S. Ganesh Kumar

SRM Institute of Science and Technology

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B. Muruganantham

SRM Institute of Science and Technology

A. Murugan

SRM Institute of Science and Technology

B. Sriman

SRM Institute of Science and Technology

M. Mohideen AbdulKader

SRM Institute of Science and Technology

D. Praveena Anjelin

SRM Institute of Science and Technology

P. Shamili

SRM Institute of Science and Technology

Tushar Chechare

SRM Institute of Science and Technology

S Hemanth Kumar

PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

S. V. Manikanthan

Udit Hasija

Bharati Vidyapeeth Deemed University

Cooperative Institutions

SRM Institute of Science and Technology

SRM University

University of Abou Bekr Belkaïd

Bharati Vidyapeeth Deemed University

KPR Institute of Engineering and Technology

Botswana International University of Science and Technology

Guru Gobind Singh Indraprastha University

Periyar Maniammai Institute of Science & Technology

Sathyabama Institute of Science and Technology

PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH

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A time efficient and accurate retrieval of range aggregate queries using fuzzy clustering means (FCM) approach

International Journal of Electrical and Computer Engineering (IJECE) (2019)

A. Murugan D. Gobinath S. Ganesh Kumar B. Muruganantham Sarubala Velusamy

Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Statistical analysis: Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of static variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the static variable might leads to the less efficient partitioning of tables. Findings: This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of static variable. Improvements: This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy.</p>

Centroid

10.11591/ijece.v10i1.pp415-420

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Citations (3)

ODMM - AN ONTOLOGY BASED DEEP MINING METHOD TO CLUSTER THE CONTENT FROM WEB SERVERS

S. Ganesh Kumar K. Vivekanandan

ODMM aims to present a novel ontology-based content-mining approach to cluster research proposals deep web search based on their similarities. The used method is efficient and effective for clustering research proposals with English texts. Text-mining methods have been proposed to solve the problem by automatically classifying text documents. Current search methods for grouping proposals are based on manual matching of similar search discipline keywords. The advantages of this method are that it can extract three types of data records, namely, synonyms data records, hypernymy data records, and hyponyms data records, and also provides options for aligning iterative and disjunctive data items. The proposed ODMM is used together with statistical method and optimization models and consists of reference to the ontology; the new proposals in each discipline are clustered using a self-organized mapping (SOM) algorithm. The SOM algorithm is a typical unsupervised learning neural network model that clusters input data with similarities. Our new techniques used in data extraction from deep webs needs to be improved to achieve the efficiency and finally the result is given like the result comes for user query on multi view point like web links, news contents and the synonym hyponym and hypernym for the input term specified. Key terms : Semantic Web Services, ontology, mapping, ontology search engine

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Scalability Challenges and Solutions in Blockchain Technology

Lecture notes in networks and systems (2022)

K. Harshini Poojaa S. Ganesh Kumar

Electronic cash

Proof-of-work system

10.1007/978-981-16-6723-7_44

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Citations (18)

Blockchain Technology for Data Sharing in Decentralized Storage System

Advances in intelligent systems and computing (2020)

D. Praveena Anjelin S. Ganesh Kumar

10.1007/978-981-15-5566-4_32

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Citations (11)

Blockchain Technology: Consensus Protocol Proof of Work and Proof of Stake

Advances in intelligent systems and computing (2020)

B. Sriman S. Ganesh Kumar P. Shamili

Proof-of-work system

Distributed ledger

Consensus algorithm

10.1007/978-981-15-5566-4_34

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Citations (66)

Personalized web service recommendation through mishmash technique and deep learning model

Multimedia Tools and Applications (2022)

S. Ganesh Kumar S. S. Sridhar Azham Hussain S. V. Manikanthan S. Padmapriya

10.1007/s11042-021-11452-4

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Citations (5)

Quantum cryptography for secured communication networks

International Journal of Electrical and Computer Engineering (IJECE) (2019)

B. Muruganantham P. Shamili S. Ganesh Kumar A. Murugan

Quantum cryptography is a method for accessing data with the cryptosystem more efficiently. The network security and the cryptography are the two major properties in securing the data in the communication network. The quantum cryptography uses the single photon passing through the polarization of a photon. In Quantum Cryptography, it's impossible for the eavesdropper to copy or modify the encrypted messages in the quantum states in which we are sending through the optical fiber channels. Cryptography performed by using the protocols BB84 and B92 protocols. The two basic algorithms of quantum cryptography are Shor’s algorithm and the Grover’s’s algorithm. For finding the number of integer factorization of each photon, Shor’s algorithm is used. Grover’s’s algorithm used for searching the unsorted data. Shor’s algorithm overcomes RSA algorithm by high security. By the implementation of quantum cryptography, we are securing the information from the eavesdropper and thereby preventing data in the communication channel.

Neural cryptography

BB84

Lattice-based Cryptography

Financial cryptography

PKCS #1

Quantum key distribution

10.11591/ijece.v10i1.pp407-414

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Citations (20)

SBM - A SEMANTIC BASED MINING METHOD TO CLUSTER THE CONTENT OF THE WEB

Navin M. S S. Ganesh Kumar K. Vivekanadan

SBM aims to present a novel ontology-based content-mining approach to cluster research proposals deep web search based on their similarities. The used method is efficient and effective for clustering research proposals with English texts. Text-mining methods have been proposed to solve the problem by automatically classifying text documents. Current search methods for grouping proposals are based on manual matching of similar search discipline keywords. The advantages of this method are that it can extract three types of data records, namely, synonyms data records, hypernymy data records, and hyponyms data records, and also provides options for aligning iterative and disjunctive data items. The proposed SBM is used together with statistical method and optimization models and consists of reference to the ontology; the new proposals in each discipline are clustered using a self-organized mapping (SOM) algorithm. The SOM algorithm is a typical unsupervised learning neural network model that clusters input data with similarities. Our new techniques used in data extraction from deep webs needs to be improved to achieve the efficiency and finally the result is given like the result comes for user query on multi view point like web links, news contents and the synonym hyponym and hypernym for the input term specified.

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A novel ALM based security framework for block chain in healthcare platform

International Journal of Systems Assurance Engineering and Management (2024)

D. Praveena Anjelin S. Ganesh Kumar

10.1007/s13198-024-02443-3

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Citations (0)

Privacy Challenges and Enhanced Protection in Blockchain Using Erasable Ledger Mechanism

Lecture notes in networks and systems (2021)