Abstract: In this study, communities of ammonia-oxidizing
archaea (AOA) and ammonia-oxidizing bacteria (AOB) in nitrifying
activated sludge (NAS) prepared by enriching sludge from a
municipal wastewater treatment plant in three continuous-flow
reactors receiving an inorganic medium containing different
ammonium concentrations of 2, 10, and 30 mM NH4
+-N (NAS2,
NAS10, and NAS30, respectively) were investigated using molecular
analysis. Results suggested that almost all AOA clones from NAS2,
NAS10, and NAS30 fell into the same AOA cluster and AOA
communities in NAS2 and NAS10 were more diverse than those of
NAS30. In contrast to AOA, AOB communities obviously shifted
from the seed sludge to enriched NASs and in each enriched NAS,
communities of AOB varied particularly. The seed sludge contained
members of N. communis cluster and N. oligotropha cluster. After it
was enriched under various ammonium loads, members of N.
communis cluster disappeared from all enriched NASs. AOB with
high affinity to ammonia presented in NAS 2, AOB with low affinity
to ammonia presented in NAS 30, and both types of AOB survived in
NAS 10. These demonstrated that ammonium load significantly
influenced AOB communities, but not AOA communities in enriched
NASs.
Abstract: The cellular network is one of the emerging areas of
communication, in which the mobile nodes act as member for one
base station. The cluster based communication is now an emerging
area of wireless cellular multimedia networks. The cluster renders
fast communication and also a convenient way to work with
connectivity. In our scheme we have proposed an optimization
technique for the fuzzy cluster nodes, by categorizing the group
members into three categories like long refreshable member, medium
refreshable member and short refreshable member. By considering
long refreshable nodes as static nodes, we compute the new
membership values for the other nodes in the cluster. We compare
their previous and present membership value with the threshold value
to categorize them into three different members. By which, we
optimize the nodes in the fuzzy clusters. The simulation results show
that there is reduction in the cluster computational time and
iterational time after optimization.
Abstract: A Decision Support System/Expert System for stock
portfolio selection presented where at first step, both technical and
fundamental data used to estimate technical and fundamental return
and risk (1st phase); Then, the estimated values are aggregated with
the investor preferences (2nd phase) to produce convenient stock
portfolio.
In the 1st phase, there are two expert systems, each of which is
responsible for technical or fundamental estimation. In the technical
expert system, for each stock, twenty seven candidates are identified
and with using rough sets-based clustering method (RC) the effective
variables have been selected. Next, for each stock two fuzzy rulebases
are developed with fuzzy C-Mean method and Takai-Sugeno-
Kang (TSK) approach; one for return estimation and the other for
risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation
method. In parallel, for fundamental expert systems,
fuzzy rule-bases have been identified in the form of “IF-THEN" rules
through brainstorming with the stock market experts and the input
data have been derived from financial statements; as a result two
fuzzy rule-bases have been generated for all the stocks, one for return
and the other for risk.
In the 2nd phase, user preferences represented by four criteria and
are obtained by questionnaire. Using an expert system, four estimated
values of return and risk have been aggregated with the respective
values of user preference. At last, a fuzzy rule base having four rules,
treats these values and produce a ranking score for each stock which
will lead to a satisfactory portfolio for the user.
The stocks of six manufacturing companies and the period of
2003-2006 selected for data gathering.
Abstract: This paper describes a novel approach for deriving
modules from protein-protein interaction networks, which combines
functional information with topological properties of the network.
This approach is based on weighted clustering coefficient, which
uses weights representing the functional similarities between the
proteins. These weights are calculated according to the semantic
similarity between the proteins, which is based on their Gene
Ontology terms. We recently proposed an algorithm for identification
of functional modules, called SWEMODE (Semantic WEights for
MODule Elucidation), that identifies dense sub-graphs containing
functionally similar proteins. The rational underlying this approach is
that each module can be reduced to a set of triangles (protein triplets
connected to each other). Here, we propose considering semantic
similarity weights of all triangle-forming edges between proteins. We
also apply varying semantic similarity thresholds between
neighbours of each node that are not neighbours to each other (and
hereby do not form a triangle), to derive new potential triangles to
include in module-defining procedure. The results show an
improvement of pure topological approach, in terms of number of
predicted modules that match known complexes.
Abstract: In this paper, we present a novel approach to accurately
detect text regions including shop name in signboard images with
complex background for mobile system applications. The proposed
method is based on the combination of text detection using edge
profile and region segmentation using fuzzy c-means method. In the
first step, we perform an elaborate canny edge operator to extract all
possible object edges. Then, edge profile analysis with vertical and
horizontal direction is performed on these edge pixels to detect
potential text region existing shop name in a signboard. The edge
profile and geometrical characteristics of each object contour are
carefully examined to construct candidate text regions and classify the
main text region from background. Finally, the fuzzy c-means
algorithm is performed to segment and detected binarize text region.
Experimental results show that our proposed method is robust in text
detection with respect to different character size and color and can
provide reliable text binarization result.
Abstract: A new dynamic clustering approach (DCPSO), based
on Particle Swarm Optimization, is proposed. This approach is
applied to unsupervised image classification. The proposed approach
automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference.
The algorithm starts by partitioning the data set into a relatively large
number of clusters to reduce the effects of initial conditions. Using
binary particle swarm optimization the "best" number of clusters is
selected. The centers of the chosen clusters is then refined via the Kmeans
clustering algorithm. The experiments conducted show that
the proposed approach generally found the "optimum" number of
clusters on the tested images.
Abstract: This paper represents four unsupervised clustering algorithms namely sIB, RandomFlatClustering, FarthestFirst, and FilteredClusterer that previously works have not been used for network traffic classification. The methodology, the result, the products of the cluster and evaluation of these algorithms with efficiency of each algorithm from accuracy are shown. Otherwise, the efficiency of these algorithms considering form the time that it use to generate the cluster quickly and correctly. Our work study and test the best algorithm by using classify traffic anomaly in network traffic with different attribute that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and compare it to the previously used (K-Means). Our research will be use to develop anomaly detection system to more efficiency and more require in the future.
Abstract: In this paper, an efficient technique is proposed to manage the cache memory. The proposed technique introduces some modifications on the well-known set associative mapping technique. This modification requires a little alteration in the structure of the cache memory and on the way by which it can be referenced. The proposed alteration leads to increase the set size virtually and consequently to improve the performance and the utilization of the cache memory. The current mapping techniques have accomplished good results. In fact, there are still different cases in which cache memory lines are left empty and not used, whereas two or more processes overwrite the lines of each other, instead of using those empty lines. The proposed algorithm aims at finding an efficient way to deal with such problem.
Abstract: Bladder carcinoma is an important worldwide health problem. Both cystoscopy and urine cytology used in detecting bladder cancer suffer from drawbacks where cystoscopy is an invasive method and urine cytology shows low sensitivity in low grade tumors. This study validates easier and less time-consuming techniques to evaluate the value of combined use of angiogenin and clusterin in comparison and combination with voided urine cytology in the detection of bladder cancer patients. This study includes malignant (bladder cancer patients, n= 50), benign (n=20) and healthy (n=20) groups. The studied groups were subjected to cystoscopic examination, detection of bilharzial antibodies, urine cytology, and estimation of urinary angiogenin and clusterin by ELISA. The overall sensitivity and specificity were 66% and 75% for angiogenin, 70% and 82.5% for clusterin and 46% and 80% for voided urine cytology. Combined sensitivity of angiogenin and clusterin with urine cytology increased from 82 to 88%.
Abstract: The tracking allows to detect the tumor affections of cervical cancer, it is particularly complex and consuming time, because it consists in seeking some abnormal cells among a cluster of normal cells. In this paper, we present our proposed computer system for helping the doctors in tracking the cervical cancer. Knowing that the diagnosis of the malignancy is based in the set of atypical morphological details of all cells, herein, we present an unsupervised genetic algorithm for the separation of cell components since the diagnosis is doing by analysis of the core and the cytoplasm. We give also the various algorithms used for computing the morphological characteristics of cells (Ratio core/cytoplasm, cellular deformity, ...) necessary for the recognition of illness.
Abstract: China apparel industry, which is deeply embedded in
the global production network (GPN), faces the dual pressures of
social upgrading and economic upgrading. Based on the survey in
Ningbo apparel cluster, the paper shows the state of corporate social
responsibility (CSR) in China apparel industry is better than before.
And the investigation indicates that the firms who practice CSR
actively perform better both socially and economically than those who
inactively. The research demonstrates that CSR can be an initial
capital rather than cost, and “doing well by doing good" is also existed
in labor intensive industry.
Abstract: Color image segmentation plays an important role in
computer vision and image processing areas. In this paper, the
features of Volterra filter are utilized for color image segmentation.
The discrete Volterra filter exhibits both linear and nonlinear
characteristics. The linear part smoothes the image features in
uniform gray zones and is used for getting a gross representation of
objects of interest. The nonlinear term compensates for the blurring
due to the linear term and preserves the edges which are mainly used
to distinguish the various objects. The truncated quadratic Volterra
filters are mainly used for edge preserving along with Gaussian noise
cancellation. In our approach, the segmentation is based on K-means
clustering algorithm in HSI space. Both the hue and the intensity
components are fully utilized. For hue clustering, the special cyclic
property of the hue component is taken into consideration. The
experimental results show that the proposed technique segments the
color image while preserving significant features and removing noise
effects.
Abstract: In this paper, a comparative study of application of
supervised and unsupervised learning algorithms on illumination
invariant face recognition has been carried out. The supervised
learning has been carried out with the help of using a bi-layered
artificial neural network having one input, two hidden and one output
layer. The gradient descent with momentum and adaptive learning
rate back propagation learning algorithm has been used to implement
the supervised learning in a way that both the inputs and
corresponding outputs are provided at the time of training the
network, thus here is an inherent clustering and optimized learning of
weights which provide us with efficient results.. The unsupervised
learning has been implemented with the help of a modified
Counterpropagation network. The Counterpropagation network
involves the process of clustering followed by application of Outstar
rule to obtain the recognized face. The face recognition system has
been developed for recognizing faces which have varying
illumination intensities, where the database images vary in lighting
with respect to angle of illumination with horizontal and vertical
planes. The supervised and unsupervised learning algorithms have
been implemented and have been tested exhaustively, with and
without application of histogram equalization to get efficient results.
Abstract: The issue of real-time and reliable report delivery is extremely important for taking effective decision in a real world mission critical Wireless Sensor Network (WSN) based application. The sensor data behaves differently in many ways from the data in traditional databases. WSNs need a mechanism to register, process queries, and disseminate data. In this paper we propose an architectural framework for data placement and management. We propose a reliable and real time approach for data placement and achieving data integrity using self organized sensor clusters. Instead of storing information in individual cluster heads as suggested in some protocols, in our architecture we suggest storing of information of all clusters within a cell in the corresponding base station. For data dissemination and action in the wireless sensor network we propose to use Action and Relay Stations (ARS). To reduce average energy dissipation of sensor nodes, the data is sent to the nearest ARS rather than base station. We have designed our architecture in such a way so as to achieve greater energy savings, enhanced availability and reliability.
Abstract: Image clustering is a process of grouping images
based on their similarity. The image clustering usually uses the color
component, texture, edge, shape, or mixture of two components, etc.
This research aims to explore image clustering using color
composition. In order to complete this image clustering, three main
components should be considered, which are color space, image
representation (feature extraction), and clustering method itself. We
aim to explore which composition of these factors will produce the
best clustering results by combining various techniques from the
three components. The color spaces use RGB, HSV, and L*a*b*
method. The image representations use Histogram and Gaussian
Mixture Model (GMM), whereas the clustering methods use KMeans
and Agglomerative Hierarchical Clustering algorithm. The
results of the experiment show that GMM representation is better
combined with RGB and L*a*b* color space, whereas Histogram is
better combined with HSV. The experiments also show that K-Means
is better than Agglomerative Hierarchical for images clustering.
Abstract: Wireless Sensor Networks can be used to monitor the
physical phenomenon in such areas where human approach is nearly
impossible. Hence the limited power supply is the major constraint of
the WSNs due to the use of non-rechargeable batteries in sensor
nodes. A lot of researches are going on to reduce the energy
consumption of sensor nodes. Energy map can be used with
clustering, data dissemination and routing techniques to reduce the
power consumption of WSNs. Energy map can also be used to know
which part of the network is going to fail in near future. In this paper,
Energy map is constructed using the prediction based approach.
Adaptive alpha GM(1,1) model is used as the prediction model.
GM(1,1) is being used worldwide in many applications for predicting
future values of time series using some past values due to its high
computational efficiency and accuracy.
Abstract: I/O workload is a critical and important factor to
analyze I/O pattern and to maximize file system performance.
However to measure I/O workload on running distributed parallel file
system is non-trivial due to collection overhead and large volume of
data. In this paper, we measured and analyzed file system activities on
two large-scale cluster systems which had TFlops level high
performance computation resources. By comparing file system
activities of 2009 with those of 2006, we analyzed the change of I/O
workloads by the development of system performance and high-speed
network technology.
Abstract: This research tries to analyze the role that knowledge
about foreign markets has in increasing firms- exports in clustered
spaces. We consider two interrelated sources of knowledge: firms-
direct experience and indirect experience from other clustered firms –
export externalities. In particular, it is proposed that firms would
improve their export performance by accessing to export externalities
if they have some previous direct experience that allows them to
identify, understand and exploit them. Also, we propose that this
positive influence of previous direct experience on export
externalities keeps only up to a point, where it becomes negative,
creating an inverted “U" shape. Empirical evidence gathered among
wine producers located in La Rioja tends to confirm that firms enjoy
of export externalities if they have export experience along several
years and countries increase their export performance. While this
relationship becomes less relevant as they develop a higher
experience, we could not confirm the existence of a curvilinear
relationship in their influence on export externalities and export
performance.
Abstract: Landslide susceptibility map delineates the potential
zones for landslide occurrence. Previous works have applied
multivariate methods and neural networks for mapping landslide
susceptibility. This study proposed a new approach to integrate
decision tree model and spatial cluster statistic for assessing landslide
susceptibility spatially. A total of 2057 landslide cells were digitized
for developing the landslide decision tree model. The relationships of
landslides and instability factors were explicitly represented by using
tree graphs in the model. The local Getis-Ord statistics were used to
cluster cells with high landslide probability. The analytic result from
the local Getis-Ord statistics was classed to create a map of landslide
susceptibility zones. The map was validated using new landslide data
with 482 cells. Results of validation show an accuracy rate of 86.1% in
predicting new landslide occurrence. This indicates that the proposed
approach is useful for improving landslide susceptibility mapping.
Abstract: We present a non standard Euclidean vehicle
routing problem adding a level of clustering, and we revisit the use
of self-organizing maps as a tool which naturally handles such
problems. We present how they can be used as a main operator
into an evolutionary algorithm to address two conflicting
objectives of route length and distance from customers to bus stops
minimization and to deal with capacity constraints. We apply the
approach to a real-life case of combined clustering and vehicle
routing for the transportation of the 780 employees of an
enterprise. Basing upon a geographic information system we
discuss the influence of road infrastructures on the solutions
generated.