Abstract: One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.
Abstract: Mobile agents are a powerful approach to develop distributed systems since they migrate to hosts on which they have the resources to execute individual tasks. In a dynamic environment like a peer-to-peer network, Agents have to be generated frequently and dispatched to the network. Thus they will certainly consume a certain amount of bandwidth of each link in the network if there are too many agents migration through one or several links at the same time, they will introduce too much transferring overhead to the links eventually, these links will be busy and indirectly block the network traffic, therefore, there is a need of developing routing algorithms that consider about traffic load. In this paper we seek to create cooperation between a probabilistic manner according to the quality measure of the network traffic situation and the agent's migration decision making to the next hop based on decision tree learning algorithms.
Abstract: An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.
Abstract: A learning content management system (LCMS) is an
environment to support web-based learning content development.
Primary function of the system is to manage the learning process as
well as to generate content customized to meet a unique requirement
of each learner. Among the available supporting tools offered by
several vendors, we propose to enhance the LCMS functionality to
individualize the presented content with the induction ability. Our
induction technique is based on rough set theory. The induced rules
are intended to be the supportive knowledge for guiding the content
flow planning. They can also be used as decision rules to help
content developers on managing content delivered to individual
learner.
Abstract: According to dramatic growth of internet services, an easy and prompt service deployment has been important for internet service providers to successfully maintain time-to-market. Before global service deployment, they have to pay the big cost for service evaluation to make a decision of the proper system location, system scale, service delay and so on. But, intra-Lab evaluation tends to have big gaps in the measured data compared with the realistic situation, because it is very difficult to accurately expect the local service environment, network congestion, service delay, network bandwidth and other factors. Therefore, to resolve or ease the upper problems, we propose multiple cloud based GPES Broker system and use case that helps internet service providers to alleviate the above problems in beta release phase and to make a prompt decision for their service launching. By supporting more realistic and reliable evaluation information, the proposed GPES Broker system saves the service release cost and enables internet service provider to make a prompt decision about their service launching to various remote regions.
Abstract: This main purpose of the study reported here was to
investigate the extent to which the form of school
governance (particularly decision-making) had an impact upon the
effectiveness of the school with reference to parental involvement,
planning and budgeting, professional development of teachers,
school facilities and resources, and student outcomes. Particular
attention was given to decision-making within the governance
arrangements. The study was based on four case studies of high
schools in New South Wales, Australia including one government
school, one independent Christian community school, one
independent Catholic school, and one Catholic systemic school.
The focus of the research was principals, teachers, parents, and
students of four schools with varying governance structures. To
gain a greater insight into the issues, the researchers collected
information by questionnaire, semi-structured interview, and
review of school key documents. This study found that it was not
so much structure but the centrality of the school Principal and the
way that the Principal perceived his/her roles in relation to others
that impacted most on school governance.
Abstract: Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.
Abstract: There has been considerable growth in the issue of
food & beverage safety in Thailand. This is important because the
level of satisfaction in food & beverage safety has impacts on travel
decision made by foreign tourists. Therefore, this paper was aimed to
test if there is any significant gender effect in the level of satisfaction
of food & beverage safety made by foreign tourists in Thailand. In
addition, this paper utilized the Chi Square test of independent to test
if there was an association between gender and sickness because of
food and if there was an association between gender and the
perception of food safety standard. During January to June, 2012, a
total of 400 foreign tourist respondents, 200 male as well as 200
female foreign tourists, were interviewed at the departure lounge at
Suvarnabhumi airport, Thailand. The findings revealed the
astonishing result that there was no significant effect of gender. Also,
there was no significant difference in the association between gender
and being sick because of food as well as the association between
gender and the perception on the standard of food safety during their
trip in Thailand.
Abstract: Several researchers have proposed methods about
combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of
GA to obtain fuzzy rules and application of fuzzy logic in
optimization of GA). In this paper, we suggest a new method in
which fuzzy decision making is used to improve the performance of
genetic algorithm. In the suggested method, we determine the alleles
that enhance the fitness of chromosomes and try to insert them to the
next generation.
In this algorithm we try to present an innovative vaccination in the
process of reproduction in genetic algorithm, with considering the
trade off between exploration and exploitation.
Abstract: In a representative democracy political parties
promote vital competition on different policy issues and play
essential roles by offering ideological alternatives. They also give
channels for citizens- participation in government decision-making
processes and they are significant conduits and interpreters of
information about government. This paper attempts to examine how
opposition political parties and rebel fronts emerged in Ethiopia, and
examines their present conditions. In this paper, selected case studies
of political parties and rebel fronts are included to highlight the status
and the role of opposition groups in the country in the three
successive administrations: Haile Selassie (1930-1974), Derg (1974-
1991), and EPRDF (1991-Present).
Abstract: The decision of information technology (IT) outsourcing requires close attention to the evaluation of supplier selection process because the selection decision involves conflicting multiple criteria and is replete with complex decision making problems. Selecting the most appropriate suppliers is considered an important strategic decision that may impact the performance of outsourcing engagements. The objective of this paper is to aid decision makers to evaluate and assess possible IT outsourcing suppliers. An axiomatic design based fuzzy group decision making is adopted to evaluate supplier alternatives. Finally, a case study is given to demonstrate the potential of the methodology. KeywordsIT outsourcing, Supplier selection, Multi-criteria decision making, Axiomatic design, Fuzzy logic.
Abstract: In the end of the day, meteorological data and environmental data becomes widely used such as plant varieties selection system. Variety plant selection for planted area is of almost importance for all crops, including varieties of sugarcane. Since sugarcane have many varieties. Variety plant non selection for planting may not be adapted to the climate or soil conditions for planted area. Poor growth, bloom drop, poor fruit, and low price are to be from varieties which were not recommended for those planted area. This paper presents plant varieties selection system for planted areas in Thailand from meteorological data and environmental data by the use of decision tree techniques. With this software developed as an environmental data analysis tool, it can analyze resulting easier and faster. Our software is a front end of WEKA that provides fundamental data mining functions such as classify, clustering, and analysis functions. It also supports pre-processing, analysis, and decision tree output with exporting result. After that, our software can export and display data result to Google maps API in order to display result and plot plant icons effectively.
Abstract: Leprosy is an infectious disease caused by
Mycobacterium Leprae, this disease, generally, compromises
the neural fibers, leading to the development of disability.
Disabilities are changes that limit daily activities or social life
of a normal individual. When comes to leprosy, the study of
disability considered the functional limitation (physical
disabilities), the limitation of activity and social participation,
which are measured respectively by the scales: EHF, SALSA
and PARTICIPATION SCALE. The objective of this work is
to propose an on-line monitoring of leprosy patients, which is
based on information scales EHF, SALSA and
PARTICIPATION SCALE. It is expected that the proposed
system is applied in monitoring the patient during treatment
and after healing therapy of the disease. The correlations that
the system is between the scales create a variety of
information, presented the state of the patient and full of
changes or reductions in disability. The system provides
reports with information from each of the scales and the
relationships that exist between them. This way, health
professionals, with access to patient information, can
intervene with techniques for the Prevention of Disability.
Through the automated scale, the system shows the level of
the patient and allows the patient, or the responsible, to take a
preventive measure. With an online system, it is possible take
the assessments and monitor patients from anywhere.
Abstract: Until recently, researchers have developed various
tools and methodologies for effective clinical decision-making.
Among those decisions, chest pain diseases have been one of
important diagnostic issues especially in an emergency department. To
improve the ability of physicians in diagnosis, many researchers have
developed diagnosis intelligence by using machine learning and data
mining. However, most of the conventional methodologies have been
generally based on a single classifier for disease classification and
prediction, which shows moderate performance. This study utilizes an
ensemble strategy to combine multiple different classifiers to help
physicians diagnose chest pain diseases more accurately than ever.
Specifically the ensemble strategy is applied by using the integration
of decision trees, neural networks, and support vector machines. The
ensemble models are applied to real-world emergency data. This study
shows that the performance of the ensemble models is superior to each
of single classifiers.
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: In this paper a new method is suggested for risk
management by the numerical patterns in data-mining. These patterns
are designed using probability rules in decision trees and are cared to
be valid, novel, useful and understandable. Considering a set of
functions, the system reaches to a good pattern or better objectives.
The patterns are analyzed through the produced matrices and some
results are pointed out. By using the suggested method the direction
of the functionality route in the systems can be controlled and best
planning for special objectives be done.
Abstract: Ranking of fuzzy numbers play an important role in
decision making, optimization, forecasting etc. Fuzzy numbers must
be ranked before an action is taken by a decision maker. In this
paper, with the help of several counter examples it is proved that
ranking method proposed by Chen and Chen (Expert Systems with
Applications 36 (2009) 6833-6842) is incorrect. The main aim of this
paper is to propose a new approach for the ranking of generalized
trapezoidal fuzzy numbers. The main advantage of the proposed
approach is that the proposed approach provide the correct ordering
of generalized and normal trapezoidal fuzzy numbers and also the
proposed approach is very simple and easy to apply in the real life
problems. It is shown that proposed ranking function satisfies all
the reasonable properties of fuzzy quantities proposed by Wang and
Kerre (Fuzzy Sets and Systems 118 (2001) 375-385).
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.
Abstract: The number of users supported in a DS-CDMA
cellular system is typically less than spreading factor (N), and the
system is said to be underloaded. Overloading is a technique to
accommodate more number of users than the spreading factor N. In
O/O overloading scheme, the first set is assigned to the N
synchronous users and the second set is assigned to the additional
synchronous users. An iterative multistage soft decision interference
cancellation (SDIC) receiver is used to remove high level of
interference between the two sets. Performance is evaluated in terms
of the maximum number acceptable users so that the system
performance is degraded slightly compared to the single user
performance at a specified BER. In this paper, the capacity of CDMA
based O/O overloading scheme is evaluated with SDIC receiver. It is
observed that O/O scheme using orthogonal Gold codes provides
25% channel overloading (N=64) for synchronous DS-CDMA
system on an AWGN channel in the uplink at a BER of 1e-5.For a
Rayleigh faded channel, the critical capacity is 40% at a BER of 5e-5
assuming synchronous users. But in practical systems, perfect chip
timing is very difficult to maintain in the uplink.. We have shown that
the overloading performance reduces to 11% for a timing
synchronization error of 0.02Tc for a BER of 1e-5.
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.