Effect of fuzzy discretization in the association performance with continuous attributes

Flood is one of the natural disasters caused by complex factors such as natural, breeding and environmental.The variability of such factors on multiple heterogeneous spatial scales may cause difficulties in finding correlation or association between regions.The interaction between these factors has resulted in provision of either diverse or repeated information which can be detrimental to prediction accuracy.The complex and diverse available database has triggered this study to incorporate multi source heterogeneous data source in finding association between regions.Bayesian Network based method has been used to quantify dependency patterns in spatial data.However, a group of variables may be relevant for a particular region but may not be relevant to other region.To overcome the weakness of Bayesian network in handling continuous variable, this study has proposed data discretization technique to produce spatial correlation model.The effect of the proposed fuzzy discretization on the association performance is investigated.The comparison between different data discretization techniques proved that the proposed fuzzy discretization method gives better result with high precision, good F-measure, and a better receiver operating characteristic area compared with other methods.The results of correlation between the spatial patterns gives detailed information that may help the government, planners, decision makers, and researchers to perform actions that help to prevent and mitigate flood events in the future

Improving the geospatial consistency of digital libraries metadata

Consistency is an essential aspect of the quality of metadata. Inconsistent metadata records are harmful: given a themed query, the set of retrieved metadata records would contain descriptions of unrelated or irrelevant resources, and may even not contain some resources considered obvious. This is even worse when the description of the location is inconsistent. Inconsistent spatial descriptions may yield invisible or hidden geographical resources that cannot be retrieved by means of spatially themed queries. Therefore, ensuring spatial consistency should be a primary goal when reusing, sharing and developing georeferenced digital collections. We present a methodology able to detect geospatial inconsistencies in metadata collections based on the combination of spatial ranking, reverse geocoding, geographic knowledge organization systems and information-retrieval techniques. This methodology has been applied to a collection of metadata records describing maps and atlases belonging to the Library of Congress. The proposed approach was able to automatically identify inconsistent metadata records (870 out of 10,575) and propose fixes to most of them (91.5%) These results support the ability of the proposed methodology to assess the impact of spatial inconsistency in the retrievability and visibility of metadata records and improve their spatial consistency

Fuzzy discretization techique for bayesian flood disaster model

The use of Bayesian Networks in the domain of disaster management has proven its efficiency in developing the disaster model and has been widely used to represent the logical relationships between variables.Prior to modelling the correlation between the flood factors, it was necessary to discretize the continuous data due to the weakness of the Bayesian Network to handle such variables.Therefore, this paper aimed to propose a data discretization technique and compare the existing discretization techniques to produce a spatial correlation model.In particular, the main contribution of this paper was to propose a fuzzy discretization method for the Bayesian-based flood model. The performance of the model is based on precision, recall, F-measure, and the receiver operating characteristic area.The experimental results demonstrated that the fuzzy discretization method provided the best measurements for the correlation model. Consequently, the proposed fuzzy discretization technique facilitated the data input for the flood model and was able to help the researchers in developing effective early warning systems in the future. In addition, the results of correlation were prominent in disaster management to provide reference that may help the government, planners, and decision-makers to perform actions and mitigate flood events

A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level

This doctoral thesis deals with a number of challenges related to investigating and devising solutions to the Sentiment Analysis Problem, a subset of the discipline known as Natural Language Processing (NLP), following a path that differs from the most common approaches currently in-use. The majority of the research and applications building in Sentiment Analysis (SA) / Opinion Mining (OM) have been conducted and developed using Supervised Machine Learning techniques. It is our intention to prove that a hybrid approach merging fuzzy sets, a solid sentiment lexicon, traditional NLP techniques and aggregation methods will have the effect of compounding the power of all the positive aspects of these tools. In this thesis we will prove three main aspects, namely: 1. That a Hybrid Classification Model based on the techniques mentioned in the previous paragraphs will be capable of: (a) performing same or better than established Supervised Machine Learning techniques -namely, Naïve Bayes and Maximum Entropy (ME)- when the latter are utilised respectively as the only classification methods being applied, when calculating subjectivity polarity, and (b) computing the intensity of the polarity previously estimated. 2. That cross-ratio uninorms can be used to effectively fuse the classification outputs of several algorithms producing a compensatory effect. 3. That the Induced Ordered Weighted Averaging (IOWA) operator is a very good choice to model the opinion of the majority (consensus) when the outputs of a number of classification methods are combined together. For academic and experimental purposes we have built the proposed methods and associated prototypes in an iterative fashion: Step 1: we start with the so-called Hybrid Standard Classification (HSC) method, responsible for subjectivity polarity determination. Step 2: then, we have continued with the Hybrid Advanced Classification (HAC) method that computes the polarity intensity of opinions/sentiments. Step 3: in closing, we present two methods that produce a semantic-specific aggregation of two or more classification methods, as a complement to the HSC/HAC methods when the latter cannot generate a classification value or when we are looking for an aggregation that implies consensus, respectively: *the Hybrid Advanced Classification with Aggregation by Cross-ratio Uninorm (HACACU) method