scholarly journals Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

2021 ◽  
pp. 293-319
Author(s):  
Emre Cihan Ateş
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Deep Learning ◽  
Mining Machine ◽  
Crime Data

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

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 Cross-Device Computation Coordination for Mobile Collocated Interactions with Wearables

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 796 ◽  
Author(s):  
Hyoseok Yoon ◽  
Choonsung Shin
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Energy Efficient ◽  
Form Factors ◽  
Analysis Data ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Coordination Problem ◽  
Multiple Devices

Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices should communicate collectively to share computation burdens and stay energy-efficient. In this paper, we present a cross-device computation coordination method for scenarios of mobile collocated interactions with wearables. We formally define a cross-device computation coordination problem and propose a method for solving this problem. Lastly, we demonstrate the feasibility of our approach through experiments and exemplar cases using 12 commercial Android devices with varying computation capabilities.

Decoding search and partnerships across news, journalism, and social media

2020 ◽  
pp. 67-106
Author(s):  
Cate Dowd
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Deep Learning ◽  
Learning Systems ◽  
Third Party ◽  
Viral Video ◽  
News Agency ◽  
Cloud Servers

Semantic news tags processed via cloud servers are in amongst big data and machine learning systems. The latter may have influenced Murdoch’s acquisition of a ‘social media news agency’, and other partnerships, as a mix of new roles across journalism, analytics, and search emerged. Some editing roles in journalism focus on SEO, but Murdoch’s Storyful, which started as a verification business created jobs for cloud operations engineers, viral video editors, and trends editors. Data-mining techniques were a lure for news and social media partnerships circa 2013–2016. In the name of verification, access to big data was matched by social media gaining credibility, evident in Facebook Newswire and other journalism projects. Deep learning methods in search, referrals, and automated tagging have also produced mutual benefits, mostly via third party agreements. However, data sharing for political ends by targeting particular users, and verification projects, have not stopped fake news.

scholarly journals Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 121
Author(s):  
Marco Sánchez-Aguayo ◽  
Luis Urquiza-Aguiar ◽  
José Estrada-Jiménez
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Learning Strategies ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
General Interest ◽  
Fraud Triangle ◽  
Learning Techniques

Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis.

Use of semantics in bio-informatics

2017 ◽  
Vol 01 (01) ◽  
pp. 1630017
Author(s):  
Charles C. N. Wang ◽  
Jeffrey J. P. Tsai
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Bioinformatics Analysis ◽  
Learning Technologies ◽  
Biological Data ◽  
Mining Machine ◽  
Biological Processes ◽  
Primary Methods ◽  
Modeling Data

Bioinformatics conceptualizes biological processes in terms of genomics and applies computer science (derived from disciplines such as applied modeling, data mining, machine learning and statistics) to extract knowledge from biological data. This paper introduces the working definitions of bioinformatics and its applications and challenges. We also identify the bioinformatics resources that are popular among bioinformatics analysis, review some primary methods used to analyze bioinformatics problems, and review the data mining, semantic computing and deep learning technologies that may be applied in bioinformatics analysis.

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