A5061204834- COVID-19 epidemiological studies
- Advanced Text Analysis Techniques
- Anomaly Detection Techniques and Applications
- Handwritten Text Recognition Techniques
- Stock Market Forecasting Methods
- Time Series Analysis and Forecasting
- Natural Language Processing Techniques
- COVID-19 diagnosis using AI
- Mental Health Research Topics
- Advanced Image and Video Retrieval Techniques
- Topic Modeling
- Image Retrieval and Classification Techniques
- Forecasting Techniques and Applications
- Data-Driven Disease Surveillance
University of Georgia
2023
University of Dhaka
2018-2022
Deep Learning has been successfully applied to many application domains, yet its advantages have slow emerge for time series forecasting. For example, in the well-known Makridakis (M) Competitions, hybrids of traditional statistical or machine learning techniques only recently become top performers. With recent architectural advances deep being forecasting (e.g., encoder-decoders with attention, transformers, and graph neural networks), begun show significant advantages. Still, area pandemic...
Abstractive text summarization is one of the most interesting problems in research field Natural Language Processing. Recent advances sequence to model have made it possible apply new approaches for abstractive and perform significantly. But existing systems suffer from some drawbacks like word repetition, producing inaccurate or irrelevant information etc. In this work we propose a novel architecture incorporating advanced embedding layer topical feature with pointer generator network...
The research on document layout analysis has been widespread over a large arena recently and is craving for more efficiency day by day. Document segmentation an important preprocessing step before analyzing the layouts. This paper presents language-independent system that segments heterogeneous printed into homogeneous components like halftones graphics, texts tables including its individual cells. From input page are segmented in three steps with separate modules, which are- extraction of...
Deep Learning has been successfully applied to many problem domains, yet its advantages have slow emerge for time series forecasting. For example, in the well-known M Competitions, until recently, hybrids of traditional statistical or machine learning (e.g., gradient boosting) techniques were top performers. With recent architectural advances deep being forecasting, such as encoder-decoders with attention, transformers, representation learning, and graph neural networks, begun show...