Abstract: This paper contributes to our knowledge about buyerseller
relations by identifying barriers and conflict situations
associated with maintaining and developing durable business
relationships by small companies. The contribution of prior studies
with regard to negative aspects of marketing relationships is
presented in the first section. The international research results are
discussed with regard to the existing conceptualizations and main
research implications identified at the end.
Abstract: Website plays a significant role in success of an e-business. It is the main start point of any organization and corporation for its customers, so it's important to customize and design it according to the visitors' preferences. Also, websites are a place to introduce services of an organization and highlight new service to the visitors and audiences. In this paper, we will use web usage mining techniques, as a new field of research in data mining and knowledge discovery, in an Iranian government website. Using the results, a framework for web content layour is proposed. An agent is designed to dynamically update and improve web links locations and layout. Then, we will explain how it is used to directly enable top managers of the organization to influence on the arrangement of web contents and also to enhance customization of web site navigation due to online users' behaviors.
Abstract: WikID is a wiki for industrial design engineers. An
important aspect for the viability of a wiki is the loyalty of the user
community to share their information and knowledge by adding this
knowledge to the wiki. For the initiators of a wiki it is therefore
important to use every aspect to stimulate the user community to
actively participate. In this study the focus is on the styling of the
website. The central question is: How could the WikID website be
visually designed to achieve a user experience which will incite the
user to actively participate in the WikID community? After a
literature study on the influencing factors of a website, a new
interface has been designed by applying the rules found, in order to
expand this website-s active user community. An online
questionnaire regarding the old or the new website gave insights in
the opinions of users. As expected, the new website was rated more
positively than the old website. However, the differences are limited.
Abstract: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.
Abstract: This work aims to explore the factors that have an incidence in reading comprehension process, with different type of texts. In a recent study with 2nd, 3rd and 4th grade children, it was observed that reading comprehension of narrative texts was better than comprehension of expository texts. Nevertheless it seems that not only the type of text but also other textual factors would account for comprehension depending on the cognitive processing demands posed by the text. In order to explore this assumption, three narrative and three expository texts were elaborated with different degree of complexity. A group of 40 fourth grade Spanish-speaking children took part in the study. Children were asked to read the texts and answer orally three literal and three inferential questions for each text. The quantitative and qualitative analysis of children responses showed that children had difficulties in both, narrative and expository texts. The problem was to answer those questions that involved establishing complex relationships among information units that were present in the text or that should be activated from children’s previous knowledge to make an inference. Considering the data analysis, it could be concluded that there is some interaction between the type of text and the cognitive processing load of a specific text.
Abstract: The efficient knowledge management system (KMS)
is one of the important strategies to help firms to achieve sustainable
competitive advantages, but little research has been conducted to
understand what contributes to the KMS success. This study thus set
to investigate the determinants of KMS success in the context of Thai
banking industry. A questionnaire survey was conducted in four
major Thai Banks to test the proposed KMS Success model.
The result of this study shows that KMS use and user satisfaction
relate significantly to the success of KMS, and knowledge quality,
service quality and trust lead to system use, and knowledge quality,
system quality and trust lead to user satisfaction. However, this
research focuses only on system and user-related factors. Future
research thus can extend to study factors such as management support
and organization readiness.
Abstract: This paper describes a 3D modeling system in
Augmented Reality environment, named 3DARModeler. It can be
considered a simple version of 3D Studio Max with necessary
functions for a modeling system such as creating objects, applying
texture, adding animation, estimating real light sources and casting
shadows. The 3DARModeler introduces convenient, and effective
human-computer interaction to build 3D models by combining both
the traditional input method (mouse/keyboard) and the tangible input
method (markers). It has the ability to align a new virtual object with
the existing parts of a model. The 3DARModeler targets nontechnical
users. As such, they do not need much knowledge of
computer graphics and modeling techniques. All they have to do is
select basic objects, customize their attributes, and put them together
to build a 3D model in a simple and intuitive way as if they were
doing in the real world. Using the hierarchical modeling technique,
the users are able to group several basic objects to manage them as a
unified, complex object. The system can also connect with other 3D
systems by importing and exporting VRML/3Ds Max files. A
module of speech recognition is included in the system to provide
flexible user interfaces.
Abstract: Propagation of solitons in single-mode birefringent fibers is considered under the presence of third-order dispersion (TOD). The behavior of two neighboring solitons and their interaction is investigated under the presence of third-order dispersion with different group velocity dispersion (GVD) parameters. It is found that third-order dispersion makes the resultant soliton to deviate from its ideal position and increases the interaction between adjacent soliton pulses. It is also observed that this deviation due to third-order dispersion is considerably small when the optical pulse propagates at wavelengths relatively far from the zerodispersion. Modified coupled nonlinear Schrödinger-s equations (CNLSE) representing the propagation of optical pulse in single mode fiber with TOD are solved using split-step Fourier algorithm. The results presented in this paper reveal that the third-order dispersion can substantially increase the interaction between the solitons, but large group velocity dispersion reduces the interaction between neighboring solitons.
Abstract: Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.
Abstract: Within the last years, several technologies have been developed to help building e-learning portals. Most of them follow approaches that deliver a vast amount of functionalities, suitable for class-like learning. The SuGI project, as part of the D-Grid (funded by the BMBF), targets on delivering a highly scalable and sustainable learning solution to provide materials (e.g. learning modules, training systems, webcasts, tutorials, etc.) containing knowledge about Grid computing to the D-Grid community. In this article, the process of the development of an e-learning portal focused on the requirements of this special user group is described. Furthermore, it deals with the conceptual and technical design of an e-learning portal, addressing the special needs of heterogeneous target groups. The main focus lies on the quality management of the software development process, Web templates for uploading new contents, the rich search and filter functionalities which will be described from a conceptual as well as a technical point of view. Specifically, it points out best practices as well as concepts to provide a sustainable solution to a relatively unknown and highly heterogeneous community.
Abstract: This research aims to study value-creation process of
producing monk-s bowls, Thai traditional handicrafts, which is facing problems in adapting to the changing society. It also aims to identify
problems and obstacles to value creation. This research is based on a case study of monk-s bowl manufactures from Ban-Baat Village,
Bangkok. The conceptual framework is based on the model of value
chain to analyze the process.
The research methodology is qualitative. This research found that the value-creation process of monk-s bowls consists of eight
activities contributing to adding value to the products and increasing
profits to the producers in return. Five major problems and obstacles
are found.
The research suggests that these problems and obstacles limit the manufacturers- potential for creating more valued product and lead to business stagnation. These problems should be addressed and solved with collaboration among the government, the private sector and the
manufacturers.
Abstract: Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.
Abstract: The decision to recruit manpower in an organization
requires clear identification of the criteria (attributes) that distinguish
successful from unsuccessful performance. The choice of appropriate
attributes or criteria in different levels of hierarchy in an organization
is a multi-criteria decision problem and therefore multi-criteria
decision making (MCDM) techniques can be used for prioritization
of such attributes. Analytic Hierarchy Process (AHP) is one such
technique that is widely used for deciding among the complex criteria
structure in different levels. In real applications, conventional AHP
still cannot reflect the human thinking style as precise data
concerning human attributes are quite hard to be extracted. Fuzzy
logic offers a systematic base in dealing with situations, which are
ambiguous or not well defined. This study aims at defining a
methodology to improve the quality of prioritization of an
employee-s performance measurement attributes under fuzziness. To
do so, a methodology based on the Extent Fuzzy Analytic Hierarchy
Process is proposed. Within the model, four main attributes such as
Subject knowledge and achievements, Research aptitude, Personal
qualities and strengths and Management skills with their subattributes
are defined. The two approaches conventional AHP
approach and the Extent Fuzzy Analytic Hierarchy Process approach
have been compared on the same hierarchy structure and criteria set.
Abstract: Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.
Abstract: The objective of this work is to explicit knowledge on the interactions between the chlorophyll-a and nine meroplankton larvae of epibenthonic fauna. The studied case is the Arraial do Cabo upwelling system, Southeastern of Brazil, which provides different environmental conditions. To assess this information a network approach based in probability estimative was used. Comparisons among the generated graphs are made in the light of different water masses, application of Shannon biodiversity index, and the closeness and betweenness centralities measurements. Our results show the main pattern among different water masses and how the core organisms belonging to the network skeleton are correlated to the main environmental variable. We conclude that the approach of complex networks is a promising tool for environmental diagnostic.
Abstract: Result of the study on knowledge management systems in businesses was shown that the most of these businesses provide internet accessibility for their employees in order to study new knowledge via internet, corporate website, electronic mail, and electronic learning system. These business organizations use information technology application for knowledge management because of convenience, time saving, ease of use, accuracy of information and knowledge usefulness. The result indicated prominent improvements for corporate knowledge management systems as the following; 1) administrations must support corporate knowledge management system 2) the goal of corporate knowledge management must be clear 3) corporate culture should facilitate the exchange and sharing of knowledge within the organization 4) cooperation of personnel of all levels must be obtained 5) information technology infrastructure must be provided 6) they must develop the system regularly and constantly.
Abstract: The conventional GA combined with a local search
algorithm, such as the 2-OPT, forms a hybrid genetic algorithm(HGA)
for the traveling salesman problem (TSP). However, the geometric
properties which are problem specific knowledge can be used to
improve the search process of the HGA. Some tour segments (edges)
of TSPs are fine while some maybe too long to appear in a short tour.
This knowledge could constrain GAs to work out with fine tour
segments without considering long tour segments as often.
Consequently, a new algorithm is proposed, called intelligent-OPT
hybrid genetic algorithm (IOHGA), to improve the GA and the 2-OPT
algorithm in order to reduce the search time for the optimal solution.
Based on the geometric properties, all the tour segments are assigned
2-level priorities to distinguish between good and bad genes. A
simulation study was conducted to evaluate the performance of the
IOHGA. The experimental results indicate that in general the IOHGA
could obtain near-optimal solutions with less time and better accuracy
than the hybrid genetic algorithm with simulated annealing algorithm
(HGA(SA)).
Abstract: Fossil fuels are the major source to meet the world
energy requirements but its rapidly diminishing rate and adverse
effects on our ecological system are of major concern. Renewable
energy utilization is the need of time to meet the future challenges.
Ocean energy is the one of these promising energy resources. Threefourths
of the earth-s surface is covered by the oceans. This enormous
energy resource is contained in the oceans- waters, the air above the
oceans, and the land beneath them. The renewable energy source of
ocean mainly is contained in waves, ocean current and offshore solar
energy. Very fewer efforts have been made to harness this reliable
and predictable resource. Harnessing of ocean energy needs detail
knowledge of underlying mathematical governing equation and their
analysis. With the advent of extra ordinary computational resources
it is now possible to predict the wave climatology in lab simulation.
Several techniques have been developed mostly stem from numerical
analysis of Navier Stokes equations. This paper presents a brief over
view of such mathematical model and tools to understand and
analyze the wave climatology. Models of 1st, 2nd and 3rd generations
have been developed to estimate the wave characteristics to assess the
power potential. A brief overview of available wave energy
technologies is also given. A novel concept of on-shore wave energy
extraction method is also presented at the end. The concept is based
upon total energy conservation, where energy of wave is transferred
to the flexible converter to increase its kinetic energy. Squeezing
action by the external pressure on the converter body results in
increase velocities at discharge section. High velocity head then can
be used for energy storage or for direct utility of power generation.
This converter utilizes the both potential and kinetic energy of the
waves and designed for on-shore or near-shore application. Increased
wave height at the shore due to shoaling effects increases the
potential energy of the waves which is converted to renewable
energy. This approach will result in economic wave energy
converter due to near shore installation and more dense waves due to
shoaling. Method will be more efficient because of tapping both
potential and kinetic energy of the waves.
Abstract: Text Mining is around applying knowledge discovery
techniques to unstructured text is termed knowledge discovery in text
(KDT), or Text data mining or Text Mining. In decision tree
approach is most useful in classification problem. With this
technique, tree is constructed to model the classification process.
There are two basic steps in the technique: building the tree and
applying the tree to the database. This paper describes a proposed
C5.0 classifier that performs rulesets, cross validation and boosting
for original C5.0 in order to reduce the optimization of error ratio.
The feasibility and the benefits of the proposed approach are
demonstrated by means of medial data set like hypothyroid. It is
shown that, the performance of a classifier on the training cases from
which it was constructed gives a poor estimate by sampling or using a
separate test file, either way, the classifier is evaluated on cases that
were not used to build and evaluate the classifier are both are large. If
the cases in hypothyroid.data and hypothyroid.test were to be
shuffled and divided into a new 2772 case training set and a 1000
case test set, C5.0 might construct a different classifier with a lower
or higher error rate on the test cases. An important feature of see5 is
its ability to classifiers called rulesets. The ruleset has an error rate
0.5 % on the test cases. The standard errors of the means provide an
estimate of the variability of results. One way to get a more reliable
estimate of predictive is by f-fold –cross- validation. The error rate of
a classifier produced from all the cases is estimated as the ratio of the
total number of errors on the hold-out cases to the total number of
cases. The Boost option with x trials instructs See5 to construct up to
x classifiers in this manner. Trials over numerous datasets, large and
small, show that on average 10-classifier boosting reduces the error
rate for test cases by about 25%.
Abstract: This study endeavors to evaluate the effects of farmers’ training program on the adoption of improved farming practices, the output of rice farming, and the income as well as the profit from rice farming by employing an ex-post non-experimental data in Sierra Leone. It was established that participating in farmers’ training program increased the possibility of adoption of the improved farming activities that were implemented in the study area. Through the training program also, the proceeds from rice production was also established to have increased considerably. These results were in line with the assumption that one of the main constraints on the growth in agricultural output particularly rice cultivation in most African states is the lack of efficient extension programs.