Predicting Energy Demands Constructed on Ensemble of Classifiers

Daniel, A. K.; Kannan, Balasubramanian; Yuvaraj, N.; Kousik, N.V.

doi:10.1007/978-981-15-5566-4_52

Cited by 6 publications

(2 citation statements)

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“…The result shows that the prediction accuracy of BEE is better than the previous CapsNet Models (Table 3). 3009 (3,4,5) and 3009 (3,4,5,6) at times provides higher rate of validation accuracy, where it is seen that with increasing channel size in CL, the accuracy increases. In other words, higher the validation accuracy, lower is the computational cost using the proposed method.…”

Section: Error Inspectionmentioning

confidence: 94%

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

Kumar

Srihari²,

Chakrapani

et al. 2022

Sādhanā

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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 sub-tasks, 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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Section: Error Inspectionmentioning

confidence: 94%

“…Based on the concept of Biomedical event [4] which consists of an event-type trigger term and multiple arguments according to BioNLP. In this, an argument has a relationship between an event trigger and another event.…”

Section: Introductionmentioning

confidence: 99%