Research on video anomaly detection has mainly been based data. However, many real-world cases involve users who can conceive potential normal and abnormal situations within the domain. This domain knowledge be conveniently expressed as text descriptions, such “walking” or “people fighting”, which easily obtained, customized for specific applications, applied to unseen videos not included in tr...

Availability of large data storage systems has resulted in digitization information. Question and answering communities like Quora stack overflow take advantage such to provide information users. However, as the amount stored gets larger, it becomes difficult keep track existing information, especially duplication. This work presents a similarity detection technique that can be used identify le...

Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time effort. Evaluating selecting the most sentences articles is always challenging. This study aims to develop a dual-mode text summarization model achieve enhanced coverage information. The research also includes checking fitment of appr...

This paper presents a deep architecture for learning a similarity metric on variablelength character sequences. The model combines a stack of character-level bidirectional LSTM’s with a Siamese architecture. It learns to project variablelength strings into a fixed-dimensional embedding space by using only information about the similarity between pairs of strings. This model is applied to the ta...