Lie-detection has been an evergreen and evolving subject. Polygraph techniques have been the most popular and successful technique till date. The main drawback of the polygraph is that good results cannot be attained without maintaining a physical contact, of the subject under test. In general, this physical contact would induce extra consciousness in the subject. Also, any sort of arousal in the subject triggers false positives while performing the traditional polygraph based tests. With all these drawbacks in the polygraph, also, due to rapid developments in the fields of computer vision and artificial intelligence, with newer and faster algorithms, have compelled mankind to search and adapt to contemporary methods in lie-detection. Observing the facial expressions of emotions in a person without any physical contact and implementing these techniques using artificial intelligence is one such method. The concept of magnifying a micro expression and trying to decipher them is rather premature at this stage but would evolve in future. Magnification using EVM technique has been proposed recently and it is rather new to extract these micro expressions from magnified EVM based on HOG features. Till date, HOG features have been used in conjunction with SVM, and generally for person/pedestrian detection. A newer, simpler and contemporary method of applying EVM with HOG features and Back-propagation Neural Network jointly has been introduced and proposed to extract and decipher the micro-expressions on the face. Micro-expressions go unnoticed due to its involuntary nature, but EVM is used to magnify them and makes them noticeable. Emotions behind the micro-expressions are extracted and recognized using the HOG features \& Back-Propagation Neural Network. One of the important aspects that has to be dealt with human beings is a biased mind. Since, an investigator is also a human and, he too, has to deal with his own assumptions and emotions, a Neural Network is used to give the investigator an unbiased start in identifying the true emotions behind every micro-expression. On the whole, this proposed system is not a lie-detector, but helps in detecting the emotions of the subject under test. By further investigation, a lie can be detected. / This thesis uses a magnification technique to magnify the subtle, faint and spontaneous facial muscle movements or more precisely, micro-expressions. This magnification would help a system in classifying them and estimating the emotion behind them. This technique additionally magnifies the color changes, which could be used to extract the pulse without a physical contact with the subject. The results are presented in a GUI. / Gautam: +46(0)739528573, +91-9701534064 Tushal: +46(0)723219833, +91-9000242241 Venu: +46(0)734780266, +91-9298653191 Sai: +91-9989410111

Les systèmes réels sont généralement de nature non-linéaire, et leurs modélisations etsurveillance restent une tâche difficile à accomplir. Néanmoins, avec les progrès technologiqueson dispose maintenant d'un atout de taille sur ces systèmes qui est les données.Ce travail présente une technique de diagnostic de défaut et de modélisation basée en grandepartie sur la méthode d'apprentissage automatique « Les Machines à Vecteurs de Support,SVM » qui est basée sur les données. La méthodologie proposée est appliquée à différentessystèmes multivariables et non linéaires, à savoir : un procédé de traitement des eaux usées, unsystème éolien et un réacteur chimique parfaitement agité.L'objectif de cette thèse de doctorat est d'examiner la possibilité d'extraire le maximumd'information à partir de données afin de surveiller efficacement le comportement de systèmesréels et de détecter rapidement tout défaut qui peut compromettre leur bon fonctionnement. Lamême méthode est utilisée pour la modélisation des différents systèmes. Plusieurs défis ont étérelevés tels que la complexité du comportement des systèmes, le grand nombre de mesuresvariant à différentes échelles de temps, la présence de bruit et les perturbations. Une méthodegénérique de diagnostic de défauts est proposée par la génération des caractéristiques de chaquedéfaut suivie d’une étape d'évaluation de ces caractéristiques avec une amélioration du transfertde connaissances en modélisation.Dans cette thèse ont a démontré l'utilité de l'outil Machines à Vecteurs de Support, enclassification par la construction de modèles de décision SVM dédiés à l'évaluation descaractéristiques de défaut, et aussi en tant qu'estimateur non linéaire/ou pour la modélisation parl'utilisation des machines à vecteurs de support dédiés pour la régression (SVR).La combinaison de SVM et d’une méthode basée sur le modèle "observateur" a été aussi étudiéeet a été nécessaire dans certains cas pour garantir un bon diagnostic de défauts. / Real systems are usually nonlinear and their modeling and monitoring remains adifficult task. However, with advances in technology and the availability of big amounts of data,we have a facility to operate these systems.This work presents a methodology for fault diagnosis and modeling which is in large part basedon the method of Support Vector Machines (SVM) which data-based. The proposedmethodology is applied to various nonlinear multivariable systems including: wastewatertreatment processes, wind turbines and stirred tank reactors.The objective of this PhD is to examine the possibility of extracting the maximum of informationfrom data to effectively monitor the behavior of real systems and rapidly detect any faults whichmay impair their proper functioning. The same method is used for modeling the differentsystems. Several challenges were identified and surmounted such as the complexity of thesystem behavior, large amount of data varying at different time scales, the presence of noise anddisturbances. A generic method of fault diagnosis is proposed for the generation of the faultcharacteristics followed by an evaluation of these characteristics as well as an improved transferof knowledge in modeling.In this thesis the usefulness of the tool Support Vector Machines in Classification has beendemonstrated by the construction of decision models dedicated to evaluating the characteristicsof faults, and also its usefulness for modeling/ or as estimator for the nonlinear systems usingsupport vector machines dedicated for regression (SVR).The combination of SVM and a method based on models “observer” was also considered andwas found to be interesting in some cases to ensure proper fault diagnosis.

Within the context of the assessment of laryngeal function, acoustic analysis has an important place because the speech signal may be recorded non-invasively and it forms the base on which the perceptual assessment of voice is founded. Given the limitations of perceptual ratings, one has investigated vocal cues of disordered voices that are clinically relevant, summarize properties of speech signals and report on a speaker's phonation in general and voice in particular. Ideally, the acoustic descriptors should also be correlates of auditory-perceptual ratings of voice. Generally speaking, the goal of acoustic analysis is to document quantitatively the degree of severity of a voice disorder and monitor the evolution of the voice of dysphonic speakers.<p><p><p>The first part of this thesis is devoted to the analysis of disordered connected speech. The aim is to investigate vocal cues that are clinically relevant and correlated with auditory-perceptual ratings. Two approaches are investigated. The variogram-based method in the temporal domain is addressed first. The second approach is in the cepstral domain. In particular, the first rahmonic amplitude is used as an acoustic cue to describe voice quality. A multi-dimensional approach combining temporal and spectral aspects is also investigated. The goal is to check whether acoustic cues in both domains report complementary information when predicting perceptual scores.<p><p><p>Both methods are tested first on a corpus of synthetic sound stimuli that has been obtained by means of a synthesizer of disordered voices. The purpose is to learn about the link between the signal properties (fixed by the synthesis parameters) and acoustic cues.<p>In this study, we had the opportunity to use two large natural speech corpora. One of them has been perceptually rated. <p><p><p>The final part of the text is devoted to the automatic classification of voice with regard to perceived voice quality. Many studies have proposed a binary (normal/pathological) classification of voice samples. An automatic categorization according to perceived degrees of hoarseness appears, however, to be more attractive to both clinicians and technologists and more likely to be clinically relevant. Indeed, one way to reduce inter-rater variability of an auditory-perceptual evaluation is to ask several experts to participate and then to average the perceptual scores. However, auditory-perceptual evaluation of a corpus by several judges is a very laborious, time-consuming and costly task. Making this perceptual evaluation task automatic is therefore desirable. <p>The aim of this study is to exploit the support vector machine classifier that has become, over the last years, a popular tool for classification, to carry out categorization of voices according to perceived degrees of hoarseness. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.

Abstracts in English, Afrikaans and Northern Sotho / More time series forecasting methods were researched and made available in recent years. This is mainly due to the emergence of machine learning methods which also found applicability in time series forecasting. The emergence of a variety of methods and their variants presents a challenge when choosing appropriate forecasting methods. This study explored the performance of four advanced forecasting methods: autoregressive integrated moving averages (ARIMA); artificial neural networks (ANN); support vector machines (SVM) and regression models with ARIMA errors. To improve their performance, bagging was also applied. The performance of the different methods was illustrated using South African air passenger data collected for planning purposes by the Airports Company South Africa (ACSA). The dissertation discussed the different forecasting methods at length. Characteristics such as strengths and weaknesses and the applicability of the methods were explored. Some of the most popular forecast accuracy measures were discussed in order to understand how they could be used in the performance evaluation of the methods. It was found that the regression model with ARIMA errors outperformed all the other methods, followed by the ARIMA model. These findings are in line with the general findings in the literature. The ANN method is prone to overfitting and this was evident from the results of the training and the test data sets. The bagged models showed mixed results with marginal improvement on some of the methods for some performance measures. It could be concluded that the traditional statistical forecasting methods (ARIMA and the regression model with ARIMA errors) performed better than the machine learning methods (ANN and SVM) on this data set, based on the measures of accuracy used. This calls for more research regarding the applicability of the machine learning methods to time series forecasting which will assist in understanding and improving their performance against the traditional statistical methods / Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse. Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes. Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes: outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute. Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is. Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te evalueer. Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig. Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige prestasiemaatstawwe vir party metodes marginaal verbeter. Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes (ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om hul prestasie vergeleke met dié van die tradisionele metodes te verbeter. / Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya maleba ya go akanya. Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo (ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo (SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe. Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye. Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga mešomo. Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya dipalopalo. / Decision Sciences / M. Sc. (Operations Research)

The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.

This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.

The diploma thesis is focused on the use of artificial intelligence methods for evaluating the fault condition of machinery. The evaluated data are from a vibrodiagnostic model for simulation of static and dynamic unbalances. The machine learning methods are applied, specifically supervised learning. The thesis describes the Spyder software environment, its alternatives, and the Python programming language, in which the scripts are written. It contains an overview with a description of the libraries (Scikit-learn, SciPy, Pandas ...) and methods — K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT) and Random Forests Classifiers (RF). The results of the classification are visualized in the confusion matrix for each method. The appendix includes written scripts for feature engineering, hyperparameter tuning, evaluation of learning success and classification with visualization of the result.

This master's thesis deals with the use of text classification for sorting of incoming emails. First, there is described the Knowledge Discovery in Databases and there is also analyzed in detail the text classification with selected methods. Further, this thesis describes the email communication and SMTP, POP3 and IMAP protocols. The next part contains design of the system that classifies incoming emails and there are also described realated technologie ie Apache James Server, PostgreSQL and RapidMiner. Further, there is described the implementation of all necessary components. The last part contains an experiments with email server using Enron Dataset.

This diploma thesis deals with the method which are useful for analysis of reliability in specific circuit applications. It also deals with fault analysis in frequency, time and DC domain. Methods for these domains are described in other chapter of this thesis. Finally methods for diagnostics analog circuit are evaluated based on simulation and practical testing of selected methods.