Abstract: Myoelectric control system is the fundamental
component of modern prostheses, which uses the myoelectric signals
from an individual’s muscles to control the prosthesis movements.
The surface electromyogram signal (sEMG) being noninvasive has
been used as an input to prostheses controllers for many years.
Recent technological advances has led to the development of
implantable myoelectric sensors which enable the internal
myoelectric signal (MES) to be used as input to these prostheses
controllers. The intramuscular measurement can provide focal
recordings from deep muscles of the forearm and independent signals
relatively free of crosstalk thus allowing for more independent
control sites. However, little work has been done to compare the two
inputs. In this paper we have compared the classification accuracy of
six pattern recognition based myoelectric controllers which use
surface myoelectric signals recorded using untargeted (symmetric)
surface electrode arrays to the same controllers with multichannel
intramuscular myolectric signals from targeted intramuscular
electrodes as inputs. There was no significant enhancement in the
classification accuracy as a result of using the intramuscular EMG
measurement technique when compared to the results acquired using
the surface EMG measurement technique. Impressive classification
accuracy (99%) could be achieved by optimally selecting only five
channels of surface EMG.
Abstract: Banda Sea Collision Zone (BSCZ) is the result of the
interaction and convergence of Indo-Australian plate, Eurasian plate
and Pacific plate. This location is located in eastern Indonesia. This
zone has a very high seismic activity. In this research, we will
calculate the rate (λ) and Mean Square Error (MSE). By this result,
we will classification earthquakes distribution in the BSCZ with the
point process approach. Chi-square is used to determine the type of
earthquakes distribution in the sub region of BSCZ. The data used in
this research is data of earthquakes with a magnitude ≥ 6 SR for the
period 1964-2013 and sourced from BMKG Jakarta. This research is
expected to contribute to the Moluccas Province and surrounding
local governments in performing spatial plan document related to
disaster management.
Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: The occurrences of precipitation, also commonly
referred as rain, in the form of "convective" and "stratiform" have
been identified to exist worldwide. In this study, the radar return
echoes or known as reflectivity values acquired from radar scans
have been exploited in the process of classifying the type of rain
endured. The investigation use radar data from Malaysian
Meteorology Department (MMD). It is possible to discriminate the
types of rain experienced in tropical region by observing the vertical
characteristics of the rain structure. .Heavy rain in tropical region
profoundly affects radiowave signals, causing transmission
interference and signal fading. Required wireless system fade margin
depends on the type of rain. Information relating to the two
mentioned types of rain is critical for the system engineers and
researchers in their endeavour to improve the reliability of
communication links. This paper highlights the quantification of
percentage occurrences over one year period in 2009.
Abstract: Social networking sites such as Twitter and Facebook
attracts over 500 million users across the world, for those users, their
social life, even their practical life, has become interrelated. Their
interaction with social networking has affected their life forever.
Accordingly, social networking sites have become among the main
channels that are responsible for vast dissemination of different kinds
of information during real time events. This popularity in Social
networking has led to different problems including the possibility of
exposing incorrect information to their users through fake accounts
which results to the spread of malicious content during life events.
This situation can result to a huge damage in the real world to the
society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting the
fake accounts on Twitter. The study determines the minimized set of
the main factors that influence the detection of the fake accounts on
Twitter, and then the determined factors are applied using different
classification techniques. A comparison of the results of these
techniques has been performed and the most accurate algorithm is
selected according to the accuracy of the results. The study has been
compared with different recent researches in the same area; this
comparison has proved the accuracy of the proposed study. We claim
that this study can be continuously applied on Twitter social network
to automatically detect the fake accounts; moreover, the study can be
applied on different social network sites such as Facebook with minor
changes according to the nature of the social network which are
discussed in this paper.
Abstract: The main purpose of this study is to assess the
sediment quality and potential ecological risk in marine sediments in
Gymea Bay located in south Sydney, Australia. A total of 32 surface
sediment samples were collected from the bay. Current track
trajectories and velocities have also been measured in the bay. The
resultant trace elements were compared with the adverse biological
effect values Effect Range Low (ERL) and Effect Range Median
(ERM) classifications. The results indicate that the average values of
chromium, arsenic, copper, zinc, and lead in surface sediments all
reveal low pollution levels and are below ERL and ERM values. The
highest concentrations of trace elements were found close to
discharge points and in the inner bay, and were linked with high
percentages of clay minerals, pyrite and organic matter, which can
play a significant role in trapping and accumulating these elements.
The lowest concentrations of trace elements were found to be on the
shoreline of the bay, which contained high percentages of sand
fractions. It is postulated that the fine particles and trace elements are
disturbed by currents and tides, then transported and deposited in
deeper areas. The current track velocities recorded in Gymea Bay had
the capability to transport fine particles and trace element pollution
within the bay. As a result, hydrodynamic measurements were able to
provide useful information and to help explain the distribution of
sedimentary particles and geochemical properties. This may lead to
knowledge transfer to other bay systems, including those in remote
areas. These activities can be conducted at a low cost, and are
therefore also transferrable to developing countries. The advent of
portable instruments to measure trace elements in the field has also
contributed to the development of these lower cost and easily applied
methodologies available for use in remote locations and low-cost
economies.
Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: We present probabilistic multinomial Dirichlet
classification model for multidimensional data and Gaussian process
priors. Here, we have considered efficient computational method that
can be used to obtain the approximate posteriors for latent variables
and parameters needed to define the multiclass Gaussian process
classification model. We first investigated the process of inducing a
posterior distribution for various parameters and latent function by
using the variational Bayesian approximations and important sampling
method, and next we derived a predictive distribution of latent
function needed to classify new samples. The proposed model is
applied to classify the synthetic multivariate dataset in order to verify
the performance of our model. Experiment result shows that our model
is more accurate than the other approximation methods.
Abstract: This paper introduces an effective method of
segmenting Korean text (place names in Korean) from a Korean road
sign image. A Korean advanced directional road sign is composed of
several types of visual information such as arrows, place names in
Korean and English, and route numbers. Automatic classification of
the visual information and extraction of Korean place names from the
road sign images make it possible to avoid a lot of manual inputs to a
database system for management of road signs nationwide. We
propose a series of problem-specific heuristics that correctly segments
Korean place names, which is the most crucial information, from the
other information by leaving out non-text information effectively. The
experimental results with a dataset of 368 road sign images show 96%
of the detection rate per Korean place name and 84% per road sign
image.
Abstract: The underutilization of biomass resources in the
Philippines, combined with its growing population and the rise in
fossil fuel prices confirms demand for alternative energy sources. The
goal of this paper is to provide a comparison of MODIS-based and
Landsat-based agricultural land cover maps when used in the
estimation of rice hull’s available energy potential. Biomass resource
assessment was done using mathematical models and remote sensing
techniques employed in a GIS platform.
Abstract: In this paper, we propose the variational EM inference
algorithm for the multi-class Gaussian process classification model
that can be used in the field of human behavior recognition. This
algorithm can drive simultaneously both a posterior distribution of a
latent function and estimators of hyper-parameters in a Gaussian
process classification model with multiclass. Our algorithm is based
on the Laplace approximation (LA) technique and variational EM
framework. This is performed in two steps: called expectation and
maximization steps. First, in the expectation step, using the Bayesian
formula and LA technique, we derive approximately the posterior
distribution of the latent function indicating the possibility that each
observation belongs to a certain class in the Gaussian process
classification model. Second, in the maximization step, using a derived
posterior distribution of latent function, we compute the maximum
likelihood estimator for hyper-parameters of a covariance matrix
necessary to define prior distribution for latent function. These two
steps iteratively repeat until a convergence condition satisfies.
Moreover, we apply the proposed algorithm with human action
classification problem using a public database, namely, the KTH
human action data set. Experimental results reveal that the proposed
algorithm shows good performance on this data set.
Abstract: Polycyclic Aromatic Hydrocarbons (PAHs) are
formed mainly because of incomplete combustion of organic
materials during industrial, domestic activities or natural occurrence.
Their toxicity and contamination of terrestrial and aquatic ecosystem
have been established. However, with limited validity index, previous
research has focused on PAHs isomer pair ratios of variable
physicochemical properties in source identification. The objective of
this investigation was to determine the empirical validity of Pearson
Correlation Coefficient (PCC) and Cluster Analysis (CA) in PAHs
source identification along soil samples of different land uses.
Therefore, 16 PAHs grouped, as Endocrine Disruption Substances
(EDSs) were determined in 10 sample stations in top and sub soils
seasonally. PAHs was determined the use of Varian 300 gas
chromatograph interfaced with flame ionization detector. Instruments
and reagents used are of standard and chromatographic grades
respectively. PCC and CA results showed that the classification of
PAHs along pyrolitic and petrogenic organics used in source
signature is about the predominance PAHs in environmental matrix.
Therefore, the distribution of PAHs in the studied stations revealed
the presence of trace quantities of the vast majority of the sixteen
PAHs, which may ultimately inhabit the actual source signature
authentication. Therefore, factors to be considered when evaluating
possible sources of PAHs could be; type and extent of bacterial
metabolism, transformation products/substrates, and environmental
factors such as salinity, pH, oxygen concentration, nutrients, light
intensity, temperature, co-substrates, and environmental medium are
hereby recommended as factors to be considered when evaluating
possible sources of PAHs.
Abstract: A clay soil classified as A-7-6 and CH soil according
to AASHTO and unified soil classification system respectively, was
stabilized using A-3 soil (AASHTO soil classification system). The
clay soil was replaced with 0%, 10%, 20%, to 100% A-3 soil,
compacted at both British Standard Light (BSL) and British Standard
Heavy (BSH) compaction energy levels and using Unconfined
Compressive Strength (UCS) as evaluation criteria. The Maximum
Dry Density (MDD) of the treated soils at both the BSL and BSH
compaction energy levels showed increase from 0% to 40% A-3 soil
replacement after which the values reduced to 100% replacement.
The trend of the Optimum Moisture Content (OMC) with varied A-3
soil replacement was similar to that of MDD but in a reversed order.
The OMC reduced from 0% to 40% A-3 soil replacement after which
the values increased to 100% replacement. This trend was attributed
to the observed reduction in void ratio from 0% to 40% replacement
after which the void ratio increased to 100% replacement. The
maximum UCS for the soil at varied A-3 soil replacement increased
from 272 and 770 kN/m2 for BSL and BSH compaction energy level
at 0% replacement to 295 and 795 kN/m2 for BSL and BSH
compaction energy level respectively at 10% replacement after which
the values reduced to 22 and 60 kN/m2 for BSL and BSH compaction
energy level respectively at 70% replacement. Beyond 70%
replacement, the mixtures could not be moulded for UCS test.
Abstract: The article deals with the readiness of military
professionals for challenging situations. It discusses higher
requirements on the psychical endurance of military professionals
arising from the specific nature of the military occupation, which is
typical for being very difficult to maintain regularity, which is in
accordance with the hygiene of work alternated by relaxation. The
soldier must be able to serve in the long term and constantly intense
performance that goes beyond human tolerance to stress situations. A
challenging situation is always associated with overcoming
difficulties, obstacles and complicated circumstances or using
unusual methods, ways and means to achieve the desired (expected)
objectives, performing a given task or satisfying an important need.
This paper describes the categories of challenging situations, their
classification and characteristics. Attention is also paid to the
formation of personality in challenging situations, coping with stress
in challenging situations, Phases of solutions of stressful situations,
resistance to challenging life situations and its factors. Finally, the
article is focused on increasing the readiness of military professionals
for challenging situations.
Abstract: Wheat is the first and the most important grain of the
world and its bakery property is due to glutenin and gliadin qualities.
Wheat seed proteins were divided into four groups according to
solubility including albumin, globulin, glutenin and prolamin or
gliadin. Gliadins are major components of the storage proteins in
wheat endosperm. It seems that little information is available about
gliadin genes in Iranian wild relatives of wheat. Thus, the aim of this
study was the evaluation of the wheat wild relatives collected from
different origins of Zagros Mountains in Iran, in terms of coding
gliadin genes using specific primers. For this, forty accessions of
Triticum boeoticum and Triticum urartu were selected for this study.
For each accession, genomic DNA was extracted and PCRs were
performed in total volumes of 15 μl. The amplification products were
separated on 1.5% agarose gels. In results, for Gli-2A locus three
allelic variants were detected by Gli-2As primer pairs. The sizes of
PCR products for these alleles were 210, 490 and 700 bp. Only five
(13%) and two accessions (5%) produced 700 and 490 bp fragments
when their DNA was amplified with the Gli.As.2 primer pairs.
However, 93% of the accessions carried allele 210 bp, and only 8%
did not any product for this marker. Therefore, these germplasm
could be used as rich gene pool to broaden the genetic base of bread
wheat.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: Recently, numerous documents including large
volumes of unstructured data and text have been created because of the
rapid increase in the use of social media and the Internet. Usually,
these documents are categorized for the convenience of users. Because
the accuracy of manual categorization is not guaranteed, and such
categorization requires a large amount of time and incurs huge costs.
Many studies on automatic categorization have been conducted to help
mitigate the limitations of manual categorization. Unfortunately, most
of these methods cannot be applied to categorize complex documents
with multiple topics because they work on the assumption that
individual documents can be categorized into single categories only.
Therefore, to overcome this limitation, some studies have attempted to
categorize each document into multiple categories. However, the
learning process employed in these studies involves training using a
multi-categorized document set. These methods therefore cannot be
applied to the multi-categorization of most documents unless
multi-categorized training sets using traditional multi-categorization
algorithms are provided. To overcome this limitation, in this study, we
review our novel methodology for extending the category of a
single-categorized document to multiple categorizes, and then
introduce a survey-based verification scenario for estimating the
accuracy of our automatic categorization methodology.
Abstract: Due to the fast and flawless technological innovation
there is a tremendous amount of data dumping all over the world in
every domain such as Pattern Recognition, Machine Learning, Spatial
Data Mining, Image Analysis, Fraudulent Analysis, World Wide
Web etc., This issue turns to be more essential for developing several
tools for data mining functionalities. The major aim of this paper is to
analyze various tools which are used to build a resourceful analytical
or descriptive model for handling large amount of information more
efficiently and user friendly. In this survey the diverse tools are
illustrated with their extensive technical paradigm, outstanding
graphical interface and inbuilt multipath algorithms in which it is
very useful for handling significant amount of data more indeed.