Data mining and machine learning approaches on engineering materials — A review

2016 ◽  
Author(s):  
P. J. Antony ◽  
Prajna Manujesh ◽  
N. A Jnanesh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Approaches ◽  
Engineering Materials

scholarly journals A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Antonio Hernández-Blanco ◽  
Boris Herrera-Flores ◽  
David Tomás ◽  
Borja Navarro-Colorado
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Learning Approaches ◽  
Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

scholarly journals The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Omer F. Akmese ◽  
Gul Dogan ◽  
Hakan Kor ◽  
Hasan Erbay ◽  
Emre Demir
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Acute Appendicitis ◽  
Learning Algorithms ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Learning Approaches ◽  
Predictive Data Mining ◽  
Clinical Records

Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

scholarly journals Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets

2017 ◽  
Vol 106 (11) ◽  
pp. 3270-3279 ◽  
Author(s):  
Maulik K. Nariya ◽  
Jae Hyun Kim ◽  
Jian Xiong ◽  
Peter A. Kleindl ◽  
Asha Hewarathna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Data Sets ◽  
Learning Approaches ◽  
Comparative Characterization ◽  
Analytical Stability ◽  
Stability Data ◽  
Using Data

scholarly journals Data mining tools -a case study for network intrusion detection

2020 ◽  
Author(s):  
Soodeh Hosseini ◽  
Saman Rafiee Sardo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Learning Approaches ◽  
Learning Tools ◽  
Network Intrusion ◽  
The Face ◽  
Level Of Knowledge ◽  
Mining Tools

Abstract With the growth of data mining and machine learning approaches in recent years, many efforts have been made to generalize these sciences so that researchers from any field can easily utilize these sciences. One of the most important of these efforts is the development of data mining tools that try to hide the complexities from researchers so that they can achieve a professional output with any level of knowledge. This paper is focused on reviewing and comparing data mining and machine learning tools including WEKA, KNIME, Keel, Orange, Azure, IBM SPSS Modeler, R and Scikit-Learn to show what approach each of these methods has taken in the face of the complexities and problems of different scenarios of generalization of data mining and machine learning. In addition, for a more detailed review, this paper examines the challenge of network intrusion detection in two tools, Knime with graphical interface and Scikit-Learn with coding environment.

scholarly journals A lifelong spam emails classification model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rami Mustafa A. Mohammad
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Positive Impact ◽  
Concept Drift ◽  
Classification Model ◽  
Learning Approaches ◽  
Suggested Model ◽  
Sound Classification ◽  
Internet Users ◽  
Using Data

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

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