Abstract: In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain visual
characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Abstract: In many countries, governments have been promoting the involvement of private sector entities to enter into long-term agreements for the development and delivery of large infrastructure projects, with a focus on overcoming the limitations upon public fund of the traditional approach. The involvement of private sector through public private partnerships (PPP) brings in new capital investments, value for money and additional risks to handle. Worldwide research studies have shown that an objective, systematic, reliable and useroriented risk assessment process and an optimal allocation mechanism among different stakeholders is crucial to the successful completion. In this framework, this paper, which is the first stage of a research study, aims to identify the main risks for the delivery of PPP projects. A review of cross-countries research projects and case studies was performed to map the key risks affecting PPP infrastructure delivery. The matrix of mapping offers a summary of the frequency of factors, clustered in eleven categories: construction, design, economic, legal, market, natural, operation, political, project finance, project selection and relationship. Results will highlight the most critical risk factors, and will hopefully assist the project managers in directing the managerial attention in the further stages of risk allocation.
Abstract: Web mining is to discover and extract useful
Information. Different users may have different search goals when
they search by giving queries and submitting it to a search engine.
The inference and analysis of user search goals can be very useful for
providing an experience result for a user search query. In this project,
we propose a novel approach to infer user search goals by analyzing
search web logs. First, we propose a novel approach to infer user
search goals by analyzing search engine query logs, the feedback
sessions are constructed from user click-through logs and it
efficiently reflect the information needed for users. Second we
propose a preprocessing technique to clean the unnecessary data’s
from web log file (feedback session). Third we propose a technique
to generate pseudo-documents to representation of feedback sessions
for clustering. Finally we implement k-medoids clustering algorithm
to discover different user search goals and to provide a more optimal
result for a search query based on feedback sessions for the user.
Abstract: In this paper, we present a new segmentation approach
for focal liver lesions in contrast enhanced ultrasound imaging. This
approach, based on a two-cluster Fuzzy C-Means methodology,
considers type-II fuzzy sets to handle uncertainty due to the image
modality (presence of speckle noise, low contrast, etc.), and to
calculate the optimum inter-cluster threshold. Fine boundaries are
detected by a local recursive merging of ambiguous pixels. The
method has been tested on a representative database. Compared to
both Otsu and type-I Fuzzy C-Means techniques, the proposed
method significantly reduces the segmentation errors.
Abstract: Speaker Identification (SI) is the task of establishing
identity of an individual based on his/her voice characteristics. The SI
task is typically achieved by two-stage signal processing: training and
testing. The training process calculates speaker specific feature
parameters from the speech and generates speaker models
accordingly. In the testing phase, speech samples from unknown
speakers are compared with the models and classified. Even though
performance of speaker identification systems has improved due to
recent advances in speech processing techniques, there is still need of
improvement. In this paper, a Closed-Set Tex-Independent Speaker
Identification System (CISI) based on a Multiple Classifier System
(MCS) is proposed, using Mel Frequency Cepstrum Coefficient
(MFCC) as feature extraction and suitable combination of vector
quantization (VQ) and Gaussian Mixture Model (GMM) together
with Expectation Maximization algorithm (EM) for speaker
modeling. The use of Voice Activity Detector (VAD) with a hybrid
approach based on Short Time Energy (STE) and Statistical
Modeling of Background Noise in the pre-processing step of the
feature extraction yields a better and more robust automatic speaker
identification system. Also investigation of Linde-Buzo-Gray (LBG)
clustering algorithm for initialization of GMM, for estimating the
underlying parameters, in the EM step improved the convergence rate
and systems performance. It also uses relative index as confidence
measures in case of contradiction in identification process by GMM
and VQ as well. Simulation results carried out on voxforge.org
speech database using MATLAB highlight the efficacy of the
proposed method compared to earlier work.
Abstract: Low Temperature Matrix Isolation - Electron
Paramagnetic Resonance (LTMI-EPR) Spectroscopy was utilized to
identify the species of iron oxide nanoparticles generated during the
oxidative pyrolysis of 1-methylnaphthalene (1-MN). The otherwise
gas-phase reactions of 1--MN were impacted by a polypropylenimine
tetra-hexacontaamine dendrimer complexed with iron (III) nitrate
nonahydrate diluted in air under atmospheric conditions. The EPR
fine structure of Fe (III)2O3 nanoparticles clusters, characterized by gfactors
of 2.00, 2.28, 3.76 and 4.37 were detected on a cold finger
maintained at 77 K after accumulation over a multitude of
experiments. Additionally, a high valence Fe (IV) paramagnetic
intermediate and superoxide anion-radicals, O2•- adsorbed on
nanoparticle surfaces in the form of Fe (IV) --- O2•- were detected
from the quenching area of Zone 1 in the gas-phase.
Abstract: Wireless Sensor Networks (WSNs) enable new
applications and need non-conventional paradigms for the protocol
because of energy and bandwidth constraints, In WSN, sensor node’s
life is a critical parameter. Research on life extension is based on
Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme,
which rotates Cluster Head (CH) among sensor nodes to distribute
energy consumption over all network nodes. CH selection in WSN
affects network energy efficiency greatly. This study proposes an
improved CH selection for efficient data aggregation in sensor
networks. This new algorithm is based on Bacterial Foraging
Optimization (BFO) incorporated in LEACH.
Abstract: In this paper, we present a new segmentation approach
for liver lesions in regions of interest within MRI (Magnetic
Resonance Imaging). This approach, based on a two-cluster Fuzzy CMeans
methodology, considers the parameter variable compactness
to handle uncertainty. Fine boundaries are detected by a local
recursive merging of ambiguous pixels with a sequential forward
floating selection with Zernike moments. The method has been tested
on both synthetic and real images. When applied on synthetic images,
the proposed approach provides good performance, segmentations
obtained are accurate, their shape is consistent with the ground truth,
and the extracted information is reliable. The results obtained on MR
images confirm such observations. Our approach allows, even for
difficult cases of MR images, to extract a segmentation with good
performance in terms of accuracy and shape, which implies that the
geometry of the tumor is preserved for further clinical activities (such
as automatic extraction of pharmaco-kinetics properties, lesion
characterization, etc.).
Abstract: In this study, nuclear magnetic resonance
spectroscopy and nuclear quadrupole resonance spectroscopy
parameters of 14N (Nitrogen in imidazole ring) in N–H…O hydrogen
bonding for Histidine hydrochloride monohydrate were calculated via
density functional theory. We considered a five-molecule model
system of Histidine hydrochloride monohydrate. Also we examined
the trends of environmental effect on hydrogen bonds as well as
cooperativity. The functional used in this research is M06-2X which
is a good functional and the obtained results has shown good
agreement with experimental data. This functional was applied to
calculate the NMR and NQR parameters. Some correlations among
NBO parameters, NMR and NQR parameters have been studied
which have shown the existence of strong correlations among them.
Furthermore, the geometry optimization has been performed using
M062X/6-31++G(d,p) method. In addition, in order to study
cooperativity and changes in structural parameters, along with
increase in cluster size, natural bond orbitals have been employed.
Abstract: The aim of this investigation is to elaborate nearinfrared
methods for testing and recognition of chemical components
and quality in “Pannon wheat” allied (i.e. true to variety or variety
identified) milling fractions as well as to develop spectroscopic
methods following the milling processes and evaluate the stability of
the milling technology by different types of milling products and
according to sampling times, respectively. These wheat categories
produced under industrial conditions where samples were collected
versus sampling time and maximum or minimum yields. The changes
of the main chemical components (such as starch, protein, lipid) and
physical properties of fractions (particle size) were analysed by
dispersive spectrophotometers using visible (VIS) and near-infrared
(NIR) regions of the electromagnetic radiation. Close correlation
were obtained between the data of spectroscopic measurement
techniques processed by various chemometric methods (e.g. principal
component analysis [PCA], cluster analysis [CA]) and operation
condition of milling technology. It is obvious that NIR methods are
able to detect the deviation of the yield parameters and differences of
the sampling times by a wide variety of fractions, respectively. NIR
technology can be used in the sensitive monitoring of milling
technology.
Abstract: Due to rapid advancement of powerful image
processing software, digital images are easy to manipulate and
modify by ordinary people. Lots of digital images are edited for a
specific purpose and more difficult to distinguish form their original
ones. We propose a clustering method to detect a copy-move image
forgery of JPEG, BMP, TIFF, and PNG. The process starts with
reducing the color of the photos. Then, we use the clustering
technique to divide information of measuring data by Hausdorff
Distance. The result shows that the purposed methods is capable of
inspecting the image file and correctly identify the forgery.
Abstract: This study focuses on a novel method for dispersion
and distribution of reinforcement under high intensive shear stress to
produce metal composites. The polyacrylonitrile (PAN)-based short
carbon fiber (Csf) and Nextel 610 alumina fiber were dispersed under
high intensive shearing at mushy zone in semi-solid of A356 by a
novel method. The bundles and clusters were embedded by
infiltration of slurry into the clusters, thus leading to a uniform
microstructure. The fibers were embedded homogenously into the
aluminum around 576-580°C with around 46% of solid fraction.
Other experiments at 615°C and 568°C which are contained 0% and
90% solid respectively were not successful for dispersion and
infiltration of aluminum into bundles of Csf. The alumina fiber has
been cracked by high shearing load. The morphologies and
crystalline phase were evaluated by SEM and XRD. The adopted
thixo-process effectively improved the adherence and distribution of
Csf into Al that can be developed to produce various composites by
thixomixing.
Abstract: In the present study, RBF neural networks were used
for predicting the performance and emission parameters of a
biodiesel engine. Engine experiments were carried out in a 4 stroke
diesel engine using blends of diesel and Honge methyl ester as the
fuel. Performance parameters like BTE, BSEC, Tex and emissions
from the engine were measured. These experimental results were
used for ANN modeling.
RBF center initialization was done by random selection and by
using Clustered techniques. Network was trained by using fixed and
varying widths for the RBF units. It was observed that RBF results
were having a good agreement with the experimental results.
Networks trained by using clustering technique gave better results
than using random selection of centers in terms of reduced MRE and
increased prediction accuracy. The average MRE for the performance
parameters was 3.25% with the prediction accuracy of 98% and for
emissions it was 10.4% with a prediction accuracy of 80%.
Abstract: During the post-Civil War era, the city of Nashville,
Tennessee, had the highest mortality rate in the United States. The
elevated death and disease rates among former slaves were
attributable to lack of quality healthcare. To address the paucity of
healthcare services, Meharry Medical College, an institution with the
mission of educating minority professionals and serving the
underserved population, was established in 1876.
Purpose: The social ecological framework and partial least squares
(PLS) path modeling were used to quantify the impact of
socioeconomic status and adverse health outcome on primary care
professionals serving the disadvantaged community. Thus, the study
results could demonstrate the accomplishment of the College’s
mission of training primary care professionals to serve in underserved
areas.
Methods: Various statistical methods were used to analyze alumni
data from 1975 – 2013. K-means cluster analysis was utilized to
identify individual medical and dental graduates in the cluster groups
of the practice communities (Disadvantaged or Non-disadvantaged
Communities). Discriminant analysis was implemented to verify the
classification accuracy of cluster analysis. The independent t-test was
performed to detect the significant mean differences of respective
clustering and criterion variables. Chi-square test was used to test if
the proportions of primary care and non-primary care specialists are
consistent with those of medical and dental graduates practicing in
the designated community clusters. Finally, the PLS path model was
constructed to explore the construct validity of analytic model by
providing the magnitude effects of socioeconomic status and adverse
health outcome on primary care professionals serving the
disadvantaged community.
Results: Approximately 83% (3,192/3,864) of Meharry Medical
College’s medical and dental graduates from 1975 to 2013 were
practicing in disadvantaged communities. Independent t-test confirmed the content validity of the cluster analysis model. Also, the
PLS path modeling demonstrated that alumni served as primary care
professionals in communities with significantly lower socioeconomic
status and higher adverse health outcome (p < .001). The PLS path
modeling exhibited the meaningful interrelation between primary
care professionals practicing communities and surrounding
environments (socioeconomic statues and adverse health outcome),
which yielded model reliability, validity, and applicability.
Conclusion: This study applied social ecological theory and
analytic modeling approaches to assess the attainment of Meharry
Medical College’s mission of training primary care professionals to
serve in underserved areas, particularly in communities with low
socioeconomic status and high rates of adverse health outcomes. In
summary, the majority of medical and dental graduates from Meharry
Medical College provided primary care services to disadvantaged
communities with low socioeconomic status and high adverse health
outcome, which demonstrated that Meharry Medical College has
fulfilled its mission. The high reliability, validity, and applicability of
this model imply that it could be replicated for comparable
universities and colleges elsewhere.
Abstract: This paper is concerned with knowledge representation
and extraction of fuzzy if-then rules using Interval Type-2
Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of
fuzzy granulation. This proposed clustering algorithm is based on
information granulation in the form of IT2 based Fuzzy C-Means
(IT2-FCM) clustering and estimates the cluster centers by preserving
the homogeneity between the clustered patterns from the IT2 contexts
produced in the output space. Furthermore, we can obtain the
automatic knowledge representation in the design of Radial Basis
Function Networks (RBFN), Linguistic Model (LM), and Adaptive
Neuro-Fuzzy Networks (ANFN) from the numerical input-output data
pairs. We shall focus on a design of ANFN in this paper. The
experimental results on an estimation problem of energy performance
reveal that the proposed method showed a good knowledge
representation and performance in comparison with the previous
works.
Abstract: The capability of exploiting the electronic charge and
spin properties simultaneously in a single material has made diluted
magnetic semiconductors (DMS) remarkable in the field of
spintronics. We report the designing of DMS based on zinc-blend
ZnO doped with Cr impurity. The full potential linearized augmented
plane wave plus local orbital FP-L(APW+lo) method in density
functional theory (DFT) has been adapted to carry out these
investigations. For treatment of exchange and correlation energy,
generalized gradient approximations have been used. Introducing Cr
atoms in the matrix of ZnO has induced strong magnetic moment
with ferromagnetic ordering at stable ground state. Cr:ZnO was found
to favor the short range magnetic interaction that
reflect tendency of Cr clustering. The electronic structure of ZnO is
strongly influenced in the presence of Cr impurity atoms where
impurity bands appear in the band gap.
Abstract: This study was conducted Razakı grape variety (Vitis
vinifera L.) and its vine which was aged 19 was grown on 5 BB
rootstock in a vegetation period of 2014 in Afyon province in Turkey.
In this research, it was investigated whether the applications of
Control (C), 1/3 Cluster Tip Reduction (1/3 CTR), Shoot Tip
Reduction (STR), 1/3 CTR + STR, Boric Acid (BA), 1/3 CTR + BA,
STR + BA, 1/3 CTR + STR + BA on yield and yield components of
Razakı grape variety. The results were obtained as the highest fresh
grape yield (7.74 kg/vine) with C application; as the highest cluster
weight (244.62 g) with STR application; as the highest 100 berry
weight (504.08 g) with C application; as the highest maturity index
(36.89) with BA application; as the highest must yield (695.00 ml)
with BA and (695.00 ml) with 1/3 CTR + STR + BA applications; as
the highest intensity of L* color (46.93) with STR and (46.10) with
1/3 CTR + STR + BA applications; as the highest intensity of a*
color (-5.37) with 1/3 CTR + STR and (-5.01) with STR, as the
highest intensity of b* color (12.59) with STR application. The shoot
tip reduction to increase cluster weight and boric acid application to
increase maturity index of Razakı grape variety can be recommended.
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: We have been grouping and developing various kinds
of practical, promising sensing applied systems concerning
agricultural advancement and technical tradition (guidance). These
include advanced devices to secure real-time data related to worker
motion, and we analyze by methods of various advanced statistics and
human dynamics (e.g. primary component analysis, Ward system
based cluster analysis, and mapping). What is more, we have been
considering worker daily health and safety issues. Targeted fields are
mainly common farms, meadows, and gardens. After then, we
observed and discussed time-line style, changing data. And, we made
some suggestions. The entire plan makes it possible to improve both
the aforementioned applied systems and farms.
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.