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Rathore / Piuri / Babo Universal Threats in Expert Applications and Solutions
Erscheinungsjahr 2025
ISBN: 978-981-967292-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
Proceedings of 4th UNI-TEAS 2025, Volume 2
E-Book, Englisch, 546 Seiten
Reihe: Intelligent Technologies and Robotics
ISBN: 978-981-967292-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book presents high-quality, peer-reviewed papers from 4th International Conference on “Universal Threats in Expert Applications and Solutions" (UNI-TEAS 2025), jointly being organized by IES University, Bhopal, and Shree KKarni Universe College, Jaipur, in association with CSI Jaipur Chapter and Jaipur ACM Professional Chapter during February 1–4, 2025. The book is a collection of innovative ideas from researchers, scientists, academicians, industry professionals, and students. The book covers a variety of topics, such as expert applications and artificial intelligence/machine learning; advance web technologies such as IoT, big data, cloud computing in expert applications; information and cyber security threats and solutions, multimedia applications in forensics, security and intelligence; advancements in app development; management practices for expert applications; and social and ethical aspects in expert applications through applied sciences.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Leveraging AI to Enhanced ESG Impact Assessment in Sustainable Investments: A Sentiment Analysis.- Ray Tracing: Impact on the Gaming Environment.- Graphical Passwords and Blockchain: A Dual Approach for Secure Authentication and Encrypted Document Sharing.- Mitigating Cloud Security Risks through Role-Based Access and Multi-Level Authentication with Third-Party Auditing.- Design and Proposal of Algorithms for Blockchain Security: Multi-Factor Identity Authentication and Fault Tolerance.- Advanced Classification of Ischemic and Hemorrhagic Stroke using Deep learning Framework.- Exploring AI Approaches and Challenges in Anomaly Detection for Cybersecurity ML.