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Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (10 October 2021) | Viewed by 18925

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Special Issue Editors

Prof. Dr. Agapito Ledezma Espino

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Guest Editor

Computer Science and Engineering Department, Carlos III University of Madrid, 28911 Leganés, Madrid, Spain
Interests: computer science; Artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Araceli Sanchis de Miguel

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Guest Editor

Computer Science and Engineering Department, Universidad Carlos III de Madrid, 28911 Leganés (Madrid), Spain
Interests: computer science; Artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the mid-1990s, terms such as data mining, machine learning, or intelligent agents began to be part of the vocabulary not only of the academic world but also of companies encouraged by the digitization of companies and institutions. Initially, the techniques and methods associated with these terms were used as non-priority task support tools in companies, but over time, they became in the core of the business. In the last two decades, the number of applications of different machine learning techniques and intelligent agents has experienced exponential growth either directly named or under the umbrella of terms such as data mining, business intelligence, or data analytics.

The exponential growth of the data available for analysis allows companies to have an asset that they can put at the service of the business through the application of machine learning and intelligent agents. In this Special Issue, we are particularly interested in the application of machine learning techniques and intelligent agents aimed at turning data into insights that are actable by companies/institutions. We encourage submissions of conceptual, empirical, and literature review work that focuses on this field.

Possible topics of interest include but are not limited to machine learning and intelligent agents and their impact on:

Economics and finance;
Supply chain management;
Human resources management;
Creating values;
Customer management;
Innovation;
Ethical aspects;
Sustainable development;
Agent-based simulation.

Prof. Dr. Agapito Ledezma Espino
Prof. Dr. Araceli Sanchis de Miguel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Machine learning
Intelligent agents
Business intelligence
Agent-based simulation
Data analytics
Data mining.

Published Papers (6 papers)

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Research

17 pages, 1900 KiB

Open AccessFeature PaperArticle

An Automated Recognition of Work Activity in Industrial Manufacturing Using Convolutional Neural Networks

by Justyna Patalas-Maliszewska, Daniel Halikowski and Robertas Damaševičius

Electronics 2021, 10(23), 2946; https://doi.org/10.3390/electronics10232946 - 26 Nov 2021

Cited by 17 | Viewed by 2617

Abstract

The automated assessment and analysis of employee activity in a manufacturing enterprise, operating in accordance with the concept of Industry 4.0, is essential for a quick and precise diagnosis of work quality, especially in the process of training a new employee. In the case of industrial solutions, many approaches involving the recognition and detection of work activity are based on Convolutional Neural Networks (CNNs). Despite the wide use of CNNs, it is difficult to find solutions supporting the automated checking of work activities performed by trained employees. We propose a novel framework for the automatic generation of workplace instructions and real-time recognition of worker activities. The proposed method integrates CNN, CNN Support Vector Machine (SVM), CNN Region-Based CNN (Yolov3 Tiny) for recognizing and checking the completed work tasks. First, video recordings of the work process are analyzed and reference video frames corresponding to work activity stages are determined. Next, work-related features and objects are determined using CNN with SVM (achieving 94% accuracy) and Yolov3 Tiny network based on the characteristics of the reference frames. Additionally, matching matrix between the reference frames and the test frames using mean absolute error (MAE) as a measure of errors between paired observations was built. Finally, the practical usefulness of the proposed approach by applying the method for supporting the automatic training of new employees and checking the correctness of their work done on solid fuel boiler equipment in a manufacturing company was demonstrated. The developed information system can be integrated with other Industry 4.0 technologies introduced within an enterprise. Full article

(This article belongs to the Special Issue Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence)

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28 pages, 2852 KiB

Open AccessArticle

Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company

by Juan Manuel Rozas Andaur, Gonzalo A. Ruz and Marcos Goycoolea

Electronics 2021, 10(22), 2787; https://doi.org/10.3390/electronics10222787 - 14 Nov 2021

Cited by 4 | Viewed by 6082

Abstract

For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a certain commodity while attempting to buy it. This paper focuses on addressing this problem from a manufacturer’s perspective, conducting a case study in a retail packaged foods manufacturing company located in Latin America. We developed two machine learning based systems to detect OOS events automatically. The first is based on a single Random Forest classifier with balanced data, and the second is an ensemble of six different classification algorithms. We used transactional data from the manufacturer information system and physical audits. The novelty of this work is our use of new predictor variables of OOS events. The system was successfully implemented and tested in a retail packaged foods manufacturer company. By incorporating the new predictive variables in our Random Forest and Ensemble classifier, we were able to improve their system’s predictive power. In particular, the Random Forest classifier presented the best performance in a real-world setting, achieving a detection precision of 72% and identifying 68% of the total OOS events. Finally, the incorporation of our new predictor variables allowed us to improve the performance of the Random Forest by 0.24 points in the F-measure. Full article

(This article belongs to the Special Issue Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence)

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24 pages, 4707 KiB

Open AccessArticle

Assessment of the Readiness of Industrial Enterprises for Automation and Digitalization of Business Processes

by Irina Krakovskaya and Julia Korokoshko

Electronics 2021, 10(21), 2722; https://doi.org/10.3390/electronics10212722 - 08 Nov 2021

Cited by 6 | Viewed by 2270

Abstract

The purpose of this article is to identify the promising areas of digitalization in the work of industrial enterprises at the national and regional level. The study was conducted on the basis of industrial enterprises of the Republic of Mordovia using the methods of a systematic approach, comparative and strategic analysis, mathematical statistics, etc. As a result, we assessed the impact of the digital transformation of the economy on the development of industrial enterprises in Russia and the Republic of Mordovia, changes in the efficiency of enterprises associated with the expansion of the use IT, the degree of satisfaction of enterprises with the use of specific tools of information and communication technologies, etc. Spearman’s rank linear correlation demonstrates positive and negative effects of ICT using the industrial enterprises. The novelty and practical value of the obtained results consists in the fact that confirmed research hypotheses reflect both specific regional factors and systemic nationwide problems of digitalization of the Russian industry, automation of business process, allow us to outline the priority areas of digital transformation of business models not only of the studied enterprises industry of the region, but also the non-resource sector of the industry in general. Full article

(This article belongs to the Special Issue Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence)

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Graphical abstract

16 pages, 2109 KiB

Open AccessArticle

Intelligent Performance Prediction: The Use Case of a Hadoop Cluster

by Dimitris Uzunidis, Panagiotis Karkazis, Chara Roussou, Charalampos Patrikakis and Helen C. Leligou

Electronics 2021, 10(21), 2690; https://doi.org/10.3390/electronics10212690 - 03 Nov 2021

Cited by 12 | Viewed by 1840

Abstract

The optimum utilization of infrastructural resources is a highly desired yet cumbersome task for service providers to achieve. This is because the optimal amount of such resources is a function of various parameters, such as the desired/agreed quality of service (QoS), the service characteristics/profile, workload and service life-cycle. The advent of frameworks that foresee the dynamic establishment and placement of service and network functions further contributes to a decrease in the effectiveness of traditional resource allocation methods. In this work, we address this problem by developing a mechanism which first performs service profiling and then a prediction of the resources that would lead to the desired QoS for each newly deployed service. The main elements of our approach are as follows: (a) the collection of data from all three layers of the deployed infrastructure (hardware, virtual and service), instead of a single layer of the deployed infrastructure, to provide a clearer picture on the potential system break points, (b) the study of well-known container based implementations following that microservice paradigm and (c) the use of a data analysis routine that employs a set of machine learning algorithms and performs accurate predictions of the required resources for any future service requests. We investigate the performance of the proposed framework using our open-source implementation to examine the case of a Hadoop cluster. The results show that running a small number of tests is adequate to assess the main system break points and at the same time to attain accurate resource predictions for any future request. Full article

(This article belongs to the Special Issue Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence)

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23 pages, 1724 KiB

Open AccessArticle

Monte Carlo Tree Search as a Tool for Self-Learning and Teaching People to Play Complete Information Board Games

by Víctor Gonzalo-Cristóbal, Edward Rolando Núñez-Valdez, Vicente García-Díaz, Cristian González García, Alba Cotarelo and Alberto Gómez

Electronics 2021, 10(21), 2609; https://doi.org/10.3390/electronics10212609 - 26 Oct 2021

Cited by 2 | Viewed by 2665

Abstract

Artificial intelligence allows computer systems to make decisions similar to those of humans. However, the expert knowledge that artificial intelligence systems have is rarely used to teach non-expert humans in a specific knowledge domain. In this paper, we want to explore this possibility by proposing a tool which presents and explains recommendations for playing board games generated by a Monte Carlo Tree Search algorithm combined with Neural Networks. The aim of the aforementioned tool is to showcase the information in an easily interpretable way and to effectively transfer knowledge: in this case, which movements should be avoided, and which action is recommended. Our system displays the state of the game in the form of a tree, showing all the movements available from the current state and a set of their successors. To convince and try to teach people, the tool offers a series of queries and all information available about every possible movement. In addition, it produces a brief textual explanation for those which are recommended or not advisable. To evaluate the tool, we performed a series of user tests, observing and assessing how participants learn while using this system. Full article

(This article belongs to the Special Issue Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence)

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Graphical abstract

12 pages, 3267 KiB

Open AccessArticle

A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling

by Marco Antonio Islas, José de Jesús Rubio, Samantha Muñiz, Genaro Ochoa, Jaime Pacheco, Jesus Alberto Meda-Campaña, Dante Mujica-Vargas, Carlos Aguilar-Ibañez, Guadalupe Juliana Gutierrez and Alejandro Zacarias

Electronics 2021, 10(4), 448; https://doi.org/10.3390/electronics10040448 - 11 Feb 2021

Cited by 28 | Viewed by 2199

Abstract

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data. Full article

(This article belongs to the Special Issue Machine Learning and Intelligent Agents Applications: From Data Mining to Business Intelligence)

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