DCIT Lecturers' Papers
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Papers, abstracts and publications authored by staff of the Department of Computing and Information Technology, UWI St. Augustine.
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Browsing DCIT Lecturers' Papers by Author "Nikov, Alexander"
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Item Item Computational intelligence-based personalization of interactive web systems(2011-06-14) Rambharose, Tricia; Nikov, AlexanderThe main Computational Intelligence (CI) models for personalization of interactive web systems are identified as Fuzzy Systems, Genetic Algorithms, Neural Networks, Artificial Immune Systems and Swarm Intelligence which includes Particle Swarm Optimization, Ant Colony Optimization, Bee Colony Optimization and Wasp Colony Optimization. These models are reviewed and compared regarding their inception, functions, performance and application to personalization of interactive web systems. A taxonomy for personalization of interactive web systems based on CI methods is proposed. It uses two approaches to personalize web-based systems as profile generation and profile exploitation. Based on this taxonomy a general procedure and recommendations for personalization of interactive web systems are suggested. Future directions for application of CI modelling for personalization are discussed.Item Education in Geographic information systems usability and user-adaptivity(2011-06-14) McAdams, Michael Andrew; Nikov, AlexanderThe objective of this paper is to advance the usability and adaption for users of geographic information systems(GIS) by developing suitably trained professionals via a tailored graduate program. Human-computer interaction models are useful in analyzing the use of GIS in specific task situations.Such analysis provides a sound basis for GIS use optiomization.GIS applications,i.e., in environmental protection or urban and regional planning, require entirely different user interfaces that for those where users are experts. Building such interfaces taking into account GIS usability and adaptation to the user appears to be a promising approach.This could be achieved by careful analysis of GIS utilization and dynamic adaptation to user preferences and interests, the given task,goals and actual work context.The students should learn the usablity principles and how to apply them for designing GIS user interfaces.In addition, they should know to analyze GIS user tasks,interests, and preferences and adapt GIS user interfaces to them.This paper integrates these two complementary disciplines.Examples using ArcView illustrating GIS usability and user adaptivity are given.A MS GIS program with specification in usability and user-adaptive systems is proposed.Item A fuzzy backpropagation algorithm(2011-06-14) Stoeva, Stefka; Nikov, AlexanderThis paper presents an extension of the standard backpropagation algorithm (SBP). The proposed learning algorithm is based on the fuzzy integral of Sugeno and thus called fuzzy backpropagation (FBP) algorithm. Necessary and sufficient conditions for convergence of FBP algorithm for single-output networks in case of single- and multiple-training patterns are proved. A computer simulation illustrates and confirms the theoretical results. FBP algorithm shows considerably greater convergence rate compared to SBP algorithm. Other advantages of FBP algorithm are that it reaches forward to the target value without oscillations, requires no assumptions about probability distribution and independence of input data. The convergence conditions enable training by automation of weights tuning process (quasi-unsupervised learning) pointing out the interval where the target value belongs to. This supports acquisition of implicit knowledge and ensures wide application, e.g. for creation of adaptable user interfaces, assessment of products, intelligent data analysis, etc.Item A methodology for human factors analysis in office automation systems(2011-06-14) Nikov, Alexander; Matarazzo, Giacinto; Orlando, AntoninoA methodology for computer-aided human factors analysis in office automation systems (OAS) design and implementation process has been developed. It incorporates a fuzzy knowledge-based evaluation mechanism which is employed to aggregate data measured in scales of different type. The methodology has a high degree of flexibility which allows it to be adjusted to the individual client situation. A case study in public administration for assessing OAS introduction from users' point of view has been carried out. On the basis of the results recommendations on further implementation have been proposed. The advantages, disadvantages, and further developments are discussedItem NN-AirPol: a neural-networks-based method for air pollution evaluation and control(2011-06-14) Karaca, Ferhat; Nikov, Alexander; Alagha, OmarA method for air pollution evaluation and control, based on one of the most popular neural networks – the backpropagation algorithm, is proposed. After the backpropagation training, the neural network, based on weather forecasting data, determines the future concentration of critical air pollution indicators. Depending on these concentrations, relevant episode warnings and actions are activated. A case study is carried out to illustrate and validate the method proposed, based on Istanbul air pollution data. Sulphur dioxide and inhalable particulate matter are selected as air pollution indicators (neural network outputs). Relevant episode measures are proposed. Among ten backpropagation algorithms, the BFGS algorithm (Quasi-Newton algorithms) is adopted since it showed the lowest training error. A comparison of NN-AirPol method against regression and perceptron models showed significantly better performanceItem Quick fuzzy backpropagation algorithm(2011-06-14) Nikov, Alexander; Stoeva, StefkaA modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.Item A Study of the Effectiveness of the Routing Decision Support Algorithm(2010-11-03T15:11:32Z) Sahai, Ashok; Nikov, Alexander; Goodridge, WayneMulti criteria decision making (MCDM) methods like analytic hierarchy process, ELECTRE and multi-attribute utility theory are critically studied. They have irregularities in terms of the reliability of ranking of the best alternatives. The Routing Decision Support (RDS) algorithm is trying to improve some of their deficiencies. This paper gives a mathematical verification that the RDS algorithm conforms to the test criteria for an effective MCDM method when a linear preference function is consideredItem Swarm-Optimization-Based Affective Product Design Illustrated by a Pen Case-Study(2011-06-14) Mohais, Arvind; Nikov, Alexander; Sahai, Ashok; Nesil, SelahattinAn optimization approach for suggesting product design parameters based on emotive responses is proposed that combines Kansei techniques and particle swarm optimization algorithm (PSO). The approach involves designing a Kansei survey for collecting data on customers’ affective responses to various aspects of a product, using several exemplars of the product. After information gathering, the PSO algorithm is employed to build a prediction binary linear model that aggregates the survey data. Subsequently, another binary linear model links product design parameters to the outputs of the first model to establish mathematical connections between the subjective impression of a product (Kansei) and its properties. This approach is illustrated by a case study for pen design. Three PSO neighborhood configurations are tested, and the results yield insight into the nature of the optimization function. Experimental work in implementing the proposed approach was able to suggest customers’ preferences for pen design attributes that would be considered optimal by all of those surveyed. They can be used for improvement and development of new future products.Item UWIS: An assessment methodology for usability of web-based information systems(2011-06-14) Oztekin, Asil; Nikov, Alexander; Zaim, SelimA methodology for usability assessment and design of web-based information systems (UWIS) is proposed. It combines web-based service quality and usability dimensions of information systems. Checklist items with the highest and the lowest contribution to the usability performance of a web-based information system can be specified by UWIS. A case study by a student information system at Fatih University is carried out to validate the methodology. UWIS reveals a strong relationship between quality and usability which is assumed to exist by many researchers but not experimentally analyzed yet. This study depicts a strong relevance between web-based service quality and usability of web-based information systems. UWIS methodology can be used for designing more usable and higher quality web-based information systems.