An abundance of biomedical data is generated in the form clinical notes, reports, and research articles available online. This holds valuable information that requires extraction, retrieval, transformation into actionable knowledge. However, this has various access challenges due to need for precise machine-interpretable semantic metadata required by search engines. Despite engines' efforts int...

The goal of this work is to recognize words, phrases, and sentences being spoken by a talking face without given the audio. Current deep learning approaches for lip reading focus on exploring appearance optical flow information videos. However, these methods do not fully exploit characteristics motion. In addition flow, mouth contour deformation usually conveys significant that complementary ot...

Twitter is a fast communication channel for gathering and spreading breaking news, it generates large volume of tweets most events. Automatically creating summary an event necessary important. In this study, we explored two extractive approaches summarizing events on Twitter. The first one exploits the semantic types related terms, ranks based score computed from these terms. second utilizes gr...

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since vehicles need to navigate in dynamically changing environments, they are expected make accurate predictions regardless where and what driving circumstances encountered. Several methodologies have been proposed solve prediction problems under different situations. These ...

The field of visual question answering (VQA) has seen a growing trend integrating external knowledge sources to improve performance. However, owing the potential incompleteness and inherent mismatch between different forms data, current knowledge-based (KBVQA) techniques are still confronted with challenge effectively utilizing multiple heterogeneous data. To address this issue, novel approach ...