Abstract: Support vector machines (SVMs) have shown
superior performance compared to other machine learning techniques,
especially in classification problems. Yet one limitation of SVMs is
the lack of an explanation capability which is crucial in some
applications, e.g. in the medical and security domains. In this paper, a
novel approach for eclectic rule-extraction from support vector
machines is presented. This approach utilizes the knowledge acquired
by the SVM and represented in its support vectors as well as the
parameters associated with them. The approach includes three stages;
training, propositional rule-extraction and rule quality evaluation.
Results from four different experiments have demonstrated the value
of the approach for extracting comprehensible rules of high accuracy
and fidelity.
Abstract: Social, culture and artistic status of a society in
various historical eras is affected by numerous, and sometimes
imposed, factors that better understanding requires analysis of such
conditions. Throughout history Iran has been involved with
determining and significant events that examining each of these
events can improve the understanding of social conditions of this
country in the intended time. Mongolian conquest of Iran is one of
most significant events in the history of Iran with consequences that
never left Iranian societies. During this tragic invasion and
subsequent devastating wars, which led to establishment of Ilkhanate
dynasty, numerous cultural and artistic changes occurred both in
Mongolian conquerors and Iranian society. This study examines these
changes with a glimpse towards art and architecture as important part
of cultural aspects and social communication.
Abstract: The fuzzy technique is an operator introduced in order
to simulate at a mathematical level the compensatory behavior in
process of decision making or subjective evaluation. The following
paper introduces such operators on hand of computer vision
application.
In this paper a novel method based on fuzzy logic reasoning
strategy is proposed for edge detection in digital images without
determining the threshold value. The proposed approach begins by
segmenting the images into regions using floating 3x3 binary matrix.
The edge pixels are mapped to a range of values distinct from each
other. The robustness of the proposed method results for different
captured images are compared to those obtained with the linear Sobel
operator. It is gave a permanent effect in the lines smoothness and
straightness for the straight lines and good roundness for the curved
lines. In the same time the corners get sharper and can be defined
easily.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.
Abstract: Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
Abstract: The dilute acid pretreatment and enzymatic
saccharification of lignocellulosic substrate, cogon grass (Imperata
cylindrical, L.) was optimized prior ethanol fermentation using
simultaneous saccharification and fermentation (SSF) method. The
optimum pretreatment conditions, temperature, sulfuric acid
concentration, and reaction time were evaluated by determining the
maximum sugar yield at constant enzyme loading. Cogon grass, at
10% w/v substrate loading, has optimum pretreatment conditions of
126°C, 0.6% v/v H2SO4, and 20min reaction time. These
pretreatment conditions were used to optimize enzymatic
saccharification using different enzyme combinations. The maximum
saccharification yield of 36.68mg/mL (71.29% reducing sugar) was
obtained using 25FPU/g-cellulose cellulase complex combined with
1.1% w/w of cellobiase, ß-glucosidase, and 0.225% w/w of
hemicellulase complex, after 96 hours of saccharification. Using the
optimum pretreatment and saccharification conditions, SSF of treated
substrates was done at 37°C for 120 hours using industrial yeast
strain HBY3, Saccharomyces cerevisiae. The ethanol yield for cogon
grass at 4% w/w loading was 9.11g/L with 5.74mg/mL total residual
sugar.
Abstract: This research were investigated, determined, and
analyzed of the climate characteristically change in the provincial
Udon Thani in the period of 60 surrounding years from 1951 to 2010
A.D. that it-s transferred to effects of climatologically data for
determining global warming. Statistically significant were not found
for the 60 years- data (R2
Abstract: This study analyzes the effect of discretization on
classification of datasets including continuous valued features. Six
datasets from UCI which containing continuous valued features are
discretized with entropy-based discretization method. The
performance improvement between the dataset with original features
and the dataset with discretized features is compared with k-nearest
neighbors, Naive Bayes, C4.5 and CN2 data mining classification
algorithms. As the result the classification accuracies of the six
datasets are improved averagely by 1.71% to 12.31%.
Abstract: This paper describes the performance of TCP Vegas
over the wireless IPv6 network. The performance of TCP Vegas is
evaluated using network simulator (ns-2). The simulation experiment
investigates how packet spacing affects the network delay, network
throughput and network efficiency of TCP Vegas. Moreover, we
investigate how the variable FTP packet sizes affect the network
performance. The result of the simulation experiment shows that as
the packet spacing is implements, the network delay is reduces,
network throughput and network efficiency is optimizes. As the FTP
packet sizes increase, the ratio of delay per throughput decreases.
From the result of experiment, we propose the appropriate packet size
in transmitting file transfer protocol application using TCP Vegas
with packet spacing enhancement over wireless IPv6 environment in
ns-2. Additionally, we suggest the appropriate ratio in determining
the appropriate RTT and buffer size in a network.
Abstract: Nowadays predicting political risk level of country
has become a critical issue for investors who intend to achieve
accurate information concerning stability of the business
environments. Since, most of the times investors are layman and
nonprofessional IT personnel; this paper aims to propose a
framework named GECR in order to help nonexpert persons to
discover political risk stability across time based on the political
news and events.
To achieve this goal, the Bayesian Networks approach was
utilized for 186 political news of Pakistan as sample dataset.
Bayesian Networks as an artificial intelligence approach has been
employed in presented framework, since this is a powerful technique
that can be applied to model uncertain domains. The results showed
that our framework along with Bayesian Networks as decision
support tool, predicted the political risk level with a high degree of
accuracy.
Abstract: Needs of an efficient information retrieval in recent
years in increased more then ever because of the frequent use of
digital information in our life. We see a lot of work in the area of
textual information but in multimedia information, we cannot find
much progress. In text based information, new technology of data
mining and data marts are now in working that were started from the
basic concept of database some where in 1960.
In image search and especially in image identification,
computerized system at very initial stages. Even in the area of image
search we cannot see much progress as in the case of text based
search techniques. One main reason for this is the wide spread roots
of image search where many area like artificial intelligence,
statistics, image processing, pattern recognition play their role. Even
human psychology and perception and cultural diversity also have
their share for the design of a good and efficient image recognition
and retrieval system.
A new object based search technique is presented in this paper
where object in the image are identified on the basis of their
geometrical shapes and other features like color and texture where
object-co-relation augments this search process.
To be more focused on objects identification, simple images are
selected for the work to reduce the role of segmentation in overall
process however same technique can also be applied for other
images.
Abstract: Operating rooms are important assets for hospitals as
they generate the largest revenue and, at the same time, produce the
largest cost for hospitals. The model presented in this paper helps
make capacity planning decisions on the combination of open
operating rooms (ORs) and estimated overtime to satisfy the
allocated OR time to each specialty. The model combines both
decisions on determining the amount of OR time to open and to
allocate to different surgical specialties. The decisions made are
based on OR costs, overutilization and underutilization costs, and
contribution margins from allocating OR time. The results show the
importance of having a good estimate of specialty usage of OR time
to determine the amount of needed capacity and highlighted the
tradeoff that the OR manager faces between opening more ORs
versus extending the working time of the ORs already in use.
Abstract: Data mining and knowledge engineering have become a tough task due to the availability of large amount of data in the web nowadays. Validity and reliability of data also become a main debate in knowledge acquisition. Besides, acquiring knowledge from different languages has become another concern. There are many language translators and corpora developed but the function of these translators and corpora are usually limited to certain languages and domains. Furthermore, search results from engines with traditional 'keyword' approach are no longer satisfying. More intelligent knowledge engineering agents are needed. To address to these problems, a system known as Multilingual Word Semantic Network is proposed. This system adapted semantic network to organize words according to concepts and relations. The system also uses open source as the development philosophy to enable the native language speakers and experts to contribute their knowledge to the system. The contributed words are then defined and linked using lexical and semantic relations. Thus, related words and derivatives can be identified and linked. From the outcome of the system implementation, it contributes to the development of semantic web and knowledge engineering.
Abstract: The purpose of this research study is to investigate the manner in which various loads affect the mechanical properties of the formed mild steel plates. The investigation focuses on examining the cross-sectional area of the metal plate at the centre of the formed mild steel plate. Six mild steel plates were deformed with different loads. The loads applied on the plates had a magnitude of 5 kg, 10 kg, 15 kg, 20 kg, 25 kg and 30 kg. The radius of the punching die was 120 mm and the loads were applied at room temperature. The investigations established that the applied load causes the Vickers microhardness at the cross-sectional area of the plate to increase due to strain hardening. Hence, the percentage increase of the hardness due to the load was found to be directly proportional to the increase in the load. Furthermore, the tensile test results for the parent material showed that the average Ultimate Tensile Strength (UTS) for the three samples was 308 MPa while the average Yield Strength and Percentage Elongation were 227 MPa and 38 % respectively. Similarly, the UTS of the formed components increased after the deformation of the plate, as such it can be concluded that the forming loads alter the mechanical properties of the materials by improving and strengthening the material properties.
Abstract: The density estimates considered in this paper comprise
a base density and an adjustment component consisting of a linear
combination of orthogonal polynomials. It is shown that, in the
context of density approximation, the coefficients of the linear combination
can be determined either from a moment-matching technique
or a weighted least-squares approach. A kernel representation of
the corresponding density estimates is obtained. Additionally, two
refinements of the Kronmal-Tarter stopping criterion are proposed
for determining the degree of the polynomial adjustment. By way of
illustration, the density estimation methodology advocated herein is
applied to two data sets.
Abstract: Clustering is one of an interesting data mining topics
that can be applied in many fields. Recently, the problem of cluster
analysis is formulated as a problem of nonsmooth, nonconvex optimization,
and an algorithm for solving the cluster analysis problem
based on nonsmooth optimization techniques is developed. This
optimization problem has a number of characteristics that make it
challenging: it has many local minimum, the optimization variables
can be either continuous or categorical, and there are no exact
analytical derivatives. In this study we show how to apply a particular
class of optimization methods known as pattern search methods
to address these challenges. These methods do not explicitly use
derivatives, an important feature that has not been addressed in
previous studies. Results of numerical experiments are presented
which demonstrate the effectiveness of the proposed method.
Abstract: The bridge vibration due to traffic loading has been a
subject of extensive research during the last decades. A number of
these studies are concerned with the effects of the unevenness of
roadways on the dynamic responses of highway bridges. The road
unevenness is often described as a random process that constitutes
of different wavelengths. Thus, the study focuses on examining
the effects of the random description of roadways on the dynamic
response and its variance. A new setting of variance based sensitivity
analysis is proposed and used to identify and quantify the
contributions of the roadway-s wavelengths to the variance of the
dynamic response. Furthermore, the effect of the vehicle-s speed on
the dynamic response is studied.
Abstract: The purpose of this study is to examine the selfefficacy
and life satisfaction levels of students receiving education in
schools of physical education and sports. The population of the study
consisted 263 students, among which 154 were male and 109 were
female ( X age=19,4905 + 2,5605), that received education in the
schools of physical education and sports of Selcuk University, Inonu
University, Gazi University and Karamanoglu Mehmetbey
University. In order to achieve the purpose of the study, the selfefficacy
scale, which was developed by Jarrusselam and Shwarzer
(1981) [1] and adapted to Turkish by Yesillay (1993) [2], and the
life satisfaction scale, developed by Diener, Emmos, Larsen and
Griffin (1985) [3] and adapted to Turkish by Kokler (1991) [4], were
utilized.For analyzing and interpreting data Kolmogorov-Smirnov
test, t-test and one way anova test were used, while for determining
the difference between the groups Tukey test and Multiple Linear
Regression test were employed and significance was accepted at
P
Abstract: In an electric power system, spinning reserve
requirements can be determined by using deterministic and/or
probabilistic measures. Although deterministic methods are usual in
many systems, application of probabilistic methods becomes
increasingly important in the new environment of the electric power
utility industry. This is because of the increased uncertainty
associated with competition. In this paper 1) a new probabilistic
method is presented which considers the reliability of transmission
system in a simplified manner and 2) deterministic and probabilistic
methods are compared. The studied methods are applied to the Roy
Billinton Test System (RBTS).
Abstract: In the present study, the effects of ultrasound as
emerging technology were investigated on germination stimulation,
amount of alpha-amylase activity on dry barley seeds before steeping
stage of malting process. All experiments were carried out at 20 KHz
on the ultrasonic generator in 3 different ultrasonic intensities (20, 60
and 100% setting from total power of device) and time (5, 10 and 15
min) at constant temperature (30C). For determining the effects of
these parameters on enzyme the Fuwa method assay based on the
decreased staining value of blue starch–iodine complexes employed
for measurement an activity. The results of these assays were
analyzed by Qualitek4 software using the Taguchi statistical method
to evaluate the factor-s effects on enzyme activity. It has been found
that when malting barley is irradiated with an ultrasonic power, a
stimulating effect occurs as to the enzyme activity.