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Books > Computing & IT > Applications of computing > Databases
The concept of quantum computing is based on two fundamental
principles of quantum mechanics: superposition and entanglement.
Instead of using bits, qubits are used in quantum computing, which
is a key indicator in the high level of safety and security this
type of cryptography ensures. If interfered with or eavesdropped
in, qubits will delete or refuse to send, which keeps the
information safe. This is vital in the current era where sensitive
and important personal information can be digitally shared online.
In computer networks, a large amount of data is transferred
worldwide daily, including anything from military plans to a
country's sensitive information, and data breaches can be
disastrous. This is where quantum cryptography comes into play. By
not being dependent on computational power, it can easily replace
classical cryptography. Limitations and Future Applications of
Quantum Cryptography is a critical reference that provides
knowledge on the basics of IoT infrastructure using quantum
cryptography, the differences between classical and quantum
cryptography, and the future aspects and developments in this
field. The chapters cover themes that span from the usage of
quantum cryptography in healthcare, to forensics, and more. While
highlighting topics such as 5G networks, image processing,
algorithms, and quantum machine learning, this book is ideally
intended for security professionals, IoT developers, computer
scientists, practitioners, researchers, academicians, and students
interested in the most recent research on quantum computing.
Over the last two decades, researchers are looking at imbalanced
data learning as a prominent research area. Many critical
real-world application areas like finance, health, network, news,
online advertisement, social network media, and weather have
imbalanced data, which emphasizes the research necessity for
real-time implications of precise fraud/defaulter detection, rare
disease/reaction prediction, network intrusion detection, fake news
detection, fraud advertisement detection, cyber bullying
identification, disaster events prediction, and more. Machine
learning algorithms are based on the heuristic of
equally-distributed balanced data and provide the biased result
towards the majority data class, which is not acceptable
considering imbalanced data is omnipresent in real-life scenarios
and is forcing us to learn from imbalanced data for foolproof
application design. Imbalanced data is multifaceted and demands a
new perception using the novelty at sampling approach of data
preprocessing, an active learning approach, and a cost perceptive
approach to resolve data imbalance. The Handbook of Research on
Data Preprocessing, Active Learning, and Cost Perceptive Approaches
for Resolving Data Imbalance offers new aspects for imbalanced data
learning by providing the advancements of the traditional methods,
with respect to big data, through case studies and research from
experts in academia, engineering, and industry. The chapters
provide theoretical frameworks and the latest empirical research
findings that help to improve the understanding of the impact of
imbalanced data and its resolving techniques based on data
preprocessing, active learning, and cost perceptive approaches.
This book is ideal for data scientists, data analysts, engineers,
practitioners, researchers, academicians, and students looking for
more information on imbalanced data characteristics and solutions
using varied approaches.
Learn application security from the very start, with this
comprehensive and approachable guide! Alice and Bob Learn
Application Security is an accessible and thorough resource for
anyone seeking to incorporate, from the beginning of the System
Development Life Cycle, best security practices in software
development. This book covers all the basic subjects such as threat
modeling and security testing, but also dives deep into more
complex and advanced topics for securing modern software systems
and architectures. Throughout, the book offers analogies, stories
of the characters Alice and Bob, real-life examples, technical
explanations and diagrams to ensure maximum clarity of the many
abstract and complicated subjects. Topics include: Secure
requirements, design, coding, and deployment Security Testing (all
forms) Common Pitfalls Application Security Programs Securing
Modern Applications Software Developer Security Hygiene Alice and
Bob Learn Application Security is perfect for aspiring application
security engineers and practicing software developers, as well as
software project managers, penetration testers, and chief
information security officers who seek to build or improve their
application security programs. Alice and Bob Learn Application
Security illustrates all the included concepts with
easy-to-understand examples and concrete practical applications,
furthering the reader's ability to grasp and retain the
foundational and advanced topics contained within.
The success of many companies through the assistance of bitcoin
proves that technology continually dominates and transforms how
economics operate. However, a deeper, more conceptual understanding
of how these technologies work to identify innovation opportunities
and how to successfully thrive in an increasingly competitive
environment is needed for the entrepreneurs of tomorrow.
Transforming Businesses With Bitcoin Mining and Blockchain
Applications provides innovative insights into IT infrastructure
and emerging trends in the realm of digital business technologies.
This publication analyzes and extracts information from Bitcoin
networks and provides the necessary steps to designing open
blockchain. Highlighting topics that include financial markets,
risk management, and smart technologies, the research contained
within the title is ideal for entrepreneurs, business
professionals, managers, executives, academicians, researchers, and
business students.
Data-Driven Solutions to Transportation Problems explores the
fundamental principle of analyzing different types of
transportation-related data using methodologies such as the data
fusion model, the big data mining approach, computer vision-enabled
traffic sensing data analysis, and machine learning. The book
examines the state-of-the-art in data-enabled methodologies,
technologies and applications in transportation. Readers will learn
how to solve problems relating to energy efficiency under connected
vehicle environments, urban travel behavior, trajectory data-based
travel pattern identification, public transportation analysis,
traffic signal control efficiency, optimizing traffic networks
network, and much more.
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