Abstract: In this paper, we propose a hybrid machine learning
system based on Genetic Algorithm (GA) and Support Vector
Machines (SVM) for stock market prediction. A variety of indicators
from the technical analysis field of study are used as input features.
We also make use of the correlation between stock prices of different
companies to forecast the price of a stock, making use of technical
indicators of highly correlated stocks, not only the stock to be
predicted. The genetic algorithm is used to select the set of most
informative input features from among all the technical indicators.
The results show that the hybrid GA-SVM system outperforms the
stand alone SVM system.
Abstract: Burnishing is a method of finishing and hardening
machined parts by plastic deformation of the surface. Experimental
work based on central composite second order rotatable design has
been carried out on a lathe machine to establish the effects of ball
burnishing parameters on the surface roughness of brass material.
Analysis of the results by the analysis of variance technique and the
F-test show that the parameters considered, have significant effects
on the surface roughness.
Abstract: The proper assessment of interaxial distance and
convergence control are important factors in stereoscopic imaging
technology to make an efficient 3D image. To control interaxial
distance and convergence for efficient 3D shooting, horizontal 3D
camera rig is designed using some hardware components like 'LM
Guide', 'Goniometer' and 'Rotation Stage'. The horizontal 3D camera
rig system can be properly aligned by moving the two cameras
horizontally in same or opposite directions, by adjusting the camera
angle and finally considering horizontal swing as well as vertical
swing. In this paper, the relationship between interaxial distance and
convergence angle control are discussed and intensive experiments are
performed in order to demonstrate an easy and effective 3D shooting.
Abstract: Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.