Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models

There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data

Context-Aware and Adaptable eLearning Systems

The full text file attached to this record contains a copy of the thesis without the authors publications attached. The list of publications that are attached to the complete thesis can be found on pages 6-7 in the thesis.This thesis proposed solutions to some shortcomings to current eLearning architectures. The proposed DeLC architecture supports context-aware and adaptable provision of eLearning services and electronic content. The architecture is fully distributed and integrates service-oriented development with agent technology. Central to this architecture is that a node is our unit of computation (known as eLearning node) which can have purely service-oriented architecture, agent-oriented architecture or mixed architecture. Three eLeaerning Nodes have been implemented in order to demonstrate the vitality of the DeLC concept. The Mobile eLearning Node uses a three-level communication network, called InfoStations network, supporting mobile service provision. The services, displayed on this node, are to be aware of its context, gather required learning material and adapted to the learner request. This is supported trough a multi-layered hybrid (service- and agent-oriented) architecture whose kernel is implemented as middleware. For testing of the middleware a simulation environment has been developed. In addition, the DeLC development approach is proposed. The second eLearning node has been implemented as Education Portal. The architecture of this node is poorly service-oriented and it adopts a client-server architecture. In the education portal, there are incorporated education services and system services, called engines. The electronic content is kept in Digital Libraries. Furthermore, in order to facilitate content creators in DeLC, the environment Selbo2 was developed. The environment allows for creating new content, editing available content, as well as generating educational units out of preexisting standardized elements. In the last two years, the portal is used in actual education at the Faculty of Mathematics and Informatics, University of Plovdiv. The third eLearning node, known as Agent Village, exhibits a purely agent-oriented architecture. The purpose of this node is to provide intelligent assistance to the services deployed on the Education Pportal. Currently, two kinds of assistants are implemented in the node - eTesting Assistants and Refactoring eLearning Environment (ReLE). A more complex architecture, known as Education Cluster, is presented in this thesis as well. The Education Cluster incorporates two eLearning nodes, namely the Education Portal and the Agent Village. eLearning services and intelligent agents interact in the cluster

A recent review of conventional vs. automated cybersecurity anti-phishing techniques

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link."In the era of electronic and mobile commerce, massive numbers of financial transactions are conducted online on daily basis, which created potential fraudulent opportunities. A common fraudulent activity that involves creating a replica of a trustful website to deceive users and illegally obtain their credentials is website phishing. Website phishing is a serious online fraud, costing banks, online users, governments, and other organisations severe financial damages. One conventional approach to combat phishing is to raise awareness and educate novice users on the different tactics utilised by phishers by conducting periodic training or workshops. However, this approach has been criticised of being not cost effective as phishing tactics are constantly changing besides it may require high operational cost. Another anti- phishing approach is to legislate or amend existing cyber security laws that persecute online fraudsters without minimising its severity. A more promising anti-phishing approach is to prevent phishing attacks using intelligent machine learning (ML) technology. Using this technology, a classification system is integrated in the browser in which it will detect phishing activities and communicate these with the end user. This paper reviews and critically analyses legal, training, educational and intelligent anti-phishing approaches. More importantly, ways to combat phishing by intelligent and conventional are highlighted, besides revealing these approaches differences, similarities and positive and negative aspects from the user and performance prospective. Different stakeholders such as computer security experts, researchers in web security as well as business owners may likely benefit from this review on website phishing.

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.Qatar National Librar

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

Programación por metas con niveles de aspiración imprecisos

En la programación por metas borrosa se trabaja con metas imprecisas del tipo “esencialmente menor (mayor) que bi”, las cuales se modelan mediante conjuntos borrosos, cuya función de pertenencia mide el grado de satisfacción respecto del logro de la meta. Para valores menores (mayores) que el nivel de aspiración bi el grado de satisfacción va disminuyendo monótonamente hasta un umbral, fijado por el decisor, a partir del cual el grado de satisfacción es nulo. El hecho de que el umbral no se pueda sobrepasar conduce, en algunos casos, a problemas infactibles, para evitar lo cual se tiende a ampliar excesivamente dicho umbral. En este trabajo, en lugar de maximizar el grado de satisfacción, proponemos un enfoque similar al de la programación por metas estándar, de manera que lo que pretendemos es minimizar la distancia a los niveles de aspiración. Obtenemos un programa matemático con función objetivo cuadrática y restricciones lineales y, por tanto, fácil de resolver. Se incluye un ejemplo numérico en el que se compara el enfoque propuesto con la programación por metas estándar y con el enfoque borroso ordinario.KUTXA; Vicerrectorado de Campus de Gipuzkoa de la UPV/EH

An Investigation into the Data Collection Process for the Development of Cost Models

This thesis is the result of many years of research in the field of manufacturing cost modelling. It particularly focuses on the Data Collection Process for the development of manufacturing cost models in the UK Aerospace Industry with no less important contributions from other areas such as construction, process and software development. The importance of adopting an effective model development process is discussed and a new CMD Methodology is proposed. In this respect, little research has considered the development of the cost model from the point of view of a standard and systematic Methodology, which is essential if an optimum process is to be achieved. A Model Scoping 3 Framework, a functional Data Source and Data Collection Library and a referential Data Type Library are the core elements of the proposed Cost Model Development Methodology. The research identified a number of individual data collection methods, along with a comprehensive list of data sources and data types, from which essential data for developing cost models could be collected. A Taxonomy based upon sets of generic characteristics for describing the individual data collection, data sources and data types was developed. The methods, tools and techniques were identified and categorised according to these generic characteristics. This provides information for selecting between alternative methods, tools and techniques. The need to perform frequent iterations of data collection, data identification, data analysis and decision making tasks until an acceptable cost model has been developed has become an inherent feature of the CMDP. It is expected that the proposed model scoping framework will assist cost engineering and estimating practitioners in: defining the features, activities of the process and the attributes of the product for which a cost model is required, and also in identifying the cost model characteristics before the tasks of data identification and collection start. It offers a structured way of looking at the relationship between data sources, cost model characteristics and data collection tools and procedures. The aim was to make the planning process for developing cost models more effective and efficient and consequently reduce the time to generate cost models

Autonomous Sailboat Navigation

The purpose of this study was to investigate novel methods on an unmanned sailing boat, which enables it to sail fully autonomously, navigate safely, and perform long-term missions. The author used robotic sailing boat prototypes for field experiments as his main research method. Two robotic sailing boats have been developed especially for this purpose. A compact software model of a sailing boat's behaviour allowed for further evaluation of routing and obstacle avoidance methods in a computer simulation. The results of real-world experiments and computer simulations are validated against each other. It has been demonstrated that autonomous boat sailing is possible by the effective combination of appropriate new and novel techniques that will allow autonomous sailing boats to create appropriate routes, to react properly on obstacles and to carry out sailing manoeuvres by controlling rudder and sails. Novel methods for weather routing, collision avoidance, and autonomous manoeuvre execution have been proposed and successfully demonstrated. The combination of these techniques in a layered hybrid subsumption architecture make robotic sailing boats a promising tool for many applications, especially in ocean observation