Visualization and Analysis of 3D Images Using Data Mining Approaches

2020 ◽  
pp. 289-295
Author(s):  
Parimala Boobalan
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Realistic Model ◽  
Natural Sciences ◽  
The Internet ◽  
3D Images ◽  
Using Data ◽  
Numeric Data ◽  
Innovative Techniques ◽  
Insight Into

With the recent advancements in supercomputer technologies, large-scale, high-precision, and realistic model 3D simulations have been dominant in the field of solar-terrestrial physics, virtual reality, and health. Since 3D numeric data generated through simulation contain more valuable information than available in the past, innovative techniques for efficiently extracting such useful information are being required. One such technique is visualization—the process of turning phenomena, events, or relations not directly visible to the human eye into a visible form. Visualizing numeric data generated by observation equipment, simulations, and other means is an effective way of gaining intuitive insight into an overall picture of the data of interest. Meanwhile, data mining is known as the art of extracting valuable information from a large amount of data relative to finance, marketing, the internet, and natural sciences, and enhancing that information to knowledge.

Using data-mining to identify and study patterns in lexical innovation on the web

2018 ◽  
Vol 25 (1) ◽  
pp. 174-200
Author(s):  
Daphné Kerremans ◽  
Jelena Prokić ◽  
Quirin Würschinger ◽  
Hans-Jörg Schmid
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Web Mining ◽  
The Internet ◽  
Mining Tool ◽  
Lexical Innovation ◽  
Innovation And Diffusion ◽  
Using Data ◽  
And Diffusion ◽  
The Web

Abstract This paper presents the NeoCrawler – a tailor-made webcrawler, which identifies and retrieves neologisms from the Internet and systematically monitors the use of detected neologisms on the web by means of weekly searches. It enables researchers to use the web as a corpus in order to investigate the dynamics of lexical innovation on a large-scale and systematic basis. The NeoCrawler represents an innovative web-mining tool which opens up new opportunities for linguists to tackle a number of unresolved and under-researched issues in the field of lexical innovation. This paper presents the design as well as the most important characteristics of two modules, the Discoverer and the Observer, with regard to the usage-based study of lexical innovation and diffusion.

A Quick Reaction System Using Energy, Process and Quality Data for Process Characterization and Holistic Monitoring of Large Scale Assembly Lines

2017 ◽  
Vol 871 ◽  
pp. 44-51
Author(s):  
Christian Sand ◽  
Florian Renz ◽  
Akin Cüneyt Aslanpinar ◽  
Jörg Franke
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Reaction System ◽  
Quality Data ◽  
Assembly Lines ◽  
Major Step ◽  
Electromagnetic Actuators ◽  
Energy Data ◽  
Process Characterization ◽  
Using Data

Modern large-scale assembly lines need to deliver a highly varied and flexible output, while achieving 0 ppm scrap. This is becoming more and more demanding due to an increasing complexity of the products. Thus, it will be a major step in manufacturing processes to develop process monitoring strategies which increase productivity as well as flexibility and reliability of the entire assembly process. Therefore, it is necessary to advance the entire chained assembly line instead of only isolated processes and stations. For this reason, technological processes have to be assessed as a chain of upstream and downstream partial processes instead of being considered in isolation. [3] Moreover, data mining projects depend on the available data bases, while additional data sources may increase the derived knowledge. [2] These ideas are extendable by energy data measurements, besides process and quality data. Existing monitoring approaches to reduce scrap usually use dashboards linked with process and quality data. [5] Therefore, this paper presents a new methodology using data mining analysis of energy data for assembly presses as well as complete assembly lines for electromagnetic actuators. This novel holistic approach realized by a Quick Reaction System allows to increase efficiency, while decreasing energy and resource consumption for actuator manufacturing on large scale assembly lines. In particular, the data base consists of process and quality data, enriched by energy data measurements. This approach enables a comprehensive process characterization as well as monitoring of whole assembly lines by using data mining tools. Furthermore, this paper describes a quantitative evaluation of its data mining based event detection of critical process parameters.

Enhancing Decision Tree with AdaBoost for Predicting Schizophrenia Readmission

2014 ◽  
Vol 931-932 ◽  
pp. 1467-1471 ◽  
Author(s):  
Jaree Thongkam ◽  
Vatinee Sukmak
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Random Forests ◽  
Prediction Models ◽  
Adverse Outcome ◽  
Random Tree ◽  
Psychiatric Readmission ◽  
Using Data ◽  
Insight Into ◽  
F Measure

A psychiatric readmission is argued to be an adverse outcome because it is costly and occurs when relapse to the illness is so severe. An analysis of systematic models in readmission data can provide useful insight into the quicker and sicker patients with schizophrenia. This research aims to develop and investigate schizophrenia readmission prediction models using data mining techniques including decision tree, Random Tree, Random Forests, AdaBoost, Bagging and a combination of AdaBoost with decision tree, AdaBoost with Random Tree, AdaBoost with Random Forests, Bagging with decision tree, Bagging with Random Tree and Bagging with Random Forests. The experimental results successfully showed that AdaBoost with decision tree has the highest precision, recall and F-measure up to 98.11%, 98.79% and 98.41%, respectively.

Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques

2020 ◽  
pp. 234-253
Author(s):  
Daniel Kobla Gasu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Threat Detection ◽  
Use Of The Internet ◽  
Using Data

The internet has become an indispensable resource for exchanging information among users, devices, and organizations. However, the use of the internet also exposes these entities to myriad cyber-attacks that may result in devastating outcomes if appropriate measures are not implemented to mitigate the risks. Currently, intrusion detection and threat detection schemes still face a number of challenges including low detection rates, high rates of false alarms, adversarial resilience, and big data issues. This chapter describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection and cyber-attack detection. Key literature on ML and DM methods for intrusion detection is described. ML and DM methods and approaches such as support vector machine, random forest, and artificial neural networks, among others, with their variations, are surveyed, compared, and contrasted. Selected papers were indexed, read, and summarized in a tabular format.

Manzai Robots: Entertainment Robots Based on Auto-Created Manzai Scripts from Web News Articles

2014 ◽  
Vol 26 (5) ◽  
pp. 662-664 ◽  
Author(s):  
Tomohiro Umetani ◽  
◽  
Ryo Mashimo ◽  
Akiyo Nadamoto ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Data Mining ◽  
The Internet ◽  
System Configuration ◽  
Robot System ◽  
Using Data ◽  
Web News

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260005/18.jpg"" width=""300"" />Manzai robots</div> This paper introduces manzai robots – entertainment robots that automatically create manzai scripts from Internet articles based on keywords given by the audiences and perform manzai based on created manzai scripts. The robot consists two robots connected to the Internet that automatically create manzai scripts from Web news articles in response to a user’s keywords using data mining and manzai techniques. After manzai scripts are created, the two robots perform manzai using these scripts. This paper reviews the robot system configuration, manzai script creation, and robot-based management. </span>

Malware Detection in Android Using Data Mining

2017 ◽  
Vol 6 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Suparna Dasgupta ◽  
Soumyabrata Saha ◽  
Suman Kumar Das
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Execution Time ◽  
Classification Tree ◽  
The Internet ◽  
Data Set ◽  
Software Packages ◽  
Using Data ◽  
Day By Day ◽  
User Location

This article describes how as day-to-day Android users are increasing, the Internet has become the type of environment preferred by attackers to inject malicious packages. This is content with the intention of gathering critical information, spying on user details, credentials, call logs, contact details, and tracking user location. Regrettably it is very hard to detect malware even with antivirus software/packages. In addition, this type of attack is increasing day by day. In this article the authors have chosen a Supervised Learning Classification Tree-based algorithm to detect malware on the data set. Comparison amongst all the classifiers on the basis of accuracy and execution time are used to build the classifier model which has the highest executed detections.

scholarly journals Accurate Prediction of Protein Functional Class from Sequence in theMycobacterium tuberculosisandEscherichia coliGenomes Using Data Mining

Yeast ◽  
2000 ◽  
Vol 1 (4) ◽  
pp. 283-293 ◽  
Author(s):  
Ross D. King ◽  
Andreas Karwath ◽  
Amanda Clare ◽  
Luc Dehaspe
Keyword(s):  
Data Mining ◽  
Evolutionary History ◽  
Inductive Logic ◽  
Rule Learning ◽  
Functional Class ◽  
E Coli ◽  
Data Mining Approach ◽  
History Of ◽  
Using Data ◽  
Insight Into

The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on theM. tuberculosisandE. coligenomes, and identify biologically interpretable rules which predict protein functional class from information only available from the sequence. These rules predict 65% of the ORFs with no assigned function inM. tuberculosisand 24% of those inE. coli, with an estimated accuracy of 60–80% (depending on the level of functional assignment). The rules are founded on a combination of detection of remote homology, convergent evolution and horizontal gene transfer. We identify rules that predict protein functional class even in the absence of detectable sequence or structural homology. These rules give insight into the evolutionary history ofM. tuberculosisandE. coli.

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