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Informationen zum Autor Dr. Mehdi Roopaei has strong background on dynamic systems and control with experience in advance cloud machine learning. He is a Research Assistant Professor in Open Cloud Institute at University of Texas at San Antonio. He has several peer publications with more than 850 citations and serves as program committee member in many conferences. He is the pioneer in feedback image processing and deep learning control. Paul Rad! Ph.D. is the co-founder and assistant director of Open Cloud Institute (OCI)! at the University of Texas at San Antonio (UTSA). Dr. Rad's research interests relate to data analytics! deep learning! cybersecurity! and cloud computing with applications to spam and malware threat detection and analysis! network intrusion detection and prevention! machine vision and sensing! Internet of Things and Machine to Machine de-centralized decision making and security. He holds 11 US patents on cyber infrastructure! virtualization! cloud computing and big data analytics. Dr. Rad has advised over 200 companies on cyber infrastructure and cloud computing with over 70 industry and academic keynote presentations and peer-reviewed publications that have been cited over 300 times. Zusammenfassung This book investigates anomaly detection with novel deep and wide semantic and cognitive models using high performance computing and cloud-based platforms. It also provides deep learning models with extraordinary capabilities to extract multiple levels of representations with increasing abstraction. Inhaltsverzeichnis 1 Large-Scale Video Event Detection Using Deep Neural Networks 2 Leveraging Selectional Preferences for Anomaly Detection in Newswire Events 3 Abnormal Event Recognition in Crowd Environments 4 Cognitive Sensing: Adaptive Anomalies Detection with Deep Networks 5 Language-Guided Visual Recognition 6 Deep Learning for Font Recognition and Retrieval 7 A Distributed Secure Machine-Learning Cloud Architecture for Semantic Analysis 8 A Practical Look at Anomaly Detection Using Autoencoders with H2O and the R Programming Language ...
