measurements on the same specimen, is becoming increasingly frequent. To be able to take full advantage of this complementarity, joint analysis of such multiOMICs data is necessary, but this is yet an underdeveloped area. In this thesis, a theoretical background is given on general component-based methods for dimensionality reduction such as PCA, PLS for single block analysis, and multiblock PLS for co-analysis of OMICs data. This is followed by a rotation of an unsupervised analysis method. The aim of this method is to divide dimensionality-reduced data in block-distinct and common variance partitions, using the DISCO-SCA approach. Finally, an algorithm for a similar rotation of a supervised (PLS) solution is presented using data available in the literature. To the best of our knowledge, this is the first time that such an approach for rotation of a supervised analysis in block-distinct and common partitions has been developed and tested.This newly developed DISCO-PLS algorithm clearly showed an increased potential for visualisation and interpretation of data, compared to standard PLS. This is shown bybiplots of observation scores and multiblock variable loadings.

Multiple Signal Classification (MUSIC) Algorithm based and Wigner Distribution-Principal Component Analysis (WD-PCA) based classification techniques are very recently suggested resonance region approaches for electromagnetic target classification. In this thesis, performances of these two techniques will be compared concerning their robustness for noise and their capacity to handle large number of candidate targets. In this context, classifier design simulations will be demonstrated for target libraries containing conducting and dielectric spheres and for dielectric coated conducting spheres. Small scale aircraft targets modeled by thin conducting wires will also be used in classifier design demonstrations.

Diversas aplicações industriais relevantes envolvem os processos de adsorção, citando como exemplos a purificação de produtos, separação de substâncias, controle de poluição e umidade entre outros. O interesse crescente pelos processos de purificação de biomoléculas deve-se principalmente ao desenvolvimento da biotecnologia e à demanda das indústrias farmacêutica e química por produtos com alto grau de pureza. O leito móvel simulado (LMS) é um processo cromatográfico contínuo que tem sido aplicado para simular o movimento do leito de adsorvente, de forma contracorrente ao movimento do líquido, através da troca periódica das posições das correntes de entrada e saída, sendo operado de forma contínua, sem prejuízo da pureza das correntes de saída. Esta consiste no extrato, rico no componente mais fortemente adsorvido, e no rafinado, rico no componente mais fracamente adsorvido, sendo o processo particularmente adequado a separações binárias. O objetivo desta tese é estudar e avaliar diferentes abordagens utilizando métodos estocásticos de otimização para o problema inverso dos fenômenos envolvidos no processo de separação em LMS. Foram utilizados modelos discretos com diferentes abordagens de transferência de massa, com a vantagem da utilização de um grande número de pratos teóricos em uma coluna de comprimento moderado, neste processo a separação cresce à medida que os solutos fluem através do leito, isto é, ao maior número de vezes que as moléculas interagem entre a fase móvel e a fase estacionária alcançando assim o equilíbrio. A modelagem e a simulação verificadas nestas abordagens permitiram a avaliação e a identificação das principais características de uma unidade de separação do LMS. A aplicação em estudo refere-se à simulação de processos de separação do Baclofen e da Cetamina. Estes compostos foram escolhidos por estarem bem caracterizados na literatura, estando disponíveis em estudos de cinética e de equilíbrio de adsorção nos resultados experimentais. De posse de resultados experimentais avaliou-se o comportamento do problema direto e inverso de uma unidade de separação LMS visando comparar os resultados obtidos com os experimentais, sempre se baseando em critérios de eficiência de separação entre as fases móvel e estacionária. Os métodos estudados foram o GA (Genetic Algorithm) e o PCA (Particle Collision Algorithm) e também foi feita uma hibridização entre o GA e o PCA. Como resultado desta tese analisouse e comparou-se os métodos de otimização em diferentes aspectos relacionados com o mecanismo cinético de transferência de massa por adsorção e dessorção entre as fases sólidas do adsorvente.<br>Several important industrial applications involving adsorption processes, citing as an example the product purification, separation of substances, pollution control and moisture among others. The growing interest in processes of purification of biomolecules is mainly due to the development of biotechnology and the demand of pharmaceutical and chemical products with high purity. The simulated moving bed (SMB) chromatography is a continuous process that has been applied to simulate the movement of the adsorbent bed, in a countercurrent to the movement of liquid through the periodic exchange of the positions of input and output currents, being operated so continuous, notwithstanding the purity of the outlet streams. This is the extract, rich in the more strongly adsorbed component, and the raffinate, rich in the more weakly adsorbed component, the method being particularly suited to binary separations. The aim of this thesis is to study and evaluate different approaches using stochastic optimization methods for the inverse problem of the phenomena involved in the separation process in LMS. We used discrete models with different approaches to mass transfer. With the benefit of using a large number of theoretical plates in a column of moderate length, in this process the separation increases as the solute flowing through the bed, i.e. as many times as molecules interact between the mobile phase and stationary phase thus achieving the equilibrium. The modeling and simulation verified in these approaches allowed the assessment and identification of the main characteristics of a separation unit LMS. The application under consideration refers to the simulation of the separation of Ketamine and Baclofen. These compounds were chosen because they are well characterized in the literature and are available in kinetic studies and equilibrium adsorption on experimental results. With the results of experiments evaluated the behavior of the direct and inverse problem of a separation unit LMS in order to compare these results, always based on the criteria of separation efficiency between the mobile and stationary phases. The methods studied were the GA (Genetic Algorithm) and PCA (Particle Collision Algorithm) and we also made a hybridization between the GA and PCA. This thesis, we analyzed and compared the optimization methods in different aspects of the kinetic mechanism for mass transfer between the adsorption and desorption of the adsorbent solid phases.

COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of civilians and affected millions around the world. Mostly as result, numerous technologies that enable both the rapid and accurate identification of COVID-19 illnesses will provide much assistance to healthcare practitioners. A machine learning- based approach is used for the detection of COVID-19. In general, artificial intelligence (AI) approaches have yielded positive outcomes in healthcare visual processing and analysis. CXR is the digital image processing method that plays a vital role in the analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated image databases, excessive success has been done using multiclass support vector machines for image classification. Image classification is the main challenge to detect medical diagnosis. The existing work used CNN with a transfer learning mechanism that can give a solution by transferring information from GENETIC object recognition tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization PSO+MSVM method has worked with irregularities in the CXR images database by studying its group distances using a group or class mechanism. At the initial phase of the process, a median filter is used for the noise reduction from the image. Edge detection is an essential step in the process of COVID-19 detection. The canny edge detector is implemented for the detection of edges in the chest x-ray images. The PCA (Principal Component Analysis) method is implemented for the feature extraction phase. There are multiple features extracted through PCA and the essential features are optimized by an optimization technique known as swarm optimization is used for feature optimization. For the detection of COVID-19 through CXR images, a hybrid multi-class support vector machine technique is implemented. The PSO (particle swarm optimization) technique is used for feature optimization. The comparative analysis of various existing techniques is also depicted in this work. The proposed system has achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN. The proposed system is compared with existing systems and achieved better performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.

Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifier’s minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVM’s framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fisher’s IRIS database and German’s GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.

Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifier’s minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVM’s framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fisher’s IRIS database and German’s GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Microelectronic Engineering<br>Full Text

Genotype data, consisting large numbers of markers, is used as demographic and association studies to determine genes related to specific traits or diseases. Handling of these datasets usually takes a significant amount of time in its application of population structure inference. Therefore, we suggested applying PCA on genotyped data and then clustering algorithms to specify the individuals to their particular subpopulations. We collected both real and simulated datasets in this study. We studied PCA and selected significant features, then applied five different clustering techniques to obtain better results. Furthermore, we studied three different methods for predicting the optimal number of subpopulations in a collected dataset. The results of four different simulated datasets and two real human genotype datasets show that our approach performs well in the inference of population structure. NbClust is more effective to infer subpopulations in the population. In this study, we showed that centroid-based clustering: such as k-means and PAM, performs better than model-based, spectral, and hierarchical clustering algorithms. This approach also has the benefit of being fast and flexible in the inference of population structure.

Face recognition is a complex and difficult process due to various factors such as variability of illumination, occlusion, face specific characteristics like hair, glasses, beard, etc., and other similar problems affecting computer vision problems. Using a system that offers robust and consistent results for face recognition, various applications such as identification for law enforcement, secure system access, computer human interaction, etc., can be automated successfully. Different methods exist to solve the face recognition problem. Principal component analysis, Independent component analysis, and linear discriminant analysis are few other statistical techniques that are commonly used in solving the face recognition problem. Genetic algorithm, elastic bunch graph matching, artificial neural network, etc. are few of the techniques that have been proposed and implemented. The objective of this thesis paper is to provide insight into different methods available for face recognition, and explore methods that provided an efficient and feasible solution. Factors affecting the result of face recognition and the preprocessing steps that eliminate such abnormalities are also discussed briefly. Principal Component Analysis (PCA) is the most efficient and reliable method known for at least past eight years. Elastic bunch graph matching (EBGM) technique is one of the promising techniques that we studied in this thesis work. We also found better results with EBGM method than PCA in the current thesis paper. We recommend use of a hybrid technique involving the EBGM algorithm to obtain better results. Though, the EBGM method took a long time to train and generate distance measures for the given gallery images compared to PCA. But, we obtained better cumulative match score (CMS) results for the EBGM in comparison to the PCA method. Other promising techniques that can be explored separately in other paper include Genetic algorithm based methods, Mixture of principal components, and Gabor wavelet techniques.

Because of the increasing demand for the provision of energy worldwide and the numerous damages caused by a major use of fossil sources, the contribution of renewable energies has been increasing significantly in the global energy mix with the aim at moving towards a more sustainable development. In this context, this work aims at the development of a general methodology for designing PV systems based on ecodesign principles and taking into account simultaneously both techno-economic and environmental considerations. In order to evaluate the environmental performance of PV systems, an environmental assessment technique was used based on Life Cycle Assessment (LCA). The environmental model was successfully coupled with the design stage model of a PV grid-connected system (PVGCS). The PVGCS design model was then developed involving the estimation of solar radiation received in a specific geographic location, the calculation of the annual energy generated from the solar radiation received, the characteristics of the different components and the evaluation of the techno-economic criteria through Energy PayBack Time (EPBT) and PayBack Time (PBT). The performance model was then embedded in an outer multi-objective genetic algorithm optimization loop based on a variant of NSGA-II. A set of Pareto solutions was generated representing the optimal trade-off between the objectives considered in the analysis. A multi-variable statistical method (i.e., Principal Componet Analysis, PCA) was then applied to detect and omit redundant objectives that could be left out of the analysis without disturbing the main features of the solution space. Finally, a decision-making tool based on M-TOPSIS was used to select the alternative that provided a better compromise among all the objective functions that have been investigated. The results showed that while the PV modules based on c-Si have a better performance in energy generation, the environmental aspect is what makes them fall to the last positions. TF PV modules present the best trade-off in all scenarios under consideration. A special attention was paid to recycling process of PV module even if there is not yet enough information currently available for all the technologies evaluated. The main cause of this lack of information is the lifetime of PV modules. The data relative to the recycling processes for m-Si and CdTe PV technologies were introduced in the optimization procedure for ecodesign. By considering energy production and EPBT as optimization criteria into a bi-objective optimization cases, the importance of the benefits of PV modules end-of-life management was confirmed. An economic study of the recycling strategy must be investigated in order to have a more comprehensive view for decision making.

L’objet principale de cette thèse est l’étude théorique et la conception d’algorithmes d’apprentissage concevant des classificateurs par vote de majorité. En particulier, nous présentons un théorème PAC-Bayes s’appliquant pour borner, entre autres, la variance de la perte de Gibbs (en plus de son espérance). Nous déduisons de ce théorème une borne du risque du vote de majorité plus serrée que la fameuse borne basée sur le risque de Gibbs. Nous présentons également un théorème permettant de borner le risque associé à des fonctions de perte générale. À partir de ce théorème, nous concevons des algorithmes d’apprentissage construisant des classificateurs par vote de majorité pondérés par une distribution minimisant une borne sur les risques associés aux fonctions de perte linéaire, quadratique, exponentielle, ainsi qu’à la fonction de perte du classificateur de Gibbs à piges multiples. Certains de ces algorithmes se comparent favorablement avec AdaBoost.<br>The main purpose of this thesis is the theoretical study and the design of learning algorithms returning majority-vote classifiers. In particular, we present a PAC-Bayes theorem allowing us to bound the variance of the Gibbs’ loss (not only its expectation). We deduce from this theorem a bound on the risk of a majority vote tighter than the famous bound based on the Gibbs’ risk. We also present a theorem that allows to bound the risk associated with general loss functions. From this theorem, we design learning algorithms building weighted majority vote classifiers minimizing a bound on the risk associated with the following loss functions : linear, quadratic and exponential. Also, we present algorithms based on the randomized majority vote. Some of these algorithms compare favorably with AdaBoost.