A Hybrid Model for Document Retrieval Systems.

A methodology for the design of document retrieval systems is presented. First, a composite index term weighting model is developed based on term frequency statistics, including document frequency, relative frequency within document and relative frequency within collection, which can be adjusted by selecting various coefficients to fit into different indexing environments. Then, a composite retrieval model is proposed to process a user\u27s information request in a weighted Phrase-Oriented Fixed-Level Expression (POFLE), which may apply more than Boolean operators, through two phases. That is, we have a search for documents which are topically relevant to the information request by means of a descriptor matching mechanism, which incorporate a partial matching facility based on a structurally-restricted relationship imposed by indexing model, and is more general than matching functions of the traditional Boolean model and vector space model, and then we have a ranking of these topically relevant documents, by means of two types of heuristic-based selection rules and a knowledge-based evaluation function, in descending order of a preference score which predicts the combined effect of user preference for quality, recency, fitness and reachability of documents

Uncertainty and Interpretability Studies in Soft Computing with an Application to Complex Manufacturing Systems

In systems modelling and control theory, the benefits of applying neural networks have been extensively studied. Particularly in manufacturing processes, such as the prediction of mechanical properties of heat treated steels. However, modern industrial processes usually involve large amounts of data and a range of non-linear effects and interactions that might hinder their model interpretation. For example, in steel manufacturing the understanding of complex mechanisms that lead to the mechanical properties which are generated by the heat treatment process is vital. This knowledge is not available via numerical models, therefore an experienced metallurgist estimates the model parameters to obtain the required properties. This human knowledge and perception sometimes can be imprecise leading to a kind of cognitive uncertainty such as vagueness and ambiguity when making decisions. In system classification, this may be translated into a system deficiency - for example, small input changes in system attributes may result in a sudden and inappropriate change for class assignation. In order to address this issue, practitioners and researches have developed systems that are functional equivalent to fuzzy systems and neural networks. Such systems provide a morphology that mimics the human ability of reasoning via the qualitative aspects of fuzzy information rather by its quantitative analysis. Furthermore, these models are able to learn from data sets and to describe the associated interactions and non-linearities in the data. However, in a like-manner to neural networks, a neural fuzzy system may suffer from a lost of interpretability and transparency when making decisions. This is mainly due to the application of adaptive approaches for its parameter identification. Since the RBF-NN can be treated as a fuzzy inference engine, this thesis presents several methodologies that quantify different types of uncertainty and its influence on the model interpretability and transparency of the RBF-NN during its parameter identification. Particularly, three kind of uncertainty sources in relation to the RBF-NN are studied, namely: entropy, fuzziness and ambiguity. First, a methodology based on Granular Computing (GrC), neutrosophic sets and the RBF-NN is presented. The objective of this methodology is to quantify the hesitation produced during the granular compression at the low level of interpretability of the RBF-NN via the use of neutrosophic sets. This study also aims to enhance the disitnguishability and hence the transparency of the initial fuzzy partition. The effectiveness of the proposed methodology is tested against a real case study for the prediction of the properties of heat-treated steels. Secondly, a new Interval Type-2 Radial Basis Function Neural Network (IT2-RBF-NN) is introduced as a new modelling framework. The IT2-RBF-NN takes advantage of the functional equivalence between FLSs of type-1 and the RBF-NN so as to construct an Interval Type-2 Fuzzy Logic System (IT2-FLS) that is able to deal with linguistic uncertainty and perceptions in the RBF-NN rule base. This gave raise to different combinations when optimising the IT2-RBF-NN parameters. Finally, a twofold study for uncertainty assessment at the high-level of interpretability of the RBF-NN is provided. On the one hand, the first study proposes a new methodology to quantify the a) fuzziness and the b) ambiguity at each RU, and during the formation of the rule base via the use of neutrosophic sets theory. The aim of this methodology is to calculate the associated fuzziness of each rule and then the ambiguity related to each normalised consequence of the fuzzy rules that result from the overlapping and to the choice with one-to-many decisions respectively. On the other hand, a second study proposes a new methodology to quantify the entropy and the fuzziness that come out from the redundancy phenomenon during the parameter identification. To conclude this work, the experimental results obtained through the application of the proposed methodologies for modelling two well-known benchmark data sets and for the prediction of mechanical properties of heat-treated steels conducted to publication of three articles in two peer-reviewed journals and one international conference

Geographical information modelling for land resource survey

The increasing popularity of geographical information systems (GIS) has at least three major implications for land resources survey. Firstly, GIS allows alternative and richer representation of spatial phenomena than is possible with the traditional paper map. Secondly, digital technology has improved the accessibility of ancillary data, such as digital elevation models and remotely sensed imagery, and the possibilities of incorporating these into target database production. Thirdly, owing to the greater distance between data producers and consumers there is a greater need for uncertainty analysis. However, partly due to disciplinary gaps, the introduction of GIS has not resulted in a thorough adjustment of traditional survey methods. Against this background, the overall objective of this study was to explore and demonstrate the utility of new concepts and tools within the context of pedological and agronomical land surveys. To this end, research was conducted on the interface between five fields of study: geographic information theory, land resource survey, remote sensing, statistics and fuzzy set theory. A demonstration site was chosen around the village of Alora in southern Spain.Fuzzy set theory provides a formalism to deal with classes that are partly indistinct as a result of vague class intensions. Fuzzy sets are characterised by membership functions that assign real numbers from the interval [0, 1] to elements, thereby indicating the grade of membership in that set. When fuzzy membership functions are used to classify attribute data linked to geometrical elements, presence of spatial dependence among these elements ensures that they form spatially contiguous regions. These can be interpreted as objects with indeterminate boundaries or fuzzy objects. Fuzzy set theory thus adds to the conventional conceptual data models that assume either discrete spatial objects or continuous fields.This thesis includes two case studies that demonstrate the use of the fuzzy set theory in the acquisition and querying of geographical information. The first study explored the use of fuzzy c -means clustering of attribute data derived from a digital elevation model to represent transition zones in a soil-landscape model. Validity evaluation of the resulting terrain descriptions was based on the coefficient of determination of regressing topsoil clay data on membership grades. Vaguely bounded regions were more closely related to the observed variation of clay content () than crisply bounded units as used in a conventional soil survey ().The second case study involved the use of the fuzzy set theory in querying uncertain geographical data. It explains differences between fuzziness and stochastic uncertainty on the basis of an example query concerning loss of forest and ease of access. Relationships between probabilities and fuzzy set memberships were established using a linguistic probability qualifier (high probability) and the expectation of a membership function defined on a stochastic travel time. Fuzzy query processing was compared with crisp processing. The fuzzy query response contained more information because, unlike the crisp response, it indicated the degree to which individual locations matched the vague selection criteria.In a land resource survey, data acquisition typically involves collecting a small sample of precisely measured primary data as well as a larger or even exhaustive sample of related secondary data. Soil surveyors often rely on soil-landscape relationships and image interpretation to enable efficient mapping of soil properties. Yet, they generally fail to communicate about the knowledge and methods employed in deriving map units and statements about their content.In this thesis, a methodological framework is formulated and demonstrated that takes advantage of GIS to interactively formalise soil-landscape knowledge using stepwise image interpretation and inductive learning of soil-landscape relationships. It examines topology to record potential part of links between hierarchically nested terrain objects corresponding to distinct soil formation regimes. These relationships can be applied in similar areas to facilitate image interpretation by restricting possible lower level objects. GIS visualisation tools can be used to create images (e.g. perspective views) illustrating the landscape configuration of interpreted terrain objects. The framework is expected to support different methods for analysing and describing soil variation in relation to a terrain description, including those requiring alternative conceptual data models. In this thesis, though, it is only demonstrated with the discrete object model.Satellite remote sensing has become an important tool in land cover mapping, providing an attractive supplement to relatively inefficient ground surveys. A common approach to extract land cover data from remotely sensed imagery is by probabilistic classification of multispectral data. Additional information can be incorporated into such classification, for example by translating it into Bayesian prior probabilities for each land cover type. This is particularly advantageous in the case of spectral overlap among target classes, i.e. when unequivocal class assignment based on spectral data alone is impossible.This thesis demonstrates a procedure to iteratively estimate regional prior class probabilities pertaining to areas resulting from image stratification. This method thus allows the incorporation of additional information into the classification process without the requirement of known prior class probabilities. The demonstration project involved Landsat TM imagery from 1984 and 1995. Image stratification was based on a geological map of the study area. Overall classification accuracy improved from 76% to 90% (1984) and from 64% to 69% (1995) when employing iteratively estimated prior probabilities.The fact that any landscape description is a model based on a limited sample of measured target attribute data implies that it is never completely certain. The presence of error or inaccuracy in the data contributes significantly to such uncertainty. Usually, the accuracy of land survey datasets is indicated using global indices (e.g. see above). Error modelling, on the other hand, allows an indication of the spatial distribution of possible map inaccuracies to be given. This study explored two approaches to error modelling, which are demonstrated within the context of land cover analysis using remotely sensed imagery.The first approach involves the use of local class probabilities conditional to the pixels' spectral data. These probabilities are intermediate results of probabilistic image classification and indicate the magnitude and distribution of classification uncertainty. A case study demonstrated the implication of such uncertainty on change detection by comparing independently classified images. A major shortcoming of this approach is that it implicitly assumes data in neighbouring pixels to be independent. Moreover, it does not make full use of available reference data as it ignores their spatial component. It does not consider data locations nor does it use spatial dependence models that can be derived from the reference data.The assumption of independent pixels obviously impedes proper assessment of spatial uncertainty, such as joint uncertainty about the land cover class at several pixels taken together. Therefore, the second approach was based on geostatistical methods, which exploit spatial dependence rather than ignoring it. It is demonstrated how the above conditional probabilities can be updated by conditioning on sampled reference data at their locations. Stochastic simulation was used to generate a set of 500 equally probable maps, from which uncertainties regarding the spatial extent of contiguous olive orchards could be inferred.Future challenges include studies on other quality aspects of land survey datasets. The present research was limited to uncertainty analysis, so that, for example, data precision and fitness for use were not addressed. Other potential extensions to this work concern full inclusion of the third spatial dimension and modelling of temporal aspects.</p

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Glossarium BITri 2016 : Interdisciplinary Elucidation of Concepts, Metaphors, Theories and Problems Concerning Information

222 p.Terms included in this glossary recap some of the main concepts, theories, problems and metaphors concerning INFORMATION in all spheres of knowledge. This is the first edition of an ambitious enterprise covering at its completion all relevant notions relating to INFORMATION in any scientific context. As such, this glossariumBITri is part of the broader project BITrum, which is committed to the mutual understanding of all disciplines devoted to information across fields of knowledge and practic

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example