Abstract: Attributes and methods are the basic contents of an
object-oriented class. The connectivity among these class members
and the relationship between the class and other classes play an
important role in determining the quality of an object-oriented
system. Class cohesion evaluates the degree of relatedness of class
attributes and methods, whereas class coupling refers to the degree to
which a class is related to other classes. Researchers have proposed
several class cohesion and class coupling measures. However, the
correlation between class coupling and class cohesion measures has
not been thoroughly studied. In this paper, using classes of three
open-source Java systems, we empirically investigate the correlation
between several measures of connectivity-based class cohesion and
coupling. Four connectivity-based cohesion measures and eight
coupling measures are considered in the empirical study. The
empirical study results show that class connectivity-based cohesion
and coupling internal quality attributes are inversely correlated. The
strength of the correlation depends highly on the cohesion and
coupling measurement approaches.
Abstract: Starting in 2020, an EU-wide CO2-limitation of
95 g/km is scheduled for the average of an OEMs passenger car fleet.
Taking that into consideration additional improvement measures of
the Diesel cycle are necessary in order to reduce fuel consumption
and emissions while boosting, or at the least, keeping performance
values at the same time.
The present article deals with the possibilities of an optimized
air/water charge air cooler, also called iCAC (indirect Charge Air
Cooler) for a Diesel passenger car amongst extreme-boundary
conditions. In this context, the precise objective was to show the
impact of improved intercooling with reference to the engine working
process (fuel consumption and NOx-emissions). Several extremeboundaries
- e.g. varying ambient temperatures or mountainous
routes - that will become very important in the near future regarding
RDE (Real Driving emissions) were subject of the investigation.
With the introduction of RDE in 2017 (EU6c measure), the
controversial NEDC (New European Driving Cycle) will belong to
the past and the OEMs will have to avoid harmful emissions in any
conceivable real life situation.
This is certainly going to lead to optimization-measurements at the
powertrain, which again is going to make the implementation of
iCACs, presently solely used for the premium class, more and more
attractive for compact class cars. The investigations showed a benefit
in FC between 1 and 3% for the iCAC in real world conditions.
Abstract: Class cohesion is a key object-oriented software
quality attribute that is used to evaluate the degree of relatedness of
class attributes and methods. Researchers have proposed several class
cohesion measures. However, the effect of considering the special
methods (i.e., constructors, destructors, and access and delegation
methods) in cohesion calculation is not thoroughly theoretically
studied for most of them. In this paper, we address this issue for three
popular connectivity-based class cohesion measures. For each of the
considered measures we theoretically study the impact of including
or excluding special methods on the values that are obtained by
applying the measure. This study is based on analyzing the
definitions and formulas that are proposed for the measures. The
results show that including/excluding special methods has a
considerable effect on the obtained cohesion values and that this
effect varies from one measure to another. For each of the three
connectivity-based measures, the proposed theoretical study
recommended excluding the special methods in cohesion
measurement.
Abstract: Stoneware clay, fired clay (as a grog), calcite waste and class C fly ash in various mixing rations were the basic raw materials for the mixture for production of dry pressed ceramic tiles. Mechanical properties (water absorption, bulk density, apparent porosity, flexural strength) as well as mineralogical composition were studied on samples with different source of calcium oxide after firing at 900, 1000, 1100 and 1200°C. It was found that samples with addition of calcite waste contain dmisteinbergit and anorthite. This minerals help to improve the strength of the body and reduce porosity fired at lower temperatures. Class C fly ash has not significantly influence on properties of the fired body as calcite waste.
Abstract: Class cohesion is an important object-oriented software quality attributes, and it refers to the degree of relatedness of class attributes and methods. Several class cohesion measures are proposed in the literature, and the impact of considering the special methods (i.e., constructors, destructors, and access and delegation methods) in cohesion calculation is not thoroughly theoretically studied for most of them. In this paper, we address this issue for three popular similarity-based class cohesion measures. For each of the considered measures we theoretically study the impact of including or excluding special methods on the values that are obtained by applying the measure. This study is based on analyzing the definitions and formulas that are proposed for the measures. The results show that including/excluding special methods has a considerable effect on the obtained cohesion values and that this effect varies from one measure to another. The study shows the importance of considering the types of methods that have to be accounted for when proposing a similarity-based cohesion measure.
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: Bioinformatics methods for predicting the T cell
coreceptor usage from the array of membrane protein of HIV-1 are
investigated. In this study, we aim to propose an effective prediction
method for dealing with the three-class classification problem of
CXCR4 (X4), CCR5 (R5) and CCR5/CXCR4 (R5X4). We made
efforts in investigating the coreceptor prediction problem as follows: 1)
proposing a feature set of informative physicochemical properties
which is cooperated with SVM to achieve high prediction test
accuracy of 81.48%, compared with the existing method with
accuracy of 70.00%; 2) establishing a large up-to-date data set by
increasing the size from 159 to 1225 sequences to verify the proposed
prediction method where the mean test accuracy is 88.59%, and 3)
analyzing the set of 14 informative physicochemical properties to
further understand the characteristics of HIV-1coreceptors.
Abstract: A mathematical model for the transmission of SARS is developed. In addition to dividing the population into susceptible (high and low risk), exposed, infected, quarantined, diagnosed and recovered classes, we have included a class called untraced. The model simulates the Gompertz curves which are the best representation of the cumulative numbers of probable SARS cases in Hong Kong and Singapore. The values of the parameters in the model which produces the best fit of the observed data for each city are obtained by using a differential evolution algorithm. It is seen that the values for the parameters needed to simulate the observed daily behaviors of the two epidemics are different.
Abstract: This paper proposes a novel approach to the question of lithofacies classification based on an assessment of the uncertainty in the classification results. The proposed approach has multiple neural networks (NN), and interval neutrosophic sets (INS) are used to classify the input well log data into outputs of multiple classes of lithofacies. A pair of n-class neural networks are used to predict n-degree of truth memberships and n-degree of false memberships. Indeterminacy memberships or uncertainties in the predictions are estimated using a multidimensional interpolation method. These three memberships form the INS used to support the confidence in results of multiclass classification. Based on the experimental data, our approach improves the classification performance as compared to an existing technique applied only to the truth membership. In addition, our approach has the capability to provide a measure of uncertainty in the problem of multiclass classification.
Abstract: Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.
Abstract: Face authentication for access control is a face
membership authentication which passes the person of the incoming
face if he turns out to be one of an enrolled person based on face
recognition or rejects if not. Face membership authentication belongs
to the two class classification problem where SVM(Support Vector
Machine) has been successfully applied and shows better performance
compared to the conventional threshold-based classification. However,
most of previous SVMs have been trained using image feature vectors
extracted from face images of each class member(enrolled
class/unenrolled class) so that they are not robust to variations in
illuminations, poses, and facial expressions and much affected by
changes in member configuration of the enrolled class
In this paper, we propose an effective face membership
authentication method based on SVM using class discriminating
features which represent an incoming face image-s associability with
each class distinctively. These class discriminating features are weakly
related with image features so that they are less affected by variations
in illuminations, poses and facial expression.
Through experiments, it is shown that the proposed face
membership authentication method performs better than the threshold
rule-based or the conventional SVM-based authentication methods and
is relatively less affected by changes in member size and membership.
Abstract: This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
Abstract: In general, small-scale vegetables farmers experience
problems in improving the safety and quality of vegetables supplied
to high-class consumers in modern retailers. They also lack of
information to access market. The farmers group and/or cooperative
(FGC) should be able to assist its members by providing training in
handling and packing vegetables and enhancing marketing
capabilities to sell commodities to the modern retailers. This study
proposes an agri-food supply chain (ASC) model that involves the
corporate social responsibility (CSR) activities to cultivate the
capabilities of farmers to access market. Multi period ASC model is
formulated as Weighted Goal Programming (WGP) to analyze the
impacts of CSR programs to empower the FGCs in managing the
small-scale vegetables farmers. The results show that the proposed
model can be used to determine the priority of programs in order to
maximize the four goals to be achieved in the CSR programs.
Abstract: Ensemble learning algorithms such as AdaBoost and
Bagging have been in active research and shown improvements in
classification results for several benchmarking data sets with mainly
decision trees as their base classifiers. In this paper we experiment to
apply these Meta learning techniques with classifiers such as random
forests, neural networks and support vector machines. The data sets
are from MAGIC, a Cherenkov telescope experiment. The task is to
classify gamma signals from overwhelmingly hadron and muon
signals representing a rare class classification problem. We compare
the individual classifiers with their ensemble counterparts and
discuss the results. WEKA a wonderful tool for machine learning has
been used for making the experiments.
Abstract: In this study, aeroelastic response and performance
analyses have been conducted for a 5MW-Class composite wind
turbine blade model. Advanced coupled numerical method based on
computational fluid dynamics (CFD) and computational flexible
multi-body dynamics (CFMBD) has been developed in order to
investigate aeroelastic responses and performance characteristics of
the rotating composite blade. Reynolds-Averaged Navier-Stokes
(RANS) equations with k-ω SST turbulence model were solved for
unsteady flow problems on the rotating turbine blade model. Also,
structural analyses considering rotating effect have been conducted
using the general nonlinear finite element method. A fully implicit
time marching scheme based on the Newmark direct integration
method is applied to solve the coupled aeroelastic governing equations
of the 3D turbine blade for fluid-structure interaction (FSI) problems.
Detailed dynamic responses and instantaneous velocity contour on the
blade surfaces which considering flow-separation effects were
presented to show the multi-physical phenomenon of the huge rotating
wind- turbine blade model.
Abstract: In this article we are going to discuss the improvement
of the multi classes- classification problem using multi layer
Perceptron. The considered approach consists in breaking down the
n-class problem into two-classes- subproblems. The training of each
two-class subproblem is made independently; as for the phase of test,
we are going to confront a vector that we want to classify to all two
classes- models, the elected class will be the strongest one that won-t
lose any competition with the other classes. Rates of recognition
gotten with the multi class-s approach by two-class-s decomposition
are clearly better that those gotten by the simple multi class-s
approach.
Abstract: The one-class support vector machine “support vector
data description” (SVDD) is an ideal approach for anomaly or outlier
detection. However, for the applicability of SVDD in real-world
applications, the ease of use is crucial. The results of SVDD are
massively determined by the choice of the regularisation parameter C
and the kernel parameter of the widely used RBF kernel. While for
two-class SVMs the parameters can be tuned using cross-validation
based on the confusion matrix, for a one-class SVM this is not
possible, because only true positives and false negatives can occur
during training. This paper proposes an approach to find the optimal
set of parameters for SVDD solely based on a training set from
one class and without any user parameterisation. Results on artificial
and real data sets are presented, underpinning the usefulness of the
approach.
Abstract: The biomarker for colorectal cancer (CRC) is CEACAM-6 antigen (C6AG). Therefore, this study aims to develop a novel, simple and low-cost CEACAM-6 antigen immumosensor (C6AG-IMS), based on electrical impedance measurement, for precise determination of C6AG. A low-cost screen-printed graphite electrode was constructed and used as the sensor, with CEACAM-6 antibody (C6AB) immobilized on it. The procedures of sensor fabrication and antibody immobilization are simple and low-cost. Measurement of the electrical impedance at a definite frequency ranges (0.43 – 1.26 MHz) showed that the C6AG-IMS has an excellent linear (r2>0.9) response range (8.125 – 65 pg/mL), covering the normal physiological and pathological ranges of blood C6AG levels. Also, the C6AG-IMS has excellent reliability and validity, with the intraclass correlation coefficient being 0.97. In conclusion, a novel, simple, low-cost and reliable C6AG-IMS was designed and developed, being able to accurately determine blood C6AG levels in the range of pathological and normal physiological regions. The C6AG-IMS can provide a point-of-care and immediate screening results to the user at home.
Abstract: Steel surface defect detection is essentially one of
pattern recognition problems. Support Vector Machines (SVMs) are
known as one of the most proper classifiers in this application. In this
paper, we introduce a more accurate classification method by using
SVMs as our final classifier of the inspection system. In this scheme,
multiclass classification task is performed based on the "one-againstone"
method and different kernels are utilized for each pair of the
classes in multiclass classification of the different defects.
In the proposed system, a decision tree is employed in the first
stage for two-class classification of the steel surfaces to "defect" and
"non-defect", in order to decrease the time complexity. Based on
the experimental results, generated from over one thousand images,
the proposed multiclass classification scheme is more accurate than
the conventional methods and the overall system yields a sufficient
performance which can meet the requirements in steel manufacturing.
Abstract: This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.