Improved Density-Based Learning to Cluster for User Web Log in Data Mining

Kousik, N.V.; Sivaram, M.; Yuvaraj, N.; Mahaveerakannan, R.

doi:10.1007/978-981-33-4305-4_59

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…This data may be used by lenders to evaluate and approve loan applications. It's worth noting that, although the CIBIL Score is significant in the loan application process, it doesn't always provide all of the information needed [37]- [45]. When it comes to financing, a lender's first consideration is your CIBIL Score; the higher your score, the greater your chances of getting your loan investigated and approved.…”

Section: Existing Workmentioning

confidence: 99%

Loan Eligibility Prediction using Data Science Algorithms A Comparative Analysis

Ramkumar,

Johny,

Darak

et al. 2022

JEMM

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Loan is a amount that is provided to someone else in exchange for repayment of the loan principle amount plus interest. The different variety of loans is Personal loan, Home loan, Education loan, etc. Here, Decision Tree and Naïve Bayes algorithms are used to predict the loan eligibility of a particular individual. Data mining techniques are used to extract data and predict the loan eligibility. By providing loan, the banks help in the overall flow of the economy of the country. The borrower can avail the loan whenever required and the bank can get back the principle amount with interest based on some pre-defined percentages.

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Section: Existing Workmentioning

confidence: 99%

Loan Eligibility Prediction using Data Science Algorithms A Comparative Analysis

Ramkumar,

Johny,

Darak

et al. 2022

JEMM

View full text Add to dashboard Cite

show abstract

“…Apart from that sometimes companies fail to maintain privacy in some data that contains personal information about the preferences of the customers. On the other hand, data visualisation is a crucial process in data mining as it is a prime interaction that highlights the output in an effective and respectable manner to the purchasers [14]. The data extracted must pass in a remarkable way by keeping a balance with the plan.…”

Section: Issues To Implement Data Mining To Analysis Financesmentioning

confidence: 99%

Data Mining Techniques and Modelling for Financial Analysis

Lumacad¹,

MP²

2022

2583-195X

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Data mining methods are used to identify the needs of the customers and this also helps the companies to stay competitive in the global market. Effective data mining process allows the firms to stay in the running in the competitive world by looking into the strength of the company. Data mining process has the capability of analysing financial stability and that in turn helps the companies implicate suitable policy and strategy to achieve competitive advantages. There are several types of data mining process that helps the companies to analysis their needs and allows the companies to evaluate finances. Different the types have their own approaches and advantages and helps companies to assess their finances to make concrete decision. The study has been carried out with the help of suitable methods to secure the success of the study. Qualitative methods, inductive approach and cross-sectional research design has been utilised to accomplish the study. From the assessment it is evident that despite of the advantages there are some issues that has been faced by the companies is mainly due to the large size of the data and lack of expertise of the higher authority.

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“…The following changes are contemplated across the network to attain such an objective: The critical details are updated with an ECC (Elliptic Curve Cryptography) algorithm with XNOR capabilities during the job planning process. This study applies a principle of insecurity to select additional communication with hostile nodes in the game model [6].…”

Section: Introductionmentioning

confidence: 99%

Implementation of ID-based Audit Protocols to Enhance Security and Productivity

Hariharan¹,

Komarasamy²,

Raja³

2023

Computer Systems Science and Engineering

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Cloud storage has gained increasing popularity, as it helps cloud users arbitrarily store and access the related outsourced data. Numerous public audit buildings have been presented to ensure data transparency. However, modern developments have mostly been constructed on the public key infrastructure. To achieve data integrity, the auditor must first authenticate the legality of the public key certificate, which adds to an immense workload for the auditor, in order to ensure that data integrity is accomplished. The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server. One of the main problems for individuals, though, is how to detect data integrity on a term where people have a backup of local files. Meanwhile, a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved. In this work, a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols. The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity. The idea has already been proved safe; its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.

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Improved Density-Based Learning to Cluster for User Web Log in Data Mining

Cited by 13 publications

References 20 publications

Loan Eligibility Prediction using Data Science Algorithms A Comparative Analysis

Loan Eligibility Prediction using Data Science Algorithms A Comparative Analysis

Data Mining Techniques and Modelling for Financial Analysis

Implementation of ID-based Audit Protocols to Enhance Security and Productivity

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