Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: This study aims to analyze ceramic employees’
occupational health and safety training expectations. To that general
objective, the study tries to examine whether occupational health and
safety training expectations of ceramic employees meaningfully
differentiate depending on demographic features and professional,
social and economic conditions. For this purpose, a questionnaire was
developed by the researcher. The research data were collected
through this questionnaire called “Questionnaire of Occupational
Health and Safety Training Expectation” (QSOHSTE). QSOHSTE
was applied to 125 ceramic employees working in Kütahya, Turkey.
Data obtained from questionnaire were analyzed via SPSS 21.
The findings, obtained from the study, revealed that employees’
agreement level to occupational health and safety training expectation
statements is generally high-level. The findings reveal that employees
expect professional interest such as increased development and
investment, preventive measures for accidents, interventions to
evaluate the working conditions, establishment of safe working
environments and sustainment of adequate equipment for
occupational health and safety training process.
Besides these findings, employees’ agreement level to
occupational health and safety training expectation statements also
varies in terms of educational level, professional seniority, income
level and perception of economic condition.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.
Abstract: The wide use of the Internet-based applications bring many challenges to the researchers to guarantee the continuity of the connections needed by the mobile hosts and provide reliable Internet access for them. One of proposed solutions by Internet Engineering Task Force (IETF) is to connect the local, multi-hop, and infrastructure-less Mobile Ad hoc Network (MANET) with Internet structure. This connection is done through multi-interface devices known as Internet Gateways. Many issues are related to this connection like gateway discovery, handoff, address auto-configuration and selecting the optimum gateway when multiple gateways exist. Many studies were done proposing gateway selection schemes with a single selection criterion or weighted multiple criteria. In this research, a review of some of these schemes is done showing the differences, the features, the challenges and the drawbacks of each of them.
Abstract: Development of a method to estimate gene functions is
an important task in bioinformatics. One of the approaches for the
annotation is the identification of the metabolic pathway that genes are
involved in. Since gene expression data reflect various intracellular
phenomena, those data are considered to be related with genes’
functions. However, it has been difficult to estimate the gene function
with high accuracy. It is considered that the low accuracy of the
estimation is caused by the difficulty of accurately measuring a gene
expression. Even though they are measured under the same condition,
the gene expressions will vary usually. In this study, we proposed a
feature extraction method focusing on the variability of gene
expressions to estimate the genes' metabolic pathway accurately. First,
we estimated the distribution of each gene expression from replicate
data. Next, we calculated the similarity between all gene pairs by KL
divergence, which is a method for calculating the similarity between
distributions. Finally, we utilized the similarity vectors as feature
vectors and trained the multiclass SVM for identifying the genes'
metabolic pathway. To evaluate our developed method, we applied the
method to budding yeast and trained the multiclass SVM for
identifying the seven metabolic pathways. As a result, the accuracy
that calculated by our developed method was higher than the one that
calculated from the raw gene expression data. Thus, our developed
method combined with KL divergence is useful for identifying the
genes' metabolic pathway.
Abstract: The Cone Penetration Test (CPT) is a common in-situ
test which generally investigates a much greater volume of soil more
quickly than possible from sampling and laboratory tests. Therefore,
it has the potential to realize both cost savings and assessment of soil
properties rapidly and continuously. The principle objective of this
paper is to demonstrate the feasibility and efficiency of using
artificial neural networks (ANNs) to predict the soil angle of internal
friction (Φ) and the soil modulus of elasticity (E) from CPT results
considering the uncertainties and non-linearities of the soil. In
addition, ANNs are used to study the influence of different
parameters and recommend which parameters should be included as
input parameters to improve the prediction. Neural networks discover
relationships in the input data sets through the iterative presentation
of the data and intrinsic mapping characteristics of neural topologies.
General Regression Neural Network (GRNN) is one of the powerful
neural network architectures which is utilized in this study. A large
amount of field and experimental data including CPT results, plate
load tests, direct shear box, grain size distribution and calculated data
of overburden pressure was obtained from a large project in the
United Arab Emirates. This data was used for the training and the
validation of the neural network. A comparison was made between
the obtained results from the ANN's approach, and some common
traditional correlations that predict Φ and E from CPT results with
respect to the actual results of the collected data. The results show
that the ANN is a very powerful tool. Very good agreement was
obtained between estimated results from ANN and actual measured
results with comparison to other correlations available in the
literature. The study recommends some easily available parameters
that should be included in the estimation of the soil properties to
improve the prediction models. It is shown that the use of friction
ration in the estimation of Φ and the use of fines content in the
estimation of E considerable improve the prediction models.
Abstract: The performance and analysis of speech recognition
system is illustrated in this paper. An approach to recognize the
English word corresponding to digit (0-9) spoken by 2 different
speakers is captured in noise free environment. For feature extraction,
speech Mel frequency cepstral coefficients (MFCC) has been used
which gives a set of feature vectors from recorded speech samples.
Neural network model is used to enhance the recognition
performance. Feed forward neural network with back propagation
algorithm model is used. However other speech recognition
techniques such as HMM, DTW exist. All experiments are carried
out on Matlab.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: Doxorubicin, also known as Adriamycin, is an
anthracycline class of drug used in cancer chemotherapy. It is used in
the treatment of non-Hodgkin’s lymphoma, multiple myeloma, acute
leukemia, breast cancer, lung cancer, endometrium cancer and ovary
cancers. It functions via intercalating DNA and ultimately killing
cancer cells. The major side effects of doxorubicin are hair loss,
myelosuppression, nausea & vomiting, oesophagitis, diarrhea, heart
damage and liver dysfunction. The minor modifications in the
structure of compound exhibit large variation in the biological
activity, has prompted us to carry out the synthesis of sulfonamide
derivatives. Sulfonamide is an important feature with broad spectrum
of biological activity such as antiviral, antifungal, diuretics, antiinflammatory,
antibacterial and anticancer activities. Structure of the
synthesized compound N-(1-methyl-2-oxo-2-N-methyl anilinoethyl)
benzene sulfonamide confirmed by proton nuclear magnetic
resonance (1H NMR),13C NMR, Mass and FTIR spectroscopic tools
to assure the position of all protons and hence stereochemistry of the
molecule. Further we have reported the binding potential of
synthesized sulfonamide analogues in comparison to doxorubicin
drug using Auto Dock 4.2 software. Computational binding energy
(B.E.) and inhibitory constant (Ki) has been evaluated for the
synthesized compound in comparison of doxorubicin against Poly
(dA-dT).Poly (dA-dT) and Poly (dG-dC).Poly (dG-dC) sequences.
The in vitro cytotoxic study against human breast cancer cell lines
confirms the better anticancer activity of the synthesized compound
over currently in use anticancer drug doxorubicin. The IC50 value of
the synthesized compound is 7.12 μM whereas for doxorubicin is 7.2
μM.
Abstract: The decision-making process is theoretically clearly
defined. Generally, it includes the problem identification and
analysis, data gathering, goals and criteria setting, alternatives
development and optimal alternative choice and its implementation.
In practice however, various modifications of the theoretical
decision-making process can occur. The managers can consider some
of the phases to be too complicated or unfeasible and thus they do not
carry them out and conversely some of the steps can be
overestimated.
The aim of the paper is to reveal and characterize the perception of
the individual phases of decision-making process by the managers.
The research is concerned with managers in the military environment
– commanders. Quantitative survey is focused cross-sectionally in the
individual levels of management of the Ministry of Defence of the
Czech Republic. On the total number of 135 respondents the analysis
focuses on which of the decision-making process phases are
problematic or not carried out in practice and which are again
perceived to be the easiest. Then it is examined the reasons of the
findings.
Abstract: Nature is a great source of inspiration for solving
complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which
helps in solving routing problem in networks. The dynamic features,
changing of topology frequently and limited bandwidth make the
routing, challenging in MANET. Implementation of appropriate
routing algorithms leads to the efficient transmission of data in
mobile ad hoc networks. The algorithms that are inspired by the
principles of naturally-distributed/collective behavior of social
colonies have shown excellence in dealing with complex
optimization problems. Thus some of the bio-inspired metaheuristic
algorithms help to increase the efficiency of routing in ad hoc
networks. This survey work presents the overview of bio-inspired
metaheuristic algorithms which support the efficiency of routing in
mobile ad hoc networks.
Abstract: In this paper, we propose an automatic verification
technology of software patches for user virtual environments on IaaS
Cloud to decrease verification costs of patches. In these days, IaaS
services have been spread and many users can customize virtual
machines on IaaS Cloud like their own private servers. Regarding to
software patches of OS or middleware installed on virtual machines,
users need to adopt and verify these patches by themselves. This task
increases operation costs of users. Our proposed method replicates
user virtual environments, extracts verification test cases for user
virtual environments from test case DB, distributes patches to virtual
machines on replicated environments and conducts those test cases
automatically on replicated environments. We have implemented the
proposed method on OpenStack using Jenkins and confirmed the
feasibility. Using the implementation, we confirmed the effectiveness
of test case creation efforts by our proposed idea of 2-tier abstraction
of software functions and test cases. We also evaluated the automatic
verification performance of environment replications, test cases
extractions and test cases conductions.
Abstract: The web’s increased popularity has included a huge
amount of information, due to which automated web page
classification systems are essential to improve search engines’
performance. Web pages have many features like HTML or XML
tags, hyperlinks, URLs and text contents which can be considered
during an automated classification process. It is known that Webpage
classification is enhanced by hyperlinks as it reflects Web page
linkages. The aim of this study is to reduce the number of features to
be used to improve the accuracy of the classification of web pages. In
this paper, a novel feature selection method using an improved
Particle Swarm Optimization (PSO) using principle of evolution is
proposed. The extracted features were tested on the WebKB dataset
using a parallel Neural Network to reduce the computational cost.
Abstract: A large amount of data is typically stored in relational
databases (DB). The latter can efficiently handle user queries which
intend to elicit the appropriate information from data sources.
However, direct access and use of this data requires the end users to
have an adequate technical background, while they should also cope
with the internal data structure and values presented. Consequently
the information retrieval is a quite difficult process even for IT or DB
experts, taking into account the limited contributions of relational
databases from the conceptual point of view. Ontologies enable users
to formally describe a domain of knowledge in terms of concepts and
relations among them and hence they can be used for unambiguously
specifying the information captured by the relational database.
However, accessing information residing in a database using
ontologies is feasible, provided that the users are keen on using
semantic web technologies. For enabling users form different
disciplines to retrieve the appropriate data, the design of a Graphical
User Interface is necessary. In this work, we will present an
interactive, ontology-based, semantically enable web tool that can be
used for information retrieval purposes. The tool is totally based on
the ontological representation of underlying database schema while it
provides a user friendly environment through which the users can
graphically form and execute their queries.
Abstract: The air transport impact on environment is more than
ever a limitative obstacle to the aeronautical industry continuous
growth. Over the last decades, considerable effort has been carried
out in order to obtain quieter aircraft solutions, whether by changing
the original design or investigating more silent maneuvers. The
noise propagated by rotating surfaces is one of the most important
sources of annoyance, being present in most aerial vehicles. Bearing
this is mind, CEIIA developed a new computational chain for
noise prediction with in-house software tools to obtain solutions in
relatively short time without using excessive computer resources. This
work is based on the new acoustic tool, which aims to predict the
rotor noise generated during steady and maneuvering flight, making
use of the flexibility of the C language and the advantages of GPU
programming in terms of velocity. The acoustic tool is based in the
Formulation 1A of Farassat, capable of predicting two important
types of noise: the loading and thickness noise. The present work
describes the most important features of the acoustic tool, presenting
its most relevant results and framework analyses for helicopters and
UAV quadrotors.
Abstract: This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.
Abstract: The inherent skin patterns created at the joints in the
finger exterior are referred as finger knuckle-print. It is exploited to
identify a person in a unique manner because the finger knuckle print
is greatly affluent in textures. In biometric system, the region of
interest is utilized for the feature extraction algorithm. In this paper,
local and global features are extracted separately. Fast Discrete
Orthonormal Stockwell Transform is exploited to extract the local
features. Global feature is attained by escalating the size of Fast
Discrete Orthonormal Stockwell Transform to infinity. Two features
are fused to increase the recognition accuracy. A matching distance is
calculated for both the features individually. Then two distances are
merged mutually to acquire the final matching distance. The
proposed scheme gives the better performance in terms of equal error
rate and correct recognition rate.
Abstract: Graphical User Interface (GUI) is essential to
programming, as is any other characteristic or feature, due to the fact
that GUI components provide the fundamental interaction between
the user and the program. Thus, we must give more interest to GUI
during building and development of systems. Also, we must give a
greater attention to the user who is the basic corner in the dealing
with the GUI. This paper introduces an approach for designing GUI
from one of the models of business workflows which describe the
workflow behavior of a system, specifically through Activity
Diagrams (AD).