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Full-Text Articles in Theory and Algorithms
Anomaly Detection In Sequential Data: A Deep Learning-Based Approach, Jayesh Soni
Anomaly Detection In Sequential Data: A Deep Learning-Based Approach, Jayesh Soni
FIU Electronic Theses and Dissertations
Anomaly Detection has been researched in various domains with several applications in intrusion detection, fraud detection, system health management, and bio-informatics. Conventional anomaly detection methods analyze each data instance independently (univariate or multivariate) and ignore the sequential characteristics of the data. Anomalies in the data can be detected by grouping the individual data instances into sequential data and hence conventional way of analyzing independent data instances cannot detect anomalies. Currently: (1) Deep learning-based algorithms are widely used for anomaly detection purposes. However, significant computational overhead time is incurred during the training process due to static constant batch size and learning …
Integrating Deep Learning With Correlation-Based Multimedia Semantic Concept Detection, Hsin-Yu Ha
Integrating Deep Learning With Correlation-Based Multimedia Semantic Concept Detection, Hsin-Yu Ha
FIU Electronic Theses and Dissertations
The rapid advances in technologies make the explosive growth of multimedia data possible and available to the public. Multimedia data can be defined as data collection, which is composed of various data types and different representations. Due to the fact that multimedia data carries knowledgeable information, it has been widely adopted to different genera, like surveillance event detection, medical abnormality detection, and many others. To fulfil various requirements for different applications, it is important to effectively classify multimedia data into semantic concepts across multiple domains. In this dissertation, a correlation-based multimedia semantic concept detection framework is seamlessly integrated with the …