Application of Big Data and Machine Learning

Sharma, Neha; Gautam, Sunil Kumar; Henry, Azriel; Kumar, Abhimanyu

doi:10.1002/9781119654834.ch12

Cited by 3 publications

(6 citation statements)

References 62 publications

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“…10,11 The characteristic of BD found in the literature is numerous versus, namely variety, velocity, veracity, value, and others, as tabulated in Table 1. 5,[13][14][15][16] In the context of safety science, safety BD is the data collected from the target production system for safety investigation. 13 Safetyrelated data (SRD) could be the most valuable asset for safety decision-making in a more scientific and practical approach, 17 and it is a crucial feature for safety intelligence.…”

Section: Big Data Analyticsmentioning

confidence: 99%

“…16 This has become a key mechanism in analyzing and predicting by turning raw data into structured information to extract knowledge and valuable information. 16 General descriptive statistical analysis is commonly used in workplace accidents analysis, and machine learning predicts future events. 40 Machine learning is a form of applied statistics that can detect the pattern in data, classify and predict future data or make another decision under uncertainty.…”

Section: Machine Learningmentioning

confidence: 99%

“…ML is a study using past data to train a machine and build logic using algorithms 16 . This has become a key mechanism in analyzing and predicting by turning raw data into structured information to extract knowledge and valuable information 16 . General descriptive statistical analysis is commonly used in workplace accidents analysis, and machine learning predicts future events 40 .…”

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

“…In general, BD generates data from multiple sources and is a plentiful and constantly enriching resource 10,11 . The characteristic of BD found in the literature is numerous versus, namely variety, velocity, veracity, value, and others, as tabulated in Table 1 5,13‐16 …”

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

“…The most recent attention to the risk and reliability assessment is the application of Machine Learning (ML), in which computers are using an algorithm to learn and make a decision from the observed data 39 . ML is a study using past data to train a machine and build logic using algorithms 16 . This has become a key mechanism in analyzing and predicting by turning raw data into structured information to extract knowledge and valuable information 16 .…”

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

See 4 more Smart Citations

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Chong

Razak

Biak

et al. 2022

Process Safety Progress

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The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision‐making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision‐making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries.

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Section: Big Data Analyticsmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

Section: Introduction To Industry Revolution 40 (Ir 40)mentioning

confidence: 99%

See 3 more Smart Citations

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Chong

Razak

Biak

et al. 2022

Process Safety Progress

View full text Add to dashboard Cite

show abstract

Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma

Jiang

Teng

et al. 2022

Eur J Nucl Med Mol Imaging

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Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models

2023

AJCIS

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Travel insurance is a crucial component for any traveller as it offers protection against financial losses resulting from unforeseen events during a trip, such as trip cancellations, medical emergencies, lost luggage, and related issues. This study aims to investigate the potential of machine learning (ME) techniques for predicting the probability of travel insurance claims. In order to tackle the issue of managing extensive and intricate datasets, advanced statistical techniques were employed, including keyword extraction, feature extraction, and Chi-squared tests. Our evaluation of four popular ML models, namely balanced random forest (BRF), support vector machines (SVM), logistic regression (LR), and balanced bagging (BB), highlight that the BRF model outperforms the other models in predicting travel insurance claims. Our study emphasises the advantages of utilising machine learning algorithms in processing large datasets, producing predictions on future insurance claims, and adapting to changing circumstances, thus serving as a valuable tool for practitioners in the travel insurance industry.

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Application of Big Data and Machine Learning

Cited by 3 publications

References 62 publications

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma

Exploring the Potential of Machine Learning Techniques for Predicting Travel Insurance Claims: A Comparative Analysis of Four Models

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