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This book presents and standardizes statistical models and methods
that can be directly applied to both reliability and survival
analysis. These two types of analysis are widely used in many
fields, including engineering, management, medicine, actuarial
science, the environmental sciences, and the life sciences. Though
there are a number of books on reliability analysis and a handful
on survival analysis, there are virtually no books on both topics
and their overlapping concepts. Offering an essential textbook,
this book will benefit students, researchers, and practitioners in
reliability and survival analysis, reliability engineering,
biostatistics, and the biomedical sciences.
Warranty Data Collection and Analysis deals with warranty data
collection and analysis and the problems associated with these
activities. The book is a both a research monograph and a handbook
for practitioners. As a research monograph, it unifies the
literature on warranty data collection and analysis, and presents
the important results in an integrated manner. In the process, it
highlights topics that require further research. As a handbook, it
provides the essential methodology needed by practitioners involved
with warranty data collection and analysis, along with extensive
references to further results. Models and techniques needed for
proper and effective analysis of data are included, together with
guidelines for their use in warranty management, product
improvement, and new product development. Warranty Data Collection
and Analysis will be of interest to researchers (engineers and
statisticians) and practitioners (engineers, applied statisticians,
and managers) involved with product warranty and reliability. It is
also suitable for use as a reference text for graduate-level
reliability programs in engineering, applied statistics, operations
research, and management.
Warranty Data Collection and Analysis deals with warranty data
collection and analysis and the problems associated with these
activities. The book is a both a research monograph and a handbook
for practitioners. As a research monograph, it unifies the
literature on warranty data collection and analysis, and presents
the important results in an integrated manner. In the process, it
highlights topics that require further research. As a handbook, it
provides the essential methodology needed by practitioners involved
with warranty data collection and analysis, along with extensive
references to further results. Models and techniques needed for
proper and effective analysis of data are included, together with
guidelines for their use in warranty management, product
improvement, and new product development. Warranty Data Collection
and Analysis will be of interest to researchers (engineers and
statisticians) and practitioners (engineers, applied statisticians,
and managers) involved with product warranty and reliability. It is
also suitable for use as a reference text for graduate-level
reliability programs in engineering, applied statistics, operations
research, and management.
The Ganges-Brahmaputra-Meghna delta has newly emerged 'char'
islands, resulting from the deposition of sediment, which are very
vulnerable, socially, institutionally and environmentally. This
book explains how the governments of Bangladesh and the Netherlands
and the International Fund for Agricultural Development cooperated
on a land-based rural development project to give settlers security
and purpose. It details how they engaged communities and civil
societies, and implemented an infrastructure aimed at reducing
flooding, improving drainage, and providing adequate drinking water
and sanitation. The book describes the project's application to
crop and animal agriculture, and the development of value chains
and encouragement of female participation. It considers the
financial underpinning and infrastructure, as well as how to ensure
the impacts of the scheme are enduring. The scheme serves as a
model for support projects to vulnerable groups faced with climate
change and other environmental challenges. This book is suitable
for students, researchers, specialists and practitioners in rural
development, water resources, land management and soil science.
Implement popular deep learning techniques to make your IoT
applications smarter Key Features Understand how deep learning
facilitates fast and accurate analytics in IoT Build intelligent
voice and speech recognition apps in TensorFlow and Chainer Analyze
IoT data for making automated decisions and efficient predictions
Book DescriptionArtificial Intelligence is growing quickly, which
is driven by advancements in neural networks(NN) and deep learning
(DL). With an increase in investments in smart cities, smart
healthcare, and industrial Internet of Things (IoT),
commercialization of IoT will soon be at peak in which massive
amounts of data generated by IoT devices need to be processed at
scale. Hands-On Deep Learning for IoT will provide deeper insights
into IoT data, which will start by introducing how DL fits into the
context of making IoT applications smarter. It then covers how to
build deep architectures using TensorFlow, Keras, and Chainer for
IoT. You'll learn how to train convolutional neural networks(CNN)
to develop applications for image-based road faults detection and
smart garbage separation, followed by implementing voice-initiated
smart light control and home access mechanisms powered by recurrent
neural networks(RNN). You'll master IoT applications for indoor
localization, predictive maintenance, and locating equipment in a
large hospital using autoencoders, DeepFi, and LSTM networks.
Furthermore, you'll learn IoT application development for
healthcare with IoT security enhanced. By the end of this book, you
will have sufficient knowledge need to use deep learning
efficiently to power your IoT-based applications for smarter
decision making. What you will learn Get acquainted with different
neural network architectures and their suitability in IoT
Understand how deep learning can improve the predictive power in
your IoT solutions Capture and process streaming data for
predictive maintenance Select optimal frameworks for image
recognition and indoor localization Analyze voice data for speech
recognition in IoT applications Develop deep learning-based IoT
solutions for healthcare Enhance security in your IoT solutions
Visualize analyzed data to uncover insights and perform accurate
predictions Who this book is forIf you're an IoT developer, data
scientist, or deep learning enthusiast who wants to apply deep
learning techniques to build smart IoT applications, this book is
for you. Familiarity with machine learning, a basic understanding
of the IoT concepts, and some experience in Python programming will
help you get the most out of this book.
Supervised and unsupervised machine learning made easy in Scala
with this quick-start guide. Key Features Construct and deploy
machine learning systems that learn from your data and give
accurate predictions Unleash the power of Spark ML along with
popular machine learning algorithms to solve complex tasks in
Scala. Solve hands-on problems by combining popular neural network
architectures such as LSTM and CNN using Scala with DeepLearning4j
library Book DescriptionScala is a highly scalable integration of
object-oriented nature and functional programming concepts that
make it easy to build scalable and complex big data applications.
This book is a handy guide for machine learning developers and data
scientists who want to develop and train effective machine learning
models in Scala. The book starts with an introduction to machine
learning, while covering deep learning and machine learning basics.
It then explains how to use Scala-based ML libraries to solve
classification and regression problems using linear regression,
generalized linear regression, logistic regression, support vector
machine, and Naive Bayes algorithms. It also covers tree-based
ensemble techniques for solving both classification and regression
problems. Moving ahead, it covers unsupervised learning techniques,
such as dimensionality reduction, clustering, and recommender
systems. Finally, it provides a brief overview of deep learning
using a real-life example in Scala. What you will learn Get
acquainted with JVM-based machine learning libraries for Scala such
as Spark ML and Deeplearning4j Learn RDDs, DataFrame, and Spark SQL
for analyzing structured and unstructured data Understand
supervised and unsupervised learning techniques with best practices
and pitfalls Learn classification and regression analysis with
linear regression, logistic regression, Naive Bayes, support vector
machine, and tree-based ensemble techniques Learn effective ways of
clustering analysis with dimensionality reduction techniques Learn
recommender systems with collaborative filtering approach Delve
into deep learning and neural network architectures Who this book
is forThis book is for machine learning developers looking to train
machine learning models in Scala without spending too much time and
effort. Some fundamental knowledge of Scala programming and some
basics of statistics and linear algebra is all you need to get
started with this book.
Build efficient data flow and machine learning programs with this
flexible, multi-functional open-source cluster-computing framework
Key Features Master the art of real-time big data processing and
machine learning Explore a wide range of use-cases to analyze large
data Discover ways to optimize your work by using many features of
Spark 2.x and Scala Book DescriptionApache Spark is an in-memory,
cluster-based data processing system that provides a wide range of
functionalities such as big data processing, analytics, machine
learning, and more. With this Learning Path, you can take your
knowledge of Apache Spark to the next level by learning how to
expand Spark's functionality and building your own data flow and
machine learning programs on this platform. You will work with the
different modules in Apache Spark, such as interactive querying
with Spark SQL, using DataFrames and datasets, implementing
streaming analytics with Spark Streaming, and applying machine
learning and deep learning techniques on Spark using MLlib and
various external tools. By the end of this elaborately designed
Learning Path, you will have all the knowledge you need to master
Apache Spark, and build your own big data processing and analytics
pipeline quickly and without any hassle. This Learning Path
includes content from the following Packt products: Mastering
Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data
Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x
Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi
Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn
Get to grips with all the features of Apache Spark 2.x Perform
highly optimized real-time big data processing Use ML and DL
techniques with Spark MLlib and third-party tools Analyze
structured and unstructured data using SparkSQL and GraphX
Understand tuning, debugging, and monitoring of big data
applications Build scalable and fault-tolerant streaming
applications Develop scalable recommendation engines Who this book
is forIf you are an intermediate-level Spark developer looking to
master the advanced capabilities and use-cases of Apache Spark 2.x,
this Learning Path is ideal for you. Big data professionals who
want to learn how to integrate and use the features of Apache Spark
and build a strong big data pipeline will also find this Learning
Path useful. To grasp the concepts explained in this Learning Path,
you must know the fundamentals of Apache Spark and Scala.
Build and deploy powerful neural network models using the latest
Java deep learning libraries Key Features Understand DL with Java
by implementing real-world projects Master implementations of
various ANN models and build your own DL systems Develop
applications using NLP, image classification, RL, and GPU
processing Book DescriptionJava is one of the most widely used
programming languages. With the rise of deep learning, it has
become a popular choice of tool among data scientists and machine
learning experts. Java Deep Learning Projects starts with an
overview of deep learning concepts and then delves into advanced
projects. You will see how to build several projects using
different deep neural network architectures such as multilayer
perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization
Machines. You will get acquainted with popular deep and machine
learning libraries for Java such as Deeplearning4j, Spark ML, and
RankSys and you'll be able to use their features to build and
deploy projects on distributed computing environments. You will
then explore advanced domains such as transfer learning and deep
reinforcement learning using the Java ecosystem, covering various
real-world domains such as healthcare, NLP, image classification,
and multimedia analytics with an easy-to-follow approach. Expert
reviews and tips will follow every project to give you insights and
hacks. By the end of this book, you will have stepped up your
expertise when it comes to deep learning in Java, taking it beyond
theory and be able to build your own advanced deep learning
systems. What you will learn Master deep learning and neural
network architectures Build real-life applications covering image
classification, object detection, online trading, transfer
learning, and multimedia analytics using DL4J and open-source APIs
Train ML agents to learn from data using deep reinforcement
learning Use factorization machines for advanced movie
recommendations Train DL models on distributed GPUs for faster deep
learning with Spark and DL4J Ease your learning experience through
69 FAQs Who this book is forIf you are a data scientist, machine
learning professional, or deep learning practitioner keen to expand
your knowledge by delving into the practical aspects of deep
learning with Java, then this book is what you need! Get ready to
build advanced deep learning models to carry out complex numerical
computations. Some basic understanding of machine learning concepts
and a working knowledge of Java are required.
Learn how to solve real life problems using different methods like
logic regression, random forests and SVM's with TensorFlow. Key
Features Understand predictive analytics along with its challenges
and best practices Embedded with assessments that will help you
revise the concepts you have learned in this book Book
DescriptionPredictive analytics discovers hidden patterns from
structured and unstructured data for automated decision making in
business intelligence. Predictive decisions are becoming a huge
trend worldwide, catering to wide industry sectors by predicting
which decisions are more likely to give maximum results.
TensorFlow, Google's brainchild, is immensely popular and
extensively used for predictive analysis. This book is a quick
learning guide on all the three types of machine learning, that is,
supervised, unsupervised, and reinforcement learning with
TensorFlow. This book will teach you predictive analytics for
high-dimensional and sequence data. In particular, you will learn
the linear regression model for regression analysis. You will also
learn how to use regression for predicting continuous values. You
will learn supervised learning algorithms for predictive analytics.
You will explore unsupervised learning and clustering using
K-meansYou will then learn how to predict neighborhoods using
K-means, and then, see another example of clustering audio clips
based on their audio features. This book is ideal for developers,
data analysts, machine learning practitioners, and deep learning
enthusiasts who want to build powerful, robust, and accurate
predictive models with the power of TensorFlow. This book is
embedded with useful assessments that will help you revise the
concepts you have learned in this book. What you will learn Learn
TensorFlow features in a real-life problem, followed by detailed
TensorFlow installation and configuration Explore computation
graphs, data, and programming models also get an insight into an
example of implementing linear regression model for predictive
analytics Solve the Titanic survival problem using logistic
regression, random forests, and SVMs for predictive analytics Dig
deeper into predictive analytics and find out how to take advantage
of it to cluster records belonging to the certain group or class
for a dataset of unsupervised observations Learn several examples
of how to apply reinforcement learning algorithms for developing
predictive models on real-life datasets Who this book is forThis
book is aimed at developers, data analysts, machine learning
practitioners, and deep learning enthusiasts who want to build
powerful, robust, and accurate predictive models with the power of
TensorFlow.
One stop guide to implementing award-winning, and cutting-edge CNN
architectures Key Features Fast-paced guide with use cases and
real-world examples to get well versed with CNN techniques
Implement CNN models on image classification, transfer learning,
Object Detection, Instance Segmentation, GANs and more Implement
powerful use-cases like image captioning, reinforcement learning
for hard attention, and recurrent attention models Book
DescriptionConvolutional Neural Network (CNN) is revolutionizing
several application domains such as visual recognition systems,
self-driving cars, medical discoveries, innovative eCommerce and
more.You will learn to create innovative solutions around image and
video analytics to solve complex machine learning and computer
vision related problems and implement real-life CNN models. This
book starts with an overview of deep neural networkswith the
example of image classification and walks you through building your
first CNN for human face detector. We will learn to use concepts
like transfer learning with CNN, and Auto-Encoders to build very
powerful models, even when not much of supervised training data of
labeled images is available. Later we build upon the learning
achieved to build advanced vision related algorithms for object
detection, instance segmentation, generative adversarial networks,
image captioning, attention mechanisms for vision, and recurrent
models for vision. By the end of this book, you should be ready to
implement advanced, effective and efficient CNN models at your
professional project or personal initiatives by working on complex
image and video datasets. What you will learn From CNN basic
building blocks to advanced concepts understand practical areas
they can be applied to Build an image classifier CNN model to
understand how different components interact with each other, and
then learn how to optimize it Learn different algorithms that can
be applied to Object Detection, and Instance Segmentation Learn
advanced concepts like attention mechanisms for CNN to improve
prediction accuracy Understand transfer learning and implement
award-winning CNN architectures like AlexNet, VGG, GoogLeNet,
ResNet and more Understand the working of generative adversarial
networks and how it can create new, unseen images Who this book is
forThis book is for data scientists, machine learning and deep
learning practitioners, Cognitive and Artificial Intelligence
enthusiasts who want to move one step further in building
Convolutional Neural Networks. Get hands-on experience with extreme
datasets and different CNN architectures to build efficient and
smart ConvNet models. Basic knowledge of deep learning concepts and
Python programming language is expected.
Powerful smart applications using deep learning algorithms to
dominate numerical computing, deep learning, and functional
programming. Key Features Explore machine learning techniques with
prominent open source Scala libraries such as Spark ML, H2O, MXNet,
Zeppelin, and DeepLearning4j Solve real-world machine learning
problems by delving complex numerical computing with Scala
functional programming in a scalable and faster way Cover all key
aspects such as collection, storing, processing, analyzing, and
evaluation required to build and deploy machine models on computing
clusters using Scala Play framework. Book DescriptionMachine
learning has had a huge impact on academia and industry by turning
data into actionable information. Scala has seen a steady rise in
adoption over the past few years, especially in the fields of data
science and analytics. This book is for data scientists, data
engineers, and deep learning enthusiasts who have a background in
complex numerical computing and want to know more hands-on machine
learning application development. If you're well versed in machine
learning concepts and want to expand your knowledge by delving into
the practical implementation of these concepts using the power of
Scala, then this book is what you need! Through 11 end-to-end
projects, you will be acquainted with popular machine learning
libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the
end, you will be able to use numerical computing and functional
programming to carry out complex numerical tasks to develop, build,
and deploy research or commercial projects in a production-ready
environment. What you will learn Apply advanced regression
techniques to boost the performance of predictive models Use
different classification algorithms for business analytics Generate
trading strategies for Bitcoin and stock trading using ensemble
techniques Train Deep Neural Networks (DNN) using H2O and Spark ML
Utilize NLP to build scalable machine learning models Learn how to
apply reinforcement learning algorithms such as Q-learning for
developing ML application Learn how to use autoencoders to develop
a fraud detection application Implement LSTM and CNN models using
DeepLearning4j and MXNet Who this book is forIf you want to
leverage the power of both Scala and Spark to make sense of Big
Data, then this book is for you. If you are well versed with
machine learning concepts and wants to expand your knowledge by
delving into the practical implementation using the power of Scala,
then this book is what you need! Strong understanding of Scala
Programming language is recommended. Basic familiarity with machine
Learning techniques will be more helpful.
Delve into neural networks, implement deep learning algorithms, and
explore layers of data abstraction with the help of TensorFlow. Key
Features Learn how to implement advanced techniques in deep
learning with Google's brainchild, TensorFlow Explore deep neural
networks and layers of data abstraction with the help of this
comprehensive guide Gain real-world contextualization through some
deep learning problems concerning research and application Book
DescriptionDeep learning is a branch of machine learning algorithms
based on learning multiple levels of abstraction. Neural networks,
which are at the core of deep learning, are being used in
predictive analytics, computer vision, natural language processing,
time series forecasting, and to perform a myriad of other complex
tasks. This book is conceived for developers, data analysts,
machine learning practitioners and deep learning enthusiasts who
want to build powerful, robust, and accurate predictive models with
the power of TensorFlow, combined with other open source Python
libraries. Throughout the book, you'll learn how to develop deep
learning applications for machine learning systems using
Feedforward Neural Networks, Convolutional Neural Networks,
Recurrent Neural Networks, Autoencoders, and Factorization
Machines. Discover how to attain deep learning programming on GPU
in a distributed way. You'll come away with an in-depth knowledge
of machine learning techniques and the skills to apply them to
real-world projects. What you will learn Apply deep machine
intelligence and GPU computing with TensorFlow Access public
datasets and use TensorFlow to load, process, and transform the
data Discover how to use the high-level TensorFlow API to build
more powerful applications Use deep learning for scalable object
detection and mobile computing Train machines quickly to learn from
data by exploring reinforcement learning techniques Explore active
areas of deep learning research and applications Who this book is
forThe book is for people interested in machine learning and
machine intelligence. A rudimentary level of programming in one
language is assumed, as is a basic familiarity with computer
science techniques and technologies, including a basic awareness of
computer hardware and algorithms. Some competence in mathematics is
needed to the level of elementary linear algebra and calculus.
Harness the power of Scala to program Spark and analyze tonnes of
data in the blink of an eye! About This Book * Learn Scala's
sophisticated type system that combines Functional Programming and
object-oriented concepts * Work on a wide array of applications,
from simple batch jobs to stream processing and machine learning *
Explore the most common as well as some complex use-cases to
perform large-scale data analysis with Spark Who This Book Is For
Anyone who wishes to learn how to perform data analysis by
harnessing the power of Spark will find this book extremely useful.
No knowledge of Spark or Scala is assumed, although prior
programming experience (especially with other JVM languages) will
be useful to pick up concepts quicker. What You Will Learn *
Understand object-oriented & functional programming concepts of
Scala * In-depth understanding of Scala collection APIs * Work with
RDD and DataFrame to learn Spark's core abstractions * Analysing
structured and unstructured data using SparkSQL and GraphX *
Scalable and fault-tolerant streaming application development using
Spark structured streaming * Learn machine-learning best practices
for classification, regression, dimensionality reduction, and
recommendation system to build predictive models with widely used
algorithms in Spark MLlib & ML * Build clustering models to
cluster a vast amount of data * Understand tuning, debugging, and
monitoring Spark applications * Deploy Spark applications on real
clusters in Standalone, Mesos, and YARN In Detail Scala has been
observing wide adoption over the past few years, especially in the
field of data science and analytics. Spark, built on Scala, has
gained a lot of recognition and is being used widely in
productions. Thus, if you want to leverage the power of Scala and
Spark to make sense of big data, this book is for you. The first
part introduces you to Scala, helping you understand the
object-oriented and functional programming concepts needed for
Spark application development. It then moves on to Spark to cover
the basic abstractions using RDD and DataFrame. This will help you
develop scalable and fault-tolerant streaming applications by
analyzing structured and unstructured data using SparkSQL, GraphX,
and Spark structured streaming. Finally, the book moves on to some
advanced topics, such as monitoring, configuration, debugging,
testing, and deployment. You will also learn how to develop Spark
applications using SparkR and PySpark APIs, interactive data
analytics using Zeppelin, and in-memory data processing with
Alluxio. By the end of this book, you will have a thorough
understanding of Spark, and you will be able to perform full-stack
data analytics with a feel that no amount of data is too big. Style
and approach Filled with practical examples and use cases, this
book will hot only help you get up and running with Spark, but will
also take you farther down the road to becoming a data scientist.
Delve into neural networks, implement deep learning algorithms, and
explore layers of data abstraction with the help of this
comprehensive TensorFlow guide About This Book * Learn how to
implement advanced techniques in deep learning with Google's
brainchild, TensorFlow * Explore deep neural networks and layers of
data abstraction with the help of this comprehensive guide *
Real-world contextualization through some deep learning problems
concerning research and application Who This Book Is For The book
is intended for a general audience of people interested in machine
learning and machine intelligence. A rudimentary level of
programming in one language is assumed, as is a basic familiarity
with computer science techniques and technologies, including a
basic awareness of computer hardware and algorithms. Some
competence in mathematics is needed to the level of elementary
linear algebra and calculus. What You Will Learn * Learn about
machine learning landscapes along with the historical development
and progress of deep learning * Learn about deep machine
intelligence and GPU computing with the latest TensorFlow 1.x *
Access public datasets and utilize them using TensorFlow to load,
process, and transform data * Use TensorFlow on real-world
datasets, including images, text, and more * Learn how to evaluate
the performance of your deep learning models * Using deep learning
for scalable object detection and mobile computing * Train machines
quickly to learn from data by exploring reinforcement learning
techniques * Explore active areas of deep learning research and
applications In Detail Deep learning is the step that comes after
machine learning, and has more advanced implementations. Machine
learning is not just for academics anymore, but is becoming a
mainstream practice through wide adoption, and deep learning has
taken the front seat. As a data scientist, if you want to explore
data abstraction layers, this book will be your guide. This book
shows how this can be exploited in the real world with complex raw
data using TensorFlow 1.x. Throughout the book, you'll learn how to
implement deep learning algorithms for machine learning systems and
integrate them into your product offerings, including search, image
recognition, and language processing. Additionally, you'll learn
how to analyze and improve the performance of deep learning models.
This can be done by comparing algorithms against benchmarks, along
with machine intelligence, to learn from the information and
determine ideal behaviors within a specific context. After
finishing the book, you will be familiar with machine learning
techniques, in particular the use of TensorFlow for deep learning,
and will be ready to apply your knowledge to research or commercial
projects. Style and approach This step-by-step guide will explore
common, and not so common, deep neural networks and show how these
can be exploited in the real world with complex raw data. With the
help of practical examples, you will learn how to implement
different types of neural nets to build smart applications related
to text, speech, and image data processing.
Research Paper from the year 2013 in the subject Sociology - Work,
Profession, Education, Organisation, printed single-sided, grade:
-, National Institute of Development Administration (Graduate
School of Public Administration), course: PhD, language: English,
abstract: The effects of globalisation on gendered division of
labour, global market strategies, structural adjustment programmes,
use of information technology and changing work organisation,
working conditions are changed in different formats and there has
been an increase of female participation in employment This change
becomes more significant especially for the involvement of female
workers at the workplace with a great responsibility at home for
family activities. Like their male counterparts women are also
earners for their families. However, they have to maintain their
family activities such as caring young children, looking after
ageing and disabled people that seems women have to do more than
their men peers. Having realized this fact, family-friendly
policies have been enacted and practised in the organization on the
basis of recommendations of policy makers, researchers with an
intention to utilize the women capital and play an important role
in uplifting living conditions of women. These FFPs serve as the
balancing between work and family live. These issues are considered
as the benefit for working women with or without children who enjoy
the advantage to utilise their time for family responsibilities As
these are the concerns, the issue of quality of life has become a
new aspect for the increase of female participation in labour force
. Bangladesh is waved by the globalisation, internationalisation,
pressure of donor agencies and recent change in the growth of
female employment. This research has been framed to examine the
causal relations between the family friendly policies and working
life of women in the public sector training organizations.The
population size comprises of all working wome
Essay from the year 2013 in the subject Economics - Case Scenarios,
printed single-sided, grade: "none," - (National Institute of
Development Administration, Bangkok - Graduate School of Public
Administration), course: PhD, language: English, abstract:
Abstract: To reduce inequality in income distribution and reduce
the poverty social welfare spending in Thailand particularly on
education and health services is regarded as one of the effective
instruments. Policy makers agree that public subsidies on education
and health produce positive externalities and have spill over
effect in the society. This research is designed to analyse the
effects of public spending of education and health on income
distribution which examine the pre-expenditure and post-expenditure
income distribution in Thailand. It follows the benefit incidence
analysis (BIA) that is a method of computing the distribution of
public expenditure across different demographic groups, such as
women and men. The procedure involves allocating per unit public
subsidies (for example, expenditure per student for the education
sector) according to individual utilization rates of public
services. This paper aims at examine who are the real beneficiaries
from the government expenditure. The study uses the quantitative
method where data are used of 2010. From the benefit incidence
analysis on the public expenditure on education it seems that
education system is pro-poor and health care system are not
pro-poor rather pro-rich. The poor people can be benefited more
from the primary and secondary education and less benefited from
the tertiary education. However, overall expenditure on education
is favourable to the poor which proves from the income share of
household. In this perspective, The Thai government should
emphasize on higher education for poor by providing special loan
created only for them and universities should also be adopted the
policy so that poor income class people can access the opportunity.
Digital forensics is a very important area that carries special
sensibility in analyzing and studying digital evidence not only in
cyber crime cases, but also in house or localized digital crimes.
eDiscovery makes it necessary to identify the evidence which is in
the form of metadata. To discover and analyze the metadata in a
proper manner for a litigation process in different environments,
interoperability is one of the central problems. In order to attain
interoperability, there is a standard termed as EDRM XML v1.1.
However, this standard needs further modifications and enhancements
to ensure real interoperability in an arbitrary eDiscovery process.
A novel XML schema is proposed along with the necessary changes
from the current EDRM XML v1.1. The assessment and the evaluation
of the system demonstrated that XSD can ensure interoperability in
eDiscovery process indeed.
First publication work of the author from a dissertation that has
been applied real-time for neonatal monitoring purposes in delivery
room. This book explains digital lock-in detection in
photoplethysmography, a technique of creating graphical user
interface, transferring data via RS232, Low pass filtering in java
for a real-time project using object oriented programming
methodology. Block diagram and flow-chart is presented where
necessary.
The use of nanomaterials has extended to a broad range of
applications, for example chemical synthesis, microporous media
synthesis and catalytic combustion, contributing to achievement of
improved or promising results. Electrically -conjugated conducting
polymers (CP) and their nanomaterials has been the subject of
tremendous interest in recent years because of their unique
electrical, mechanical, and physical properties. Here is given
different CP (polyaniline, polythiophene, and polypyrrole)
nano-meter scale materials/composites synthesis routes by using the
in-situ chemical oxidative and/or radiation-induced polymerization
method. To enhance the properties of conducting polymers in several
orders of magnitudes, different host matrixes (single & multi
wall carbon nanotube, activated carbon and metals or metal oxides)
are used. The properties of the resultant nanomaterials have been
investigated by using various techniques. The overall objectives of
this manuscript is to present the versatility of electrically
conducting polymer nanomaterials synthesis and their properties
improvement mechanism with different host matrixes for increasing
their applications capability.
A graph is an abstract structure that is used to model information.
Many real-world situations can conveniently be described by means
of graphs. Smaller area of a drawing increases the readability of
the drawing. Compact drawing of a circuit is preferable for VLSI
fabrication since a compact drawing helps us to avoid wasting of
valuable wafer space. This book deals with area efficient
straight-line drawings of planar graphs. We have introduced some
classes of planar graphs that admit straight-line grid drawing with
sub-quadratic area. We introduce doughnut graphs,'' a subclass of
5-connected planar graphs as well as 3-outerplanar graphs, which
admits a straight-line grid drawing on a grid of area O(n). We
introduce a subclass of 4-connected planar graphs that admits
straight-line grid drawing with linear area. We also introduce a
subclass of outerplanar graphs, which we call label-constrained
outerplanar graphs,'' that admits straight-line grid drawings with
O(nlog n) area. We give linear-time algorithms to find such
drawings. We also give linear-time algorithms for recognition of
these classes of graphs.
ELISA: History, Types and Applications contains unique combinations
of chapters in which the abstract of each chapter defines the work
considered for the entire chapter. One of the essential goals of
this book is to delineate vital information about different types
of ELISA and their applications. The beginning chapter describes
the history and background of enzyme-linked immunosorbent assay
(ELISA), as well as the remarkable changes that were brought about
by this technique. The chapter also describes the commercial
application techniques used in food and diagnostic industries. The
second chapter, on the other hand, describes various types of ELISA
and their steps to perform ELISA. The third chapter discusses the
practical applications and their mechanisms from theory. It also
discusses possible multiple sources of error which can exist at
each stage of the process; how to minimize the error; and the
principles of data analysis. The fourth chapter discusses the
applications of ELISA in various fields, which also include the
uses of ELISA in the diagnosis of infectious diseases, parasitic
diseases, and non-communicable disease caused by a variety of
pathogenic microorganisms. While Chapters One to Four describe a
detailed history, a variety of ELISA, their steps, their sources of
application error, their solutions and their applications in
different fields, the remaining chapters discuss more specific
issues, including: The role of ELISA in advanced glycerin end
products and their cell surface receptors in metabolic diseases,
indirect ELISA for the diagnosis of Brucella Canis, ELISA based
detection in cancer research, etc.
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