Sustainable Computing Based Deep Learning Framework for Writing Research Manuscripts

Writing research manuscripts is always a tough task at the eleventh hour. Often researchers do not find time to rewrite the manuscript to satisfaction, which is not quantifiable though. This paper proposes a sustainable computing based deep learning framework for iterated accumulation of ideas while writing research manuscripts. The framework suggests Deep Author Topic Models (DATM) where every author of the manuscript is modeled. For this, we have assumed time based sustainable computing as a measure of evaluation for research manuscript effectiveness. Using respective DATM, the region contributed by every author in the manuscriptis analyzed and fine-tuned semantically such that the manuscript is made to perfection in least time.