Abstract: Chronic hepatitis B can evolve to cirrhosis and liver
cancer. Interferon is the only effective treatment, for carefully selected
patients, but it is very expensive. Some of the selection criteria are
based on liver biopsy, an invasive, costly and painful medical procedure.
Therefore, developing efficient non-invasive selection systems,
could be in the patients benefit and also save money. We investigated
the possibility to create intelligent systems to assist the Interferon
therapeutical decision, mainly by predicting with acceptable accuracy
the results of the biopsy. We used a knowledge discovery in integrated
medical data - imaging, clinical, and laboratory data. The resulted
intelligent systems, tested on 500 patients with chronic hepatitis
B, based on C5.0 decision trees and boosting, predict with 100%
accuracy the results of the liver biopsy. Also, by integrating the other
patients selection criteria, they offer a non-invasive support for the
correct Interferon therapeutic decision. To our best knowledge, these
decision systems outperformed all similar systems published in the
literature, and offer a realistic opportunity to replace liver biopsy in
this medical context.
Abstract: Reference point effects of top managers exerts an influence on managerial decision-making behaviors. We introduces the main idea of developing the decision behavior testing system designed for top manager in team task circumstance. According to the theory of the reference point effect, study of testing experiments in the reference point effect is carried out. Under managerial decision-making simulation environment, a platform is designed for testing reference point effect. The system uses the outcome of the value of the reference point to report the characteristics of the decision behavior of top managers.
Abstract: This paper proposes an innovative methodology for
Acceptance Sampling by Variables, which is a particular category of
Statistical Quality Control dealing with the assurance of products
quality. Our contribution lies in the exploitation of machine learning
techniques to address the complexity and remedy the drawbacks of
existing approaches. More specifically, the proposed methodology
exploits Artificial Neural Networks (ANNs) to aid decision making
about the acceptance or rejection of an inspected sample. For any
type of inspection, ANNs are trained by data from corresponding
tables of a standard-s sampling plan schemes. Once trained, ANNs
can give closed-form solutions for any acceptance quality level and
sample size, thus leading to an automation of the reading of the
sampling plan tables, without any need of compromise with the
values of the specific standard chosen each time. The proposed
methodology provides enough flexibility to quality control engineers
during the inspection of their samples, allowing the consideration of
specific needs, while it also reduces the time and the cost required for
these inspections. Its applicability and advantages are demonstrated
through two numerical examples.
Abstract: This paper investigates the optimization problem of
multi-product aggregate production planning (APP) with fuzzy data.
From a comprehensive viewpoint of conserving the fuzziness of input
information, this paper proposes a method that can completely
describe the membership function of the performance measure. The
idea is based on the well-known Zadeh-s extension principle which
plays an important role in fuzzy theory. In the proposed solution
procedure, a pair of mathematical programs parameterized by
possibility level a is formulated to calculate the bounds of the
optimal performance measure at a . Then the membership function of
the optimal performance measure is constructed by enumerating
different values of a . Solutions obtained from the proposed method
contain more information, and can offer more chance to achieve the
feasible disaggregate plan. This is helpful to the decision-maker in
practical applications.
Abstract: Higher productivity and less cost in the ship
manufacturing process are required to maintain the international
competitiveness of morden manufacturing industries. In shipbuilding,
however, the Engineering To Order (ETO) production method and
production process is very difficult. Thus, designs change frequently.
In accordance with production, planning should be set up according
to scene changes. Therefore, fixed production planning is very
difficult. Thus, a scheduler must first make sketchy plans, then
change the plans based on the work progress and modifications.
Thus, data sharing in a shipbuilding block assembly shop is very
important. In this paper, we proposed to scheduling method
applicable to the shipbuilding industry and decision making support
system through web based visualization system.
Abstract: This paper discusses the performance modeling and availability analysis of Yarn Dyeing System of a Textile Industry. The Textile Industry is a complex and repairable engineering system. Yarn Dyeing System of Textile Industry consists of five subsystems arranged in series configuration. For performance modeling and analysis of availability, a performance evaluating model has been developed with the help of mathematical formulation based on Markov-Birth-Death Process. The differential equations have been developed on the basis of Probabilistic Approach using a Transition Diagram. These equations have further been solved using normalizing condition in order to develop the steady state availability, a performance measure of the system concerned. The system performance has been further analyzed with the help of decision matrices. These matrices provide various availability levels for different combinations of failure and repair rates for various subsystems. The findings of this paper are therefore, considered to be useful for the analysis of availability and determination of the best possible maintenance strategies which can be implemented in future to enhance the system performance.
Abstract: This paper analyzes the patterns of the Monte Carlo
data for a large number of variables and minterms, in order to
characterize the circuit path length behavior. We propose models
that are determined by training process of shortest path length
derived from a wide range of binary decision diagram (BDD)
simulations. The creation of the model was done use of feed forward
neural network (NN) modeling methodology. Experimental results
for ISCAS benchmark circuits show an RMS error of 0.102 for the
shortest path length complexity estimation predicted by the NN
model (NNM). Use of such a model can help reduce the time
complexity of very large scale integrated (VLSI) circuitries and
related computer-aided design (CAD) tools that use BDDs.
Abstract: Decisions are regularly made during a project or
daily life. Some decisions are critical and have a direct impact on
project or human success. Formal evaluation is thus required,
especially for crucial decisions, to arrive at the optimal solution
among alternatives to address issues. According to microeconomic
theory, all people-s decisions can be modeled as indifference curves.
The proposed approach supports formal analysis and decision by
constructing indifference curve model from the previous experts-
decision criteria. These knowledge embedded in the system can be
reused or help naïve users select alternative solution of the similar
problem. Moreover, the method is flexible to cope with unlimited
number of factors influencing the decision-making. The preliminary
experimental results of the alternative selection are accurately
matched with the expert-s decisions.
Abstract: This study proposes a multi-response surface
optimization problem (MRSOP) for determining the proper choices
of a process parameter design (PPD) decision problem in a noisy
environment of a grease position process in an electronic industry.
The proposed models attempts to maximize dual process responses
on the mean of parts between failure on left and right processes. The
conventional modified simplex method and its hybridization of the
stochastic operator from the hunting search algorithm are applied to
determine the proper levels of controllable design parameters
affecting the quality performances. A numerical example
demonstrates the feasibility of applying the proposed model to the
PPD problem via two iterative methods. Its advantages are also
discussed. Numerical results demonstrate that the hybridization is
superior to the use of the conventional method. In this study, the
mean of parts between failure on left and right lines improve by
39.51%, approximately. All experimental data presented in this
research have been normalized to disguise actual performance
measures as raw data are considered to be confidential.
Abstract: In this paper a new method is suggested for
distributed data-mining by the probability patterns. These patterns
use decision trees and decision graphs. The patterns are cared to be
valid, novel, useful, and understandable. Considering a set of
functions, the system reaches to a good pattern or better objectives.
By using the suggested method we will be able to extract the useful
information from massive and multi-relational data bases.
Abstract: A new deployment of the multiple criteria decision
making (MCDM) techniques: the Simple Additive Weighting
(SAW), and the Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) for portfolio allocation, is demonstrated in
this paper. Rather than exclusive reference to mean and variance as in
the traditional mean-variance method, the criteria used in this
demonstration are the first four moments of the portfolio distribution.
Each asset is evaluated based on its marginal impacts to portfolio
higher moments that are characterized by trapezoidal fuzzy numbers.
Then centroid-based defuzzification is applied to convert fuzzy
numbers to the crisp numbers by which SAW and TOPSIS can be
deployed. Experimental results suggest the similar efficiency of these
MCDM approaches to selecting dominant assets for an optimal
portfolio under higher moments. The proposed approaches allow
investors flexibly adjust their risk preferences regarding higher
moments via different schemes adapting to various (from
conservative to risky) kinds of investors. The other significant
advantage is that, compared to the mean-variance analysis, the
portfolio weights obtained by SAW and TOPSIS are consistently
well-diversified.
Abstract: The steady-state operation of maintaining voltage
stability is done by switching various controllers scattered all over
the power network. When a contingency occurs, whether forced or
unforced, the dispatcher is to alleviate the problem in a minimum
time, cost, and effort. Persistent problem may lead to blackout. The
dispatcher is to have the appropriate switching of controllers in terms
of type, location, and size to remove the contingency and maintain
voltage stability. Wrong switching may worsen the problem and that
may lead to blackout. This work proposed and used a Fuzzy CMeans
Clustering (FCMC) to assist the dispatcher in the decision
making. The FCMC is used in the static voltage stability to map
instantaneously a contingency to a set of controllers where the types,
locations, and amount of switching are induced.
Abstract: Amongst the consistently fluctuating conditions
prevailing today, changeability represents a strategic key factor for a
manufacturing company to achieve success on the international
markets. In order to cope with turbulences and the increasing level of
incalculability, not only the flexible design of production systems but
in particular the employee as enabler of change provide the focus
here. It is important to enable employees from manufacturing
companies to participate actively in change events and in change
decisions. To this end, the learning factory has been created, which is
intended to serve the development of change-promoting competences
and the sensitization of employees for the necessity of changes.
Abstract: Lighting upgrades involve relatively lower costs which
allow the benefits to be spread more widely than is possible with any
other energy efficiency measure. In order to popularize the adoption of
CFL in Taiwan, the authority proposes to implement a new energy efficient lamp comparative label system. The current study was
accordingly undertaken to investigate the factors affecting the performance and the deviation of actual and labeled performance of
commercially available integrated CFLs. In this paper, standard test
methods to determine the electrical and photometric performances of
CFL were developed based on CIE 84-1989 and CIE 60901-1987,
then 55 selected CFLs from market were tested. The results show that
with higher color temperature of CFLs lower efficacy are achieved. It
was noticed that the most packaging of CFL often lack the information of Color Rendering Index. Also, there was no correlation between
price and performance of the CFLs was indicated in this work. The results of this paper might help consumers to make more informed
CFL-purchasing decisions.
Abstract: The demand of the energy management systems (EMS) set forth by modern power systems requires fast energy management systems. Contingency analysis is among the functions in EMS which is time consuming. In order to handle this limitation, this paper introduces agent based technology in the contingency analysis. The main function of agents is to speed up the performance. Negotiations process in decision making is explained and the issue set forth is the minimization of the operating costs. The IEEE 14 bus system and its line outage have been used in the research and simulation results are presented.
Abstract: The right to housing is a basic need while good
quality and affordable housing is a reflection of a high quality of life.
However, housing remains a major problem for most, especially for
the bottom billions. Satisfaction on housing and neighbourhood
conditions are one of the important indicators that reflect quality of
life. These indicators are also important in the process of evaluating
housing policy with the objective to increase the quality of housing
and neighbourhood. The research method is purely based on a
quantitative method, using a survey. The findings show that housing
purchasing trend in urban Malaysia is determined by demographic
profiles, mainly by education level, age, gender and income. The
period of housing ownership also influenced the socio-cultural
interactions and satisfaction of house owners with their
neighbourhoods. The findings also show that the main concerns for
house buyers in urban areas are price and location of the house.
Respondents feel that houses in urban Malaysia is too expensive and
beyond their affordability. Location of houses and distance from
work place are also regarded as the main concern. However,
respondents are fairly satisfied with religious and socio-cultural
facilities in the housing areas and most importantly not many regard
ethnicity as an issue in their decision-making, when buying a house.
Abstract: This work presents a multiple objective linear programming (MOLP) model based on the desirability function approach for solving the aggregate production planning (APP) decision problem upon Masud and Hwang-s model. The proposed model minimises total production costs, carrying or backordering costs and rates of change in labor levels. An industrial case demonstrates the feasibility of applying the proposed model to the APP problems with three scenarios of inventory levels. The proposed model yields an efficient compromise solution and the overall levels of DM satisfaction with the multiple combined response levels. There has been a trend to solve complex planning problems using various metaheuristics. Therefore, in this paper, the multi-objective APP problem is solved by hybrid metaheuristics of the hunting search (HuSIHSA) and firefly (FAIHSA) mechanisms on the improved harmony search algorithm. Results obtained from the solution of are then compared. It is observed that the FAIHSA can be used as a successful alternative solution mechanism for solving APP problems over three scenarios. Furthermore, the FAIHSA provides a systematic framework for facilitating the decision-making process, enabling a decision maker interactively to modify the desirability function approach and related model parameters until a good optimal solution is obtained with proper selection of control parameters when compared.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: Term Extraction, a key data preparation step in Text
Mining, extracts the terms, i.e. relevant collocation of words,
attached to specific concepts (e.g. genetic-algorithms and decisiontrees
are terms associated to the concept “Machine Learning" ). In
this paper, the task of extracting interesting collocations is achieved
through a supervised learning algorithm, exploiting a few
collocations manually labelled as interesting/not interesting. From
these examples, the ROGER algorithm learns a numerical function,
inducing some ranking on the collocations. This ranking is optimized
using genetic algorithms, maximizing the trade-off between the false
positive and true positive rates (Area Under the ROC curve). This
approach uses a particular representation for the word collocations,
namely the vector of values corresponding to the standard statistical
interestingness measures attached to this collocation. As this
representation is general (over corpora and natural languages),
generality tests were performed by experimenting the ranking
function learned from an English corpus in Biology, onto a French
corpus of Curriculum Vitae, and vice versa, showing a good
robustness of the approaches compared to the state-of-the-art Support
Vector Machine (SVM).
Abstract: Process capability index Cpk is the most widely
used index in making managerial decisions since it provides bounds
on the process yield for normally distributed processes. However,
existent methods for assessing process performance which
constructed by statistical inference may unfortunately lead to fine
results, because uncertainties exist in most real-world applications.
Thus, this study adopts fuzzy inference to deal with testing of Cpk .
A brief score is obtained for assessing a supplier’s process instead of
a severe evaluation.