An Optimistic Approach for Clustering Multi-version XML Documents Using Compressed Delta

Sonawane, Vijay R.; Rao, D. Srinivasa

doi:10.11591/ijece.v5i6.pp1472-1479

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…To routinely identify brain tumors, introduce a hybrid replica PSO-LLRBFNN Algorithm that achieves an accuracy of 98 % for the ADNI dataset. (11,12) A method for encephalon tumor segmentation was suggested based on a hybrid type of approach using FCM, and they reached 90 % noise level precision. (13) When compared to first-order statistical characteristics, (14) it was found that using a wavelet transform and SVM's technique resulted in better prognoses and enhanced clinical factors such as tumor volume, and tumor stage.…”

Section: Related Workmentioning

confidence: 99%

A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

U¹,

Sonawane²,

Gandhewar³

2022

Salud, Ciencia y Tecnología

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A brain tumor develops when abnormal cells in brain tissue multiply uncontrollably. For radiologists, finding and categorizing tumors manually has become a demanding and time-consuming task. When radiologists or other clinical professionals need to extract an infected tumor area from an MR picture, they have to go through a lengthy and laborious process. To improve performance and simplify the segmentation process, we investigate the FCM-predicted picture segmentation techniques in this study. In addition, classifiers for automating the detection and reclassification of encephalon tumors receive input consisting of critical information obtained from each segmented tissue. We have assessed, verified, and demonstrated the experimental efficacy of the proposed method. The purpose of this research was to develop a novel MFO (Moth-Flame Optimization) based LLRBFNN model for the automatic detection and classification of benign and malignant brain tumors. In order to alleviate the burden of manually detecting encephalon cancers from MR images, the suggested LLRBFNN model parameters are improved via MFO training. The Modified FCM method removes outlying nodes from the LLRBFNN model, and the MFO algorithm keeps the current of node centres in the aforementioned model. The proposed MFO-LLRBFNN model was evaluated alongside the Decision Tree and the Random Forest. To prove the reliability of this model, an MFO-based LLWNN (Local Linear Wavelet Neural Network) model for autonomously detecting brain cancers was presented. We extracted features from MR images using the MFCM (modified fuzzy C-Means) segmentation algorithm and the GLCM (Gray Level Co-occurrence Matrix) technique.

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Section: Related Workmentioning

confidence: 99%

A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

U¹,

Sonawane²,

Gandhewar³

2022

Salud, Ciencia y Tecnología

Self Cite

View full text Add to dashboard Cite

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“…From available literature study it is observed that no existing technique performs time efficiently to obtain most recent cluster compositions of Multiversion XML documents using the concept of compressed delta (C∆) [7]. XML version detection and management has tremendous application.…”

Section: Literature Surveymentioning

confidence: 99%

Managing Multiversion Xml Documents with Compressed Delta

Sonawane*,

Rao

2020

IJITEE

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Today web contains huge amount of information. Important part is to obtain business value from existing information. XML is useful to accomplish features of business functions. Hence to attain those promises it is important to retain those XML documents effectively. Content and structure of Dynamic XML documents changes frequently based on user behavior and produces multiple versions of it. Multiversion XML documents are having huge applicability which demands for their effective organization. Clustering is better solution to retain these documents. But due to dynamic nature of XML documents initially obtained cluster compositions becomes invalid in later stage and traditional methods are not effective to obtain most recent clustering solutions. In this paper we are proposing time efficient technique called Compressed Delta (C∆) in response to obtain most recent clusters of multiversion XML documents. C∆ contains sufficient information to get essential document version with minimum operational cost for future references. Proposed C∆ is comprehensively assessed on several real-life datasets exhibiting extreme characteristics. Experimental results shows that proposed (C∆) to obtain required XML document version outperform the related state-of-the-art approaches.

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“…Of these, MA is primarily engaged in dealing with each and every word (individual words) of the given text document. Predominantly, it comprises tokenization, stop word elimination and stemming [2]. In tokenization process, the text document is often considered as word strings which are word sequences and divides them by eliminating punctuations [3].…”

Section: Taxonomy 21 Sub Section 1 Feature Extractionmentioning

confidence: 99%

Feature selection, optimization and clustering strategies of text documents

Nikhath

Subrahmanyam

2019

IJECE

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Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.

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An Optimistic Approach for Clustering Multi-version XML Documents Using Compressed Delta

Cited by 3 publications

References 33 publications

A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

Managing Multiversion Xml Documents with Compressed Delta

Feature selection, optimization and clustering strategies of text documents

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