Abstract: Effective evaluation of software development effort is an important aspect of successful project management. Based on a large database with 4106 projects ever developed, this study statistically examines the factors that influence development effort. The factors found to be significant for effort are project size, average number of developers that worked on the project, type of development, development language, development platform, and the use of rapid application development. Among these factors, project size is the most critical cost driver. Unsurprisingly, this study found that the use of CASE tools does not necessarily reduce development effort, which adds support to the claim that the use of tools is subtle. As many of the current estimation models are rarely or unsuccessfully used, this study proposes a parsimonious parametric model for the prediction of effort which is both simple and more accurate than previous models.
Abstract: This paper presents parametric probability density
models for call holding times (CHTs) into emergency call center
based on the actual data collected for over a week in the public
Emergency Information Network (EIN) in Mongolia. When the set of
chosen candidates of Gamma distribution family is fitted to the call
holding time data, it is observed that the whole area in the CHT
empirical histogram is underestimated due to spikes of higher
probability and long tails of lower probability in the histogram.
Therefore, we provide the Gaussian parametric model of a mixture of
lognormal distributions with explicit analytical expressions for the
modeling of CHTs of PSNs. Finally, we show that the CHTs for
PSNs are fitted reasonably by a mixture of lognormal distributions
via the simulation of expectation maximization algorithm. This result
is significant as it expresses a useful mathematical tool in an explicit
manner of a mixture of lognormal distributions.
Abstract: This paper study about using of nonparametric
models for Gross National Product data in Turkey and Stanford heart
transplant data. It is discussed two nonparametric techniques called
smoothing spline and kernel regression. The main goal is to compare
the techniques used for prediction of the nonparametric regression
models. According to the results of numerical studies, it is concluded
that smoothing spline regression estimators are better than those of
the kernel regression.
Abstract: The problem of estimating time-varying regression is
inevitably concerned with the necessity to choose the appropriate
level of model volatility - ranging from the full stationarity of instant
regression models to their absolute independence of each other. In the
stationary case the number of regression coefficients to be estimated
equals that of regressors, whereas the absence of any smoothness
assumptions augments the dimension of the unknown vector by the
factor of the time-series length. The Akaike Information Criterion
is a commonly adopted means of adjusting a model to the given
data set within a succession of nested parametric model classes,
but its crucial restriction is that the classes are rigidly defined by
the growing integer-valued dimension of the unknown vector. To
make the Kullback information maximization principle underlying the
classical AIC applicable to the problem of time-varying regression
estimation, we extend it onto a wider class of data models in which
the dimension of the parameter is fixed, but the freedom of its values
is softly constrained by a family of continuously nested a priori
probability distributions.
Abstract: A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.
Abstract: In this work a surgical simulator is produced which
enables a training otologist to conduct a virtual, real-time prosthetic
insertion. The simulator provides the Ear, Nose and Throat surgeon
with real-time visual and haptic responses during virtual cochlear
implantation into a 3D model of the human Scala Tympani (ST). The
parametric model is derived from measured data as published in the
literature and accounts for human morphological variance, such as
differences in cochlear shape, enabling patient-specific pre- operative
assessment. Haptic modeling techniques use real physical data and
insertion force measurements, to develop a force model which
mimics the physical behavior of an implant as it collides with the ST
walls during an insertion. Output force profiles are acquired from the
insertion studies conducted in the work, to validate the haptic model.
The simulator provides the user with real-time, quantitative insertion
force information and associated electrode position as user inserts the
virtual implant into the ST model. The information provided by this
study may also be of use to implant manufacturers for design
enhancements as well as for training specialists in optimal force
administration, using the simulator. The paper reports on the methods
for anatomical modeling and haptic algorithm development, with
focus on simulator design, development, optimization and validation.
The techniques may be transferrable to other medical applications
that involve prosthetic device insertions where user vision is
obstructed.