Abstract: Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oil-palm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatio-temporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a non-linear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a non-linear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel k-means algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and auto-correlation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oil-palm yield by analyzing various factors affecting the
yield.
Abstract: The thermal, epithermal and fast fluxes were
calculated for three irradiation channels at Egypt Second Research
Reactor (ETRR-2) using CITVAP code. The validity of the
calculations was verified by experimental measurements. There are
some deviations between measurements and calculations. This is due
to approximations in the calculation models used, homogenization of
regions, condensation of energy groups and uncertainty in nuclear
data used. Neutron flux data for the three irradiation channels are
now available. This would enable predicting the irradiation
conditions needed for future radioisotope production.
Abstract: The aim of this study was to investigate the correlation
between Facebook involvement and internet addiction. We sampled
577 university students in Taiwan and administered a survey of
Facebook usage, Facebook involvement scale (FIS), and internet
addiction scale. The FIS comprises three factors (salience, emotional
support, and amusement). Results showed that the Facebook
involvement scale had good reliability and validity. The correlation
between Facebook involvement and internet addiction was measured
at .395. This means that a higher degree of Facebook involvement
indicates a greater degree of psychological dependency on the internet,
and a greater propensity towards social withdrawal and other negative
psychological consequences associated with internet addiction.
Besides, the correlations between three factors of FIS (salience,
emotional support, and amusement) and internet addiction ranged
from .313-372, indicating that these neither of these factors (salience,
emotional support, and amusement) is more effective than the others in
predicting internet dependency.
Abstract: This study aims at providing empirical evidence on a
comparison of two equity valuation models: (1) the dividend discount
model (DDM) and (2) the residual income model (RIM), in
estimating equity values of Thai firms during 1995-2004. Results
suggest that DDM and RIM underestimate equity values of Thai
firms and that RIM outperforms DDM in predicting cross-sectional
stock prices. Results on regression of cross-sectional stock prices on
the decomposed DDM and RIM equity values indicate that book
value of equity provides the greatest incremental explanatory power,
relative to other components in DDM and RIM terminal values,
suggesting that book value distortions resulting from accounting
procedures and choices are less severe than forecast and
measurement errors in discount rates and growth rates.
We also document that the incremental explanatory power of book
value of equity during 1998-2004, representing the information
environment under Thai Accounting Standards reformed after the
1997 economic crisis to conform to International Accounting
Standards, is significantly greater than that during 1995-1996,
representing the information environment under the pre-reformed
Thai Accounting Standards. This implies that the book value
distortions are less severe under the 1997 Reformed Thai Accounting
Standards than the pre-reformed Thai Accounting Standards.
Abstract: Estimation time and cost of work completion in a
project and follow up them during execution are contributors to
success or fail of a project, and is very important for project
management team. Delivering on time and within budgeted cost
needs to well managing and controlling the projects. To dealing with
complex task of controlling and modifying the baseline project
schedule during execution, earned value management systems have
been set up and widely used to measure and communicate the real
physical progress of a project. But it often fails to predict the total
duration of the project. In this paper data mining techniques is used
predicting the total project duration in term of Time Estimate At
Completion-EAC (t). For this purpose, we have used a project with
90 activities, it has updated day by day. Then, it is used regular
indexes in literature and applied Earned Duration Method to
calculate time estimate at completion and set these as input data for
prediction and specifying the major parameters among them using
Clem software. By using data mining, the effective parameters on
EAC and the relationship between them could be extracted and it is
very useful to manage a project with minimum delay risks. As we
state, this could be a simple, safe and applicable method in prediction
the completion time of a project during execution.
Abstract: Earth reinforcing techniques have become useful and economical to solve problems related to difficult grounds and provide satisfactory foundation performance. In this context, this paper uses radial basis function neural network (RBFNN) for predicting the bearing pressure of strip footing on reinforced granular bed overlying weak soil. The inputs for the neural network models included plate width, thickness of granular bed and number of layers of reinforcements, settlement ratio, water content, dry density, cohesion and angle of friction. The results indicated that RBFNN model exhibited more than 84 % prediction accuracy, thereby demonstrating its application in a geotechnical problem.
Abstract: It is difficult to judge ripeness by outward
characteristics such as size or external color. In this paper a nondestructive
method was studied to determine watermelon (Crimson
Sweet) quality. Responses of samples to excitation vibrations were
detected using laser Doppler vibrometry (LDV) technology. Phase
shift between input and output vibrations were extracted overall
frequency range. First and second were derived using frequency
response spectrums. After nondestructive tests, watermelons were
sensory evaluated. So the samples were graded in a range of ripeness
based on overall acceptability (total desired traits consumers).
Regression models were developed to predict quality using obtained
results and sample mass. The determination coefficients of the
calibration and cross validation models were 0.89 and 0.71
respectively. This study demonstrated feasibility of information
which is derived vibration response curves for predicting fruit
quality. The vibration response of watermelon using the LDV method
is measured without direct contact; it is accurate and timely, which
could result in significant advantage for classifying watermelons
based on consumer opinions.
Abstract: In this paper parametric analytical studies have been carried out to examine the intrinsic flow physics pertaining to the liftoff time of solid propellant rockets. Idealized inert simulators of solid rockets are selected for numerical studies to examining the preignition chamber dynamics. Detailed diagnostic investigations have been carried out using an unsteady two-dimensional k-omega turbulence model. We conjectured from the numerical results that the altered variations of the igniter jet impingement angle, turbulence level, time and location of the first ignition, flame spread characteristics, the overall chamber dynamics including the boundary layer growth history are having bearing on the time for nozzle flow chocking for establishing the required thrust for the rocket liftoff. We concluded that the altered flow choking time of strap-on motors with the pre-determined identical ignition time at the lift off phase will lead to the malfunctioning of the rocket. We also concluded that, in the light of the space debris, an error in predicting the liftoff time can lead to an unfavorable launch window amounts the satellite injection errors and/or the mission failures.
Abstract: Eco-driving allows the driver to optimize his/her behaviour in order to achieve several types of benefits: reducing pollution emissions, increasing road safety, and fuel saving. One of the main rules for adopting eco-driving is to anticipate the traffic events by avoiding strong acceleration or braking and maintaining a steady speed when possible. Therefore, drivers have to comply with speed limits and time headway. The present study explored the role of three types of motivation and social norms in predicting French drivers- intentions to comply with speed limits and time headway as eco-driving practices as well as examine the variations according to gender and age. 1234 drivers with ages between 18 and 75 years old filled in a questionnaire which was presented as part of an online survey aiming to better understand the drivers- road habits. It included items assessing: a) behavioural intentions to comply with speed limits and time headway according to three types of motivation: reducing pollution emissions, increasing road safety, and fuel saving, b) subjective and descriptive social norms regarding the intention to comply with speed limits and time headway, and c) sociodemographical variables. Drivers expressed their intention to frequently comply with speed limits and time headway in the following 6 months; however, they showed more intention to comply with speed limits as compared to time headway regardless of the type of motivation. The subjective injunctive norms were significantly more important in predicting drivers- intentions to comply with speed limits and time headway as compared to the descriptive norms. In addition, the most frequently reported type of motivation for complying with speed limits and time headway was increasing road safety followed by fuel saving and reducing pollution emissions, hence underlining a low motivation to practice eco-driving. Practical implications of the results are discussed.
Abstract: An Artificial Neural Network based modeling
technique has been used to study the influence of different
combinations of meteorological parameters on evaporation from a
reservoir. The data set used is taken from an earlier reported study.
Several input combination were tried so as to find out the importance
of different input parameters in predicting the evaporation. The
prediction accuracy of Artificial Neural Network has also been
compared with the accuracy of linear regression for predicting
evaporation. The comparison demonstrated superior performance of
Artificial Neural Network over linear regression approach. The
findings of the study also revealed the requirement of all input
parameters considered together, instead of individual parameters
taken one at a time as reported in earlier studies, in predicting the
evaporation. The highest correlation coefficient (0.960) along with
lowest root mean square error (0.865) was obtained with the input
combination of air temperature, wind speed, sunshine hours and
mean relative humidity. A graph between the actual and predicted
values of evaporation suggests that most of the values lie within a
scatter of ±15% with all input parameters. The findings of this study
suggest the usefulness of ANN technique in predicting the
evaporation losses from reservoirs.
Abstract: The aim of this paper is to identify the most suitable
model for churn prediction based on three different techniques. The
paper identifies the variables that affect churn in reverence of
customer complaints data and provides a comparative analysis of
neural networks, regression trees and regression in their capabilities
of predicting customer churn.
Abstract: Hydraulic conductivity is one parameter important for predicting the movement of water and contaminants dissolved in the water through the soil. The hydraulic conductivity is measured on soil samples in the lab and sometimes tests carried out in the field. The hydraulic conductivity has been related to soil particle diameter by a number of investigators. In this study, 25 set of soil samples with sand texture. The results show approximately success in predicting hydraulic conductivity from particle diameters data. The following relationship obtained from multiple linear regressions on data (R2 = 0.52): Where d10, d50 and d60, are the soil particle diameter (mm) that 10%, 50% and 60% of all soil particles are finer (smaller) by weight and Ks, saturated hydraulic conductivity is expressed in m/day. The results of regression analysis showed that d10 play a more significant role with respect to Ks, saturated hydraulic conductivity (m/day), and has been named as the effective parameter in Ks calculation.
Abstract: This study aimed at developing a forecasting model on the number of Dengue Haemorrhagic Fever (DHF) incidence in Northern Thailand using time series analysis. We developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models on the data collected between 2003-2006 and then validated the models using the data collected between January-September 2007. The results showed that the regressive forecast curves were consistent with the pattern of actual values. The most suitable model was the SARIMA(2,0,1)(0,2,0)12 model with a Akaike Information Criterion (AIC) of 12.2931 and a Mean Absolute Percent Error (MAPE) of 8.91713. The SARIMA(2,0,1)(0,2,0)12 model fitting was adequate for the data with the Portmanteau statistic Q20 = 8.98644 ( x20,95= 27.5871, P>0.05). This indicated that there was no significant autocorrelation between residuals at different lag times in the SARIMA(2,0,1)(0,2,0)12 model.
Abstract: Environmental impact assessment (EIA) is a procedure tool of environmental management for identifying, predicting, evaluating and mitigating the adverse effects of development proposals. EIA reports usually analyze how the amounts or concentrations of pollutants obey the relevant standards. Actually, many analytical tools can deepen the analysis of environmental impacts in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA to introduce the causal relationships among environmental hazards and damage. Incorporating the LCIA concept into ERA as an integrated tool for EIA can extend the focus of the regulatory compliance of environmental impacts to determine of the significance of environmental impacts. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations due to insufficient information; therefore, ERA should be generalized to fuzzy risk assessment (FRA). Finally, the use of the proposed methodology is demonstrated through the study case of the expansion plan of the world-s largest plastics processing factory.
Abstract: A zero dimensional model has been used to investigate
the combustion performance of a single cylinder direct injection
diesel engine fueled by biofuels with options like supercharging and
exhaust gas recirculation. The numerical simulation was performed at
constant speed. The indicated pressure, temperature diagrams are
plotted and compared for different fuels. The emissions of soot and
nitrous oxide are computed with phenomenological models. The
experimental work was also carried out with biodiesel (palm stearin
methyl ester) diesel blends, ethanol diesel blends to validate
simulation results with experimental results, and observed that the
present model is successful in predicting the engine performance with
biofuels.
Abstract: Helical milling operations are used to generate or
enlarge boreholes by means of a milling tool. The bore diameter can be
adjusted through the diameter of the helical path. The kinematics of
helical milling on a three axis machine tool is analysed firstly. The
relationships between processing parameters, cutting tool geometry
characters with machined hole feature are formulated. The feed motion
of the cutting tool has been decomposed to plane circular feed and
axial linear motion. In this paper, the time varying cutting forces acted
on the side cutting edges and end cutting edges of the flat end cylinder
miller is analysed using a discrete method separately. These two
components then are combined to produce the cutting force model
considering the complicated interaction between the cutters and
workpiece. The time varying cutting force model describes the
instantaneous cutting force during processing. This model could be
used to predict cutting force, calculate statics deflection of cutter and
workpiece, and also could be the foundation of dynamics model and
predicting chatter limitation of the helical milling operations.
Abstract: This paper introduces the application of seismic wave method in earthquake prediction and early estimation. The advantages of the seismic wave method over the traditional earthquake prediction method are demonstrated. An example is presented in this study to show the accuracy and efficiency of using the seismic wave method in predicting a medium-sized earthquake swarm occurred in Wencheng, Zhejiang, China. By applying this method, correct predictions were made on the day after this earthquake swarm started and the day the maximum earthquake occurred, which provided scientific bases for governmental decision-making.
Abstract: Nowadays predicting political risk level of country
has become a critical issue for investors who intend to achieve
accurate information concerning stability of the business
environments. Since, most of the times investors are layman and
nonprofessional IT personnel; this paper aims to propose a
framework named GECR in order to help nonexpert persons to
discover political risk stability across time based on the political
news and events.
To achieve this goal, the Bayesian Networks approach was
utilized for 186 political news of Pakistan as sample dataset.
Bayesian Networks as an artificial intelligence approach has been
employed in presented framework, since this is a powerful technique
that can be applied to model uncertain domains. The results showed
that our framework along with Bayesian Networks as decision
support tool, predicted the political risk level with a high degree of
accuracy.
Abstract: The paper presents a comparative performance of the
models developed to predict 28 days compressive strengths using
neural network techniques for data taken from literature (ANN-I) and
data developed experimentally for SCC containing bottom ash as
partial replacement of fine aggregates (ANN-II). The data used in the
models are arranged in the format of six and eight input parameters
that cover the contents of cement, sand, coarse aggregate, fly ash as
partial replacement of cement, bottom ash as partial replacement of
sand, water and water/powder ratio, superplasticizer dosage and an
output parameter that is 28-days compressive strength and
compressive strengths at 7 days, 28 days, 90 days and 365 days,
respectively for ANN-I and ANN-II. The importance of different
input parameters is also given for predicting the strengths at various
ages using neural network. The model developed from literature data
could be easily extended to the experimental data, with bottom ash as
partial replacement of sand with some modifications.
Abstract: Accurately predicting non-peak traffic is crucial to
daily traffic for all forecasting models. In the paper, least squares
support vector machines (LS-SVMs) are investigated to solve such a
practical problem. It is the first time to apply the approach and analyze
the forecast performance in the domain. For comparison purpose, two
parametric and two non-parametric techniques are selected because of
their effectiveness proved in past research. Having good
generalization ability and guaranteeing global minima, LS-SVMs
perform better than the others. Providing sufficient improvement in
stability and robustness reveals that the approach is practically
promising.