Abstract: HR is a department that enhances the power of
employee performance in regard with their services, and to make the
organization strategic objectives. The main concern of HR
department is to organize people, focus on policies and their system.
The empirical study shows the relationship between HRM (Human
Resource Management practices) and their Job Satisfaction. The
Hypothesis is testing on a sample of overall 320 employees of 5
different Pharmaceutical departments of different organizations in
Pakistan. The important thing as Relationship of Job satisfaction with
HR Practices, Impact on Job Satisfaction with HR Practices,
Participation of Staff of Different Departments, HR Practices effects
the Job satisfaction, Recruitment or Hiring and Selection effects the
Job satisfaction, Training and Development, Performance and
Appraisals, Compensation affects the Job satisfaction , and Industrial
Relationships affects the Job satisfaction. After finishing all data
analysis, the conclusion is that lots of Job related activities raise the
confidence of Job satisfaction of employees with their salary and
other benefits.
Abstract: This paper examines the relationship between
corporate governance rating and stock prices of 26 Turkish firms
listed in Turkish stock exchange (Borsa Istanbul) by using panel data
analysis over five-year period. The paper also investigates the stock
performance of firms with governance rating with regards to the
market portfolio (i.e. BIST 100 Index) both prior and after
governance scoring began. The empirical results show that there is no
relation between corporate governance rating and stock prices when
using panel data for annual variation in both rating score and stock
prices. Further analysis indicates surprising results that while the
selected firms outperform the market significantly prior to rating, the
same performance does not continue afterwards.
Abstract: Over the past era, there have been a lot of efforts and
studies are carried out in growing proficient tools for performing
various tasks in big data. Recently big data have gotten a lot of
publicity for their good reasons. Due to the large and complex
collection of datasets it is difficult to process on traditional data
processing applications. This concern turns to be further mandatory
for producing various tools in big data. Moreover, the main aim of
big data analytics is to utilize the advanced analytic techniques
besides very huge, different datasets which contain diverse sizes from
terabytes to zettabytes and diverse types such as structured or
unstructured and batch or streaming. Big data is useful for data sets
where their size or type is away from the capability of traditional
relational databases for capturing, managing and processing the data
with low-latency. Thus the out coming challenges tend to the
occurrence of powerful big data tools. In this survey, a various
collection of big data tools are illustrated and also compared with the
salient features.
Abstract: This study was conducted in Malaysia to discover how
meaning and appreciation were construed among 35 Form Five
students. Panofsky-s theory was employed to discover the levels of
reasoning among students when various types of posters were
displayed. The independent variables used were posters that carried
explicit and implicit meanings; the moderating variable was students-
visual literacy levels while the dependent variable was the implicit
interpretation level. One-way ANOVA was applied for the data
analysis. The data showed that before students were exposed to
Panofsky-s theory, there were differences in thinking between boys,
who did not think abstractly or implicit in comparison to girls. The
study showed that students- visual literacy in posters depended on the
use of visual texts and illustration. This paper discuss further on
posters with text only have a tendency to be too abstract as opposed
to posters with visuals plus text.
Abstract: Considering complexity of products, new geometrical
design and investment tolerances that are necessary, measuring and
dimensional controlling involve modern and more precise methods.
Photo digitizing method using two cameras to record pictures and
utilization of conventional method named “cloud points" and data
analysis by the use of ATOUS software, is known as modern and
efficient in mentioned context. In this paper, benefits of photo
digitizing method in evaluating sampling of machining processes
have been put forward. For example, assessment of geometrical
integrity surface in 5-axis milling process and measurement of
carbide tool wear in turning process, can be can be brought forward.
Advantages of this method comparing to conventional methods have
been expressed.
Abstract: Text data mining is a process of exploratory data
analysis. Classification maps data into predefined groups or classes.
It is often referred to as supervised learning because the classes are
determined before examining the data. This paper describes proposed
radial basis function Classifier that performs comparative crossvalidation
for existing radial basis function Classifier. The feasibility
and the benefits of the proposed approach are demonstrated by means
of data mining problem: direct Marketing. Direct marketing has
become an important application field of data mining. Comparative
Cross-validation involves estimation of accuracy by either stratified
k-fold cross-validation or equivalent repeated random subsampling.
While the proposed method may have high bias; its performance
(accuracy estimation in our case) may be poor due to high variance.
Thus the accuracy with proposed radial basis function Classifier was
less than with the existing radial basis function Classifier. However
there is smaller the improvement in runtime and larger improvement
in precision and recall. In the proposed method Classification
accuracy and prediction accuracy are determined where the
prediction accuracy is comparatively high.
Abstract: The influence of full-fat soy flour (FFSF) and
extrusion conditions on the mechanical characteristics of dry
spaghetti were evaluated. Process was performed with screw speed of
10-40rpm and water circulating temperature of 35-70°C. Data
analysis using mixture design showed that this enrichment resulted in
significant differences in mechanical strength.
Abstract: Currently, slider process of Hard Disk Drive Industry
become more complex, defective diagnosis for yield improvement
becomes more complicated and time-consumed. Manufacturing data
analysis with data mining approach is widely used for solving that
problem. The existing mining approach from combining of the KMean
clustering, the machine oriented Kruskal-Wallis test and the
multivariate chart were applied for defective diagnosis but it is still
be a semiautomatic diagnosis system. This article aims to modify an
algorithm to support an automatic decision for the existing approach.
Based on the research framework, the new approach can do an
automatic diagnosis and help engineer to find out the defective
factors faster than the existing approach about 50%.
Abstract: Artemia is one of the most conspicuous invertebrates
associated with aquaculture. It can be considered as a model
organism, offering numerous advantages for comprehensive and
multidisciplinary studies using morphologic or molecular methods.
Since DNA extraction is an important step of any molecular
experiment, a new and a rapid method of DNA extraction from adult
Artemia was described in this study. Besides, the efficiency of this
technique was compared with two widely used alternative techniques,
namely Chelex® 100 resin and SDS-chloroform methods. Data
analysis revealed that the new method is the easiest and the most cost
effective method among the other methods which allows a quick and
efficient extraction of DNA from the adult animal.
Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.