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This book explores the intersection of cybersecurity and education
technologies, providing practical solutions, detection techniques,
and mitigation strategies to ensure a secure and protected learning
environment in the face of evolving cyber threats. With a wide
range of contributors covering topics from immersive learning to
phishing detection, this book is a valuable resource for
professionals, researchers, educators, students, and policymakers
interested in the future of cybersecurity in education. Provides
practical solutions, detection techniques, and mitigation
strategies to ensure a secure and protected learning environment in
the face of evolving cyber threats. Covers a wide range of topics
including immersive learning, cybersecurity education, and malware
detection, making it a valuable resource for professionals,
researchers, educators, students, and policymakers. Offers both
theoretical foundations and practical guidance for fostering a
secure and protected environment for educational advancements in
the digital age. Addresses the need for cybersecurity in education
in the context of worldwide changes in education sources and
advancements in technology. Highlights the significance of
integrating cybersecurity into educational practices and protecting
sensitive information to ensure students' performance prediction
systems are not misused.
Social media platforms are one of the main generators of textual
data where people around the world share their daily life
experiences and information with online society. The social,
personal, and professional lives of people on these social
networking sites generate not only a huge amount of data but also
open doors for researchers and academicians with numerous research
opportunities. This ample amount of data needs advanced machine
learning, deep learning, and intelligent tools and techniques to
receive, process, and interpret the information to resolve
real-life challenges and improve the online social lives of people.
Advanced Applications of NLP and Deep Learning in Social Media Data
bridges the gap between natural language processing (NLP), advanced
machine learning, deep learning, and online social media. It hopes
to build a better and safer social media space by making human
language available on different social media platforms intelligible
for machines with the blessings of AI. Covering topics such as
machine learning-based prediction, emotion recognition, and
high-dimensional text clustering, this premier reference source is
an essential resource for OSN service providers, psychiatrists,
psychologists, clinicians, sociologists, students and educators of
higher education, librarians, researchers, and academicians.
Social media platforms are one of the main generators of textual
data where people around the world share their daily life
experiences and information with online society. The social,
personal, and professional lives of people on these social
networking sites generate not only a huge amount of data but also
open doors for researchers and academicians with numerous research
opportunities. This ample amount of data needs advanced machine
learning, deep learning, and intelligent tools and techniques to
receive, process, and interpret the information to resolve
real-life challenges and improve the online social lives of people.
Advanced Applications of NLP and Deep Learning in Social Media Data
bridges the gap between natural language processing (NLP), advanced
machine learning, deep learning, and online social media. It hopes
to build a better and safer social media space by making human
language available on different social media platforms intelligible
for machines with the blessings of AI. Covering topics such as
machine learning-based prediction, emotion recognition, and
high-dimensional text clustering, this premier reference source is
an essential resource for OSN service providers, psychiatrists,
psychologists, clinicians, sociologists, students and educators of
higher education, librarians, researchers, and academicians.
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