• Devendra Kumar

      Articles written in Sadhana

    • Heat transfer to MHD oscillatory dusty fluid flow in a channel filled with a porous medium

      Om Prakash O D Makinde Devendra Kumar Y K Dwivedi

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      In this paper, we examine the combined effects of thermal radiation, buoyancy force and magnetic field on oscillatory flow of a conducting optically thin dusty fluid through a vertical channel filled with a saturated porous medium. The governing partial differential equations are obtained and solved analytically by variable separable method. Numerical results depicting the effects of various embedded parameters like radiation number, Hartmann number and Grashof number on dusty fluid velocity profiles, temperature profiles, Nusselt number and skin friction coefficient are presented graphically and discussed qualitatively.

    • Biomedical event extraction on input text corpora using combination technique based capsule network


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      Biomedical Event Extraction (BEE) is a demanding and prominent technology that attracts the researchers and scientists in the field of natural language processing (NLP). The conventional method relies mostly on external NLP packages and manual designed features, where the features engineering is complex and large. In addition, the conventional methods on BEE uses a pipeline process that splits a task into many subtasks, however, the relationship between these sub-tasks is not defined. In this paper, such limitations are avoided using the combination technique that relies on Capsule Network (CapsNet) to perform a task. The CapsNet is used for the extraction of feature representation from the input corpora and then the combination technique reconstructs the events from RNN output. This method extracts the tasks from a BEE over several annotated corpora that extract the events from the molecular level in case of multi-level events. The proposed model is compared with state-of-the-art models over various text corpora datasets. The results show an improved rate of accuracy of CapsNet classification over cancer biomedical events than the existing methods.

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