scholarly journals Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modelling and Analysis of Big Data

Technometrics ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 280-280
Author(s):  
S. Ejaz Ahmed
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Predictive Modelling ◽  
Data Mining Techniques ◽  
Learning Data

Big Data Models and the Public Sector

Web Services ◽  
2019 ◽  
pp. 105-126
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

Big Data Models and the Public Sector

2016 ◽  
pp. 245-266
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

scholarly journals SIAPKAH PARA DOSEN AKUNTANSI DI PERGURUAN TINGGI VOKASI DALAM MENYAMBUT DATANGNYA REVOLUSI INDUSTRI 4.0 ?

2020 ◽  
Vol 18 (3) ◽  
pp. 465
Author(s):  
Diana Rino Putri ◽  
Nurafni Eltivia ◽  
Ari Kamayanti ◽  
Jaswadi Jaswadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Distributed Processing ◽  
Current Level ◽  
Future Prospects ◽  
Vocational College ◽  
True Meaning ◽  
Learning Data

In developing countries such as Indonesia, a large number of academics are unfamiliar with the true meaning of terms such as Big Data, Exabyte, Petabyte, Brontobyte, Artificial Intelligence, Machine Learning, Data Mining, Data Warehousing, Distributed Processing, Grid Computing and Cloud Computing. In this paper, we report the results of a survey carried out to ascertain the current level of awareness regarding Big Data among academics in Vocational College. Respondents to a questionnaire formulated for this purpose. Results of the survey seem to indicate that there is a need for multi-faceted efforts aimed at creating awareness regarding Big Data, the related technologies, challenges and future prospects.

Machine Learning, Data Mining, and Big Data Analytics for 5G-Enabled IoT

2021 ◽  
pp. 351-375
Author(s):  
Puneet Kumar Aggarwal ◽  
Parita Jain ◽  
Jaya Mehta ◽  
Riya Garg ◽  
Kshirja Makar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Data

Big Data Analyses in the Community of Inquiry and Educational Research Spheres

2018 ◽  
pp. 34-52
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Educational Research ◽  
Learning Analytics ◽  
Community Of Inquiry ◽  
Educational Data Mining ◽  
Data Analyses ◽  
Data Volume ◽  
Learning Data

Chapter 3 builds on the previous chapters and provides a summary of big data-style research within the Community of Inquiry scholarly literature, as well as examples from educational research broadly. This chapter also connects to the broader topics of machine learning, data analytics, learning analytics, and educational data mining. Constructs from the Community of Inquiry are integrated into this synthesis and overview. Unfortunately, only a fraction of the studies in educational research broadly today exhibit the tell-tale signs of big data: data volume and variety, new environments or instrumented sources of larger data, often with emerging tools and platforms critical to the analysis of the resulting datasets. A list of additional readings is provided.

Machine learning, data mining and Big Data frameworks for network monitoring and troubleshooting

2016 ◽  
Vol 107 ◽  
pp. 1-4 ◽  
Author(s):  
Alessandro D’Alconzo ◽  
Pere Barlet-Ros ◽  
Kensuke Fukuda ◽  
David Choffnes
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Network Monitoring ◽  
Learning Data

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

scholarly journals Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

2020 ◽  
Vol 9 (5) ◽  
pp. 288-297
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
High Performance ◽  
Predictive Modelling ◽  
Medical Application ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Screening Process ◽  
Huge Data ◽  
System Data

Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

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