Improving the Training Pattern in Back-Propagation Neural Networks Using Holt-Winters’ Seasonal Method and Gradient Boosting Model

Sangeetha, S. Brilly; Blessing, N. R. Wilfred; Yuvaraj, N.; Sneha, J. Adeline

doi:10.1007/978-981-15-3357-0_13

Cited by 11 publications

(6 citation statements)

References 12 publications

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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%

“…Since extraction of biomedical events is a standard activity, where different methods are suggested by the researchers to support the BEE. In most of the previous works, three styles are possible and are followed which include rule-based methods [5,6], where it contains the standard models of low-level or deep learning.…”

Section: Introductionmentioning

confidence: 99%

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

Kumar

Srihari²,

Chakrapani

et al. 2022

Sādhanā

View full text Add to dashboard Cite

show abstract

“…Complex planning and decision-making, where definite driving performance have remained a barrier, has led to the integration of machine learning and deep learning modules. Using deep learning models [19][20][21][22][23][24][25] has been shown to be successful in various object tracking applications. However, this comes with the requirement for significant supervised training, which will increase the complexity of the tracking system [26][27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Vijayakumar¹,

Shanthini²,

Karthik³

et al. 2023

Computer Systems Science and Engineering

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The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The study develops a direct realization method for an autonomous vehicle driving. The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving. The behavioral model includes recognition, planning and prediction modules that regulates the input trajectory processing of input video datasets. A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight. This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error (RMSE) to estimate its efficacy. Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning (E-CNN-RL) offers increased accuracy of 95.45%, reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks (CNN) and Ensemble Stacked CNN.

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“…This awareness is crucial because these criteria may face implementation challenges across a supply chain (SC) due to inadequate communication and coordination among constituent organization. The adoption of these measures during crises becomes an absolute necessity (Ahamed and Karthikeyan, 2020;Brilly-Sangeetha et al, 2020;Liu et al, 2020). Key supplier selection and segmentation factors, like backup providers and risk reduction, mainly apply after catastrophic events.…”

Section: Introductionmentioning

confidence: 99%

Supply Chain Management of Drug Products in Blockchain Using Reinforcement Learning

Alla,

Thangarasu

2023

IJTech

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Supply chain management (SCM) is a complex system that consists of two parts: a management system for the medication supply chain and a recommendation system. The first part of the system is the management system powered by reinforcement learning. The reinforcement Learning model is trained to recommend the most suitable medications for a diverse set of customers. The model will be fed customer ratings and reviews via the proposed framework client application, where it will learn over time to make the most informed drug recommendations possible. The proposed system will provide patients with recommendations for successful drug therapies.

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Improving the Training Pattern in Back-Propagation Neural Networks Using Holt-Winters’ Seasonal Method and Gradient Boosting Model

Cited by 11 publications

References 12 publications

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

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

Route Planning for Autonomous Transmission of Large Sport Utility Vehicle

Supply Chain Management of Drug Products in Blockchain Using Reinforcement Learning

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