Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: Hurling a successful Construction and Demolition
Waste (C&DW) recycling operation around the globe is a challenge
today, predominantly because secondary materials markets are yet to
be integrated. Reducing, Reusing and recycling of (C&DW) have
been employed over the years, and various techniques have been
investigated. However, the economic and environmental viability of
its application seems limited. This paper discusses the costs and
benefits in using secondary materials and focus on investigating reuse
and recycling process for five major types of construction materials:
concrete, metal, wood, cardboard/paper and plasterboard. Data
obtained from demolition specialists and contractors are considered
and evaluated. The research paper found that construction material
recovery process fully incorporate a 3R’s principle contributing to
saving energy and natural resources. This scrutiny leads to the
empathy of grand challenges in construction material recovery
process. Recommendations to deepen material recovery process are
also discussed.
Abstract: The Malaysian government had consistently revived
its campaign for “Buy Malaysian Goods” from time to time. The
purpose of the campaign is to remind consumers to be ethnocentric
and patriotic when purchasing product and services. This is necessary
to ensure high demand for local products and services compared to
foreign products. However, the decline of domestic investment in
2012 has triggered concern for the Malaysian economy. Hence, this
study attempts to determine the drivers of actual purchasing behavior,
intention to purchase domestic products and ethnocentrism. The
study employs the cross-sectional primary data, self-administered on
household, selected using stratified random sampling in four
Malaysian regions. A nine factor driver of actual domestic purchasing
behavior (culture openness, conservatism, collectivism, patriotism,
control belief, interest in foreign travel, attitude, ethnocentrism and
intention) were measured utilizing 60 items, using 7-point Likertscale.
From 1000 questionnaires distributed, a sample of 486 were
returned representing 48.6 percent response rate. From the fit
generated structural model (SEM analysis), it was found that the
drivers of actual purchase behavior are collectivism, cultural
openness and patriotism; the drivers of intention to purchase
domestic product are attitude, control belief, collectivism and
conservatism; and drivers of ethnocentrism are cultural openness,
control belief, foreign travel and patriotism. It also shows that
Malaysian consumers scored high in ethnocentrism and patriotism.
The findings are discussed in the perspective of its implication to
Malaysian National Agenda.
Abstract: This work deals with parameter identification of
permanent magnet motors, a class of ac motor which is particularly
important in industrial automation due to characteristics like
applications high performance, are very attractive for applications
with limited space and reducing the need to eliminate because they
have reduced size and volume and can operate in a wide speed range,
without independent ventilation. By using experimental data and
genetic algorithm we have been able to extract values for both the
motor inductance and the electromechanical coupling constant, which
are then compared to measured and/or expected values.
Abstract: The development of allometric models is crucial to
accurate forest biomass/carbon stock assessment. The aim of this
study was to develop a set of biomass prediction models that will
enable the determination of total tree aboveground biomass for
savannah woodland area in Niger State, Nigeria. Based on the data
collected through biometric measurements of 1816 trees and
destructive sampling of 36 trees, five species specific and one site
specific models were developed. The sample size was distributed
equally between the five most dominant species in the study site
(Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa,
Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the
equations were developed for five individual species. Secondly these
five species were mixed and were used to develop an allometric
equation of mixed species. Overall, there was a strong positive
relationship between total tree biomass and the stem diameter. The
coefficient of determination (R2 values) ranging from 0.93 to 0.99 P
< 0.001 were realised for the models; with considerable low standard
error of the estimates (SEE) which confirms that the total tree above
ground biomass has a significant relationship with the dbh. F-test
values for the biomass prediction models were also significant at p
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Meeting the growth in demand for digital services
such as social media, telecommunications, and business and cloud
services requires large scale data centres, which has led to an increase
in their end use energy demand. Generally, over 30% of data centre
power is consumed by the necessary cooling overhead. Thus energy
can be reduced by improving the cooling efficiency. Air and liquid
can both be used as cooling media for the data centre. Traditional
data centre cooling systems use air, however liquid is recognised as a
promising method that can handle the more densely packed data
centres. Liquid cooling can be classified into three methods; rack heat
exchanger, on-chip heat exchanger and full immersion of the
microelectronics. This study quantifies the improvements of heat
transfer specifically for the case of immersed microelectronics by
varying the CPU and heat sink location. Immersion of the server is
achieved by filling the gap between the microelectronics and a water
jacket with a dielectric liquid which convects the heat from the CPU
to the water jacket on the opposite side. Heat transfer is governed by
two physical mechanisms, which is natural convection for the fixed
enclosure filled with dielectric liquid and forced convection for the
water that is pumped through the water jacket. The model in this
study is validated with published numerical and experimental work
and shows good agreement with previous work. The results show that
the heat transfer performance and Nusselt number (Nu) is improved
by 89% by placing the CPU and heat sink on the bottom of the
microelectronics enclosure.
Abstract: Previous studies on financial distress prediction choose
the conventional failing and non-failing dichotomy; however, the
distressed extent differs substantially among different financial
distress events. To solve the problem, “non-distressed”, “slightlydistressed”
and “reorganization and bankruptcy” are used in our article
to approximate the continuum of corporate financial health. This paper
explains different financial distress events using the two-stage method.
First, this investigation adopts firm-specific financial ratios, corporate
governance and market factors to measure the probability of various
financial distress events based on multinomial logit models.
Specifically, the bootstrapping simulation is performed to examine the
difference of estimated misclassifying cost (EMC). Second, this work
further applies macroeconomic factors to establish the credit cycle
index and determines the distressed cut-off indicator of the two-stage
models using such index. Two different models, one-stage and
two-stage prediction models are developed to forecast financial
distress, and the results acquired from different models are compared
with each other, and with the collected data. The findings show that the
one-stage model has the lower misclassification error rate than the
two-stage model. The one-stage model is more accurate than the
two-stage model.
Abstract: A well designed and executed Production Planning
and Control (PPC) system is one of the key levers for superior
performance in the current manufacturing set-up. Hence, measuring
the PPC system performance has become a necessity for long term
success. The present study examined PPC related issues which
impact the production capacity and productivity of leather companies
with special focus on Kombolcha Tannery Share Company (KTSC),
Ethiopia. Physical observation, interview, and questionnaire were
used to generate necessary information from the respondents and
reach valid conclusions. Company annual reports were referred and
analyzed to triangulate primary data. Consequently, the study
revealed that KTSC runs below its capacity due to its inefficient PPC
system being in use for which the root causes were identified. The
study thereby conceptualizes a PPC system improvement framework
comprising three pillars viz., management culture, internal capability
and performance measurement together with key considerations in
each case. The study findings enable the company to recognize the
importance of efficient PPC system as a source of competitive
advantage. It also aid managers in evaluating various PPC execution
schemes to enhance productivity.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: In this paper, reliable consensus of multi-agent systems
with sampled-data is investigated. By using a suitable
Lyapunov-Krasovskii functional and some techniques such as
Wirtinger Inequality, Schur Complement and Kronecker Product, the
results of such system are obtained by solving a set of Linear Matrix
Inequalities (LMIs). One numerical example is included to show the
effectiveness of the proposed criteria.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: Co metal supported on SiO2 and Al2O3 catalysts with
a metal loading varied from 30 of 70 wt.% were evaluated for
decomposition of methane to COx free hydrogen and carbon
nanomaterials. The catalytic runs were carried out from 550-800oC
under atmospheric pressure using fixed bed vertical flow reactor. The
fresh and spent catalysts were characterized by BET surface area
analyzer, XRD, SEM, TEM and TG analysis. The data showed that
50% Co/Al2O3 catalyst exhibited remarkable higher activity at 800oC
with respect to H2 production compared to rest of the catalysts.
However, the catalytic activity and durability was greatly declined at
higher temperature. The main reason for the catalytic inhibition of Co
containing SiO2 catalysts is the higher reduction temperature of
Co2SiO4. TEM images illustrate that the carbon materials with
various morphologies, carbon nanofibers (CNFs), helical-shaped
CNFs and branched CNFs depending on the catalyst composition and
reaction temperature were obtained.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: The paper deals with behaviour of the segment 50+ in
the financial market in the Czech Republic. This segment could be
said as the strong market power and it can be a crucial business
potential for financial business units. The main defined objective of
this paper is analysis of the customers´ behaviour of the segment 50-
60 years in the financial market in the Czech Republic and proposal
making of the suitable marketing approach to satisfy their demands in
the area of product, price, distribution and marketing communication
policy. This paper is based on data from one part of primary
marketing research. Paper determinates the basic problem areas as
well as definition of financial services marketing, defining the
primary research problem, hypothesis and primary research
methodology. Finally suitable marketing approach to selected sub
segment at age of 50-60 years is proposed according to marketing
research findings.
Abstract: This paper examines international marketing in
business practice of Czech exporting small and medium-sized
enterprises (SMEs) with regard to the strategic perspectives.
Research was focused on Czech exporting SMEs from Moravian-
Silesia region and their behavior on international markets. For
purpose of collecting data, a questionnaire was given to 262 SMEs
involved in international business. Statistics utilized in this research
included frequency, mean, percentage, and chi-square test. Data were
analyzed by Statistical Package for the Social Sciences software. The
research analysis disclosed that there is certain space for
improvement in strategic marketing especially in a marketing
research, perception of cultural and social differences, product
adaptation and usage of marketing communication tools.
Abstract: In this paper, performances of shuffled frog leaping
algorithm was investigated on the stealth laser dicing process. Effect
of problem on the performance of the algorithm was based on the
tolerance of meandering data. From the customer specification it
could be less than five microns with the target of zero microns.
Currently, the meandering levels are unsatisfactory when compared
to the customer specification. Firstly, the two-level factorial design
was applied to preliminarily study the statistically significant effects
of five process variables. In this study one influential process variable
is integer. From the experimental results, the new operating condition
from the algorithm was superior when compared to the current
manufacturing condition.
Abstract: Evaluated nuclear decay data for the 217Po nuclide is
presented in the present work. These data include recommended
values for the half-life T1/2, α-, β-- and γ-ray emission energies and
probabilities. Decay data from 221Rn α and 217Bi β—decays are
presented. Q(α) has been updated based on the recent published work
of the Atomic Mass Evaluation AME2012. In addition, the logft
values were calculated using the Logft program from the ENSDF
evaluation package. Moreover, the total internal conversion electrons
and the K-shell to L-shell and L-shell to M-shell and to N-shell
conversion electrons ratios K/L, L/M and L/N have been calculated
using Bricc program. Meanwhile, recommendation values or the
multi-polarities have been assigned based on recently measurement
yield a better intensity balance at the 254 keV and 264 keV gamma
transitions.