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.