Researches and Projects


this is  an image. Improving summary coverage + Graphical summary In this work, we propose a biomedical text summarization method aimed at improving the coverage property of summary, also producing graphical summaries. This method utilizes machine learning and data mining methods.
this is  an image. Quantifying the informativeness for biomedical text summarization: an itemset mining method In this work, we proposed a summarization method that utilizes domain knowledge and an itemset mining approach to generate a conceptual model from a text document. The informativeness of sentences is quantified according to the extent that each sentence covers the main subtopics of text.
this is  an image. A Bayesian Approach to Biomedical Text Summarization In this work, we proposed a biomedical text summarization method based on the naïve Bayes classifier. We introduced and evaluated different feature selection approaches to identify the most important concepts of the input document and select the most informative content according to the distribution of these concepts. We showed that with the use of an appropriate feature selection approach,...
this is  an image. A centralized reinforcement learning method for multi-agent job scheduling in Grid In this work, we proposed a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources.
this is  an image. Performance evaluation of SQL and MongoDB databases for big e-commerce data It has been claimed that NoSQL databases outperform their SQL counterparts. The aim of this research was to investigate the claim by evaluating the document-oriented MongoDB database with SQL in terms of the performance of common aggregated and non-aggregate queries.