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 Crossing Point of Artificial Intelligence in Cybersecurity

2015 ◽  
Vol 2 (3) ◽  
pp. 121-128
Author(s):  
Praveen Kumar Donepudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Cyber Threats ◽  
Crossing Point ◽  
Insight Into ◽  
Distinct Approach ◽  
Learning Data ◽  
Applications Of Ai

There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.  

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.

Introduction to The Special Section on Bias and Fairness in AI

2021 ◽  
Vol 23 (1) ◽  
pp. 1-3
Author(s):  
Toon Calders ◽  
Eirini Ntoutsi ◽  
Mykola Pechenizkiy ◽  
Bodo Rosenhahn ◽  
Salvatore Ruggieri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Health Care ◽  
Special Section ◽  
Resource Planning ◽  
Health Care Resource ◽  
Intelligence Community ◽  
Mining Machine ◽  
Planning Decisions

Fairness in Artificial Intelligence rightfully receives a lot of attention these days. Many life-impacting decisions are being partially automated, including health-care resource planning decisions, insurance and credit risk predictions, recidivism predictions, etc. Much of work appearing on this topic within the Data Mining, Machine Learning and Artificial Intelligence community is focused on technological aspects. Nevertheless, fairness is much wider than this as it lies at the intersection of philosophy, ethics, legislation, and practical perspectives. Therefore, to fill this gap and bring together scholars of these disciplines working on fairness, the first workshop on Bias and Fairness in AI was held online on September 18, 2020 at the ECML-PKDD 2020 conference. This special section includes six articles presenting different perspectives on bias and fairness from different angles.

Der Weg von Descriptive zu Predictive Six Sigma/The path from descriptive to predictive Six Sigma

2019 ◽  
Vol 109 (11-12) ◽  
pp. 807-810
Author(s):  
F. Schäfer ◽  
E. Schwulera ◽  
H. Otten ◽  
J. Franke
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Continuous Improvement ◽  
Six Sigma ◽  
Data Availability ◽  
Mining Machine ◽  
Production Processes ◽  
Open Source Data ◽  
Source Data

Die Entwicklungen im Bereich der Automatisierung hin zu steigender Datendurchgängigkeit in Kombination mit der Verfügbarkeit frei zugänglicher Datenanalyseplattformen   und -algorithmen erlauben neue Ansätze zur kontinuierlichen Verbesserung von Produktionsprozessen. Auch etablierte Vorgehensweisen wie Six Sigma können und müssen in diesem Rahmen weitergedacht und angereichert werden. Folglich gilt es, die klassische, hauptsächlich deskriptive Herangehensweise von Six Sigma um relevante Methoden und Algorithmen aus den Bereichen Data Mining, Machine Learning und künstliche Intelligenz zu erweitern. Die klassische Six Sigma Ausbildung bietet für diesen Wandel gute Voraussetzungen, die es auszubauen und anzupassen gilt.   The increasing data availability in combination with open source data analysis platforms and algorithms pave the way for new ways of operationalizing continuous improvement tasks in the field of production processes. Even established approaches like Six Sigma need to be enhanced and enriched in this context. Consequently, the classical and more descriptive nature of Six Sigma should consider relevant methods and algorithms out of the field of data mining, machine learning and artificial intelligence. The classical Six Sigma training provides a good basis for this change to broaden the Six Sigma scope and its toolbox.

An Interview with Dr. Michael Zeller, Winner of ACM SIGKDD 2020 Service Award

2021 ◽  
Vol 22 (2) ◽  
pp. 6-7
Author(s):  
Michael Zeller
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Data Science ◽  
Professional Services ◽  
Investment Company ◽  
Service Award ◽  
Virtual Conference ◽  
Learning Data

Michael Zeller, Ph.D. is the recipient of the 2020 ACM SIGKDD Service Award, which is the highest service award in the field of knowledge discovery and data mining. Conferred annually on one individual or group in recognition of outstanding professional services and contributions to the field of knowledge discovery and data mining, Dr. Zeller was honored for his years of service and many accomplishments as the secretary and treasurer for ACM SIGKDD, the organizing body of the annual KDD conference. Zeller is also head of AI strategy and solutions at Temasek, a global investment company seeking to make a difference always with tomorrow in mind. He sat down with SIGKDD Explorations to discuss how he first got involved in the KDD conference in 1999, what he learned from the first-ever virtual conference, his work at Temasek, and what excites him about the future of machine learning, data science and artificial intelligence.

Técnicas de inteligência artificial para reconhecimento de sepse em ambientes hospitalares: revisão integrativa

2020 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Everton Osnei Cesario ◽  
Cristiane Yumi Nakamura ◽  
Yohan Bonescki Gumiel ◽  
Deborah Ribeiro Carvalho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning

A sepse é uma inflamação generalizada com elevada morbidade e mortalidade, cujo reconhecimento e tratamento precoce são fatores essenciais para uma melhor qualidade de vida para o paciente; caso não seja identificada e tratada rapidamente, poderá levar a óbito. Este artigo de revisão integrativa objetiva identificar as técnicas baseadas em inteligência artificial adotadas, sua respectiva acurácia, sensibilidade e especificidade para a identificação precoce nos casos de sepse em ambiente hospitalar. A pesquisa, adaptada do método PRISMA, foi realizada em cinco bases de dados indexadas a partir dos seguintes descritores: sepse, septic, sepsis, forecasting, predict, prediction, detection, predicting, diagnosis, assessment, machine learning, artificial intelligence, data mining e deep learning. Foram identificados 333 artigos, sendo 21 com referência ao reconhecimento precoce da sepse por meio de 16 técnicas. Os resultados demonstram que as redes neurais tiveram melhor desempenho, variando a acurácia entre 76% e 93%, as árvores de decisão entre 69,0% e 91,5% e os métodos estatísticos entre 56% e 89%. Conclui-se que o fator mais influente na identificação precoce do diagnóstico são a variedade e a qualidade dos dados. Também se evidencia o desafio em relação ao pré-processamento, visto que os dados em geral são oriundos de fontes heterogêneas, coletados com critérios, métodos e objetivos distintos.

scholarly journals Digital Transformation in the Concept of Internal Banking Risk Management

2020 ◽  
pp. 45-50
Author(s):  
D. V. Pasinitsky
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Risk Management ◽  
Digital Technologies ◽  
Digital Transformation ◽  
Targeted Analysis ◽  
Banking Risk ◽  
Learning Data

The article is devoted to a targeted analysis of promising displacements in the guiding ideas of managing internal banking risks. Based on the study, the author proposes to intensify the introduction of digital technologies in banking practice based on: artificial intelligence, machine learning, data mining.

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.

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.

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