Analysing Computational Complexity For Prediction Function In Health Record Dataset

Sahunthala, S.; Geetha, Angelina; Parthiban, Latha

doi:10.1109/iceca49313.2020.9297598

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The accuracy and training time is evaluated and compared with the existing technique Extreme Learning Machine (ELM). The various classification technique is introduced [21]- [26] in XML data. The contribution of this paper is given as shown in: − To provide easy access to different XML datasets (social media, trade center, and surveillance data) to extract silent features points using vector space model and effectively classify XML datasets into other classes.…”

Section: Introductionmentioning

confidence: 99%

Study and innovation of effective classification of XML documents using an advanced deep learning approach

Sanmugam

Geetha²,

Parthiban³

2023

IJEECS

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<span lang="EN-US">In the digital world, classifying real sensed data in huge volumes derived from numerical problems is a challenging task due to the computational complexity of the metaheuristic searching process. The deep learning approach includes convolutional neural network (CNN), long short-term memory (LSTM), and Bidirectional (BI)-LSTM, suitable for an optimistic processing time of analyzing XML datasets (i.e., social media, trade center, and surveillance data exchanged in the internet world). However, it faces process deviation when datasets extend their range beyond the expected volume. This paper proposes a novel deep learning formwork referred to as archimed improved numerical optimization deep learning (AINODL) to improve the classification of XML datasets. The proposed AINODL framework first extracts feature from XML documents using the vector space model. Secondly, it classifies the XML data using the inbuilt function of the AINODL framework. The experiments demonstrate that the performance parameters accuracy (90%), sensitivity (93%), and specificity (94%) of the proposed AINODL framework are significantly enhanced compared with the existing approaches CNN, LSTM, and BI-LSTM.</span>

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Section: Introductionmentioning

confidence: 99%

Study and innovation of effective classification of XML documents using an advanced deep learning approach

Sanmugam

Geetha²,

Parthiban³

2023

IJEECS

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An Improved Fuzzy Clustering Method for Analyzing Clustering Quality in XML Web Data

Sahunthala

Geetha

Parthiban

2022

2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)

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Global Attractivity, Asymptotic Stability and Blow-Up Points for Nonlinear Functional-Integral Equations’ Solutions and Applications in Banach Space Bc(r+) With Computational Complexity

Karaca

2022

Fractals

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Nonlinear functional-integral equations contain the unknown function nonlinearly, occurring extensively in theory developed to a certain extent toward different applied problems and solutions thereof. Nonlinear science serves to reveal the nonlinear descriptions of widely different systems, has had a fundamental impact on complex dynamics. Accordingly, this study provides the basic definitions and results regarding Banach spaces with the proof as well as applications. Besides, proving the existence and uniqueness of the solutions by the Banach theorem is carried out. The existence results of the equations are generally obtained based on fundamental methods by which the fixed-point theorems are frequently applied. Subsequently, the definition of the measure pertaining to noncompactness on Banach space along with properties is presented. Hence, the aim is to study nonlinear functional-integral equations in the Banach space [Formula: see text] using the measure of noncompactness. In addition, the proof of the existence of global attractors and asymptotic stabilities, having at least one solution which pertains to [Formula: see text] is looked into. As the last stage, the definitions of global attractivity and asymptotical stability for equations on Banach space [Formula: see text] are given while proving the existence of solutions and global attractivity. Besides these, the existence and asymptotical stability are proven. Furthermore, the proof of the existence of global attractors and asymptotic stabilities, having at least one solution which pertains to [Formula: see text] for an example nonlinear integral equation has been demonstrated by one example premediated and the existence of blow-up point has been examined in one and more than one points regarding the nonlinear integral equation. The example nonlinear integral equation as addressed in this study has been examined in terms of its blow-up point and asymptotic stability with computational complexity for the first time. By doing so, it has been demonstrated that the existence of blow-up point has been the case in one and more than one points and besides this, the examination of its asymptotic stability has shown that it is asymptotically stable at least in one point, yet has no blow-up point pertaining to [Formula: see text]. This has enabled thorough analysis of computational complexity of the related nonlinear functional-integral equations within the example both addressing dimension of input in Banach space and that of dataset with [Formula: see text] dimension. This applicable approach assures the accurate transformation of the complex problems towards optimized solutions through the generation of advanced mathematical models in nonlinear dynamic settings characterized by complexity.

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Analysing Computational Complexity For Prediction Function In Health Record Dataset

Cited by 3 publications

References 7 publications

Study and innovation of effective classification of XML documents using an advanced deep learning approach

Study and innovation of effective classification of XML documents using an advanced deep learning approach

An Improved Fuzzy Clustering Method for Analyzing Clustering Quality in XML Web Data

Global Attractivity, Asymptotic Stability and Blow-Up Points for Nonlinear Functional-Integral Equations’ Solutions and Applications in Banach Space Bc(r+) With Computational Complexity

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