Abstract: Debates on residential satisfaction topic have been
vigorously discussed in family house setting. Nonetheless, less or
lack of attention was given to survey on student residential
satisfaction in the campus house setting. This study, however, tried to
fill in the gap by focusing more on the relationship between students-
socio-economic backgrounds and student residential satisfaction with
their on-campus student housing facilities. Two-stage cluster
sampling method was employed to classify the respondents. Then,
self-administered questionnaires were distributed face-to-face to the
students. In general, it was confirmed that the students- socioeconomic
backgrounds have significantly influence the students-
satisfaction with their on-campus student housing facilities. The main
influential factors were revealed as the economic status, sense of
sharing, and the ethnicity of roommates. Likewise, this study could
also provide some useful feedback for the universities administration
in order to improve their student housing facilities.
Abstract: In this paper, we investigated the characteristic of a
clinical dataseton the feature selection and classification
measurements which deal with missing values problem.And also
posed the appropriated techniques to achieve the aim of the activity;
in this research aims to find features that have high effect to mortality
and mortality time frame. We quantify the complexity of a clinical
dataset. According to the complexity of the dataset, we proposed the
data mining processto cope their complexity; missing values, high
dimensionality, and the prediction problem by using the methods of
missing value replacement, feature selection, and classification.The
experimental results will extend to develop the prediction model for
cardiology.
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,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.
Abstract: In this paper, an automatic system of diagnosis was
developed to detect and locate in real time the defects of the wound
rotor asynchronous machine associated to electronic converter. For
this purpose, we have treated the signals of the measured parameters
(current and speed) to use them firstly, as indicating variables of the
machine defects under study and, secondly, as inputs to the Artificial
Neuron Network (ANN) for their classification in order to detect the
defect type in progress. Once a defect is detected, the interpretation
system of information will give the type of the defect and its place of
appearance.
Abstract: We investigate sonic cues for binaural sound localization within classrooms and present a structural model for the same. Two of the primary cues for localization, interaural time difference (ITD) and interaural level difference (ILD) created between the two ears by sounds from a particular point in space, are used. Although these cues do not lend any information about the elevation of a sound source, the torso, head, and outer ear carry out elevation dependent spectral filtering of sounds before they reach the inner ear. This effect is commonly captured in head related transfer function (HRTF) which aids in resolving the ambiguity from the ITDs and ILDs alone and helps localize sounds in free space. The proposed structural model of HRTF produces well controlled horizontal as well as vertical effects. The implemented HRTF is a signal processing model which tries to mimic the physical effects of the sounds interacting with different parts of the body. The effectiveness of the method is tested by synthesizing spatial audio, in MATLAB, for use in listening tests with human subjects and is found to yield satisfactory results in comparison with existing models.
Abstract: MM-Path, an acronym for Method/Message Path, describes the dynamic interactions between methods in object-oriented systems. This paper discusses the classifications of MM-Path, based on the characteristics of object-oriented software. We categorize it according to the generation reasons, the effect scope and the composition of MM-Path. A formalized representation of MM-Path is also proposed, which has considered the influence of state on response method sequences of messages. .Moreover, an automatic MM-Path generation approach based on UML Statechart diagram has been presented, and the difficulties in identifying and generating MM-Path can be solved. . As a result, it provides a solid foundation for further research on test cases generation based on MM-Path.
Abstract: The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.
Abstract: The aim of this paper is to identify an optimum
control strategy of three-phase shunt active filters to minimize the total harmonic distortion factor of the supply current. A classical PIPI cascade control solution of the output current of the active filterand the voltage across the DC capacitor based on Modulus–Optimum
criterion is taken into consideration. The control system operation
has been simulated using Matlab-Simulink environment and the results agree with the theoretical expectation. It is shown that there is
an optimum value of the DC-bus voltage which minimizes the supply current harmonic distortion factor. It corresponds to the equality of the apparent power at the output of the active filter and the apparent power across the capacitor. Finally, predicted results are verified experimentally on a MaxSine active power filter.
Abstract: The goal of a network-based intrusion detection
system is to classify activities of network traffics into two major
categories: normal and attack (intrusive) activities. Nowadays, data
mining and machine learning plays an important role in many
sciences; including intrusion detection system (IDS) using both
supervised and unsupervised techniques. However, one of the
essential steps of data mining is feature selection that helps in
improving the efficiency, performance and prediction rate of
proposed approach. This paper applies unsupervised K-means
clustering algorithm with information gain (IG) for feature selection
and reduction to build a network intrusion detection system. For our
experimental analysis, we have used the new NSL-KDD dataset,
which is a modified dataset for KDDCup 1999 intrusion detection
benchmark dataset. With a split of 60.0% for the training set and the
remainder for the testing set, a 2 class classifications have been
implemented (Normal, Attack). Weka framework which is a java
based open source software consists of a collection of machine
learning algorithms for data mining tasks has been used in the testing
process. The experimental results show that the proposed approach is
very accurate with low false positive rate and high true positive rate
and it takes less learning time in comparison with using the full
features of the dataset with the same algorithm.
Abstract: Artificial neural networks (ANN) have the ability to model input-output relationships from processing raw data. This characteristic makes them invaluable in industry domains where such knowledge is scarce at best. In the recent decades, in order to overcome the black-box characteristic of ANNs, researchers have attempted to extract the knowledge embedded within ANNs in the form of rules that can be used in inference systems. This paper presents a new technique that is able to extract a small set of rules from a two-layer ANN. The extracted rules yield high classification accuracy when implemented within a fuzzy inference system. The technique targets industry domains that possess less complex problems for which no expert knowledge exists and for which a simpler solution is preferred to a complex one. The proposed technique is more efficient, simple, and applicable than most of the previously proposed techniques.
Abstract: The most important subtype of non-Hodgkin-s
lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately
40% of the patients suffering from it respond well to therapy,
whereas the remainder needs a more aggressive treatment, in order to
better their chances of survival. Data Mining techniques have helped
to identify the class of the lymphoma in an efficient manner. Despite
that, thousands of genes should be processed to obtain the results.
This paper presents a comparison of the use of various attribute
selection methods aiming to reduce the number of genes to be
searched, looking for a more effective procedure as a whole.
Abstract: In this paper, several improvements are proposed to
previous work of automated classification of alcoholics and nonalcoholics.
In the previous paper, multiplayer-perceptron neural
network classifying energy of gamma band Visual Evoked Potential
(VEP) signals gave the best classification performance using 800
VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the
dataset is extended to include 3560 VEP signals from 102 subjects:
62 alcoholics and 40 non-alcoholics. Three modifications are
introduced to improve the classification performance: i) increasing
the gamma band spectral range by increasing the pass-band width of
the used filter ii) the use of Multiple Signal Classification algorithm
to obtain the power of the dominant frequency in gamma band VEP
signals as features and iii) the use of the simple but effective knearest
neighbour classifier. To validate that these two modifications
do give improved performance, a 10-fold cross validation
classification (CVC) scheme is used. Repeat experiments of the
previously used methodology for the extended dataset are performed
here and improvement from 94.49% to 98.71% in maximum
averaged CVC accuracy is obtained using the modifications. This
latest results show that VEP based classification of alcoholics is
worth exploring further for system development.
Abstract: Safe drinking water is one of the biggest issues facing
the planet this century. The primary aim of this paper is to present our
research focused on theoretical and experimental analysis of potable
water and in-building water distribution systems from the point of
view of microbiological risk on the basis of confrontation between
the theoretical analysis and synthesis of gathered information in
conditions of the Slovak Republic. The presence of the bacteria
Legionella in water systems, especially in hot water distribution
system, represents in terms of health protection of inhabitants the
crucial problem which cannot be overlooked. Legionella
pneumophila discovery, its classification and its influence on
installations inside buildings are relatively new. There are a lot of
guidelines and regulations developed in many individual countries for
the design, operation and maintenance for tap water systems to avoid
the growth of bacteria Legionella pneumophila, but in Slovakia we
don-t have any. The goal of this paper is to show the necessity of
prevention and regulations for installations inside buildings verified
by simulation methods.
Abstract: The new institutional Economics helps generalization
and expansion of new classic by adding the institution theories to
Economic. It is clear that the appropriate institution is among the
factors that lead to success in Economic programs.
If the institutional are appropriate, the society will save the source
and when we make use of time to apply the program, there will be
welfare and average revenue product will also increase. In Economy,
one should not expect the real manifestation of Economic programs
only with a model for estimating and predicting rather institutions of
the same purpose and along with production are needed to form the
process of growth and development costs.
In this research, the institution role in transaction costs, financial
markets, distribution of revenue and capital and its influence on the
process of growth and development are investigated so that
handicaps and problems of Iran Economic Institutions can be
recognized. In other words, incapability, non productivity and
ambiguity of the institution in Iran Economic are some of the factors
that handicap Economic growth and development. For example, Iran
government as an important institution while having 20 ministries,83
organizations and 60 years of programming could not go along the
growth and development but why?
Abstract: Identification of cancer genes that might anticipate
the clinical behaviors from different types of cancer disease is
challenging due to the huge number of genes and small number of
patients samples. The new method is being proposed based on
supervised learning of classification like support vector machines
(SVMs).A new solution is described by the introduction of the
Maximized Margin (MM) in the subset criterion, which permits to
get near the least generalization error rate. In class prediction
problem, gene selection is essential to improve the accuracy and to
identify genes for cancer disease. The performance of the new
method was evaluated with real-world data experiment. It can give
the better accuracy for classification.
Abstract: This paper examines the available experiment data for a copper bromide vapor laser (CuBr laser), emitting at two wavelengths - 510.6 and 578.2nm. Laser output power is estimated based on 10 independent input physical parameters. A classification and regression tree (CART) model is obtained which describes 97% of data. The resulting binary CART tree specifies which input parameters influence considerably each of the classification groups. This allows for a technical assessment that indicates which of these are the most significant for the manufacture and operation of the type of laser under consideration. The predicted values of the laser output power are also obtained depending on classification. This aids the design and development processes considerably.
Abstract: In this paper, we validate crater detection in moon surface image using FLDA. This proposal assumes that it is applied to SLIM (Smart Lander for Investigating Moon) project aiming at the pin-point landing to the moon surface. The point where the lander should land is judged by the position relations of the craters obtained via camera, so the real-time image processing becomes important element. Besides, in the SLIM project, 400kg-class lander is assumed, therefore, high-performance computers for image processing cannot be equipped. We are studying various crater detection methods such as Haar-Like features, LBP, and PCA. And we think these methods are appropriate to the project, however, to identify the unlearned images obtained by actual is insufficient. In this paper, we examine the crater detection using FLDA, and compare with the conventional methods.
Abstract: This paper proposes an adaptive sliding mode
controller which combines adaptive control and sliding
mode control to control a nonlinear robotic manipulator
with uncertain parameters. We use an adaptive algorithm
based on the concept of sliding mode control to alleviate the
chattering phenomenon of control input. Adaptive laws are
developed to obtain the gain of switching input and the
boundary layer parameters. The stability and convergence
of the robotic manipulator control system are guaranteed
by applying the Lyapunov theorem. Simulation results
demonstrate that the chattering of control input can be
alleviated effectively. The proposed controller scheme can
assure robustness against a large class of uncertainties and
achieve good trajectory tracking performance.
Abstract: The unanticipated destruct of more of the steel moment frames in Northridge earthquake, altered class of regard to the beamto- column connections in moment frames. Panel zone is one the significant part of joints which, it-s stiffness and rigidity has an important effect on the behavior and ductility of the frame. Specifically that behavior of panel zone has a very significant effect on the special moment frames. In this paper , meanwhile the relations for modeling of panel zone in frames are expressed , special moment frames with different spans and stories were studied in the way of performance-based design. The frames designed in according with Iranian steel building code. The effect of panel zone is also considered and in the case of non-existence of performance level, by changing in intimacies and parameter of panel zone, performance level is considered.
Abstract: A novel application of neural network approach to
fault classification and fault location of Medium voltage cables is
demonstrated in this paper. Different faults on a protected cable
should be classified and located correctly. This paper presents the use
of neural networks as a pattern classifier algorithm to perform these
tasks. The proposed scheme is insensitive to variation of different
parameters such as fault type, fault resistance, and fault inception
angle. Studies show that the proposed technique is able to offer high
accuracy in both of the fault classification and fault location tasks.