Abstract: In this paper we propose a new approach to constructing the Delaunay Triangulation and the optimum algorithm for the case of multidimensional spaces (d ≥ 2). Analysing the modern state, it is possible to draw a conclusion, that the ideas for the existing effective algorithms developed for the case of d ≥ 2 are not simple to generalize on a multidimensional case, without the loss of efficiency. We offer for the solving this problem an effective algorithm that satisfies all the given requirements. But theoretical complexity of the problem it is impossible to improve as the Worst - Case Optimality for algorithms of solving such a problem is proved.
Abstract: Modern spatial database management systems require a unique Spatial Access Method (SAM) in order solve complex spatial quires efficiently. In this case the spatial data structure takes a prominent place in the SAM. Inadequate data structure leads forming poor algorithmic choices and forging deficient understandings of algorithm behavior on the spatial database. A key step in developing a better semantic spatial object data structure is to quantify the performance effects of semantic and outlier detections that are not reflected in the previous tree structures (R-Tree and its variants). This paper explores a novel SSRO-Tree on SAM to the Topo-Semantic approach. The paper shows how to identify and handle the semantic spatial objects with outlier objects during page overflow/underflow, using gain/loss metrics. We introduce a new SSRO-Tree algorithm which facilitates the achievement of better performance in practice over algorithms that are superior in the R*-Tree and RO-Tree by considering selection queries.
Abstract: Matrix metalloproteinases (MMP) are a class of
structural and functional related enzymes involved in altering the
natural elements of the extracellular matrix. Most of the MMP
structures are cristalographycally determined and published in
WorldWide ProteinDataBank, isolated, in full structure or bound to
natural or synthetic inhibitors. This study proposes an algorithm to
replace missing crystallographic structures in PDB database. We
have compared the results of a chosen docking algorithm with a
known crystallographic structure in order to validate enzyme sites
reconstruction there where crystallographic data are missing.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.
Abstract: As the network based technologies become
omnipresent, demands to secure networks/systems against threat
increase. One of the effective ways to achieve higher security is
through the use of intrusion detection systems (IDS), which are a
software tool to detect anomalous in the computer or network. In this
paper, an IDS has been developed using an improved machine
learning based algorithm, Locally Linear Neuro Fuzzy Model
(LLNF) for classification whereas this model is originally used for
system identification. A key technical challenge in IDS and LLNF
learning is the curse of high dimensionality. Therefore a feature
selection phase is proposed which is applicable to any IDS. While
investigating the use of three feature selection algorithms, in this
model, it is shown that adding feature selection phase reduces
computational complexity of our model. Feature selection algorithms
require the use of a feature goodness measure. The use of both a
linear and a non-linear measure - linear correlation coefficient and
mutual information- is investigated respectively
Abstract: This paper presents a software quality support tool, a
Java source code evaluator and a code profiler based on
computational intelligence techniques. It is Java prototype software
developed by AI Group [1] from the Research Laboratories at
Universidad de Palermo: an Intelligent Java Analyzer (in Spanish:
Analizador Java Inteligente, AJI). It represents a new approach to
evaluate and identify inaccurate source code usage and transitively,
the software product itself.
The aim of this project is to provide the software development
industry with a new tool to increase software quality by extending
the value of source code metrics through computational intelligence.
Abstract: In this paper, we propose an efficient hierarchical DNA
sequence search method to improve the search speed while the
accuracy is being kept constant. For a given query DNA sequence,
firstly, a fast local search method using histogram features is used as a
filtering mechanism before scanning the sequences in the database.
An overlapping processing is newly added to improve the robustness
of the algorithm. A large number of DNA sequences with low
similarity will be excluded for latter searching. The Smith-Waterman
algorithm is then applied to each remainder sequences. Experimental
results using GenBank sequence data show the proposed method
combining histogram information and Smith-Waterman algorithm is
more efficient for DNA sequence search.
Abstract: Re-entrant scheduling is an important search problem
with many constraints in the flow shop. In the literature, a number of
approaches have been investigated from exact methods to
meta-heuristics. This paper presents a genetic algorithm that encodes
the problem as multi-level chromosomes to reflect the dependent
relationship of the re-entrant possibility and resource consumption.
The novel encoding way conserves the intact information of the data
and fastens the convergence to the near optimal solutions. To test the
effectiveness of the method, it has been applied to the
resource-constrained re-entrant flow shop scheduling problem.
Computational results show that the proposed GA performs better than
the simulated annealing algorithm in the measure of the makespan
Abstract: The objective of this paper is to develop a neural
network-based residual generator to detect the fault in the actuators
for a specific communication satellite in its attitude control system
(ACS). First, a dynamic multilayer perceptron network with dynamic
neurons is used, those neurons correspond a second order linear
Infinite Impulse Response (IIR) filter and a nonlinear activation
function with adjustable parameters. Second, the parameters from the
network are adjusted to minimize a performance index specified by
the output estimated error, with the given input-output data collected
from the specific ACS. Then, the proposed dynamic neural network
is trained and applied for detecting the faults injected to the wheel,
which is the main actuator in the normal mode for the communication
satellite. Then the performance and capabilities of the proposed
network were tested and compared with a conventional model-based
observer residual, showing the differences between these two
methods, and indicating the benefit of the proposed algorithm to
know the real status of the momentum wheel. Finally, the application
of the methods in a satellite ground station is discussed.
Abstract: CEMTool is a command style design and analyzing
package for scientific and technological algorithm and a matrix based
computation language. In this paper, we present new 2D & 3D
finite element method (FEM) packages for CEMTool. We discuss
the detailed structures and the important features of pre-processor,
solver, and post-processor of CEMTool 2D & 3D FEM packages. In
contrast to the existing MATLAB PDE Toolbox, our proposed FEM
packages can deal with the combination of the reserved words. Also,
we can control the mesh in a very effective way. With the introduction
of new mesh generation algorithm and fast solving technique, our
FEM packages can guarantee the shorter computational time than
MATLAB PDE Toolbox. Consequently, with our new FEM packages,
we can overcome some disadvantages or limitations of the existing
MATLAB PDE Toolbox.
Abstract: A analysis on the conventional the blood pressure estimation method using an oscillometric sphygmomanometer was
performed through a computer simulation using an arterial pressure-volume (APV) model. Traditionally, the maximum amplitude algorithm (MAP) was applied on the oscillation waveforms of the APV model to obtain the mean arterial pressure and the characteristic ratio. The estimation of mean arterial pressure and
characteristic ratio was significantly affected with the shape of the blood pressure waveforms and the cutoff frequency of high-pass filter
(HPL) circuitry. Experimental errors are due to these effects when estimating blood pressure. To find out an algorithm independent from
the influence of waveform shapes and parameters of HPL, the volume
oscillation of the APV model and the phase shift of the oscillation with fast fourier transform (FFT) were testified while increasing the cuff
pressure from 1 mmHg to 200 mmHg (1 mmHg per second). The phase shift between the ranges of volume oscillation was then only observed between the systolic and the diastolic blood pressures. The same results were also obtained from the simulations performed on two different the arterial blood pressure waveforms and one
hyperthermia waveform.
Abstract: The paper proposes a unified model for multimedia data retrieval which includes data representatives, content representatives, index structure, and search algorithms. The multimedia data are defined as k-dimensional signals indexed in a multidimensional k-tree structure. The benefits of using the k-tree unified model were demonstrated by running the data retrieval application on a six networked nodes test bed cluster. The tests were performed with two retrieval algorithms, one that allows parallel searching using a single feature, the second that performs a weighted cascade search for multiple features querying. The experiments show a significant reduction of retrieval time while maintaining the quality of results.
Abstract: Using maximal consistent blocks of tolerance relation
on the universe in incomplete decision table, the concepts of join block
and meet block are introduced and studied. Including tolerance class,
other blocks such as tolerant kernel and compatible kernel of an object
are also discussed at the same time. Upper and lower approximations
based on those blocks are also defined. Default definite decision rules
acquired from incomplete decision table are proposed in the paper. An
incremental algorithm to update default definite decision rules is
suggested for effective mining tasks from incomplete decision table
into which data is appended. Through an example, we demonstrate
how default definite decision rules based on maximal consistent
blocks, join blocks and meet blocks are acquired and how optimization
is done in support of discernibility matrix and discernibility function
in the incomplete decision table.
Abstract: Duplicated region detection is a technical method to
expose copy-paste forgeries on digital images. Copy-paste is one
of the common types of forgeries to clone portion of an image
in order to conceal or duplicate special object. In this type of
forgery detection, extracting robust block feature and also high
time complexity of matching step are two main open problems.
This paper concentrates on computational time and proposes a local
block matching algorithm based on block clustering to enhance time
complexity. Time complexity of the proposed algorithm is formulated
and effects of two parameter, block size and number of cluster, on
efficiency of this algorithm are considered. The experimental results
and mathematical analysis demonstrate this algorithm is more costeffective
than lexicographically algorithms in time complexity issue
when the image is complex.
Abstract: detecting the deadlock is one of the important
problems in distributed systems and different solutions have been
proposed for it. Among the many deadlock detection algorithms,
Edge-chasing has been the most widely used. In Edge-chasing
algorithm, a special message called probe is made and sent along
dependency edges. When the initiator of a probe receives the probe
back the existence of a deadlock is revealed. But these algorithms are
not problem-free. One of the problems associated with them is that
they cannot detect some deadlocks and they even identify false
deadlocks. A key point not mentioned in the literature is that when
the process is waiting to obtain the required resources and its
execution has been blocked, how it can actually respond to probe
messages in the system. Also the question of 'which process should
be victimized in order to achieve a better performance when multiple
cycles exist within one single process in the system' has received
little attention. In this paper, one of the basic concepts of the
operating system - daemon - will be used to solve the problems
mentioned. The proposed Algorithm becomes engaged in sending
probe messages to the mandatory daemons and collects enough
information to effectively identify and resolve multi-cycle deadlocks
in distributed systems.
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.
Abstract: In this paper a new Genetic Algorithm based on a heuristic operator and Centre of Mass selection operator (CMGA) is designed for the unbounded knapsack problem(UKP), which is NP-Hard combinatorial optimization problem. The proposed genetic algorithm is based on a heuristic operator, which utilizes problem specific knowledge. This center of mass operator when combined with other Genetic Operators forms a competitive algorithm to the existing ones. Computational results show that the proposed algorithm is capable of obtaining high quality solutions for problems of standard randomly generated knapsack instances. Comparative study of CMGA with simple GA in terms of results for unbounded knapsack instances of size up to 200 show the superiority of CMGA. Thus CMGA is an efficient tool of solving UKP and this algorithm is competitive with other Genetic Algorithms also.
Abstract: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.