Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.
Abstract: Querying a data source and routing data towards sink
becomes a serious challenge in static wireless sensor networks if sink
and/or data source are mobile. Many a times the event to be observed
either moves or spreads across wide area making maintenance of
continuous path between source and sink a challenge. Also, sink can
move while query is being issued or data is on its way towards sink.
In this paper, we extend our already proposed Grid Based Data
Dissemination (GBDD) scheme which is a virtual grid based
topology management scheme restricting impact of movement of
sink(s) and event(s) to some specific cells of a grid. This obviates the
need for frequent path modifications and hence maintains continuous
flow of data while minimizing the network energy consumptions.
Simulation experiments show significant improvements in network
energy savings and average packet delay for a packet to reach at sink.
Abstract: In today-s new technology era, cluster has become a
necessity for the modern computing and data applications since many
applications take more time (even days or months) for computation.
Although after parallelization, computation speeds up, still time
required for much application can be more. Thus, reliability of the
cluster becomes very important issue and implementation of fault
tolerant mechanism becomes essential. The difficulty in designing a
fault tolerant cluster system increases with the difficulties of various
failures. The most imperative obsession is that the algorithm, which
avoids a simple failure in a system, must tolerate the more severe
failures. In this paper, we implemented the theory of watchdog timer
in a parallel environment, to take care of failures. Implementation of
simple algorithm in our project helps us to take care of different
types of failures; consequently, we found that the reliability of this
cluster improves.
Abstract: In wireless sensor networks,the mobile agent technology is used in data fusion. According to the node residual energy and the results of partial integration,we design the node clustering algorithm. Optimization of mobile agent in the routing within the cluster strategy for wireless sensor networks to further reduce the amount of data transfer. Through the experiments, using mobile agents in the integration process within the cluster can be reduced the path loss in some extent.
Abstract: In this paper, a new K-means clustering based
approach for identification of voltage control areas is developed.
Voltage control areas are important for efficient reactive power
management in power systems operating under deregulated
environment. Although, voltage control areas are formed using
conventional hierarchical clustering based method, but the present
paper investigate the capability of K-means clustering for the
purpose of forming voltage control areas. The proposed method is
tested and compared for IEEE 14 bus and IEEE 30 bus systems. The
results show that this K-means based method is competing with
conventional hierarchical approach
Abstract: Recently, wireless sensor networks have been paid
more interest, are widely used in a lot of commercial and military
applications, and may be deployed in critical scenarios (e.g. when a
malfunctioning network results in danger to human life or great
financial loss). Such networks must be protected against human
intrusion by using the secret keys to encrypt the exchange messages
between communicating nodes. Both the symmetric and asymmetric
methods have their own drawbacks for use in key management. Thus,
we avoid the weakness of these two cryptosystems and make use of
their advantages to establish a secure environment by developing the
new method for encryption depending on the idea of code
conversion. The code conversion-s equations are used as the key for
designing the proposed system based on the basics of logic gate-s
principals. Using our security architecture, we show how to reduce
significant attacks on wireless sensor networks.
Abstract: This paper aims at identifying and analyzing the
knowledge transmission channels in textile and clothing clusters
located in Brazil and in Europe. Primary data was obtained through
interviews with key individuals. The collection of primary data was
carried out based on a questionnaire with ten categories of indicators
of knowledge transmission. Secondary data was also collected
through a literature review and through international organizations
sites. Similarities related to the use of the main transmission channels
of knowledge are observed in all cases. The main similarities are:
influence of suppliers of machinery, equipment and raw materials;
imitation of products and best practices; training promoted by
technical institutions and businesses; and cluster companies being
open to acquire new knowledge. The main differences lie in the
relationship between companies, where in Europe the intensity of this
relationship is bigger when compared to Brazil. The differences also
occur in importance and frequency of the relationship with the
government, with the cultural environment, and with the activities of
research and development. It is also found factors that reduce the
importance of geographical proximity in transmission of knowledge,
and in generating trust and the establishment of collaborative
behavior.
Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) catalyzes the conversion of HMG-CoA to mevalonate using NADPH and the enzyme is involved in rate-controlling step of mevalonate. Inhibition of HMGR is considered as effective way to lower cholesterol levels so it is drug target to treat hypercholesterolemia, major risk factor of cardiovascular disease. To discover novel HMGR inhibitor, we performed structure-based pharmacophore modeling combined with molecular dynamics (MD) simulation. Four HMGR inhibitors were used for MD simulation and representative structure of each simulation were selected by clustering analysis. Four structure-based pharmacophore models were generated using the representative structure. The generated models were validated used in virtual screening to find novel scaffolds for inhibiting HMGR. The screened compounds were filtered by applying drug-like properties and used in molecular docking. Finally, four hit compounds were obtained and these complexes were refined using energy minimization. These compounds might be potential leads to design novel HMGR inhibitor.
Abstract: System MEMORI automatically detects and recognizes rotated and/or rescaled versions of the objects of a database within digital color images with cluttered background. This task is accomplished by means of a region grouping algorithm guided by heuristic rules, whose parameters concern some geometrical properties and the recognition score of the database objects. This paper focuses on the strategies implemented in MEMORI for the estimation of the heuristic rule parameters. This estimation, being automatic, makes the system a highly user-friendly tool.
Abstract: Nowadays, Gene Ontology has been used widely by many researchers for biological data mining and information retrieval, integration of biological databases, finding genes, and incorporating knowledge in the Gene Ontology for gene clustering. However, the increase in size of the Gene Ontology has caused problems in maintaining and processing them. One way to obtain their accessibility is by clustering them into fragmented groups. Clustering the Gene Ontology is a difficult combinatorial problem and can be modeled as a graph partitioning problem. Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. Therefore, an approach for solving the automatic clustering of the Gene Ontology is proposed by incorporating cohesion-and-coupling metric into a hybrid algorithm consisting of a genetic algorithm and a split-and-merge algorithm. Experimental results and an example of modularized Gene Ontology in RDF/XML format are given to illustrate the effectiveness of the algorithm.
Abstract: In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Abstract: The new framework the Higher Education is
immersed in involves a complete change in the way lecturers must
teach and students must learn. Whereas the lecturer was the main
character in traditional education, the essential goal now is to
increase the students' participation in the process. Thus, one of the
main tasks of lecturers in this new context is to design activities of
different nature in order to encourage such participation. Seminars
are one of the activities included in this environment. They are active
sessions that enable going in depth into specific topics as support of
other activities. They are characterized by some features such as
favoring interaction between students and lecturers or improving
their communication skills. Hence, planning and organizing strategic
seminars is indeed a great challenge for lecturers with the aim of
acquiring knowledge and abilities. This paper proposes a method
using Artificial Intelligence techniques to obtain student profiles
from their marks and preferences. The goal of building such profiles
is twofold. First, it facilitates the task of splitting the students into
different groups, each group with similar preferences and learning
difficulties. Second, it makes it easy to select adequate topics to be a
candidate for the seminars. The results obtained can be either a
guarantee of what the lecturers could observe during the development
of the course or a clue to reconsider new methodological strategies in
certain topics.
Abstract: An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
Abstract: Nevertheless the widespread application of finite
mixture models in segmentation, finite mixture model selection is
still an important issue. In fact, the selection of an adequate number
of segments is a key issue in deriving latent segments structures and
it is desirable that the selection criteria used for this end are effective.
In order to select among several information criteria, which may
support the selection of the correct number of segments we conduct a
simulation study. In particular, this study is intended to determine
which information criteria are more appropriate for mixture model
selection when considering data sets with only categorical
segmentation base variables. The generation of mixtures of
multinomial data supports the proposed analysis. As a result, we
establish a relationship between the level of measurement of
segmentation variables and some (eleven) information criteria-s
performance. The criterion AIC3 shows better performance (it
indicates the correct number of the simulated segments- structure
more often) when referring to mixtures of multinomial segmentation
base variables.
Abstract: Grid computing is a group of clusters connected over
high-speed networks that involves coordinating and sharing
computational power, data storage and network resources operating
across dynamic and geographically dispersed locations. Resource
management and job scheduling are critical tasks in grid computing.
Resource selection becomes challenging due to heterogeneity and
dynamic availability of resources. Job scheduling is a NP-complete
problem and different heuristics may be used to reach an optimal or
near optimal solution. This paper proposes a model for resource and
job scheduling in dynamic grid environment. The main focus is to
maximize the resource utilization and minimize processing time of
jobs. Grid resource selection strategy is based on Max Heap Tree
(MHT) that best suits for large scale application and root node of
MHT is selected for job submission. Job grouping concept is used to
maximize resource utilization for scheduling of jobs in grid
computing. Proposed resource selection model and job grouping
concept are used to enhance scalability, robustness, efficiency and
load balancing ability of the grid.
Abstract: Color Image quantization (CQ) is an important
problem in computer graphics, image and processing. The aim of
quantization is to reduce colors in an image with minimum distortion.
Clustering is a widely used technique for color quantization; all
colors in an image are grouped to small clusters. In this paper, we
proposed a new hybrid approach for color quantization using firefly
algorithm (FA) and K-means algorithm. Firefly algorithm is a swarmbased
algorithm that can be used for solving optimization problems.
The proposed method can overcome the drawbacks of both
algorithms such as the local optima converge problem in K-means
and the early converge of firefly algorithm. Experiments on three
commonly used images and the comparison results shows that the
proposed algorithm surpasses both the base-line technique k-means
clustering and original firefly algorithm.
Abstract: The purposes of this research are to study and develop
the algorithm of Thai spoonerism words by semi-automatic computer
programs, that is to say, in part of data input, syllables are already
separated and in part of spoonerism, the developed algorithm is
utilized, which can establish rules and mechanisms in Thai
spoonerism words for bi-syllables by utilizing analysis in elements of
the syllables, namely cluster consonant, vowel, intonation mark and
final consonant. From the study, it is found that bi-syllable Thai
spoonerism has 1 case of spoonerism mechanism, namely
transposition in value of vowel, intonation mark and consonant of
both 2 syllables but keeping consonant value and cluster word (if
any).
From the study, the rules and mechanisms in Thai spoonerism
word were applied to develop as Thai spoonerism word software,
utilizing PHP program. the software was brought to conduct a
performance test on software execution; it is found that the program
performs bi-syllable Thai spoonerism correctly or 99% of all words
used in the test and found faults on the program at 1% as the words
obtained from spoonerism may not be spelling in conformity with
Thai grammar and the answer in Thai spoonerism could be more than
1 answer.
Abstract: This study was conducted Ismailoglu grape type (Vitis
vinifera L.) and its vine which was aged 15 was grown on its own
root in a vegetation period of 2013 in Nevşehir 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, TKI-HUMAS (TKI-HM) (Soil) (S), TKIHM
(Foliar) (F), TKI-HM (S + F), 1/3 CTR + TKI-HM (S), 1/3 CTR
+ TKI-HM (F), 1/3 CTR + TKI-HM (S+F), STR + TKI-HM (S), STR
+ TKI-HM (F), STR + TKI-HM (S + F), 1/3 CTR + STR+TKI-HM
(S), 1/3 CTR + STR + TKI-HM (F), 1/3 CTR + STR + TKI-HM (S +
F) on yield and yield components of Ismailoglu grape type. The
results were obtained as the highest fresh grape yield (16.15 kg/vine)
with TKI-HM (S), as the highest cluster weight (652.39 g) with 1/3
CTR + STR, as the highest 100 berry weight (419.07 g) with 1/3
CTR + STR + TKI-HM (F), as the highest maturity index (44.06)
with 1/3 CTR, as the highest must yield (810.00 ml) with STR +
TKI-HM (F), as the highest intensity of L* color (42.04) with TKIHM
(S + F), as the highest intensity of a* color (2.60) with 1/3 CTR
+ TKI-HM (S), as the highest intensity of b* color (7.16) with 1/3
CTR + TKI-HM (S) applications. To increase the fresh grape yield of
Ismailoglu grape type can be recommended TKI-HM (S) application.