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At the moment social exclusion is a topical problem in a whole Europe. That’s why innovative decisions are prompted for social exclusive group of people in order to facilitate their integration process into the labour market. The stepping-stone of this work is e-advisor for choosing possibilities within social isolation environment. This e-advisor is created in accordance with artificial neural network and considering to individual person’s features give suggestions for the most suitable professions. Also in this work is presented disease diagnostic model, which is defined by artificial neural network.

There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text] / Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą]

Daugelio klasių atpažinimo uždaviniams spręsti yra sukurta aibė sprendimų ir ne visada vieningų rekomendacijų. Dauguma jų paremta empiriniais bandymais, retai atsižvelgiama į statistines duomenų savybes. Dėl to sprendžiant daugelio klasių klasifikavimo uždavinį kyla klausimų, kurį metodą ir kada geriausia naudoti, koks vieno ar kito metodo patikimumas. Disertacijoje nagrinėjami dviejų pakopų sprendimo priėmimo metodai, kai pirmame etape sudaromi klasifikatoriai poroms (angl. pair-wise), sugebantys geriau išnaudoti klasių tarpusavio statistines savybes, o kitame etape yra atliekamas klasifikatorių poroms rezultatų apjungimas. Tyrime ypatingas dėmesys yra skiriamas klasifikatorių poroms sudėtingumui, mokymo duomenų kiekiui bei algoritmų kokybės įvertinimo tikslumui. Tikslumas labai priklauso nuo duomenų bei atliktų eksperimentų kiekio (duomenų permaišymo klasėse, juos skirstant į mokymo ir testavimo). Parodyta, jog dėl žemo įvertinimo tikslumo kai kurių publikuotų algoritmų deklaruojamas pranašumas prieš žinomus algoritmus nėra patikimas. Darbe atliktas detalus žinomų metodų palyginimas bei pristatytas naujai sukurtas klasifikatorių poroms apjungimo algoritmas, kuris yra paremtas analogišku algoritmu daugelio klasių klasifikatorių rezultatų apjungimui. Pateiktos bendros rekomendacijos, kaip projektuotojui elgtis daugelio klasių atveju. Pasiūlyti metodai, leidžiantys sumažinti klasifikavimo klaidą atliekant klasifikatorių poroms apjungimo koregavimą, kad algoritmas nebūtų... [toliau žr. visą tekstą] / There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the second stage a particular fusion rule of the first stage results is used to fuse the first stage results in order to produce the final classification decision. Complexity issues of pair-wise classifiers, training data size and precision of method quality estimation are pointed out in the research. The precision of algorithm highly depends on the data and the number of experiments performed (data permutation, division into training and testing data). It is shown that the declared superiority of some known algorithms is not reliable due to low precision of estimation. A detailed comparison of well known multi-class classification methods is performed and a new pair-wise classifier fusion method based on similar method used in multi-class classifier fusion is presented. The recommendations for multi-class classification task designer are provided. Methods which allow reducing classification... [to full text]

This study proposes a new method for obstacle detection and avoidance using low-level MPEG-7 visual descriptors. The method includes training a neural network with a subset of MPEG-7 visual descriptors extracted from outdoor scenes. The trained neural network is then used to estimate the obstacle presence in real outdoor videos and to perform obstacle avoidance. In our proposed method, obstacle avoidance solely depends on the estimated obstacle presence data. In this study, backpropagation algorithm on multi-layer perceptron neural network is utilized as a feature learning method. MPEG-7 visual descriptors are used to describe basic features of the given scene image and by further processing these features, input data for the neural network is obtained. The learning/training phase is carried out on specially constructed synthetic video sequence with known obstacles. Validation and tests of the algorithms are performed on actual outdoor videos. Tests on indoor videos are also performed to evaluate the performance of the proposed algorithms in indoor scenes. Throughout the study, OdBot 2 robot platform, which has been developed by the author, is used as reference platform. For final testing of the obstacle detection and avoidance algorithms, simulation environment is used. From the simulation results and tests performed on video sequences, it can be concluded that the proposed obstacle detection and avoidance methods are robust against visual changes in the environment that are common to most of the outdoor videos. Findings concerning the used methods are presented and discussed as an outcome of this study.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / This dissertation reports applications of artificial neural networks to detect stator winding interturn fault of three phase induction motor drived by frequency inverter. The artificial neural networks, like Simple and Multilayer Perceptron, served as off-line classifiers to short-circuit fault condition or healthy condition. In the training of Multilayer Perceptron, two different algorithms are used: the error back-propagation, which is a classic algorithm, and the extreme learning machine, as a relative new alternative for the classic back-propagation. The new one is more worthwhile because of its implementation easiness and higher speed of computation. The database used on the training and validation of the networks is created from an experimental setting, therefore it is composed by true data. The attributes used as failuresâ indicators are selected from certain frequencies of the spectrum, based on some theories of current signature analysis. In the second instance, the technique of principal components analysis is employed. The results obtained for the designed classifiers are shown, and some considerations are made on their use in real time embedded applications, which is the most important projection for future researches. / Este trabalho deriva da aplicaÃÃo de redes neurais artificiais para a detecÃÃo de curto-circuito entre espiras em motor de induÃÃo trifÃsico, acionado por inversor de frequÃncia. As redes neurais artificiais, do tipo Perceptron Simples e Multicamadas, sÃo usadas para detectar falhas de curto-circuito no bobinamento estatÃrico de motores de induÃÃo trifÃsicos de forma off-line. Para treinamento do Perceptron Multicamadas sÃo usados dois algoritmos distintos: o error back-propagation, que figura como o algoritmo clÃssico na literatura especializada, e o extreme learning machine, que à uma alternativa, relativamente recente, ao algoritmo clÃssico. Este algoritmo à uma opÃÃo atraente para o desenvolvimento rÃpido de classificadores. O banco de dados usado para treinamento e validaÃÃo das redes à obtido a partir de experimentaÃÃo laboratorial, portanto composto de dados reais. Os atributos utilizados para a detecÃÃo da falha sÃo componentes de frequÃncia do espectro harmÃnico da corrente estatÃrica do motor. O critÃrio de escolha destas componentes, a priori, à fundamentado em resultados de investigaÃÃes prÃvias da assinatura de corrente e, em segunda instÃncia, à aplicada a tÃcnica de anÃlise de componentes principais. SÃo apresentados os resultados obtidospelos classificadores projetados, e feitas algumas consideraÃÃes quanto à utilizaÃÃo destes em aplicaÃÃo embarcada e em tempo real, que à a principal projeÃÃo de futuros trabalhos a partir do atual.

The goal of this master’s thesis is to identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analyzed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5.

In this thesis the predictive models the multilayer perceptron and random forest are evaluated to predict cloud top pressure. The dataset used in this thesis contains brightness temperatures, reflectances and other useful variables to determine the cloud top pressure from the Advanced Very High Resolution Radiometer (AVHRR) instrument on the two satellites NOAA-17 and NOAA-18 during the time period 2006-2009. The dataset also contains numerical weather prediction (NWP) variables calculated using mathematical models. In the dataset there are also observed cloud top pressure and cloud top height estimates from the more accurate instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The predicted cloud top pressure is converted into an interpolated cloud top height. The predicted pressure and interpolated height are then evaluated against the more accurate and observed cloud top pressure and cloud top height from the instrument on the satellite CALIPSO. The predictive models have been performed on the data using different sampling strategies to take into account the performance of individual cloud classes prevalent in the data. The multilayer perceptron is performed using both the original response cloud top pressure and a log transformed repsonse to avoid negative values as output which is prevalent when using the original response. Results show that overall the random forest model performs better than the multilayer perceptron in terms of root mean squared error and mean absolute error.

Konfliktprediktering handlar om att bedöma risken för våld i ett geografiskt område vid en given tid. Uppgiften lämpar sig bra för datorer som med hjälp av matematiska modeller kan hitta mönster i stora mängder data. Att prediktera konflikthändelser går att göra med olika metoder. Syftet med studien var att utvärdera multilayer perceptron (MLP), en typ av artificiella neuronnät, som metod för konfliktprediktering i relation till två andra metoder. I studien beskrivs hur MLP-neuronnätet konstruerades och hur prestationsmått togs fram för dess prediktioner. De värdena jämfördes senare med prestationsmått från andra studier för de två andra metoderna. Prediktionerna grundade sig på data om konflikthändelser, samt ekonomiska och demografiska faktorer för länder i världen. Jämförelsen visade att MLP är användbar som metod för konfliktprediktering och hade, under de förutsättningar som rådde, i viktiga avseenden högre prediktiv förmåga än de andra metoderna. Studien presenterar även fyra faktorer som kan påverka vilken modelleringsmetod som en modellerare borde använda för konfliktprediktering.

My diploma thesis deals about face detection in picture. I try to outline problems of computer vision, artificial intelligence and machine learning. I described in details the proposed detection by Viola and Jones, which uses AdaBoost learning algorithm. This method was deliberately chosen for speed and detection accuracy. This detector was made in programming language C / C + + using the OpenCV library. To a final learning was used database of faces images „MIT CVCL Face Database“. The main goal was to propose the face detector utilizable also in video-sequences.