Abstract: The main objective of our study is to collect data
about the profile of the asthmatic patients in Assam and thereby have
a comprehensive knowledge of the factors influencing the asthmatic
patients of the state and their medication pattern. We developed a
search strategy to find any publication about the community based
survey asthma related and used. These to search the MEDLINE
(1996 to current literature) CINAHL DOAJ pubmed databases using
the key phrases, Asthma, Respiratory disorders, Drug therapy of
Asthma, database decision support system and asthma. The
appropriate literature was printed out from the online source and
library (Journal) source. The study was conducted through a set of
structured and non-structured questionnaires targeted on the
asthmatic patients belonging to the rural and urban areas of Assam,
during the month of Dec 2006 to July 2007, 138 cases were studied
in Gauwathi Medical College & Hospital located in Bhangagarh,
Assam in India. The demographic characteristics a factor in 138
patients with asthma with allergic rhinitis (cases) gives the detail
profile of asthmatic patient-s distribution of Assam as classified on
the basis of age and sex. It is evident from the study that male
populations (66%) are more prone to asthma as compared to the
females (34%).Another striking features that emerged from this
survey is the maximum prevalence of asthma in the age group of 20-
30 years followed by infants belonging to the age group of 7 (0.05%)
0-10years among both male and female populations of Assam. The
high incidence of asthma in the age group of 20-30 years may
probably be due to the allergy arising out of sudden exposure to dust
and pollen which the children face while playing and going to the
school. The rural females in the age group of 30-40 years are more
prone to asthma than urban females in the same age group may be
due to sex differentiation among the tribal population of the state.
Pharmacists should educate the asthmatics how to use inhalers
considering growing menace of asthma in the state. Safer drugs
should be produced in the form of aerosol so that easy administration
by the asthmatic patients and physicians of the state is possible for
curing asthma. The health centers should be more equipped with the
medicines to cure asthma in the state like Assam.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
three feature selection methods are evaluated: Random Selection,
Information Gain (IG) and Support Vector Machine feature selection
(called SVM_FS). We show that the best results were obtained with
SVM_FS method for a relatively small dimension of the feature
vector. Also we present a novel method to better correlate SVM
kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: This paper presents the methodology from machine
learning approaches for short-term rain forecasting system. Decision
Tree, Artificial Neural Network (ANN), and Support Vector Machine
(SVM) were applied to develop classification and prediction models
for rainfall forecasts. The goals of this presentation are to
demonstrate (1) how feature selection can be used to identify the
relationships between rainfall occurrences and other weather
conditions and (2) what models can be developed and deployed for
predicting the accurate rainfall estimates to support the decisions to
launch the cloud seeding operations in the northeastern part of
Thailand. Datasets collected during 2004-2006 from the
Chalermprakiat Royal Rain Making Research Center at Hua Hin,
Prachuap Khiri khan, the Chalermprakiat Royal Rain Making
Research Center at Pimai, Nakhon Ratchasima and Thai
Meteorological Department (TMD). A total of 179 records with 57
features was merged and matched by unique date. There are three
main parts in this work. Firstly, a decision tree induction algorithm
(C4.5) was used to classify the rain status into either rain or no-rain.
The overall accuracy of classification tree achieves 94.41% with the
five-fold cross validation. The C4.5 algorithm was also used to
classify the rain amount into three classes as no-rain (0-0.1 mm.),
few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall
accuracy of classification tree achieves 62.57%. Secondly, an ANN
was applied to predict the rainfall amount and the root mean square
error (RMSE) were used to measure the training and testing errors of
the ANN. It is found that the ANN yields a lower RMSE at 0.171 for
daily rainfall estimates, when compared to next-day and next-2-day
estimation. Thirdly, the ANN and SVM techniques were also used to
classify the rain amount into three classes as no-rain, few-rain, and
moderate-rain as above. The results achieved in 68.15% and 69.10%
of overall accuracy of same-day prediction for the ANN and SVM
models, respectively. The obtained results illustrated the comparison
of the predictive power of different methods for rainfall estimation.
Abstract: Safer driver behavior promoting is the main goal of this paper. It is a fact that drivers behavior is relatively safer when being monitored. Thus, in this paper, we propose a monitoring system to report specific driving event as well as the potentially aggressive events for estimation of the driving performance. Our driving monitoring system is composed of two parts. The first part is the in-vehicle embedded system which is composed of a GPS receiver, a two-axis accelerometer, radar sensor, OBD interface, and GPRS modem. The design considerations that led to this architecture is described in this paper. The second part is a web server where an adaptive hierarchical fuzzy system is proposed to classify the driving performance based on the data that is sent by the in-vehicle embedded system and the data that is provided by the geographical information system (GIS). Our system is robust, inexpensive and small enough to fit inside a vehicle without distracting the driver.
Abstract: In this paper, a new approach for quality assessment
tasks in lossy compressed digital video is proposed. The research
activity is based on the visual fixation data recorded by an eye
tracker. The method involved both a new paradigm for subjective
quality evaluation and the subsequent statistical analysis to match
subjective scores provided by the observer to the data obtained from
the eye tracker experiments. The study brings improvements to the
state of the art, as it solves some problems highlighted in literature.
The experiments prove that data obtained from an eye tracker can be
used to classify videos according to the level of impairment due to
compression. The paper presents the methodology, the experimental
results and their interpretation. Conclusions suggest that the eye
tracker can be useful in quality assessment, if data are collected and
analyzed in a proper way.
Abstract: There is a complex situation on the transport environment in the cities of the world. For the analysis and prevention of environmental problems an accurate calculation hazardous substances concentrations at each point of the investigated area is required. In the turbulent atmosphere of the city the wellknown methods of mathematical statistics for these tasks cannot be applied with a satisfactory level of accuracy. Therefore, to solve this class of problems apparatus of mathematical physics is more appropriate. In such models, because of the difficulty as a rule the influence of uneven land surface on streams of air masses in the turbulent atmosphere of the city are not taken into account. In this paper the influence of the surface roughness, which can be quite large, is mathematically shown. The analysis of this problem under certain conditions identified the possibility of areas appearing in the atmosphere with pressure tending to infinity, i.e. so-called "wall effect".
Abstract: Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a "higher-level paradigm" with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the model-s state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than non-simulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply non-simulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzyme-catalyzed substrate conversion and a part of the bacteriophage λ lysis/lysogeny pathway.
Abstract: This research paper presents a framework on how to
build up malware dataset.Many researchers took longer time to
clean the dataset from any noise or to transform the dataset into a
format that can be used straight away for testing. Therefore, this
research is proposing a framework to help researchers to speed up
the malware dataset cleaningprocesses which later can be used for
testing. It is believed, an efficient malware dataset cleaning
processes, can improved the quality of the data, thus help to improve
the accuracy and the efficiency of the subsequent analysis. Apart
from that, an in-depth understanding of the malware taxonomy is
also important prior and during the dataset cleaning processes. A
new Trojan classification has been proposed to complement this
framework.This experiment has been conducted in a controlled lab
environment and using the dataset from VxHeavens dataset. This
framework is built based on the integration of static and dynamic
analyses, incident response method and knowledge database
discovery (KDD) processes.This framework can be used as the basis
guideline for malware researchers in building malware dataset.
Abstract: Emerging adulthood, between the ages of 18 and 25, as a new developmental stage extending from adolescence to young adulthood. According to Arnett [2004], there are experiments related to identity in three basic fields which are love, work and view of the world in emerging adulthood. When the literature related to identity is examined, it is seen that identity has been studied more with adolescent, and studies were concentrated on the relationship of identity with many demographic variables neglecting important variables such as marital status, parental status and SES. Thus, the main aim of this study is to determine whether identity statuses differenciate with marital status, parental status and SES. A total of 700 emerging adults participated in this study, and the mean age was 22,45 years [SD = 3.76]. The sample was made up of 347 female and 353 male. All participants in the study were students from colleges. Student responses to the Extended Version of the Objective Measure of Ego Identity Status [EOM-EIS-2] used to classify students into one of the four identity statuses. SPSS 15.00 program wasa used to analyse data. Percentage, frequency and X2 analysis were used in the analysis of data. When the findings of the study is viewed as a whole, the most frequently observed identity status in the group is found to be moratorium. Also, identity statuses differenciate with marital status, parental status and SES. Findings were discussed in the context of emerging adulthood.
Abstract: A method has been developed for preparing load
models for power flow and stability. The load modeling
(LOADMOD) computer software transforms data on load class mix,
composition, and characteristics into the from required for
commonly–used power flow and transient stability simulation
programs. Typical default data have been developed for load
composition and characteristics. This paper defines LOADMOD
software and describes the dynamic and static load modeling
techniques used in this software and results of initial testing for
BAKHTAR power system.
Abstract: The purpose of determining impact significance is to
place value on impacts. Environmental impact assessment review is a
process that judges whether impact significance is acceptable or not in
accordance with the scientific facts regarding environmental,
ecological and socio-economical impacts described in environmental
impact statements (EIS) or environmental impact assessment reports
(EIAR). The first aim of this paper is to summarize the criteria of
significance evaluation from the past review results and accordingly
utilize fuzzy logic to incorporate these criteria into scientific facts. The
second aim is to employ data mining technique to construct an EIS or
EIAR prediction model for reviewing results which can assist
developers to prepare and revise better environmental management
plans in advance. The validity of the previous prediction model
proposed by authors in 2009 is 92.7%. The enhanced validity in this
study can attain 100.0%.
Abstract: Analyses carried out on examples of detected defects
echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect.
This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of
this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis
(PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a
volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the
various algorithms proposed in this study.
Abstract: This paper suggests ranking alternatives under fuzzy
MCDM (multiple criteria decision making) via an centroid based
ranking approach, where criteria are classified to benefit qualitative,
benefit quantitative and cost quantitative ones. The ratings of
alternatives versus qualitative criteria and the importance weights of
all criteria are assessed in linguistic values represented by fuzzy
numbers. The membership function for the final fuzzy evaluation
value of each alternative can be developed through α-cuts and
interval arithmetic of fuzzy numbers. The distance between the
original point and the relative centroid is applied to defuzzify the
final fuzzy evaluation values in order to rank alternatives. Finally a
numerical example demonstrates the computation procedure of the
proposed model.
Abstract: Parallel Prefix addition is a technique for improving
the speed of binary addition. Due to continuing integrating intensity
and the growing needs of portable devices, low-power and highperformance
designs are of prime importance. The classical parallel
prefix adder structures presented in the literature over the years
optimize for logic depth, area, fan-out and interconnect count of logic
circuits. In this paper, a new architecture for performing 8-bit, 16-bit
and 32-bit Parallel Prefix addition is proposed. The proposed prefix
adder structures is compared with several classical adders of same
bit width in terms of power, delay and number of computational
nodes. The results reveal that the proposed structures have the least
power delay product when compared with its peer existing Prefix
adder structures. Tanner EDA tool was used for simulating the adder
designs in the TSMC 180 nm and TSMC 130 nm technologies.
Abstract: The free and forced in-plane vibrations of axially
moving plates are investigated in this work. The plate possesses an
internal damping of which the constitutive relation obeys the
Kelvin-Voigt model, and the excitations are arbitrarily distributed on
two opposite edges. First, the equations of motion and the boundary
conditions of the axially moving plate are derived. Then, the extended
Ritz method is used to obtain discretized system equations. Finally,
numerical results for the natural frequencies and the mode shapes of
the in-plane vibration and the in-plane response of the moving plate
subjected to arbitrary edge excitations are presented. It is observed that
the symmetry class of the mode shapes of the in-plane vibration
disperses gradually as the moving speed gets higher, and the u- and
v-components of the mode shapes belong to different symmetry class.
In addition, large response amplitudes having shapes similar to the
mode shapes of the plate can be excited by the edge excitations at the
resonant frequencies and with the same symmetry class of distribution
as the u-components.
Abstract: The study was carried out to gather and identify
medicinal plants their curative effects and the part of them which is
used from the reservation area of Miankaleh. The region under study
has an area of 68800 hectares situated 12 kilometers north of the city
of Behshahr and northwest of the city of Gorgan. Results obtained
showed that out of a total of 43 families, 125 genera, and 155 species
found in the region, 33 families, 52 genera and 61 species (39% of all
the species) belonged to medicinal plants, among which the class
Asteraceae with 6 species and the class Chenopodiaceae with 5
species had the most medicinal species. The most used parts of the
plants were the leaves with 31%, the whole plants with 19%, and the
roots with 15%.
Abstract: Class cohesion is an important object-oriented
software quality attribute. It indicates how much the members in a
class are related. Assessing the class cohesion and improving the
class quality accordingly during the object-oriented design phase
allows for cheaper management of the later phases. In this paper, the
notion of distance between pairs of methods and pairs of attribute
types in a class is introduced and used as a basis for introducing a
novel class cohesion metric. The metric considers the methodmethod,
attribute-attribute, and attribute-method direct interactions.
It is shown that the metric gives more sensitive values than other
well-known design-based class cohesion metrics.
Abstract: The Chiu-s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering principle to estimate both the number of clusters and the initial locations of a cluster centers. Each obtained cluster corresponds to a Mamdani fuzzy rule. Then, it optimizes the fuzzy model parameters by applying a genetic algorithm. This method is illustrated on a traffic network management application. We suggest also a Mamdani fuzzy rules generation method, where the expert wants to classify the output variables in some fuzzy predefined classes.
Abstract: In this paper a combined feature selection method is
proposed which takes advantages of sample domain filtering,
resampling and feature subset evaluation methods to reduce
dimensions of huge datasets and select reliable features. This method
utilizes both feature space and sample domain to improve the process
of feature selection and uses a combination of Chi squared with
Consistency attribute evaluation methods to seek reliable features.
This method consists of two phases. The first phase filters and
resamples the sample domain and the second phase adopts a hybrid
procedure to find the optimal feature space by applying Chi squared,
Consistency subset evaluation methods and genetic search.
Experiments on various sized datasets from UCI Repository of
Machine Learning databases show that the performance of five
classifiers (Naïve Bayes, Logistic, Multilayer Perceptron, Best First
Decision Tree and JRIP) improves simultaneously and the
classification error for these classifiers decreases considerably. The
experiments also show that this method outperforms other feature
selection methods.
Abstract: These In this work, a regular unit speed curve in six
dimensional Euclidean space, whose Frenet curvatures are constant,
is considered. Thereafter, a method to calculate Frenet apparatus of
this curve is presented.