Big data reliability: A critical review

Sharma, Shalini; Kumar, Naresh; Kaswan, Kuldeep Singh

doi:10.3233/jifs-202503

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Kaswan et al [11] suggested a student success prediction method used the WEKA Naive Bayes method for building prediction models. Six properties have been tested: income households, university, ethnicity, gender, home town and CGPA.…”

Section: Literature Surveymentioning

confidence: 99%

Student Performance Prediction Using Case Based Reasoning Knowledge Base System (CBR-KBS) Based Data Mining

Dixit¹,

Nagar²,

Dixit³

2022

IJIET

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Higher education management problems in delivering 100% of graduates who can satisfy business demands. In industry it is often difficult for qualified graduates to identify the appropriate means to evaluate problem - solving abilities as well as shortcomings in the evaluation of problem solving skills. This is partially due to the lack of an adequate methodology. The purpose of this paper is to provide the appropriate CBR-KBS model for predicting and evaluating the characteristics of the student's dataset so as to comply with the parameters of selection required by the university industry. Machine learning algorithms have been used in these study areas under supervision, uncompleted and uncontrolled; K-Nearest neighbor, Naïve Bayes, Decision Tree, Neural Network, Logistic Regression and Vector Support Machines. The proposed model would allow university management to make easier, more professional, experienced and industry-specific plans for the manufacturing of graduates and graduates who passed the type I and II examinations held by the employment opportunities.

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Section: Literature Surveymentioning

confidence: 99%

Student Performance Prediction Using Case Based Reasoning Knowledge Base System (CBR-KBS) Based Data Mining

Dixit¹,

Nagar²,

Dixit³

2022

IJIET

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“…Data integration is a little more involved, as it entails making the data useful and common throughout the process. Usually, data ingestion, cleansing, and standardization integrate the data into the process (Sharma, Kumar and Kaswan, 2021;. Data integration has five parameters:…”

Section: Parameters Identification Of Data Integrationmentioning

confidence: 99%

A Framework for Integrating Green Highway Performance Data with the Carbon Footprint Calculator in Malaysia

Yousif

Zakaria

2022

CEB

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Highway concessionaires in Malaysia spend considerable assets in collecting, analysing and controlling different forms of data during highway projects’ life cycle. Considering this massive investment, nowadays the data use and information system reliability is becoming the key concern in terms of delivering value to consumers as opposed to the amount generated. The data generated is extensive and heterogeneous. This paper provides a new paradigm for integrating green highway performance data with carbon footprint data for green highway assessment. This framework is used to improve the active use of data in the generation of information and to assist comprehensively in decision-making at all levels of management. The network approach used in this study is the main component for interlinking data with knowledge and decisions, identifying the parameters of data integration for green highway assessment, determining the criteria data for integration, and assessing the overall performance of data usage. Real-time green highway data scenarios are used to demonstrate the applicability of this framework. A new monitoring performance measure called the MyGHI-Dashboard is proposed to control green highway assessment processes and evaluate the level of data usage, which will serve as a green highway performance scorecard. Through data-driven insights, this new paradigm can be used as a benchmarking model by highway authorities to make efficient and accurate decisions. This study is expected to become the central reference to mitigate the challenge associated with the use of highway performance data in real-time for Malaysian highway development.

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