Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

"The ladder that sends us to wealth" : artisanal mining and poverty reduction in Tanzania

This article explores the contribution that artisanal and small-scale mining (ASM) makes to poverty reduction in Tanzania, based on data on gold and diamond mining in Mwanza Region. The evidence suggests that people working in mining or related services are less likely to be in poverty than those with other occupations. However, the picture is complex; while mining income can help reduce poverty and provide a buffer from livelihood shocks, peoples inability to obtain a formal mineral claim, or to effectively exploit their claims, contributes to insecurity. This is reinforced by a context in which ASM is peripheral to large-scale mining interests, is only gradually being addressed within national poverty reduction policies, and is segregated from district-level planning.

Microbial Recognition Via Toll-Like Receptor-Dependent and -Independent Pathways Determines the Cytokine Response of Murine Dendritic Cell Subsets to CD40 Triggering

Dendritic cells (DC) can produce Th-polarizing cytokines and direct the class of the adaptive immune response. Microbial stimuli, cytokines, chemokines, and T cell-derived signals all have been shown to trigger cytokine synthesis by DC, but it remains unclear whether these signals are functionally equivalent and whether they determine the nature of the cytokine produced or simply initiate a preprogrammed pattern of cytokine production, which may be DC subtype specific. Here, we demonstrate that microbial and T cell-derived stimuli can synergize to induce production of high levels of IL-12 p70 or IL-10 by individual murine DC subsets but that the choice of cytokine is dictated by the microbial pattern recognition receptor engaged. We show that bacterial components such as CpG-containing DNA or extracts from Mycobacterium tuberculosis predispose CD8alpha(+) and CD8alpha(-)CD4(-) DC to make IL-12 p70. In contrast, exposure of CD8alpha(+), CD4(+) and CD8alpha(-)CD4(-) DC to heat-killed yeasts leads to production of IL-10. In both cases, secretion of high levels of cytokine requires a second signal from T cells, which can be replaced by CD40 ligand. Consistent with their differential effects on cytokine production, extracts from M. tuberculosis promote IL-12 production primarily via Toll-like receptor 2 and an MyD88-dependent pathway, whereas heat-killed yeasts activate DC via a Toll-like receptor 2-, MyD88-, and Toll/IL-1R domain containing protein-independent pathway. These results show that T cell feedback amplifies innate signals for cytokine production by DC and suggest that pattern recognition rather than ontogeny determines the production of cytokines by individual DC subsets.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

Generalized external indexes for comparing data partitions with overlapping categories

There is a family of well-known external clustering validity indexes to measure the degree of compatibility or similarity between two hard partitions of a given data set, including partitions with different numbers of categories. A unified, fully equivalent set-theoretic formulation for an important class of such indexes was derived and extended to the fuzzy domain in a previous work by the author [Campello, R.J.G.B., 2007. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Lett., 28, 833-841]. However, the proposed fuzzy set-theoretic formulation is not valid as a general approach for comparing two fuzzy partitions of data. Instead, it is an approach for comparing a fuzzy partition against a hard referential partition of the data into mutually disjoint categories. In this paper, generalized external indexes for comparing two data partitions with overlapping categories are introduced. These indexes can be used as general measures for comparing two partitions of the same data set into overlapping categories. An important issue that is seldom touched in the literature is also addressed in the paper, namely, how to compare two partitions of different subsamples of data. A number of pedagogical examples and three simulation experiments are presented and analyzed in details. A review of recent related work compiled from the literature is also provided. (c) 2010 Elsevier B.V. All rights reserved.

Novel Zn(2+)-binding Sites in Human Transthyretin IMPLICATIONS FOR AMYLOIDOGENESIS AND RETINOL-BINDING PROTEIN RECOGNITION

Human transthyretin (TTR) is a homotetrameric protein involved in several amyloidoses. Zn(2+) enhances TTR aggregation in vitro, and is a component of ex vivo TTR amyloid fibrils. We report the first crystal structure of human TTR in complex with Zn(2+) at pH 4.6-7.5. All four structures reveal three tetra-coordinated Zn(2+)-binding sites (ZBS 1-3) per monomer, plus a fourth site (ZBS 4) involving amino acid residues from a symmetry-related tetramer that is not visible in solution by NMR.Zn(2+) binding perturbs loop E-alpha-helix-loop F, the region involved in holo-retinol-binding protein (holo-RBP) recognition, mainly at acidic pH; TTR affinity for holo-RBP decreases similar to 5-fold in the presence of Zn(2+). Interestingly, this same region is disrupted in the crystal structure of the amyloidogenic intermediate of TTR formed at acidic pH in the absence of Zn(2+). HNCO and HNCA experiments performed in solution at pH 7.5 revealed that upon Zn(2+) binding, although the alpha-helix persists, there are perturbations in the resonances of the residues that flank this region, suggesting an increase in structural flexibility. While stability of the monomer of TTR decreases in the presence of Zn(2+), which is consistent with the tertiary structural perturbation provoked by Zn(2+) binding, tetramer stability is only marginally affected by Zn(2+). These data highlight structural and functional roles of Zn(2+) in TTR-related amyloidoses, as well as in holo-RBP recognition and vitamin A homeostasis.

Multiple camera people detection and tracking using support integration

This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the ""foreground mass"" above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS`06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method. (C) 2010 Elsevier B.V. All rights reserved.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

Clustering Lab value working with medical data

Data mining is a relatively new field of research that its objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available [27]. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make rather accurate decisions. In this thesis, the goal is finding a pattern among patients who got pneumonia by clustering of lab data values which have been recorded every day. By this pattern we can generalize it to the patients who did not have been diagnosed by this disease whose lab values shows the same trend as pneumonia patients does. There are 10 tables which have been extracted from a big data base of a hospital in Jena for my work .In ICU (intensive care unit), COPRA system which is a patient management system has been used. All the tables and data stored in German Language database.

Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods

The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.