Data Mining by Soft Computing Methods for The Coronary Heart Disease Database

For improvement of data mining technology, the advantages and disadvantages on respective data mining methods should be discussed by comparison under the same condition. For this purpose, the Coronary Heart Disease database (CHD DB) was developed in 2004, and the data mining competition was held in the International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). In the competition, two methods based on soft computing were presented. In this paper, we report the overview of the CHD DB and the soft computing methods, and discuss the features of respective methods by comparison of the experimental results

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A review of model designs

The PAEQANN project aims to review current ecological theories which can help identify suited models that predict community structure in aquatic ecosystems, to select and discuss appropriate models, depending on the type of target community (i.e. empirical vs. simulation models) and to examine how results add to ecological water management objectives. To reach these goals a number of classical statistical models, artificial neural networks and dynamic models are presented. An even higher number of techniques within these groups will tested lateron in the project. This report introduces all of them. The techniques are shortly introduced, their algorithms explained, and the advantages and disadvantages discussed

Occam's Razor For Big Data?

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor

Assessing the existence of visual clues of human ovulation

Is the concealed human ovulation a myth? The author of this work tries to answer the above question by using a medium-size database of facial images specially created and tagged. Analyzing possible facial modifications during the mensal period is a formal tool to assess the veracity about the concealed ovulation. In normal view, the human ovulation remains concealed. In other words, there is no visible external sign of the mensal period in humans. These external signs are very much visible in many animals such as baboons, dogs or elephants. Some are visual (baboons) and others are biochemical (dogs). Insects use pheromones and other animals can use sounds to inform the partners of their fertility period. The objective is not just to study the visual female ovulation signs but also to understand and explain automatic image processing methods which could be used to extract precise landmarks from the facial pictures. This could later be applied to the studies about the fluctuant asymmetry. The field of fluctuant asymmetry is a growing field in evolutionary biology but cannot be easily developed because of the necessary time to manually extract the landmarks. In this work we have tried to see if any perceptible sign is present in human face during the ovulation and how we can detect formal changes, if any, in face appearance during the mensal period. We have taken photography from 50 girls for 32 days. Each day we took many photos of each girl. At the end we chose a set of 30 photos per girl representing the whole mensal cycle. From these photos 600 were chosen to be manually tagged for verification issues. The photos were organized in a rating software to allow human raters to watch and choose the two best looking pictures for each girl. These results were then checked to highlight the relation between chosen photos and ovulation period in the cycle. Results were indicating that in fact there are some clues in the face of human which could eventually give a hint about their ovulation. Later, different automatic landmark detection methods were applied to the pictures to highlight possible modifications in the face during the period. Although the precision of the tested methods, are far from being perfect, the comparison of these measurements to the state of art indexes of beauty shows a slight modification of the face towards a prettier face during the ovulation. The automatic methods tested were Active Appearance Model (AAM), the neural deep learning and the regression trees. It was observed that for this kind of applications the best method was the regression trees. Future work has to be conducted to firmly confirm these data, number of human raters should be augmented, and a proper learning data base should be developed to allow a learning process specific to this problematic. We also think that low level image processing will be necessary to achieve the final precision which could reveal more details of possible changes in human faces.A ovulação no ser humano é, em geral, considerada “oculta”, ou seja, sem sinais exteriores. Mas a ovulação ou o período mensal é uma mudança hormonal extremamente importante que se repete em cada ciclo. Acreditar que esta mudança hormonal não tem nenhum sinal visível parece simplista. Estes sinais externos são muito visíveis em animais, como babuínos, cães ou elefantes. Alguns são visuais (babuínos) e outros são bioquímicos (cães). Insetos usam feromonas e outros animais podem usar sons para informar os parceiros do seu período de fertilidade. O ser humano tem vindo a esconder ou pelo menos camuflar sinais desses durante a evolução. As razoes para esconder ou camuflar a ovulação no ser humano não são claros e não serão discutidos nesta dissertação. Na primeira parte deste trabalho, a autora deste trabalho, depois de criar um base de dados de tamanho médio de imagens faciais e anotar as fotografias vai verificar se sinais de ovulação podem ser detetados por outros pessoas. Ou seja, se modificações que ‘as priori’ são invisíveis podem ser percebidas de maneira inconsciente pelo observador. Na segunda parte, a autora vai analisar as eventuais modificações faciais durante o período, de uma maneira formal, utilizando medidas faciais. Métodos automáticos de analise de imagem aplicados permitem obter os dados necessários. Uma base de dados de imagens para efetuar este trabalho foi criado de raiz, uma vez que nenhuma base de dados existia na literatura. 50 raparigas aceitaram de participar na criação do base de dados. Durante 32 dias e diariamente, cada rapariga foi fotografada. Em cada sessão foi tirada várias fotos. As fotos foram depois apuradas para deixar só 30 fotos ao máximo, para cada rapariga. 600 fotos foram depois escolhidas para serem manualmente anotadas. Essas 600 fotos anotadas, definam a base de dados de verificação. Assim as medidas obtidas automaticamente podem ser verificadas comparando com a base de 600 fotos anotadas. O objetivo deste trabalho não é apenas estudar os sinais visuais da ovulação feminina, mas também testar e explicar métodos de processamento automático de imagens que poderiam ser usados para extrair pontos de interesse, das imagens faciais. A automatização de extração dos pontos de interesse poderia mais tarde ser aplicado aos estudos sobre a assimetria flutuante. O campo da assimetria flutuante é um campo crescente na biologia evolucionária, mas não pode ser desenvolvido facilmente. O tempo necessário para extrair referencias e pontos de interesse é proibitivo. Por além disso, estudos de assimetria flutuante, muitas vezes, baseado numa só fotografia pode vier a ser invalido, se modificações faciais temporárias existirem. Modificações temporárias, tipo durante o período mensal, revela que estudos fenotípicos baseados numa só fotografia não pode constituir uma base viável para estabelecer ligas genótipo-fenótipo. Para tentar ver se algum sinal percetível está presente no rosto humano durante a ovulação, as fotos foram organizadas num software de presentação para permitir o observador humano escolher duas fotos (as mais atraentes) de cada rapariga. Estes resultados foram então analisados para destacar a relação entre as fotos escolhidas e o período de ovulação no ciclo mensal. Os resultados sugeriam que, de facto, existem algumas indicações no rosto que poderiam eventualmente dar informações sobre o período de ovulação. Os observadores escolheram como mais atraente de cada rapariga, aquelas que tinham sido tiradas nos dias imediatos antes ou depois da ovulação. Ou seja, foi claramente estabelecido que a mesma rapariga parecia mais atraente durante os dias próximos da data da ovulação. O software também permite recolher dados sobre o observador para analise posterior de comportamento dos observadores perante as fotografias. Os dados dos observadores podem dar indicações sobre as razoes da ovulação escondida que foi desenvolvida durante a evolução. A seguir, diferentes métodos automáticos de deteção de pontos de interesse foram aplicados às imagens para detetar o tipo de modificações no rosto durante o período. A precisão dos métodos testados, apesar de não ser perfeita, permite observar algumas relações entre as modificações e os índices de atratividade. Os métodos automáticos testados foram Active Appearance Model (AAM), Convolutional Neural Networks (CNN) e árvores de regressão (Dlib-Rt). AAM e CNN foram implementados em Python utilizando o modulo Keras library. Dlib-Rt foi implementado em C++ utilizando OpenCv. Os métodos utilizados, estão todos baseados em aprendizagem e sacrificam a precisão. Comparando os resultados dos métodos automáticos com os resultados manualmente obtidos, indicaram que os métodos baseados em aprendizagem podem não ter a precisão necessária para estudos em simetria flutuante ou para estudos de modificação faciais finas. Apesar de falta de precisão, observou-se que, para este tipo de aplicação, o melhor método (entre os testados) foi as árvores de regressão. Os dados e medidas obtidas, constituíram uma base de dados com a data de período, medidas faciais, dados sociais e dados de atratividade que poderem ser utilizados para trabalhos posteriores. O trabalho futuro tem de ser conduzido para confirmar firmemente estes dados, o número de avaliadores humanos deve ser aumentado, e uma base de dados de aprendizagem adequada deve ser desenvolvida para permitir a definição de um processo de aprendizagem específico para esta problemática. Também foi observado que o processamento de imagens de baixo nível será necessário para alcançar a precisão final que poderia revelar detalhes finos de mudanças em rostos humanos. Transcrever os dados e medidas para o índice de atratividade e aplicar métodos de data-mining pode revelar exatamente quais são as modificações implicadas durante o período mensal. A autora também prevê a utilização de uma câmara fotográfica tipo true-depth permite obter os dados de profundidade e volumo que podem afinar os estudos. Os dados de pigmentação da pele e textura da mesma também devem ser considerados para obter e observar todos tipos de modificação facial durante o período mensal. Os dados também devem separar raparigas com métodos químicos de contraceção, uma vez que estes métodos podem interferir com os níveis hormonais e introduzir erros de apreciação. Por fim o mesmo estudo poderia ser efetuado nos homens, uma vez que homens não sofrem de mudanças hormonais, a aparição de qualquer modificação facial repetível pode indicar existência de fatos camuflados

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert

Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach

This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated by using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis programs of existing large complexity detection arrays for the study of nucleus-nucleus collisions at low and intermediate energies

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Incorporating user design preferences into multi-objective roof truss optimization

Automated systems for large-span roof truss optimization provide engineers with the flexibility to consider multiple alternatives during conceptual design. This investigation extends previous work on multi-objective roof truss optimization to include the design preferences of a human user. The incorporation of user preferences into the optimization process required creation of a mechanism to identify and model preferences as well as discovery of an appropriate location within the algorithm for preference application. The first stage of this investigation developed a characteristic feature vector to describe the physical appearance of an individual truss. The feature vector translates visual elements of a truss into quantifiable properties transparent to the computer algorithm. The nine elements in the feature vector were selected from an assortment of geometrical and behavioral factors and describe truss simplicity, general shape, and chord shape. Using individual feature vectors, a truss population may be divided into groups of similar design. Partitioning the population simplifies the feedback process by allowing users to identify groups that best suit their design preferences. Several unsupervised clustering mechanisms were evaluated for their ability to generate truss classifications that matched human judgment and minimized intra-group deviation. A one-dimensional Kohonen self-organizing map was selected. The characteristic feature vectors of truss designs within user-selected groups provided a basis for determining whether or not a user would like a new design. After analyzing user inputs, prediction algorithm trials sought to reproduce these inputs and apply them to the prediction of acceptable designs. This investigation developed a hybrid method combining rough set reduct techniques and a back-propagation neural network. This hybrid prediction mechanism was embedded into the operations of an Implicit Redundant Representation Genetic Algorithm. Locations within the ranking and selection processes of this algorithm formed the basis of a study to investigate the effect of user preference on truss optimization. Final results for this investigation prove that incorporating a user's aesthetic design preferences into the optimization project generates more design alternatives for the user to examine; that these alternatives are more in line with a user's conceptual perception of the project; and that these alternatives remain structurally optimal