Abstract: In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: With the advancement of knowledge about the utility
and impact of sustainability, its feasibility has been explored into
different walks of life. Scientists, however; have established their
knowledge in four areas viz environmental, economic, social and
cultural, popularly termed as four pillars of sustainability. Aspects of
environmental and economic sustainability have been rigorously
researched and practiced and huge volume of strong evidence of
effectiveness has been founded for these two sub-areas. For the social
and cultural aspects of sustainability, dependable evidence of
effectiveness is still to be instituted as the researchers and
practitioners are developing and experimenting methods across the
globe. Therefore, the present research aimed to identify globally used
practices of social and cultural sustainability and through evidence
synthesis assess their outcomes to determine the effectiveness of
those practices. A PICO format steered the methodology which
included all populations, popular sustainability practices including
walkability/cycle tracks, social/recreational spaces, privacy, health &
human services and barrier free built environment, comparators
included ‘Before’ and ‘After’, ‘With’ and ‘Without’, ‘More’ and
‘Less’ and outcomes included Social well-being, cultural coexistence,
quality of life, ethics and morality, social capital, sense of
place, education, health, recreation and leisure, and holistic
development. Search of literature included major electronic
databases, search websites, organizational resources, directory of
open access journals and subscribed journals. Grey literature,
however, was not included. Inclusion criteria filtered studies on the
basis of research designs such as total randomization, quasirandomization,
cluster randomization, observational or single studies
and certain types of analysis. Studies with combined outcomes were
considered but studies focusing only on environmental and/or
economic outcomes were rejected. Data extraction, critical appraisal
and evidence synthesis was carried out using customized tabulation,
reference manager and CASP tool. Partial meta-analysis was carried
out and calculation of pooled effects and forest plotting were done.
As many as 13 studies finally included for final synthesis explained
the impact of targeted practices on health, behavioural and social
dimensions. Objectivity in the measurement of health outcomes
facilitated quantitative synthesis of studies which highlighted the
impact of sustainability methods on physical activity, Body Mass
Index, perinatal outcomes and child health. Studies synthesized
qualitatively (and also quantitatively) showed outcomes such as
routines, family relations, citizenship, trust in relationships, social
inclusion, neighbourhood social capital, wellbeing, habitability and
family’s social processes. The synthesized evidence indicates slight
effectiveness and efficacy of social and cultural sustainability on the
targeted outcomes. Further synthesis revealed that such results of this
study are due weak research designs and disintegrated implementations. If architects and other practitioners deliver their
interventions in collaboration with research bodies and policy
makers, a stronger evidence-base in this area could be generated.
Abstract: A growing demand is felt today for realistic 3D
models enabling the cognition and popularization of historical-artistic
heritage. Evaluation and preservation of Cultural Heritage is
inextricably connected with the innovative processes of gaining,
managing, and using knowledge. The development and perfecting of
techniques for acquiring and elaborating photorealistic 3D models,
made them pivotal elements for popularizing information of objects
on the scale of architectonic structures.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: In this paper, we present a comparative study of three
methods of 2D face recognition system such as: Iso-Geodesic Curves
(IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram
(GIH). These approaches are based on computing of geodesic
distance between points of facial surface and between facial curves.
In this study we represented the image at gray level as a 2D surface in
a 3D space, with the third coordinate proportional to the intensity
values of pixels. In the classifying step, we use: Neural Networks
(NN), K-Nearest Neighbor (KNN) and Support Vector Machines
(SVM). The images used in our experiments are from two wellknown
databases of face images ORL and YaleB. ORL data base was
used to evaluate the performance of methods under conditions where
the pose and sample size are varied, and the database YaleB was used
to examine the performance of the systems when the facial
expressions and lighting are varied.
Abstract: This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
Abstract: The enormous amount of information stored on the
web increases from one day to the next, exposing the web currently
faced with the inevitable difficulties of research pertinent information
that users really want. The problem today is not limited to expanding
the size of the information highways, but to design a system for
intelligent search. The vast majority of this information is stored in
relational databases, which in turn represent a backend for managing
RDF data of the semantic web. This problem has motivated us to
write this paper in order to establish an effective approach to support
semantic transformation algorithm for SPARQL queries to SQL
queries, more precisely SPARQL SELECT queries; by adopting this
method, the relational database can be questioned easily with
SPARQL queries maintaining the same performance.
Abstract: In this paper, we are interested in the problem of
finding similar images in a large database. For this purpose we
propose a new algorithm based on a combination of the 2-D
histogram intersection in the HSV space and statistical moments. The
proposed histogram is based on a 3x3 window and not only on the
intensity of the pixel. This approach overcome the drawback of the
conventional 1-D histogram which is ignoring the spatial distribution
of pixels in the image, while the statistical moments are used to
escape the effects of the discretisation of the color space which is
intrinsic to the use of histograms. We compare the performance of
our new algorithm to various methods of the state of the art and we
show that it has several advantages. It is fast, consumes little memory
and requires no learning. To validate our results, we apply this
algorithm to search for similar images in different image databases.
Abstract: Methicillin/multiple-resistant Staphylococcus aureus
(MRSA) are infectious bacteria that are resistant to common
antibiotics. A previous in silico study in our group has identified a
hypothetical protein SAV1226 as one of the potential drug targets. In
this study, we reported the bioinformatics characterization, as well as
cloning, expression, purification and kinetic assays of hypothetical
protein SAV1226 from methicillin/vancomycin-resistant
Staphylococcus aureus Mu50 strain. MALDI-TOF/MS analysis
revealed a low degree of structural similarity with known proteins.
Kinetic assays demonstrated that hypothetical protein SAV1226 is
neither a domain of an ATP dependent dihydroxyacetone kinase nor
of a phosphotransferase system (PTS) dihydroxyacetone kinase,
suggesting that the function of hypothetical protein SAV1226 might
be misannotated on public databases such as UniProt and
InterProScan 5.
Abstract: Thousands of organisations store important and
confidential information related to them, their customers, and their
business partners in databases all across the world. The stored data
ranges from less sensitive (e.g. first name, last name, date of birth) to
more sensitive data (e.g. password, pin code, and credit card
information). Losing data, disclosing confidential information or
even changing the value of data are the severe damages that
Structured Query Language injection (SQLi) attack can cause on a
given database. It is a code injection technique where malicious SQL
statements are inserted into a given SQL database by simply using a
web browser. In this paper, we propose an effective pattern
recognition neural network model for detection and classification of
SQLi attacks. The proposed model is built from three main elements
of: a Uniform Resource Locator (URL) generator in order to generate
thousands of malicious and benign URLs, a URL classifier in order
to: 1) classify each generated URL to either a benign URL or a
malicious URL and 2) classify the malicious URLs into different
SQLi attack categories, and a NN model in order to: 1) detect either a
given URL is a malicious URL or a benign URL and 2) identify the
type of SQLi attack for each malicious URL. The model is first
trained and then evaluated by employing thousands of benign and
malicious URLs. The results of the experiments are presented in
order to demonstrate the effectiveness of the proposed approach.
Abstract: Dengue outbreaks are affected by biological,
ecological, socio-economic and demographic factors that vary over
time and space. These factors have been examined separately and still
require systematic clarification. The present study aimed to investigate
the spatial-temporal clustering relationships between these factors and
dengue outbreaks in the northern region of Sri Lanka. Remote sensing
(RS) data gathered from a plurality of satellites were used to develop
an index comprising rainfall, humidity and temperature data. RS data
gathered by ALOS/AVNIR-2 were used to detect urbanization, and a
digital land cover map was used to extract land cover information.
Other data on relevant factors and dengue outbreaks were collected
through institutions and extant databases. The analyzed RS data and
databases were integrated into geographic information systems,
enabling temporal analysis, spatial statistical analysis and space-time
clustering analysis. Our present results showed that increases in the
number of the combination of ecological factor and socio-economic
and demographic factors with above the average or the presence
contribute to significantly high rates of space-time dengue clusters.
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: In this paper, Bayesian online inference in models of
data series are constructed by change-points algorithm, which
separated the observed time series into independent series and study
the change and variation of the regime of the data with related
statistical characteristics. variation of statistical characteristics of time
series data often represent separated phenomena in the some
dynamical system, like a change in state of brain dynamical reflected
in EEG signal data measurement or a change in important regime of
data in many dynamical system. In this paper, prediction algorithm
for studying change point location in some time series data is
simulated. It is verified that pattern of proposed distribution of data
has important factor on simpler and smother fluctuation of hazard
rate parameter and also for better identification of change point
locations. Finally, the conditions of how the time series distribution
effect on factors in this approach are explained and validated with
different time series databases for some dynamical system.
Abstract: In-memory database systems are becoming popular
due to the availability and affordability of sufficiently large RAM and
processors in modern high-end servers with the capacity to manage
large in-memory database transactions. While fast and reliable inmemory
systems are still being developed to overcome cache misses,
CPU/IO bottlenecks and distributed transaction costs, disk-based data
stores still serve as the primary persistence. In addition, with the
recent growth in multi-tenancy cloud applications and associated
security concerns, many organisations consider the trade-offs and
continue to require fast and reliable transaction processing of diskbased
database systems as an available choice. For these
organizations, the only way of increasing throughput is by improving
the performance of disk-based concurrency control. This warrants a
hybrid database system with the ability to selectively apply an
enhanced disk-based data management within the context of inmemory
systems that would help improve overall throughput.
The general view is that in-memory systems substantially
outperform disk-based systems. We question this assumption and
examine how a modified variation of access invariance that we call
enhanced memory access, (EMA) can be used to allow very high
levels of concurrency in the pre-fetching of data in disk-based
systems. We demonstrate how this prefetching in disk-based systems
can yield close to in-memory performance, which paves the way for
improved hybrid database systems. This paper proposes a novel EMA
technique and presents a comparative study between disk-based EMA
systems and in-memory systems running on hardware configurations
of equivalent power in terms of the number of processors and their
speeds. The results of the experiments conducted clearly substantiate
that when used in conjunction with all concurrency control
mechanisms, EMA can increase the throughput of disk-based systems
to levels quite close to those achieved by in-memory system. The
promising results of this work show that enhanced disk-based
systems facilitate in improving hybrid data management within the
broader context of in-memory systems.
Abstract: The use of eXtensible Markup Language (XML) in
web, business and scientific databases lead to the development of
methods, techniques and systems to manage and analyze XML data.
Semi-structured documents suffer due to its heterogeneity and
dimensionality. XML structure and content mining represent
convergence for research in semi-structured data and text mining. As
the information available on the internet grows drastically, extracting
knowledge from XML documents becomes a harder task. Certainly,
documents are often so large that the data set returned as answer to a
query may also be very big to convey the required information. To
improve the query answering, a Semantic Tree Based Association
Rule (STAR) mining method is proposed. This method provides
intentional information by considering the structure, content and the
semantics of the content. The method is applied on Reuter’s dataset
and the results show that the proposed method outperforms well.
Abstract: Safety is one of the most important considerations
when buying a new car. While active safety aims at avoiding
accidents, passive safety systems such as airbags and seat belts
protect the occupant in case of an accident. In addition to legal
regulations, organizations like Euro NCAP provide consumers with
an independent assessment of the safety performance of cars and
drive the development of safety systems in automobile industry.
Those ratings are mainly based on injury assessment reference values
derived from physical parameters measured in dummies during a car
crash test.
The components and sub-systems of a safety system are designed
to achieve the required restraint performance. Sled tests and other
types of tests are then carried out by car makers and their suppliers
to confirm the protection level of the safety system. A Knowledge
Discovery in Databases (KDD) process is proposed in order to
minimize the number of tests. The KDD process is based on the
data emerging from sled tests according to Euro NCAP specifications.
About 30 parameters of the passive safety systems from different data
sources (crash data, dummy protocol) are first analysed together with
experts opinions. A procedure is proposed to manage missing data
and validated on real data sets. Finally, a procedure is developed to
estimate a set of rough initial parameters of the passive system before
testing aiming at reducing the number of tests.
Abstract: The growth in the volume of text data such as books
and articles in libraries for centuries has imposed to establish
effective mechanisms to locate them. Early techniques such as
abstraction, indexing and the use of classification categories have
marked the birth of a new field of research called "Information
Retrieval". Information Retrieval (IR) can be defined as the task of
defining models and systems whose purpose is to facilitate access to
a set of documents in electronic form (corpus) to allow a user to find
the relevant ones for him, that is to say, the contents which matches
with the information needs of the user.
Most of the models of information retrieval use a specific data
structure to index a corpus which is called "inverted file" or "reverse
index".
This inverted file collects information on all terms over the corpus
documents specifying the identifiers of documents that contain the
term in question, the frequency of each term in the documents of the
corpus, the positions of the occurrences of the word...
In this paper we use an oriented object database (db4o) instead of
the inverted file, that is to say, instead to search a term in the inverted
file, we will search it in the db4o database.
The purpose of this work is to make a comparative study to see if
the oriented object databases may be competing for the inverse index
in terms of access speed and resource consumption using a large
volume of data.
Abstract: This paper reports a structured literature review of the
application of Health Information Technology in developing
countries, defined as the World Bank categories Low-income
countries, Lower-middle-income, and Upper-middle-income
countries. The aim was to identify and classify the various
applications of health information technology to assess its current
state in developing countries and explore potential areas of research.
We offer specific analysis and application of HIT in Libya as one of
the developing countries. A structured literature review was
conducted using the following online databases: IEEE, Science
Direct, PubMed, and Google Scholar. Publication dates were set for
2000-2013. For the PubMed search, publications in English, French,
and Arabic were specified. Using a content analysis approach, 159
papers were analyzed and a total number of 26 factors were identified
that affect the adoption of health information technology. Of the 2681
retrieved articles, 159 met the inclusion criteria which were carefully
analyzed and classified. The implementation of health information
technology across developing countries is varied. Whilst it was
initially expected financial constraints would have severely limited
health information technology implementation, some developing
countries like India have nevertheless dominated the literature and
taken the lead in conducting scientific research. Comparing the
number of studies to the number of countries in each category, we
found that Low-income countries and Lower-middle-income had
more studies carried out than Upper-middle-income countries.
However, whilst IT has been used in various sectors of the economy,
the healthcare sector in developing countries is still failing to benefit
fully from the potential advantages that IT can offer.
Abstract: Different strategies and tools are available at the oil
and gas industry for detecting and analyzing tension and possible
fractures in borehole walls. Most of these techniques are based on
manual observation of the captured borehole images. While this
strategy may be possible and convenient with small images and few
data, it may become difficult and suitable to errors when big
databases of images must be treated. While the patterns may differ
among the image area, depending on many characteristics (drilling
strategy, rock components, rock strength, etc.). In this work we
propose the inclusion of data-mining classification strategies in order
to create a knowledge database of the segmented curves. These
classifiers allow that, after some time using and manually pointing
parts of borehole images that correspond to tension regions and
breakout areas, the system will indicate and suggest automatically
new candidate regions, with higher accuracy. We suggest the use of
different classifiers methods, in order to achieve different knowledge
dataset configurations.