Abstract: The under-5 mortality rate is high in sub-Saharan Africa with Lesotho being amongst the highest under-5 mortality rates in the world. The objective of the study is to determine the factors associated with under-5 mortality in Lesotho. The data used for this analysis come from the nationally representative household survey called the 2009 Lesotho Demographic and Health Survey. Odds ratios produced by the logistic regression models were used to measure the effect of each independent variable on the dependent variable. Female children were significantly 38% less likely to die than male children. Children who were breastfed for 13 to 18 months and those who were breastfed for more than 19 months were significantly less likely to die than those who were breastfed for 12 months or less. Furthermore, children of mothers who stayed in Quthing, Qacha’s Nek and Thaba Tseka ran the greatest risk of dying. The results suggested that: sex of child, type of birth, breastfeeding duration, district, source of energy and marital status were significant predictors of under-5 mortality, after correcting for all variables.
Abstract: Forecasting electricity load plays a crucial role regards
decision making and planning for economical purposes. Besides, in
the light of the recent privatization and deregulation of the power
industry, the forecasting of future electricity load turned out to be a
very challenging problem. Empirical data about electricity load
highlights a clear seasonal behavior (higher load during the winter
season), which is partly due to climatic effects. We also emphasize
the presence of load periodicity at a weekly basis (electricity load is
usually lower on weekends or holidays) and at daily basis (electricity
load is clearly influenced by the hour). Finally, a long-term trend may
depend on the general economic situation (for example, industrial
production affects electricity load). All these features must be
captured by the model.
The purpose of this paper is then to build an hourly electricity load
model. The deterministic component of the model requires non-linear
regression and Fourier series while we will investigate the stochastic
component through econometrical tools.
The calibration of the parameters’ model will be performed by
using data coming from the Italian market in a 6 year period (2007-
2012). Then, we will perform a Monte Carlo simulation in order to
compare the simulated data respect to the real data (both in-sample
and out-of-sample inspection). The reliability of the model will be
deduced thanks to standard tests which highlight a good fitting of the
simulated values.
Abstract: The current trends in affect recognition research are
to consider continuous observations from spontaneous natural
interactions in people using multiple feature modalities, and to
represent affect in terms of continuous dimensions, incorporate
spatio-temporal correlation among affect dimensions, and provide
fast affect predictions. These research efforts have been propelled
by a growing effort to develop affect recognition system that
can be implemented to enable seamless real-time human-computer
interaction in a wide variety of applications. Motivated by these
desired attributes of an affect recognition system, in this work
a multi-dimensional affect prediction approach is proposed by
integrating multivariate Relevance Vector Machine (MVRVM) with
a recently developed Output-associative Relevance Vector Machine
(OARVM) approach. The resulting approach can provide fast
continuous affect predictions by jointly modeling the multiple affect
dimensions and their correlations. Experiments on the RECOLA
database show that the proposed approach performs competitively
with the OARVM while providing faster predictions during testing.
Abstract: Recent investigations have demonstrated the global
sea level rise due to climate change impacts. In this study, climate
changes study the effects of increasing water level in the strait of
Hormuz. The probable changes of sea level rise should be
investigated to employ the adaption strategies. The climatic output
data of a GCM (General Circulation Model) named CGCM3 under
climate change scenario of A1b and A2 were used. Among different
variables simulated by this model, those of maximum correlation
with sea level changes in the study region and least redundancy
among themselves were selected for sea level rise prediction by using
stepwise regression. One of models (Discrete Wavelet artificial
Neural Network) was developed to explore the relationship between
climatic variables and sea level changes. In these models, wavelet
was used to disaggregate the time series of input and output data into
different components and then ANN was used to relate the
disaggregated components of predictors and input parameters to each
other. The results showed in the Shahid Rajae Station for scenario
A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea
level rise is among 90 t0 105 cm. Furthermore, the result showed a
significant increase of sea level at the study region under climate
change impacts, which should be incorporated in coastal areas
management.
Abstract: Presently various computational techniques are used
in modeling and analyzing environmental engineering data. In the
present study, an intra-comparison of polynomial and radial basis
kernel functions based on Support Vector Regression and, in turn, an
inter-comparison with Multi Linear Regression has been attempted in
modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ
multiple plunging jets (varying from 1 to 16 numbers). The data set
used in this study consists of four input parameters with a total of
eighty eight cases, forty four each for vertical and inclined multiple
plunging jets. For testing, tenfold cross validation was used.
Correlation coefficient values of 0.971 and 0.981 along with
corresponding root mean square error values of 0.0025 and 0.0020
were achieved by using polynomial and radial basis kernel functions
based Support Vector Regression respectively. An intra-comparison
suggests improved performance by radial basis function in
comparison to polynomial kernel based Support Vector Regression.
Further, an inter-comparison with Multi Linear Regression
(correlation coefficient = 0.973 and root mean square error = 0.0024)
reveals that radial basis kernel functions based Support Vector
Regression performs better in modeling and estimating mass transfer
by multiple plunging jets.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.
Abstract: Taiwan is a hyper endemic area for the Hepatitis B
virus (HBV). The estimated total number of HBsAg carriers in the
general population who are more than 20 years old is more than 3
million. Therefore, a case record review is conducted from January
2003 to June 2007 for all patients with a diagnosis of acute hepatitis
who were admitted to the Emergency Department (ED) of a
well-known teaching hospital. The cost for the use of medical
resources is defined as the total medical fee. In this study, principal
component analysis (PCA) is firstly employed to reduce the number of
dimensions. Support vector regression (SVR) and artificial neural
network (ANN) are then used to develop the forecasting model. A total
of 117 patients meet the inclusion criteria. 61% patients involved in
this study are hepatitis B related. The computational result shows that
the proposed PCA-SVR model has superior performance than other
compared algorithms. In conclusion, the Child-Pugh score and
echogram can both be used to predict the cost of medical resources for
patients with acute hepatitis in the ED.
Abstract: This study analyzes the innovative orientation of the
Croatian entrepreneurs. Innovative orientation is represented by the
perceived extent to which an entrepreneur’s product or service or
technology is new, and no other businesses offer the same product.
The sample is extracted from the GEM Croatia Adult Population
Survey dataset for the years 2003-2013. We apply descriptive
statistics, t-test, Chi-square test and logistic regression. Findings
indicate that innovative orientations vary with personal, firm, meso
and macro level variables, and between different stages in
entrepreneurship process. Significant predictors are occupation of the
entrepreneurs, size of the firm and export aspiration for both early
stage and established entrepreneurs. In addition, fear of failure,
expecting to start a new business and seeing an entrepreneurial career
as a desirable choice are predictors of innovative orientation among
early stage entrepreneurs.
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: Non-linear FEM calculations are indispensable when
important technical information like operating performance of a
rubber component is desired. For example rubber bumpers built into
air-spring structures may undergo large deformations under load,
which in itself shows non-linear behavior. The changing contact
range between the parts and the incompressibility of the rubber
increases this non-linear behavior further. The material
characterization of an elastomeric component is also a demanding
engineering task.
The shape optimization problem of rubber parts led to the study of
FEM based calculation processes. This type of problems was posed
and investigated by several authors. In this paper the time demand of
certain calculation methods are studied and the possibilities of time
reduction is presented.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: Near infrared (NIR) spectroscopy has always been of
great interest in the food and agriculture industries. The development
of prediction models has facilitated the estimation process in recent
years. In this study, 110 crude palm oil (CPO) samples were used to
build a free fatty acid (FFA) prediction model. 60% of the collected
data were used for training purposes and the remaining 40% used for
testing. The visible peaks on the NIR spectrum were at 1725 nm and
1760 nm, indicating the existence of the first overtone of C-H bands.
Principal component regression (PCR) was applied to the data in
order to build this mathematical prediction model. The optimal
number of principal components was 10. The results showed
R2=0.7147 for the training set and R2=0.6404 for the testing set.
Abstract: The current study explored the effect of economic
development, financial development and institutional quality on
environmental destruction in upper-middle income countries during
the time period of 1999-2011. The dependent variable is logarithm of
carbon dioxide emissions that can be considered as an index for
destruction or quality of the environment given to its effects on the
environment. Financial development and institutional development
variables as well as some control variables were considered. In order
to study cross-sectional correlation among the countries under study,
Pesaran and Friz test was used. Since the results of both tests show
cross-sectional correlation in the countries under study, seemingly
unrelated regression method was utilized for model estimation. The
results disclosed that Kuznets’ environmental curve hypothesis is
confirmed in upper-middle income countries and also, financial
development and institutional quality have a significant effect on
environmental quality. The results of this study can be considered by
policy makers in countries with different income groups to have
access to a growth accompanied by improved environmental quality.
Abstract: Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.
Abstract: After recession that began in 2007 in the United States and subsequently spilled over the Europe we could expect recovery of economic growth. According to the last estimation of economic progress of European countries, this recovery is not strong enough. Among others, it will depend on economic policy, where and in which way, the economic indicators will proceed. Economic theories postulate that the economic subjects prefer stably, continual economic policy without repeated and strong fluctuations. This policy is perceived as support of economic growth. Mostly in crises period, when the government must cope with consequences of recession, the economic policy becomes unpredictable for many subjects and economic policy uncertainty grows, which have negative influence on economic growth. The aim of this paper is to use panel regression to prove or disprove this hypothesis on the example of five largest European economies in the period 2008–2012.
Abstract: This paper proposes a bioprocess optimization procedure based on Relevance Vector Regression models and evolutionary programming technique. Relevance Vector Regression scheme allows developing a compact and stable data-based process model avoiding time-consuming modeling expenses. The model building and process optimization procedure could be done in a half-automated way and repeated after every new cultivation run. The proposed technique was tested in a simulated mammalian cell cultivation process. The obtained results are promising and could be attractive for optimization of industrial bioprocesses.
Abstract: The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90O; whereas, multiple inclined plunging jets have jet impact angle of θ = 60O. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (KLa) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.
Abstract: This study investigated published financial statement as correlate of investment decision among commercial bank stakeholders in Nigeria. A correlation research design was used in the study. 180 users of published financial statement were purposively sampled from Lagos and Ibadan. Data generated were analyzed using Pearson correlation and regression. The findings of the study revealed that, balance sheet is negatively related with investment decision (r= -.483; p
Abstract: Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-destructive techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.