Abstract: In this paper we present the deep study about the Bio-
Medical Images and tag it with some basic extracting features (e.g.
color, pixel value etc). The classification is done by using a nearest
neighbor classifier with various distance measures as well as the
automatic combination of classifier results. This process selects a
subset of relevant features from a group of features of the image. It
also helps to acquire better understanding about the image by
describing which the important features are. The accuracy can be
improved by increasing the number of features selected. Various
types of classifications were evolved for the medical images like
Support Vector Machine (SVM) which is used for classifying the
Bacterial types. Ant Colony Optimization method is used for optimal
results. It has high approximation capability and much faster
convergence, Texture feature extraction method based on Gabor
wavelets etc..
Abstract: Alzheimer is known as the loss of mental functions
such as thinking, memory, and reasoning that is severe enough to
interfere with a person's daily functioning. The appearance of
Alzheimer Disease symptoms (AD) are resulted based on which part
of the brain has a variety of infection or damage. In this case, the
MRI is the best biomedical instrumentation can be ever used to
discover the AD existence. Therefore, this paper proposed a fusion
method to distinguish between the normal and (AD) MRIs. In this
combined method around 27 MRIs collected from Jordanian
Hospitals are analyzed based on the use of Low pass -morphological
filters to get the extracted statistical outputs through intensity
histogram to be employed by the descriptive box plot. Also, the
artificial neural network (ANN) is applied to test the performance of
this approach. Finally, the obtained result of t-test with confidence
accuracy (95%) has compared with classification accuracy of ANN
(100 %). The robust of the developed method can be considered
effectively to diagnose and determine the type of AD image.
Abstract: An inverse geometry problem is solved to predict an
unknown irregular boundary profile. The aim is to minimize the
objective function, which is the difference between real and
computed temperatures, using three different versions of Conjugate
Gradient Method. The gradient of the objective function, considered
necessary in this method, obtained as a result of solving the adjoint
equation. The abilities of three versions of Conjugate Gradient
Method in predicting the boundary profile are compared using a
numerical algorithm based on the method. The predicted shapes show
that due to its convergence rate and accuracy of predicted values, the
Powell-Beale version of the method is more effective than the
Fletcher-Reeves and Polak –Ribiere versions.
Abstract: This paper proposes to use ETM+ multispectral data
and panchromatic band as well as texture features derived from the
panchromatic band for land cover classification. Four texture features
including one 'internal texture' and three GLCM based textures
namely correlation, entropy, and inverse different moment were used
in combination with ETM+ multispectral data. Two data sets
involving combination of multispectral, panchromatic band and its
texture were used and results were compared with those obtained by
using multispectral data alone. A decision tree classifier with and
without boosting were used to classify different datasets. Results
from this study suggest that the dataset consisting of panchromatic
band, four of its texture features and multispectral data was able to
increase the classification accuracy by about 2%. In comparison, a
boosted decision tree was able to increase the classification accuracy
by about 3% with the same dataset.
Abstract: As the use of registration packages spreads, the number of the aligned image pairs in image databases (either by manual or automatic methods) increases dramatically. These image pairs can serve as a set of training data. Correspondingly, the images that are to be registered serve as testing data. In this paper, a novel medical image registration method is proposed which is based on the a priori knowledge of the expected joint intensity distribution estimated from pre-aligned training images. The goal of the registration is to find the optimal transformation such that the distance between the observed joint intensity distribution obtained from the testing image pair and the expected joint intensity distribution obtained from the corresponding training image pair is minimized. The distance is measured using the divergence measure based on Tsallis entropy. Experimental results show that, compared with the widely-used Shannon mutual information as well as Tsallis mutual information, the proposed method is computationally more efficient without sacrificing registration accuracy.
Abstract: In this study we focus on improvement performance
of a cue based Motor Imagery Brain Computer Interface (BCI). For
this purpose, data fusion approach is used on results of different
classifiers to make the best decision. At first step Distinction
Sensitive Learning Vector Quantization method is used as a feature
selection method to determine most informative frequencies in
recorded signals and its performance is evaluated by frequency
search method. Then informative features are extracted by packet
wavelet transform. In next step 5 different types of classification
methods are applied. The methodologies are tested on BCI
Competition II dataset III, the best obtained accuracy is 85% and the
best kappa value is 0.8. At final step ordered weighted averaging
(OWA) method is used to provide a proper aggregation classifiers
outputs. Using OWA enhanced system accuracy to 95% and kappa
value to 0.9. Applying OWA just uses 50 milliseconds for
performing calculation.
Abstract: In this study, a high accuracy protein-protein interaction
prediction method is developed. The importance of the proposed
method is that it only uses sequence information of proteins while
predicting interaction. The method extracts phylogenetic profiles of
proteins by using their sequence information. Combining the phylogenetic
profiles of two proteins by checking existence of homologs
in different species and fitting this combined profile into a statistical
model, it is possible to make predictions about the interaction status
of two proteins.
For this purpose, we apply a collection of pattern recognition
techniques on the dataset of combined phylogenetic profiles of protein
pairs. Support Vector Machines, Feature Extraction using ReliefF,
Naive Bayes Classification, K-Nearest Neighborhood Classification,
Decision Trees, and Random Forest Classification are the methods
we applied for finding the classification method that best predicts
the interaction status of protein pairs. Random Forest Classification
outperformed all other methods with a prediction accuracy of 76.93%
Abstract: This paper investigates the problem of sampling from transactional data streams. We introduce CFISDS as a content based sampling algorithm that works on a landmark window model of data streams and preserve more informed sample in sample space. This algorithm that work based on closed frequent itemset mining tasks, first initiate a concept lattice using initial data, then update lattice structure using an incremental mechanism.Incremental mechanism insert, update and delete nodes in/from concept lattice in batch manner. Presented algorithm extracts the final samples on demand of user. Experimental results show the accuracy of CFISDS on synthetic and real datasets, despite on CFISDS algorithm is not faster than exist sampling algorithms such as Z and DSS.
Abstract: Near-infrared (NIR) spectroscopy is a widely used
method for material identification for laboratory and industrial applications.
While standard spectrometers only allow measurements at
one sampling point at a time, NIR Spectral Imaging techniques can
measure, in real-time, both the size and shape of an object as well as
identify the material the object is made of. The online classification
and sorting of recovered paper with NIR Spectral Imaging (SI)
is used with success in the paper recycling industry throughout
Europe. Recently, the globalisation of the recycling material streams
caused that water-based flexographic-printed newspapers mainly from
UK and Italy appear also in central Europe. These flexo-printed
newspapers are not sufficiently de-inkable with the standard de-inking
process originally developed for offset-printed paper. This de-inking
process removes the ink from recovered paper and is the fundamental
processing step to produce high-quality paper from recovered paper.
Thus, the flexo-printed newspapers are a growing problem for the
recycling industry as they reduce the quality of the produced paper
if their amount exceeds a certain limit within the recovered paper
material.
This paper presents the results of a research project for the
development of an automated entry inspection system for recovered
paper that was jointly conducted by CTR AG (Austria) and PTS
Papiertechnische Stiftung (Germany). Within the project an NIR
SI prototype for the identification of flexo-printed newspaper has
been developed. The prototype can identify and sort out flexoprinted
newspapers in real-time and achieves a detection accuracy
for flexo-printed newspaper of over 95%. NIR SI, the technology the
prototype is based on, allows the development of inspection systems
for incoming goods in a paper production facility as well as industrial
sorting systems for recovered paper in the recycling industry in the
near future.
Abstract: In this paper we propose a class of second derivative multistep methods for solving some well-known classes of Lane- Emden type equations which are nonlinear ordinary differential equations on the semi-infinite domain. These methods, which have good stability and accuracy properties, are useful in deal with stiff ODEs. We show superiority of these methods by applying them on the some famous Lane-Emden type equations.
Abstract: In a pilot plant scale of a fluidized bed reactor, a
reduction reaction of sodium sulfate by natural gas has been
investigated. Natural gas is applied in this study as a reductant. Feed
density, feed mass flow rate, natural gas and air flow rate
(independent parameters)and temperature of bed and CO
concentration in inlet and outlet of reactor (dependent parameters)
were monitored and recorded at steady state. The residence time was
adjusted close to value of traditional reaction [1]. An artificial neural
network (ANN) was established to study dependency of yield and
carbon gradient on operating parameters. Resultant 97% accuracy of
applied ANN is a good prove that natural gas can be used as a
reducing agent. Predicted ANN model for relation between other
sources carbon gradient (accuracy 74%) indicates there is not a
meaningful relation between other sources carbon variation and
reduction process which means carbon in granule does not have
significant effect on the reaction yield.
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: 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: Access Management is the proactive management of
vehicular access points to land parcels adjacent to all manner of
roadways. Good access management promotes safe and efficient use
of the transportation network. This study attempts to utilize archived
data from the University Technology of Malaysia on-campus area to
assess the accuracy with which access management display some
benefits. Results show that usage of access management reduces
delay and fewer crashes. Clustered development can improve
walking, cycling and transit travel, reduce parking requirements and
improve emergency responses. Effective Access Management
planning can also reduce total roadway facility costs by reducing the
number of driveways and intersections. At the end after presenting
recommendations some of the travel impact, and benefits that
can be derived if these suggestions are implemented have
been summarized with the related comments.
Abstract: One of the major challenges in the Information
Retrieval field is handling the massive amount of information
available to Internet users. Existing ranking techniques and strategies
that govern the retrieval process fall short of expected accuracy.
Often relevant documents are buried deep in the list of documents
returned by the search engine. In order to improve retrieval accuracy
we examine the issue of language effect on the retrieval process.
Then, we propose a solution for a more biased, user-centric relevance
for retrieved data. The results demonstrate that using indices based
on variations of the same language enhances the accuracy of search
engines for individual users.
Abstract: With advances in computer vision, non-contact gaze tracking systems are heading towards being much easier to operate and more comfortable for use, the technique proposed in this paper is specially designed for achieving these goals. For the convenience in operation, the proposal aims at the system with simple configuration which is composed of a fixed wide angle camera and dual infrared illuminators. Then in order to enhance the usability of the system based on single camera, a self-adjusting method which is called Real-time gaze Tracking Algorithm with head movement Compensation (RTAC) is developed for estimating the gaze direction under natural head movement and simplifying the calibration procedure at the same time. According to the actual evaluations, the average accuracy of about 1° is achieved over a field of 20×15×15 cm3.
Abstract: Functioning of a biometric system in large part
depends on the performance of the similarity measure function.
Frequently a generalized similarity distance measure function such as
Euclidian distance or Mahalanobis distance is applied to the task of
matching biometric feature vectors. However, often accuracy of a
biometric system can be greatly improved by designing a customized
matching algorithm optimized for a particular biometric application.
In this paper we propose a tailored similarity measure function for
behavioral biometric systems based on the expert knowledge of the
feature level data in the domain. We compare performance of a
proposed matching algorithm to that of other well known similarity
distance functions and demonstrate its superiority with respect to the
chosen domain.
Abstract: Linear induction motors are used in various industries
but they have some specific phenomena which are the causes for
some problems. The most important phenomenon is called end effect.
End effect decreases efficiency, power factor and output force and
unbalances the phase currents. This phenomenon is more important
in medium and high speeds machines. In this paper a factor, EEF , is
obtained by an accurate equivalent circuit model, to determine the
end effect intensity. In this way, all of effective design parameters on
end effect is described. Accuracy of this equivalent circuit model is
evaluated by two dimensional finite-element analysis using ANSYS.
The results show the accuracy of the equivalent circuit model.
Abstract: In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.
Abstract: This paper describes an application of a dual satellite
geolocation (DSG) system on identifying and locating the unknown
source of uplink sweeping interference. The geolocation system
integrates the method of joint time difference of arrival (TDOA) and
frequency difference of arrival (FDOA) with ephemeris correction
technique which successfully demonstrated high accuracy in
interference source location. The factors affecting the location error
were also discussed.