Articles written in Sadhana
Volume 40 Issue 4 June 2015 pp 1273-1282 Mechanical Sciences
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
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.
Volume 47 All articles Published: 28 September 2022 Article ID 0198
Biomedical event extraction on input text corpora using combination technique based capsule network
R N DEVENDRA KUMAR K SRIHARI C ARVIND WATTANA VIRIYASITAVAT
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.
Volume 48, 2023
Continuous Article Publishing mode
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