Abstract-Climate change is a phenomenon that is forcing the world to adapt to a different environment. In this study, Analytical Hierarchy Process (AHP) method is combined with a Geographical Information System (GIS) for flood risk analysis and evaluation in the town of Enrile, a flood-prone area located in northern Philippines. Expert opinions, together with geographical, statistical and historical data, were collected and then processed through fuzzy membership. The AHP results showed the relative weights of three identified flood risk factors, and these results were validated to be consistent, using a standard consistency index. Using the Quantum GIS software, the factor weights from the AHP were incorporated to produce a map that is color-coded representing 5 levels of estimated flood risks. Using such a GIS weighted overlay analysis map as guide, local councils and other stakeholders can act to prepare for potential flooding when the rains come or, better yet, proactively promote appropriate land-use policy that will minimize threat to lives due to flooding.Index Terms-Analytic hierarchy process (AHP), decision support system, geographic information system (GIS), land-use policy.

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Artificial Neural Networks (ANNs) have continued to be efficient models in solving classification problems. In this paper, we explore the use of an A NN with a small dataset to accurately classify whet her Filipino call center agents’ pronunciations are neutral or not based on their employer’s standards. Isolated utterances of the ten most commonly used words in the call center were recorded from eleven agents creating a dataset of 110 utterances. Two learning specialists were consulted to establish ground truths and Cohen’s Kappa was computed as 0.82, validating the reliability of the dataset. The first thirteen Mel-Frequency Cepstral Coefficients (MFCCs) were then extracted from each word and an ANN was trained with Ten-fold Stratified Cross Validation. Experimental results on the model recorded a classification accuracy of 89.60% supported by an overall F-Score of 0.92.

There has been much research on the combinatorial problem of generating the linear extensions of a given poset. This paper focuses on the reverse of that problem, where the input is a set of linear orders, and the goal is to construct a poset or set of posets that generates the input. Such a problem finds applications in computational neuroscience, systems biology, paleontology, and physical plant engineering. In this paper, two algorithms are presented for efficiently finding a single poset, if such a poset exists, whose linear extensions are exactly the same as the input set of linear orders. The variation of the problem where a minimum set of posets that cover the input is also explored. This variation is shown to be polynomially solvable for one class of simple posets (kite(2) posets) but NP-complete for a related class (hammock(2,2,2) posets).General Terms: Algorithms.

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