scholarly journals A Theoretical Evaluation of Mellitus Diabetes using Data Mining and Machine Learning

2021 ◽  
pp. 612-620
Author(s):  
Shivani Patel ◽  
Sanjay Chaudhary ◽  
Prakashsingh Tanwar
Keyword(s):  
Data Mining ◽  
Extended Period ◽  
Medical Diagnostics ◽  
Future Research ◽  
Theoretical Evaluation ◽  
Research Directions ◽  
Future Research Directions ◽  
Using Data ◽  
Sugar Levels ◽  
Gathering Data

Pattern identification, processing, and treatment are all common uses of data mining techniques in medical diagnostics. Diabetes is a metabolic illness in which elevated blood sugar levels persist for an extended period of time. Diabetes mellitus (DM) is a collection of metabolic illnesses that puts a lot of pressure on people all over the world. According to these studies, India accounts for 19% of the world's residents. Category 1 and Category 2 diabetes are covered in this overview. Theoretical basis is used to compare previous researcher methodologies and processes. To process datasets, the Weka open-source tool is employed. In the first half, we'll talk about gathering data from various medical departments; in the second part, we'll talk about data cleaning and then algorithms for removing noisy data. Also, several Algorithms were used to determine the best characteristic. Finally, we'll look at alternative machine learners for diabetes data classification and discuss future research directions.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

Unit of Analysis in Digitally-Enabled Electronic Procurement Research

2020 ◽  
pp. 83-103
Author(s):  
Md Mahbubur Rahim ◽  
Maryam Jabberzadeh ◽  
Nergiz Ilhan
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Critical Issue ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
Unit Of Analysis ◽  
Electronic Procurement ◽  
Future Research Directions ◽  
Methodological Concern

E-procurement systems that have been in place for over a decade have begun incorporating digital tools like big data, cloud computing, internet of things, and data mining. Hence, there exists a rich literature on earlier e-procurement systems and advanced digitally-enabled e-procurement systems. Existing literature on these systems addresses many research issues (e.g., adoption) associated with e-procurement. However, one critical issue that has so far received no rigorous attention is about “unit of analysis,” a methodological concern of importance, for e-procurement research context. Hence, the aim of this chapter is twofold: 1) to discuss how the notion of “unit of analysis” has been conceptualised in the e-procurement literature and 2) to discuss how its use has been justified by e-procurement scholars to address the research issues under investigation. Finally, the chapter provides several interesting findings and outlines future research directions.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2016 ◽  
pp. 212-244
Author(s):  
Boutheina Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2020 ◽  
pp. 700-725
Author(s):  
Boutheina A. Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

scholarly journals Knowledge Graph Analysis of Human Health Research Related to Climate Change

2020 ◽  
Vol 17 (20) ◽  
pp. 7395
Author(s):  
Yating Zhao ◽  
Jingjing Guo ◽  
Chao Bao ◽  
Changyong Liang ◽  
Hemant K Jain
Keyword(s):  
Climate Change ◽  
Human Health ◽  
Future Research ◽  
Research Directions ◽  
Development Status ◽  
Future Research Directions ◽  
Knowledge Support ◽  
Using Data ◽  
Research Hotspots ◽  
Impacts Of Climate Change

In order to explore the development status, knowledge base, research hotspots, and future research directions related to the impacts of climate change on human health, a systematic bibliometric analysis of 6719 published articles from 2003 to 2018 in the Web of Science was performed. Using data analytics tools such as HistCite and CiteSpace, the time distribution, spatial distribution, citations, and research hotspots were analyzed and visualized. The analysis revealed the development status of the research on the impacts of climate change on human health and analyzed the research hotspots and future development trends in this field, providing important knowledge support for researchers in this field.

scholarly journals Pre-purchase information search and consumer satisfaction: Replication and extension

1986 ◽  
Vol 17 (4) ◽  
pp. 220-224
Author(s):  
D. Rousseau
Keyword(s):  
Information Search ◽  
Consumer Satisfaction ◽  
Future Research ◽  
Eastern Cape ◽  
Market Place ◽  
Research Directions ◽  
Search Behaviour ◽  
Determining Factors ◽  
Future Research Directions ◽  
Using Data

In this paper the author examines consumer satisfaction with major household appliances and its determining factors. Hypotheses relating to pre-purchase information search and product satisfaction as well as previous satisfactory store experiences and subsequent repurchase behaviour are proposed and empirically tested using data from 55 consumers who patronized a large eastern Cape hypermarket. Results imply that product satisfaction is more related to market place variables than actual search behaviour. Repeat shopping intentions are associated with previous shopping experiences at the particular store which also contributes to product satisfaction. Marketing implications and future research directions are briefly discussed.

Methods and Techniques of Data Mining

2021 ◽  
pp. 749-767
Author(s):  
Ana Funes ◽  
Aristides Dasso
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Research And Development ◽  
Knowledge Acquisition ◽  
Future Research ◽  
Research Directions ◽  
New Methods ◽  
Methods And Techniques ◽  
Future Research Directions

Nowadays, there is an increasing number of applications where artificial intelligence has fuelled the research and development of new methods, techniques, and tools related to knowledge acquisition and data mining. The development of data mining and other related disciplines has benefited from the existence of large volumes of data proceeding from the most diverse sources and domains. KDD process and methods of data mining allows for the discovery of knowledge in data that is hidden to humans, presenting this knowledge under different ways. In this chapter, the relation of data mining with other disciplines is analyzed, an overview of data mining tasks and methods is presented, and also a possible classification of them is given. Finally, a brief discussion on issues associated to the discipline and future research directions are also given.

Data Mining

2012 ◽  
pp. 75-94 ◽  
Author(s):  
Martin Atzmueller
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Pattern Mining ◽  
Data Sources ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions

Data Mining provides approaches for the identification and discovery of non-trivial patterns and models hidden in large collections of data. In the applied natural language processing domain, data mining usually requires preprocessed data that has been extracted from textual documents. Additionally, this data is often integrated with other data sources. This chapter provides an overview on data mining focusing on approaches for pattern mining, cluster analysis, and predictive model construction. For those, we discuss exemplary techniques that are especially useful in the applied natural language processing context. Additionally, we describe how the presented data mining approaches are connected to text mining, text classification, and clustering, and discuss interesting problems and future research directions.

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