Abstract: In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.
Abstract: Energy efficient protocol design is the aim of current
researches in the area of sensor networks where limited power
resources impose energy conservation considerations. In this paper
we care for Medium Access Control (MAC) protocols and after an
extensive literature review, two adaptive schemes are discussed. Of
them, adaptive-rate MACs which were introduced for throughput
enhancement show the potency to save energy, even more than
adaptive-power schemes. Then we propose an allocation algorithm
for getting accurate and reliable results. Through a simulation study
we validated our claim and showed the power saving of adaptive-rate
protocols.
Abstract: This paper presents a generalized formulation for the
problem of buckling optimization of anisotropic, radially graded,
thin-walled, long cylinders subject to external hydrostatic pressure.
The main structure to be analyzed is built of multi-angle fibrous
laminated composite lay-ups having different volume fractions of the
constituent materials within the individual plies. This yield to a
piecewise grading of the material in the radial direction; that is the
physical and mechanical properties of the composite material are
allowed to vary radially. The objective function is measured by
maximizing the critical buckling pressure while preserving the total
structural mass at a constant value equals to that of a baseline
reference design. In the selection of the significant optimization
variables, the fiber volume fractions adjoin the standard design
variables including fiber orientation angles and ply thicknesses. The
mathematical formulation employs the classical lamination theory,
where an analytical solution that accounts for the effective axial and
flexural stiffness separately as well as the inclusion of the coupling
stiffness terms is presented. The proposed model deals with
dimensionless quantities in order to be valid for thin shells having
arbitrary thickness-to-radius ratios. The critical buckling pressure
level curves augmented with the mass equality constraint are given
for several types of cylinders showing the functional dependence of
the constrained objective function on the selected design variables. It
was shown that material grading can have significant contribution to
the whole optimization process in achieving the required structural
designs with enhanced stability limits.
Abstract: This paper presents a new approach for image
segmentation by applying Pillar-Kmeans algorithm. This
segmentation process includes a new mechanism for clustering the
elements of high-resolution images in order to improve precision and
reduce computation time. The system applies K-means clustering to
the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars- placement which should be
located as far as possible from each other to withstand against the
pressure distribution of a roof, as identical to the number of centroids
amongst the data distribution. This algorithm is able to optimize the
K-means clustering for image segmentation in aspects of precision
and computation time. It designates the initial centroids- positions
by calculating the accumulated distance metric between each data
point and all previous centroids, and then selects data points which
have the maximum distance as new initial centroids. This algorithm
distributes all initial centroids according to the maximum
accumulated distance metric. This paper evaluates the proposed
approach for image segmentation by comparing with K-means and
Gaussian Mixture Model algorithm and involving RGB, HSV, HSL
and CIELAB color spaces. The experimental results clarify the
effectiveness of our approach to improve the segmentation quality in
aspects of precision and computational time.
Abstract: The microbiological and physicochemical
characteristics of wetland soils in Eket Local Government Area were
studied between May 2001 and June 2003. Total heterotrophic
bacterial counts (THBC), total fungal counts (TFC), and total
actinomycetes counts (TAC) were determined from soil samples
taken from four locations at two depths in the wet and dry seasons.
Microbial isolates were characterized and identified. Particle size and
chemical parameters were also determined using standard methods.
THBC ranged from 5.2 (+0.17) x106 to 1.7 (+0.18) x107 cfu/g and
from 2.4 (+0.02) x106 to 1.4 (+0.04) x107cfu/g in the wet and dry
seasons, respectively. TFC ranged from 1.8 (+0.03) x106 to 6.6 (+
0.18) x106 cfu/g and from 1.0 (+0.04) x106 to 4.2 (+ 0.01) x106 cfu/g
in the wet and dry seasons, respectively .TAC ranged from 1.2
(+0.53) x106 to 6.0 (+0.05) x106 cfu/g and from 0.6 (+0.01) x106 to
3.2 (+ 0.12) x106 cfu/g in the wet and dry season, respectively.
Acinetobacter, Alcaligenes, Arthrobacter, Bacillus, Beijerinckja,
Enterobacter, Micrococcus, Flavobacterium, Serratia, Enterococcus,
and Pseudomonas species were predominant bacteria while
Aspergillus, Fusarium, Mucor, Penicillium, and Rhizopus were the
dominant fungal genera isolated. Streptomyces and Norcadia were
the actinomycetes genera isolated. The particle size analysis showed
high sand fraction but low silt and clay. The pH and % organic
matter were generally acidic and low, respectively at all locations.
Calcium dominated the exchangeable bases with low electrical
conductivity and micronutrients. These results provide the baseline
data of Eket wetland soils for its management for sustainable
agriculture.
Abstract: This paper proposes a technique to block adult images displayed in websites. The filter is designed so as to perform even in exceptional cases such as, where face detection is not possible or improper face visibility. This is achieved by using an alternative phase to extract the MFC (Most Frequent Color) from the Human Body regions estimated using a biometric of anthropometric distances between fixed rigidly connected body locations. The logical results generated can be protected from overriding by a firewall or intrusion, by encrypting the result in a SSH data packet.
Abstract: Whilst there is growing evidence that activity
across the lifespan is beneficial for improved health, there are
also many changes involved with the aging process and
subsequently the potential for reduced indices of health. The
nexus between health, physical activity and aging is complex
and has raised much interest in recent times due to the
realization that a multifaceted approached is necessary in
order to counteract a growing obesity epidemic. By
investigating age based trends within a population adhering to
competitive sport at older ages, further insight might be
gleaned to assist in understanding one of many factors
influencing this relationship.
BMI was derived using data gathered on a total of 6,071
masters athletes (51.9% male, 48.1% female) aged 25 to 91
years ( =51.5, s =±9.7), competing at the Sydney World
Masters Games (2009). Using linear and loess regression it
was demonstrated that the usual tendency for prevalence of
higher BMI increasing with age was reversed in the sample.
This trend in reversal was repeated for both male and female
only sub-sets of the sample participants, indicating the
possibility of improved prevalence of BMI with increasing
age for both the sample as a whole and these individual subgroups.
This evidence of improved classification in one index of
health (reduced BMI) for masters athletes (when compared to
the general population) implies there are either improved
levels of this index of health with aging due to adherence to
sport or possibly the reduced BMI is advantageous and
contributes to this cohort adhering (or being attracted) to
masters sport at older ages. Demonstration of this
proportionately under-investigated World Masters Games
population having an improved relationship between BMI and
increasing age over the general population is of particular
interest in the context of the measures being taken globally to
curb an obesity epidemic.
Abstract: The present study presents a new approach to automatic
data clustering and classification problems in large and complex
databases and, at the same time, derives specific types of explicit rules
describing each cluster. The method works well in both sparse and
dense multidimensional data spaces. The members of the data space
can be of the same nature or represent different classes. A number
of N-dimensional ellipsoids are used for enclosing the data clouds.
Due to the geometry of an ellipsoid and its free rotation in space
the detection of clusters becomes very efficient. The method is based
on genetic algorithms that are used for the optimization of location,
orientation and geometric characteristics of the hyper-ellipsoids. The
proposed approach can serve as a basis for the development of
general knowledge systems for discovering hidden knowledge and
unexpected patterns and rules in various large databases.
Abstract: This paper investigates the problem of designing a robust state-feedback controller for a class of uncertain Markovian jump nonlinear systems that guarantees the L2-gain from an exogenous input to a regulated output is less than or equal to a prescribed value. First, we approximate this class of uncertain Markovian jump nonlinear systems by a class of uncertain Takagi-Sugeno fuzzy models with Markovian jumps. Then, based on an LMI approach, LMI-based sufficient conditions for the uncertain Markovian jump nonlinear systems to have an H performance are derived. An illustrative example is used to illustrate the effectiveness of the proposed design techniques.
Abstract: Maintenance is one of the most important activities in
the shipyard industry. However, sometimes it is not supported by
adequate services from the shipyard, where inaccuracy in estimating
the duration of the ship maintenance is still common. This makes
estimation of ship maintenance duration is crucial. This study uses
Data Mining approach, i.e., CART (Classification and Regression
Tree) to estimate the duration of ship maintenance that is limited to
dock works or which is known as dry docking. By using the volume
of dock works as an input to estimate the maintenance duration, 4
classes of dry docking duration were obtained with different linear
model and job criteria for each class. These linear models can then be
used to estimate the duration of dry docking based on job criteria.
Abstract: Grid environments include aggregation of
geographical distributed resources. Grid is put forward in three types
of computational, data and storage. This paper presents a research on
data grid. Data grid is used for covering and securing accessibility to
data from among many heterogeneous sources. Users are not worry
on the place where data is located in it, provided that, they should get
access to the data. Metadata is used for getting access to data in data
grid. Presently, application metadata catalogue and SRB middle-ware
package are used in data grids for management of metadata. At this
paper, possibility of updating, streamlining and searching is provided
simultaneously and rapidly through classified table of preserving
metadata and conversion of each table to numerous tables.
Meanwhile, with regard to the specific application, the most
appropriate and best division is set and determined. Concurrency of
implementation of some of requests and execution of pipeline is
adaptability as a result of this technique.
Abstract: Petrology and geochemical characteristics of granitic
rocks from South Sulawesi, especially from Polewaliand Masamba
area are presented in order to elucidate their origin of magma and
geodynamic setting. The granitic rocks in these areas are dominated by
granodiorite and granite in composition. Quartz, K-feldspar and
plagioclase occur as major phases with hornblende and biotite as
major ferromagnesian minerals. All of the samples were plotted in
calc-alkaline field, show metaluminous affinity and typical of I-type
granitic rock. Harker diagram indicates that granitic rocks experienced
fractional crystallization during magmatic evolution. Both groups
displayed an extreme enrichment of LILE, LREE and a slight negative
Eu anomaly which resemble upper continental crust affinity. They
were produced from partial melting of upper continental crust and
have close relationship of sources composition within a suite. The
geochemical characteristics explained the arc related subduction
environment which later give an evidence of continent-continent
collision between Australia-derived microcontinent and Sundalandto
form continental arc environment.
Abstract: Over a million tonnes of cigarette butts (CBs) are produced worldwide annually. These CBs accumulate in the environment due to the poor biodegradability of the cellulose acetate filters and pose a serious environmental risk. This paper presents some of the results from a continuing study on recycling CBs into fired clay bricks. Properties including compressive strength, flexural strength, density, water absorption and thermal conductivity of fired clay bricks are reported and discussed. Furthermore, leaching of heavy metals from the manufactured clay bricks was tested. The results show that the density of fired bricks was reduced by about 8 – 30 %, depending on the percentage of CBs incorporated into the raw materials. The compressive strength of bricks tested was 12.57, 5.22 and 3.00 MPa for 2.5, 5.0 and 10 % CB content respectively. Water absorption and initial rate of absorption values increased as density, and hence porosity, of bricks decreased with increasing CB volume. The leaching test results revealed trace amounts of heavy metals.
Abstract: Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
Abstract: Cancer classification to their corresponding cohorts has been key area of research in bioinformatics aiming better prognosis of the disease. High dimensionality of gene data has been makes it a complex task and requires significance data identification technique in order to reducing the dimensionality and identification of significant information. In this paper, we have proposed a novel approach for classification of oral cancer into metastasis positive and negative patients. We have used significance analysis of microarrays (SAM) for identifying significant genes which constitutes gene signature. 3 different gene signatures were identified using SAM from 3 different combination of training datasets and their classification accuracy was calculated on corresponding testing datasets using k-Nearest Neighbour (kNN), Fuzzy C-Means Clustering (FCM), Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). A final gene signature of only 9 genes was obtained from above 3 individual gene signatures. 9 gene signature-s classification capability was compared using same classifiers on same testing datasets. Results obtained from experimentation shows that 9 gene signature classified all samples in testing dataset accurately while individual genes could not classify all accurately.
Abstract: When a high DC voltage is applied to a capacitor with
strongly asymmetrical electrodes, it generates a mechanical force that
affects the whole capacitor. This is caused by the motion of ions generated around the smaller of the two electrodes and their subsequent interaction with the surrounding medium. If one of the electrodes is heated, it changes the conditions around the capacitor
and influences the process of ionisation, thus changing the value of the generated force. This paper describes these changes and gives
reasons behind them. Further the experimental results are given as proof of the ionic mechanism of the phenomenon.
Abstract: The expansive nature of soils containing high
amounts of clay minerals can be altered through chemical
stabilization, resulting in a material suitable for construction
purposes. The primary objective of this investigation was to
study the changes induced in the molecular structure of
phosphoric acid stabilized bentonite and lateritic soil using
Nuclear Magnetic Resonance (NMR) and Fourier Transform
Infrared (FTIR) spectroscopy. Based on the obtained data, it
was found that a surface alteration mechanism was the main
reason responsible for the improvement of treated soils.
Furthermore, the results indicated that the Al present in the
octahedral layer of clay minerals were more amenable to
chemical attacks and also partly responsible for the formation
of new products.
Abstract: Nature conducts its action in a very private manner. To
reveal these actions classical science has done a great effort. But
classical science can experiment only with the things that can be seen
with eyes. Beyond the scope of classical science quantum science
works very well. It is based on some postulates like qubit,
superposition of two states, entanglement, measurement and
evolution of states that are briefly described in the present paper.
One of the applications of quantum computing i.e.
implementation of a novel quantum evolutionary algorithm(QEA) to
automate the time tabling problem of Dayalbagh Educational Institute
(Deemed University) is also presented in this paper. Making a good
timetable is a scheduling problem. It is NP-hard, multi-constrained,
complex and a combinatorial optimization problem. The solution of
this problem cannot be obtained in polynomial time. The QEA uses
genetic operators on the Q-bit as well as updating operator of
quantum gate which is introduced as a variation operator to converge
toward better solutions.
Abstract: In this article, the phenomenon of nonlinear
consolidation in saturated and homogeneous clay layer is studied.
Considering time-varied drainage model, the excess pore water
pressure in the layer depth is calculated. The Generalized Differential
Quadrature (GDQ) method is used for the modeling and numerical
analysis. For the purpose of analysis, first the domain of independent
variables (i.e., time and clay layer depth) is discretized by the
Chebyshev-Gauss-Lobatto series and then the nonlinear system of
equations obtained from the GDQ method is solved by means of the
Newton-Raphson approach. The obtained results indicate that the
Generalized Differential Quadrature method, in addition to being
simple to apply, enjoys a very high accuracy in the calculation of
excess pore water pressure.
Abstract: Knowledge Discovery in Databases (KDD) has
evolved into an important and active area of research because of
theoretical challenges and practical applications associated with the
problem of discovering (or extracting) interesting and previously
unknown knowledge from very large real-world databases. Rough
Set Theory (RST) is a mathematical formalism for representing
uncertainty that can be considered an extension of the classical set
theory. It has been used in many different research areas, including
those related to inductive machine learning and reduction of
knowledge in knowledge-based systems. One important concept
related to RST is that of a rough relation. In this paper we presented
the current status of research on applying rough set theory to KDD,
which will be helpful for handle the characteristics of real-world
databases. The main aim is to show how rough set and rough set
analysis can be effectively used to extract knowledge from large
databases.