Search results for: Probabilistic formalism

Authors: Hong Choon Ong

Abstract:

Naïve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Naïve Bayes. The improvement in the classification using structural learning is shown if there exist relationship between the features or when they are not independent.

Keywords: Bayesian Network, Classification, Naïve Bayes, Structural Learning.

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Authors: Eva Eggeling, Dieter W. Fellner, Torsten Ullrich

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The objective of global optimization is to find the globally best solution of a model. Nonlinear models are ubiquitous in many applications and their solution often requires a global search approach; i.e. for a function f from a set A ⊂ Rn to the real numbers, an element x0 ∈ A is sought-after, such that ∀ x ∈ A : f(x0) ≤ f(x). Depending on the field of application, the question whether a found solution x0 is not only a local minimum but a global one is very important. This article presents a probabilistic approach to determine the probability of a solution being a global minimum. The approach is independent of the used global search method and only requires a limited, convex parameter domain A as well as a Lipschitz continuous function f whose Lipschitz constant is not needed to be known.

Keywords: global optimization, probability theory, probability of globality

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Authors: Debesh R. Roy

Abstract:

The fundamental issue in understanding the origin and growth mechanism of nanomaterials, from a fundamental unit is a big challenging problem to the scientists. Recently, an immense attention is generated to the researchers for prediction of exceptionally stable atomic cluster units as the building units for future smart materials. The present study is a systematic investigation on the stability and electronic properties of a series of bimetallic (semiconductor-alkaline earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for exceptional and/ or unusual stable motifs. A very popular hybrid exchange-correlation functional, B3LYP along with a higher basis set, viz., 6-31+G[d,p] is employed for this purpose under the density functional formalism. The magic stability among the concerned clusters is explained using the jellium model. It is evident from the present study that the magic stability of B4Mg3 cluster arises due to the jellium shell closure.

Keywords: Atomic Clusters, Density Functional Theory, Jellium Model, Magic Clusters, Smart Nanomaterials.

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Authors: Vinicius R. Domingues, Luan C. S. M. Ozelim

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The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters.

Keywords: Kolmogorov-Smirnov, Anderson-Darling, Cramer-Von-Mises, Nonparametric adherence tests.

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Authors: Yasser F. Hassan, Marwa Abdeen, Mustafa Fahmy

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In recent years multi-agent systems have emerged as one of the interesting architectures facilitating distributed collaboration and distributed problem solving. Each node (agent) of the network might pursue its own agenda, exploit its environment, develop its own problem solving strategy and establish required communication strategies. Within each node of the network, one could encounter a diversity of problem-solving approaches. Quite commonly the agents can realize their processing at the level of information granules that is the most suitable from their local points of view. Information granules can come at various levels of granularity. Each agent could exploit a certain formalism of information granulation engaging a machinery of fuzzy sets, interval analysis, rough sets, just to name a few dominant technologies of granular computing. Having this in mind, arises a fundamental issue of forming effective interaction linkages between the agents so that they fully broadcast their findings and benefit from interacting with others.

Keywords: Granular computing, rough sets, agents, traffic system.

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Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER

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The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Relevance feedback, bag of words model, probabilistic model, vector space model, image retrieval

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Authors: Darby Tien-Hao Chang, Guan-Yu Shiu, You-Jie Sun

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Various cis-regulatory module (CRM) predictors have been proposed in the last decade. Several well-established CRM predictors adopted different categories of prediction strategies, including window clustering, probabilistic modeling and phylogenetic footprinting. Appropriate integration of them has a potential to achieve high quality CRM prediction. This study analyzed four existing CRM predictors (ClusterBuster, MSCAN, CisModule and MultiModule) to seek a predictor combination that delivers a higher accuracy than individual CRM predictors. 465 CRMs across 140 Drosophila melanogaster genes from the RED fly database were used to evaluate the integrated CRM predictor proposed in this study. The results show that four predictor combinations achieved superior performance than the best individual CRM predictor.

Keywords: Cis-regulatory module, transcription factor binding site.

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Authors: Rein Kuusik, Grete Lind

Abstract:

Generator of hypotheses is a new method for data mining. It makes possible to classify the source data automatically and produces a particular enumeration of patterns. Pattern is an expression (in a certain language) describing facts in a subset of facts. The goal is to describe the source data via patterns and/or IF...THEN rules. Used evaluation criteria are deterministic (not probabilistic). The search results are trees - form that is easy to comprehend and interpret. Generator of hypotheses uses very effective algorithm based on the theory of monotone systems (MS) named MONSA (MONotone System Algorithm).

Keywords: data mining, monotone systems, pattern, rule.

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Authors: A. Jabbari, R. Jedermann, W. Lang

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The new idea of this research is application of a new fault detection and isolation (FDI) technique for supervision of sensor networks in transportation system. In measurement systems, it is necessary to detect all types of faults and failures, based on predefined algorithm. Last improvements in artificial neural network studies (ANN) led to using them for some FDI purposes. In this paper, application of new probabilistic neural network features for data approximation and data classification are considered for plausibility check in temperature measurement. For this purpose, two-phase FDI mechanism was considered for residual generation and evaluation.

Keywords: Fault detection and Isolation, Neural network, Temperature measurement, measurement approximation and classification.

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Authors: Bassem Roushdy, Nahed Sobhy, Abdelrhim Abdelhamid, Ahmed Mahmoud

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Most papers model Joint Replenishment Problem (JRP) as a (kT,S) where kT is a multiple value for a common review period T,and S is a predefined order up to level. In general the (T,S) policy is characterized by a long out of control period which requires a large amount of safety stock compared to the (R,Q) policy. In this paper a probabilistic model is built where an item, call it item(i), with the shortest order time between interval (T)is modeled under (R,Q) policy and its inventory is continuously reviewed, while the rest of items (j) are periodically reviewed at a definite time corresponding to item

Keywords: Inventory management, Joint replenishment, policy evaluation, stochastic process

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Authors: Soumia Ichoua

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In this paper, we investigate the strategic stochastic air traffic flow management problem which seeks to balance airspace capacity and demand under weather disruptions. The goal is to reduce the need for myopic tactical decisions that do not account for probabilistic knowledge about the NAS near-future states. We present and discuss a scenario-based modeling approach based on a time-space stochastic process to depict weather disruption occurrences in the NAS. A solution framework is also proposed along with a distributed implementation aimed at overcoming scalability problems. Issues related to this implementation are also discussed.

Keywords: Air traffic management, sample average approximation, scenario-based approach, stochastic capacity.

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Authors: Raida El Mansouri, Elhillali Kerkouche, Allaoua Chaoui

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The Petri net tool INA is a well known tool by the Petri net community. However, it lacks a graphical environment to cerate and analyse INA models. Building a modelling tool for the design and analysis from scratch (for INA tool for example) is generally a prohibitive task. Meta-Modelling approach is useful to deal with such problems since it allows the modelling of the formalisms themselves. In this paper, we propose an approach based on the combined use of Meta-modelling and Graph Grammars to automatically generate a visual modelling tool for INA for analysis purposes. In our approach, the UML Class diagram formalism is used to define a meta-model of INA models. The meta-modelling tool ATOM3 is used to generate a visual modelling tool according to the proposed INA meta-model. We have also proposed a graph grammar to automatically generate INA description of the graphically specified Petri net models. This allows the user to avoid the errors when this description is done manually. Then the INA tool is used to perform the simulation and the analysis of the resulted INA description. Our environment is illustrated through an example.

Keywords: INA, Meta-modelling, Graph Grammars, AToM3, Automatic Code Generation.

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Authors: S. Lgarch, M. Khalidi Idrissi, S. Bennani

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For best collaboration, Asynchronous tools and particularly the discussion forums are the most used thanks to their flexibility in terms of time. To convey only the messages that belong to a theme of interest of the tutor in order to help him during his tutoring work, use of a tool for classification of these messages is indispensable. For this we have proposed a semantics classification tool of messages of a discussion forum that is based on LSA (Latent Semantic Analysis), which includes a thesaurus to organize the vocabulary. Benefits offered by formal ontology can overcome the insufficiencies that a thesaurus generates during its use and encourage us then to use it in our semantic classifier. In this work we propose the use of some functionalities that a OWL ontology proposes. We then explain how functionalities like “ObjectProperty", "SubClassOf" and “Datatype" property make our classification more intelligent by way of integrating new terms. New terms found are generated based on the first terms introduced by tutor and semantic relations described by OWL formalism.

Keywords: Classification of messages, collaborative communication tools, discussion forum, e-learning, formal description, latente semantic analysis, ontology, owl, semantic relations, semantic web, thesaurus, tutoring.

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Authors: Hooman Hoodat, Hassan Rashidi

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Despite various methods that exist in software risk management, software projects have a high rate of failure. When complexity and size of the projects are increased, managing software development becomes more difficult. In these projects the need for more analysis and risk assessment is vital. In this paper, a classification for software risks is specified. Then relations between these risks using risk tree structure are presented. Analysis and assessment of these risks are done using probabilistic calculations. This analysis helps qualitative and quantitative assessment of risk of failure. Moreover it can help software risk management process. This classification and risk tree structure can apply to some software tools.

Keywords: Risk analysis, risk assessment, risk classification, risk tree.

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Authors: J.H. Park, S. Lakshmanan, H.Y. Jung, S.M. Lee

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This paper is concerned with the delay-distributiondependent stability criteria for bidirectional associative memory (BAM) neural networks with time-varying delays. Based on the Lyapunov-Krasovskii functional and stochastic analysis approach, a delay-probability-distribution-dependent sufficient condition is derived to achieve the globally asymptotically mean square stable of the considered BAM neural networks. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulation is given to demonstrate the usefulness and effectiveness of the proposed results.

Keywords: BAM neural networks, Probabilistic time-varying delays, Stability criteria.

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Authors: Mackenzie Leake, Liyu Xia, Kamil Rocki, Wayne Imaino

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In the Hierarchical Temporal Memory (HTM) paradigm the effect of overlap between inputs on the activation of columns in the spatial pooler is studied. Numerical results suggest that similar inputs are represented by similar sets of columns and dissimilar inputs are represented by dissimilar sets of columns. It is shown that the spatial pooler produces these results under certain conditions for the connectivity and proximal thresholds. Following the discussion of the initialization of parameters for the thresholds, corresponding qualitative arguments about the learning dynamics of the spatial pooler are discussed.

Keywords: Hierarchical Temporal Memory, HTM, Learning Algorithms, Machine Learning, Spatial Pooler.

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Authors: Kevin Fong-Rey Liu, Ken Yeh, Cheng-Wu Chen, Han-Hsi Liang

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This paper proposes the use of Bayesian belief networks (BBN) as a higher level of health risk assessment for a dumping site of lead battery smelter factory. On the basis of the epidemiological studies, the actual hospital attendance records and expert experiences, the BBN is capable of capturing the probabilistic relationships between the hazardous substances and their adverse health effects, and accordingly inferring the morbidity of the adverse health effects. The provision of the morbidity rates of the related diseases is more informative and can alleviate the drawbacks of conventional methods.

Keywords: Bayesian belief networks, lead battery smelter factory, health risk assessment.

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Authors: Boukelkoul Lahcen

Abstract:

The cumulative costs for O&M may represent as much as 65%-90% of the turbine's investment cost. Nowadays the cost effectiveness concept becomes a decision-making and technology evaluation metric. The cost of energy metric accounts for the effect replacement cost and unscheduled maintenance cost parameters. One key of the proposed approach is the idea of maintaining the WTs which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating the cost of O&M is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the cost according to various options of maintenance.

Keywords: Cost, finite state, Markov model, operation, maintenance.

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Authors: Yayun Hsu, Henry Horng-Shing Lu

Abstract:

This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate crosstalk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds, since biological information is inherently included inside the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: Brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning.

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Authors: Norwati Mustapha, Manijeh Jalali, Abolghasem Bozorgniya, Mehrdad Jalali

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Web usage mining algorithms have been widely utilized for modeling user web navigation behavior. In this study we advance a model for mining of user-s navigation pattern. The model makes user model based on expectation-maximization (EM) algorithm.An EM algorithm is used in statistics for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. The experimental results represent that by decreasing the number of clusters, the log likelihood converges toward lower values and probability of the largest cluster will be decreased while the number of the clusters increases in each treatment.

Keywords: Web Usage Mining, Expectation maximization, navigation pattern mining.

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Authors: Rumena D. Stancheva, Ilona I. Iatcheva

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In working mode some unexpected changes could be arise in inner structure of electromagnetic device. They influence modification in electromagnetic field propagation map. The field values at an observed boundary are also changed. The development of the process has to be watched because the arising structural changes would provoke the device to be gone out later. The probabilistic assessment of the state is possible to be made. The numerical assessment points if the resulting changes have only accidental character or they are due to the essential inner structural disturbances. The presented application example is referring to the 200MW turbine-generator. A part of the stator core end teeth zone is simulated broken. Quasi three-dimensional electromagnetic and temperature field are solved applying FEM. The stator core state diagnosis is proposed to be solved as an identification problem on the basis of a statistical criterion.

Keywords: Identification, structural disturbance, statistical criterion.

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Authors: Nkongho Ayuketang Arreyndip, Ebobenow Joseph

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In this paper, to model a real life wind turbine, a probabilistic approach is proposed to model the dynamics of the blade elements of a small axial wind turbine under extreme stochastic wind speeds conditions. It was found that the power and the torque probability density functions even-dough decreases at these extreme wind speeds but are not infinite. Moreover, we also fund that it is possible to stabilize the power coefficient (stabilizing the output power)above rated wind speeds by turning some control parameters. This method helps to explain the effect of turbulence on the quality and quantity of the harness power and aerodynamic torque.

Keywords: Probability, Stochastic, Probability density function, Turbulence.

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Authors: Takashi Sakai, Shinsaku Kikuta, Jun-ichi Koyama

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Using a texture database, a statistical estimation of spring-back was conducted in this study on the basis of statistical analysis. Both spring-back in bending deformation and experimental data related to the crystal orientation show significant dispersion. Therefore, a probabilistic statistical approach was established for the proper quantification of these values. Correlation was examined among the parameters F(x) of spring-back, F(x) of the buildup fraction to three orientations after 92° bending, and F(x) at an as-received part on the basis of the three-parameter Weibull distribution. Consequent spring-back estimation using a texture database yielded excellent estimates compared with experimental values.

Keywords: Bending, Spring-back, Database, Crystallographic Orientation, Texture, SEM-EBSD, Weibull distribution, Statistical analysis.

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Authors: Armin Ghayoori, Faramarz Hendessi, Asrar Sheikh

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In developing a text-to-speech system, it is well known that the accuracy of information extracted from a text is crucial to produce high quality synthesized speech. In this paper, a new scheme for converting text into its equivalent phonetic spelling is introduced and developed. This method is applicable to many applications in text to speech converting systems and has many advantages over other methods. The proposed method can also complement the other methods with a purpose of improving their performance. The proposed method is a probabilistic model and is based on Smooth Ergodic Hidden Markov Model. This model can be considered as an extension to HMM. The proposed method is applied to Persian language and its accuracy in converting text to speech phonetics is evaluated using simulations.

Keywords: Hidden Markov Models, text, synthesis.

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Authors: Eleni Galiotou, Angela Ralli

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In this paper we propose a computational model for the representation and processing of morpho-phonological phenomena in a natural language, like Modern Greek. We aim at a unified treatment of inflection, compounding, and word-internal phonological changes, in a model that is used for both analysis and generation. After discussing certain difficulties cuase by well-known finitestate approaches, such as Koskenniemi-s two-level model [7] when applied to a computational treatment of compounding, we argue that a morphology-based model provides a more adequate account of word-internal phenomena. Contrary to the finite state approaches that cannot handle hierarchical word constituency in a satisfactory way, we propose a unification-based word grammar, as the nucleus of our strategy, which takes into consideration word representations that are based on affixation and [stem stem] or [stem word] compounds. In our formalism, feature-passing operations are formulated with the use of the unification device, and phonological rules modeling the correspondence between lexical and surface forms apply at morpheme boundaries. In the paper, examples from Modern Greek illustrate our approach. Morpheme structures, stress, and morphologically conditioned phoneme changes are analyzed and generated in a principled way.

Keywords: Morpho-Phonology, Natural Language Processing.

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Authors: Loredana G. Marcu, David Marcu, Sanda M. Filip

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Advanced head and neck cancers are aggressive tumours, which require aggressive treatment. Treatment efficiency is often hindered by cancer cell repopulation during radiotherapy, which is due to various mechanisms triggered by the loss of tumour cells and involves both stem and differentiated cells. The aim of the current paper is to present in silico simulations of radiotherapy schedules on a virtual head and neck tumour grown with biologically realistic kinetic parameters. Using the linear quadratic formalism of cell survival after radiotherapy, altered fractionation schedules employing various treatment breaks for normal tissue recovery are simulated and repopulation mechanism implemented in order to evaluate the impact of various cancer cell contribution on tumour behaviour during irradiation. The model has shown that the timing of treatment breaks is an important factor influencing tumour control in rapidly proliferating tissues such as squamous cell carcinomas of the head and neck. Furthermore, not only stem cells but also differentiated cells, via the mechanism of abortive division, can contribute to malignant cell repopulation during treatment.

Keywords: Radiation, tumour repopulation, squamous cell carcinoma, stem cell.

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Authors: Mikhail Vladimirovich Deryabin, Morten Willatzen

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In this work, we address theoretically the influence of red and white Gaussian noise for electronic energies and eigenstates of cylindrically shaped quantum dots. The stochastic effect can be imagined as resulting from crystal-growth statistical fluctuations in the quantum-dot material composition. In particular we obtain analytical expressions for the eigenvalue shifts and electronic envelope functions in the k . p formalism due to stochastic variations in the confining band-edge potential. It is shown that white noise in the band-edge potential leaves electronic properties almost unaffected while red noise may lead to changes in state energies and envelopefunction amplitudes of several percentages. In the latter case, the ensemble-averaged envelope function decays as a function of distance. It is also shown that, in a stochastic system, constant ensembleaveraged envelope functions are the only bounded solutions for the infinite quantum-wire problem and the energy spectrum is completely discrete. In other words, the infinite stochastic quantum wire behaves, ensemble-averaged, as an atom.

Keywords: cylindrical quantum dots, electronic eigen energies, red and white Gaussian noise, ensemble averaging effects.

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Authors: J. Šejnoha, D. Jarušková, O. Špačková, E. Novotná

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Construction of tunnels is connected with high uncertainty in the field of costs, construction period, safety and impact on surroundings. Risk management became therefore a common part of tunnel projects, especially after a set of fatal collapses occurred in 1990's. Such collapses are caused usually by combination of factors that can be divided into three main groups, i.e. unfavourable geological conditions, failures in the design and planning or failures in the execution. This paper suggests a procedure enabling quantification of the excavation risk related to extraordinary accidents using FTA and ETA tools. It will elaborate on a common process of risk analysis and enable the transfer of information and experience between particular tunnel construction projects. Further, it gives a guide for designers, management and other participants, how to deal with risk of such accidents and how to make qualified decisions based on a probabilistic approach.

Keywords: risk quantification, tunnel collapse, ETA, FTA, geotechnical risk

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Authors: Sang-Rak Kim, Jea-Yong Park, Won-Goo Lee, Jin-Shik Yu, Seog-Young Han

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This paper presents a Reliability-Based Topology Optimization (RBTO) based on Evolutionary Structural Optimization (ESO). An actual design involves uncertain conditions such as material property, operational load and dimensional variation. Deterministic Topology Optimization (DTO) is obtained without considering of the uncertainties related to the uncertainty parameters. However, RBTO involves evaluation of probabilistic constraints, which can be done in two different ways, the reliability index approach (RIA) and the performance measure approach (PMA). Limit state function is approximated using Monte Carlo Simulation and Central Composite Design for reliability analysis. ESO, one of the topology optimization techniques, is adopted for topology optimization. Numerical examples are presented to compare the DTO with RBTO.

Keywords: Evolutionary Structural Optimization, PerformanceMeasure Approach, Reliability-Based Topology Optimization, Reliability Index Approach.

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: Lexicon, sentiment analysis, stock movement prediction., computational finance.

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