Artificial Intelligence and Machine Learning Applications in Smart Production (PDF)

Review Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions Raffaele Cioffi 1, Marta Travaglioni 1, Giuseppina Piscitelli 1, Antonella Petrillo 1,* and Fabio De Felice 2 1 Department of Engineering, Parthenope University, Isola C4, Centro D...

Review Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions Raffaele Cioffi 1, Marta Travaglioni 1, Giuseppina Piscitelli 1, Antonella Petrillo 1,* and Fabio De Felice 2 1 Department of Engineering, Parthenope University, Isola C4, Centro Direzionale, 80143 Napoli NA, Italy; [email protected] (R.C.); [email protected] (M.T.); [email protected] (G.P.) 2 Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Via G. Di Biasio, 43, 03043 Cassino FR, Italy; [email protected] * Correspondence: [email protected] Received: 1 December 2019; Accepted: 5 January 2020; Published: 8 January 2020 Abstract: Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI‐based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty‐two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0. Keywords: artificial intelligence; machine learning; systematic literature review; applications; Industry 4.0; smart production; sustainability 1. Introduction Smart production systems require innovative solutions to increase the quality and sustainability of manufacturing activities while reducing costs. In this context, artificial intelligence (AI)‐driven technologies, leveraged by I4.0 Key Enabling Technologies (e.g., Internet of Thing, advanced embedded systems, cloud computing, big data, cognitive systems, virtual and augmented reality), are ready to generate new industrial paradigms. In this regard, it is interesting to remember that the father of artificial intelligence, John McCarthy , in the 1990s, defined artificial intelligence as “artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs”. Generally, the term “AI” is used when a machine simulates functions that humans associate with other human minds, such as learning and problem solving. Sustainability 2020, 12, 492; doi:10.3390/su12020492 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 492 2 of 24 On a very broad account, the areas of artificial intelligence are classified into 16 categories [4–8]. These are reasoning, programming, artificial life, belief revision, data mining, distributed AI, expert systems, genetic algorithms, systems, knowledge representation, machine learning, natural language understanding, neural networks, theorem proving, constraint satisfaction, and theory of computation [9–11]. In the 21st century, AI has become an important area of research in all fields: Engineering, science, education, medicine, business, accounting, finance, marketing, economics, stock market, and law, among others [12–18]. The range of AI has grown enormously since the intelligence of machines with machine learning capabilities has created profound impacts on business, governments, and society. They also influence the larger trends in global sustainability. Artificial intelligence can be useful to solve critical issue for sustainable manufacturing (e.g., optimization of energy resources, logistics, supply chain management, waste management, etc.). In this context, in smart production, there is a trend to incorporate AI into green manufacturing processes for stricter environmental policies. In fact, as said in March 2019 by Hendrik Fink, head of Sustainability Services at PricewaterhouseCoopers, “If we properly incorporate artificial intelligence, we can achieve a revolution with regard to sustainability. AI will be the driving force of the fourth industrial revolution”. Thus, subfields of AI, such as machine learning, natural language processing, image processing, and data mining, have also become an important topic for today’s tech giants. The subject of AI generates considerable interest in the scientific community, by virtue of the continuous evolution of the technologies available today. The development of ML as a branch of AI is now very fast. Its usage has spread to various fields, such as learning machines, which are currently used in smart manufacturing, medical science, pharmacology, agriculture, archeology, games, business, and so forth. According to the above considerations, in this work, a systematic literature review of research from 1999 to 2019 was performed on AI and the ML technique. Therefore, it is considered necessary to create a classification system that refers to the articles that jointly treat the two topics, in order to have greater variance and reflection. Furthermore, to gain a deeper understanding, the influence of other variables was explored, such as the thematic areas and the sectors in which the technologies are most influential. The main contribution of this work is that it provides an overview of the research carried out to date. A number of impressive documentations of established research methods and philosophy have been discussed for several years. Unfortunately, little comparison and integration across studies exists. In this article, a common understanding of AI and ML research and its variations was created. This paper is not attempting to provide an all‐encompassing framework on the literature on AI and ML research. Rather, it attempts to provide a starting point for integrating knowledge across research in this domain and suggests paths for future research. It explores studies in certain novel disciplines: Environmental pollution, medicine, maintenance, manufacturing, etc. Further research is needed to extend the present boundary of knowledge in AI by integrating principles and philosophies of some traditional disciplines into the existing AI frameworks [22–24]. The target that this document would like to assume is not the trigger of a sudden proliferation of an already consolidated sector, but it is hoped that this research could be an important intellectual tool for both the refocusing of the work and creating new intellectual opportunities. This paper presents valuable ideas and perspectives for undergoing research on AI and ML. The final aim was to anticipate the transformation of the discipline in the future age. This would be a journey that may experience change in its course as new generations of scholars contribute to the dialogue and to the action. As noted earlier, this work presents a review, hence it lays a foundation for future inquiry. It not only offers a basis for future comparisons but prompts a number of new questions for investigations as well. While topics that might be considered as results of this work are numerous, some are of particularly broad interest or impact. Sustainability 2020, 12, 492 3 of 24 The paper is organized as follows. Section 2 presents the proposed methodology and details the research methodology adopted for the literature survey. Section 3 analyzes the main results of the bibliometric analysis. Finally, in Section 4, the main contribution of the research is summarized. 2. Methodology The methodological approach used mixes bibliometric, content analysis, and social network techniques. In this study, a state‐of‐the‐art research was conducted through the SCOPUS and Web of Science databases. For the publication time span, the time from 1999 to 2019 was considered with the intent to understand how the level of attention towards the topic has changed before and after the introduction of Industry 4.0. The research methodology chosen for this study was a systematic literature review. The main phases of the study were as follows: 1. Phase 1: Research and Classification. The present phase was divided into three steps:  Step 1: Identification;  Step 2: Screening; and  Step 3: Inclusion. In phase 1, bibliometric data was collected (step 1). Then, a screening of the overall result was carried out to identify which documents can be taken into consideration, in line with the research areas deemed interesting and relevant (step 2). At the end of this step, the last step (step 3) aimed to select the documents to be analyzed in detail. 2. Phase 2: Analysis. Once phase 1 was completed, the next phase was phase 2, which was the analysis of the results. The approach used for the bibliometric analysis included:  The use of indicators for the parameters studied; and  SNA (social network analysis) for the keywords. The indicators chosen to perform the analysis were total papers (TPs), which is the total number of publications, and total citations (TCs), which is the total number of citations. SNA finds application in various social sciences, and has lately been employed in the study of various phenomena, such as international trade, information dissemination, the study of institutions, and the functioning of organizations. The analysis of the use of the term SNA in the scientific literature has undergone exponential growth in the use of this mode of computable representation of complex and interdependent phenomena. For the purpose of the study, UCINET, NetDraw software was used, which was expressly designed for the creation and graphic processing of networks, and was used to represent the keywords in the network, and Excel for data input. The software UCINET, NetDraw returned a sociometric network that describes the relationships between the classes, that is, data entered as input. Furthermore, NVivo 12 software, the leading program for computer‐assisted qualitative analysis (CAQDAS), was used to analyze keywords of all documents. In this specific case, it was used to identify the possible links between the keywords of the various documents examined, developing conceptual schemes from which to make interpretative hypotheses. 3. Phase 3: Discussion. At the end of the second phase, a third and final one followed, where the results were discussed, and conclusions were drawn. In Figure 1, the main phases and steps followed for the analysis are shown. Sustainability 2020, 12, 492 4 of 24 Figure 1. Process flow chart. 3. Results of the Bibliometric Analysis 3.1. Phase 1: Research and Classification The first phase consisted of the search for documents, which included the activities of collecting the material belonging to the academic universe. This first phase was divided into three steps as follows. 3.1.1. Identification (Step 1) For a comprehensive survey of the phenomenon, an investigation on the Scopus (SCP) and Web of Science (WoS) databases was carried out using Boolean operators. We began by making a search query on the Scopus and WoS databases with the general keywords “artificial intelligence” AND “machine learning” AND “application”, as shown in Table 1. In order to maintain the consistency of the results, the same keywords were used in both databases and a time horizon of 20 years was chosen, from 1999 to 2019. The choice of keywords for performing the survey was based on the awareness that AI and ML can be an important tool in the effort to adopt responsible business practices in the context of smart production. In this regard, it is worthy to note that with the increasingly urgent discussions of climate change, it seemed appropriate to focus our research on the topic of sustainability. Thus, the selection of papers also considered applications on sustainability. Sustainability 2020, 12, 492 5 of 24 Table 1. Keywords and time period. Keywords Time Period Artificial Intelligence Machine Learning 1999–2019 Application The search returned in total 13,512 documents. The results extracted by Scopus are numerically superior to Web of Science (WoS): 12,445 for the first and only 1081 for the second one (Table 2). Table 2. Total results of research on Scopus and WoS. Research Carried out on 2019 Source of research Scopus Web of Science Results 12,445 1081 The result is not entirely unexpected, and the reason is to be found in the fact that Scopus, being an Elsevier product, collects data from all the other databases, in particular Science Direct and those queried by the Scirus search engine, while Web of Science (WoS) collects fewer documents. From the documents extracted in Scopus, it was found that most of them are conference papers (57.28%) and, subsequently, articles (33.85%). On the contrary, the research on Web of Science (WoS) underlines that most of the documents are articles (46.12%) and, subsequently, proceedings papers (42.86%). All the document types are filled in Table 3. Table 3. Distribution of document types in Scopus and Web of Science. Web of Science Scopus Document Types Records Contribute % Document Types Records Contribute % Article 481 46.12 Conference Paper 7128 57.28 Proceedings paper 447 42.86 Article 4212 33.85 Review 133 12.76 Review 412 3.31 Editorial material 16 1.53 Article in Press 194 1.56 Meeting abstract 2 0.19 Book Chapter 177 1.42 Book chapter 1 0.1 Conference Review 177 1.42 Retracted publication 1 0.1 Book 90 0.72 ‐ ‐ ‐ Editorial 27 0.22 ‐ ‐ ‐ Note 10 0.08 ‐ ‐ ‐ Letter 9 0.07 ‐ ‐ ‐ Short Survey 9 0.07 AI began working in the 1940s and researchers showed strong expectations until the 1970s when they began to encounter serious difficulties and investments were greatly reduced. Since then, a long period began, known as the “AI winter” : Despite some great successes, such as IBM’s Deep Blue system, which in the late 1990s defeated the then chess world champion Garri Kasparov, the study of solutions for AI has only come back for a few years. The push for a new technological development has been given by the I4.0, which considered AI as one of the primary key enabling technologies (KETs). From this period onwards, the literature has been enriched with documents, as shown in Figure 2. Growth is apparent after 2011 when new technologies began to be implemented more frequently. In fact, the Industry 4.0 term first appeared at Hannover Messe in 2011 when Professor Wolfgang Wahlster, Director and CEO of the German Research Center for Artificial Intelligence, addressed the opening ceremony audience. In fact, this research indicates that over the time period considered (1999–2019), the number of published articles remains almost constant until 2013, from which it undergoes an increase. Sustainability 2020, 12, 492 6 of 24 Figure 2. Research growth on Scopus and Web of Science. Subsequently, the increase in the adoption of these ones has led researchers to keep pace with the growth of I4.0. 3.1.2. Screening (Step 2) Trying to give an overview of the topics and areas interface, in the screening phase, an analysis of documents characterized by free access was chosen, excluding those that have restrictions, and to restrict the field to the thematic areas of scientific interest. With this in mind, the number of open access items has been drastically reduced (1288 results for Scopus and 149 for WoS) and, also applying the filter related to the thematic areas (Table 4), it determined a further reduction: 947 for Scopus and 60 for WoS. Table 4. Subject area filter on Scopus and WoS. Subject Area Scopus Web of Science (WoS) Computer Chemical Computer Science Computer Science Artificial Automation Control Science Engineering Information Systems Intelligence Systems Materials Science Environmental Engineering Energy Environmental Sciences Multidisciplinary Studies Materials Engineering Electrical Computer Science Operations Research Decision Science Science Electronic Hardware Architecture Management Science Business Environmental Management Telecommunications Industrial Relations Labor Robotics Science and accounting Engineering Environmental Engineering Manufacturing Thermodynamics Computer Science Theory Engineering Industrial Energy Fuels Methods Computer Science Engineering Civil Engineering Mechanical Cybernetics Computer Science Software Multidisciplinary Sciences Engineering Note how the number of filters applied is different. The databases, in fact, offer the same search options, but, in the specific case of the thematic areas, the latter are more numerous and structured on Web of Science (WoS) compared to Scopus. Sustainability 2020, 12, 492 7 of 24 3.1.3. Inclusion (Step 3) At the end of the screening process, the inclusion step was started, which consisted in the selection of documents, which was extracted from the last passage, destined to be included in the sample on which bibliometric analysis was performed. In this review step, for the purposes of eligibility, we examined the complete text of each document independently. For each article, we examined whether there was interest from the academic world, and if it contained case studies or real applications, proposals for new AI and ML algorithms, or possible future scenarios. Therefore, the final sample to be analyzed consisted of 60 documents for Scopus and 22 for WoS. 3.2. Phase 2: Analysis This section presents and discusses the findings of this review. First, an overview of the selected studies is presented. Second, the review findings according to the research criteria, one by one in the separate subsections, are reported. 3.2.1. Top Highly Influential Analysis This section lists the most highly cited documents in WoS and Scopus. The list is structured by research source, date, title, authors, source title, and top citation (TP) in WoS or Scopus, according to the research source. The whole list is available in the Appendix A. Looking into the Appendix A, it is possible underline that the document by Larrañaga, Calvo, Santana et al. in 2006 has the highest citation count of 298. This article reviews machine learning methods for bioinformatics and it presents modelling methods. Moreover, the document year is 2006, so before I4.0 was introduced. Therefore, having more years than today has an advantage in terms of diffusion. This means that it is one of the most influential documents in the academic world, as it proposes some of the most useful techniques for modelling, giving the document the opportunity to become a pioneer in the computer science research area. Obviously, all documents before I4.0, in general, have more citations than the most recent documents. However, it is significant to note that even recent documents have a very high number of citations compared to the year of publication. This denotes the interest in the topic from the scientific community. The citation analysis revealed that the first article that we can identify among the most cited in the I4.0 period dates to 2016. The work, published by Krawczyk , proposes application models to further develop the field of unbalanced learning, to focus on computationally effective, adaptive, and real‐time methods, and provides a discussion and suggestions on the lines of future research in the application subject of the study. It received 119 citations. Moreover, an article published by Wuest, Weimer, Irgens et al. received much attention among the scientific community. It contributes by presenting an overview of the available machine learning techniques. Finally, the citation analysis pointed out that the average number of citations of all documents is 16.58. This value is expected to increase rapidly considering the interest in the issues of ML and AI. 3.2.2. Publications by Years Consistent with what is defined in Section 3.1.1., the study shows that the number of items included in the analysis is definitely low for the entire period before I4.0 and then suddenly increases, starting in 2012. The data shown in Figure 3 also show two holes in the 2001–2008 and 2008–2011 intervals. This means that the technological applications were limited before it became an enabling technology of I4.0 in all respects, only to have a peak of technological implementation, as was foreseeable. Sustainability 2020, 12, 492 8 of 24 Figure 3. Years of publications. With reference to 2019, the figure refers to the first months of the year, so it is plausible that during the year, there will be a further increase in the documents in the literature. Furthermore, an increase is expected in the coming years, in parallel with the growth of I4.0 3.2.3. Most Collaborative Authors The analysis highlighted that most of publications have more than one author. From this point of view, it is possible to identify the number of authors for each document. As shown in Figure 4, most of the manuscripts were produced by groups ranging from two to five authors. The indicators chosen to perform the analysis were total papers (TPs), which is the total number of publications. Figure 4. Collaborative groups. Sustainability 2020, 12, 492 9 of 24 3.2.4. Research Areas Analysis The total research area analysis collected from the 82 papers was 164 because each paper can be considered as more than one research area analysis. Given the small number of documents identified in the period before I4.0, the ranking refers mostly to the current industrial revolution. Also, in this case, the result is consistent with the introduction of paradigm 4.0, which has intensified research and the adoption of technology. The first thematic areas and disciplines that are at the top of the ranking are computer science, engineering and biochemistry, genetics, and molecular Biology, respectively, with 29%, 23%, and 6% of publications. Furthermore, the other disciplines identified for which applicative findings are found are considered transversal to the first three disciplines and this is a consequence of I4.0. In terms of the percentage contribution, the first three areas cover about 60% of the papers considered. Considering the top 20 research areas, given the frequency of the research areas’ distribution, Figure 5 shows a higher level of concentration in the disciplines indicated above. In fact, in terms of the percentage contribution, the first five areas cover about 70% of the papers considered. Regardless, by only counting research areas found once, there is a total of 27. This means two things: The large number of fields in which this kind of research is involved; and Most papers have a transversal approach, that is, the object of each research crosses more than one field of application, thus involving more research areas. This confirms the wide interest in these subjects from several fields. Figure 5. Top 20 research areas contributions. 3.2.5. Top Source Journals Analysis In this section, the top 20 sources or journals that were published most frequently were extracted. A journal is a time‐bound publication with the objective of promoting and monitoring the progress of the discipline it represents. In this specific case, the total source journals detected from the documents is 74, but, considering the top 20, given the frequency of the source journals’ distribution, only the first 13 sources have more than one paper published, with a total percentage contribution of 43% of the total. After analyzing the sources separately, the results obtained in the two databases were found to not be the same. In WoS, the top source journal was IEEE Access with two publications while in Scopus, the top source journals are Procedia Computer Science, Matec Web of Conferences, and Machine Learning with four publications, which contribute 5% of the total. Sustainability 2020, 12, 492 10 of 24 Aggregating the data collected from the two databases, the ranking moves to that obtained by Scopus, making sure that IEEE Access is no longer first in the standings, but only eighth, and that the former are precisely those of Scopus: Procedia Computer Science, Matec Web Of Conferences, and Machine Learning, with the same number of publications. Next, the 10 source journals have a 3% publication contribution while the rest have a one‐to‐one relationship (1%) with the corresponding source journal. The low level of concentration of the sources suggests that there is a great deal of interest in these topics from several scientific journals. As a matter of fact, it is foreseeable that specialized sector sources (AI Magazine and Machine Learning) are among the first 13; however, it is interesting to note that other sources are involved, such as Sustainability Switzerland or BMC Bioinformatics and Nuclear Engineering and Design. Figure 6 shows the top 20 source journals contributions. Figure 6. Top 20 source journals contributions. 3.2.6. Country Analysis The results that emerged through research on the two databases are consistent with each other. In both cases, in fact, the countries that give the greatest contribution to the research are China and the United States (Figure 8). The result is obvious since in China and the United States, more than 1.3 billion and 0.3 millions of people live, respectively, and so there are more researchers than in the single European nations. Focusing on Europe, Germany published more papers than any other European country. This is not a random result: I4.0 was born in Germany, so this outcome was expected. However, the following observation cannot be ignored from this data: The USA and China carry the first two places in the list while it is not the same for European countries. Europe, despite its talents and resources, has lost ground. Presenting its report on artificial intelligence, the French deputy and mathematician Cédric Villani declared that, “Europe must be able to compete with China and the United States while protecting its citizens and pointing the way to go on ethical issues”. If we are not careful, the 21st century rules will not be defined in Brussels, but in Shanghai. Artificial intelligence is also a land marked by intense geopolitical rivalry that could redefine global power relations. Sustainability 2020, 12, 492 11 of 24 Even so, regarding Europe, it is worthy to also note that since 2017, France, Germany, and Italy have intensified their trilateral cooperation to promote digitizing the manufacturing industry. In this regard, in the near future, we expect a significant evolution of smart production initiatives and therefore an increase in scientific research. Figure 7 shows the country contribution distribution. Figure 7. Top 20 countries contributions. 3.2.7. Affiliation Analysis The total number of affiliation detected from the 82 papers is 153. Also, in this case, considering the top 20, the frequency of the affiliation distribution shows that most papers have a one‐to‐one relationship with the corresponding affiliation. Only the first four affiliations have three papers (2% of the contribution) and the second four have two papers (1.3% of the contribution). This result gives us information about the wide interest on this subject from several universities and research centers all over the world. Then, the affiliation analysis confirms the result of the country analysis (Figure 8). In fact, if we try to sum the first eight affiliations by their own country, the outcome is: Nine papers from China; Six papers from Germany; and Five papers from the USA. In September 2018, the most important event on artificial intelligence was held in Shanghai. China is very determined to focus on future technologies. For some months, China has become the world’s leading power in terms of scientific publications. Late in the 20th century technologies, China chose to do what the English‐speaking people call a “frog jump” and focus on 21st century technologies. China, with its 800 million Internet users and without any privacy protection policy, has access to more personal data than the United States and Europe. Sustainability 2020, 12, 492 12 of 24 Figure 8. Top 20 institute affiliations contributions. 3.2.8. Top Keywords Analysis Through NVivo 12, the top 20 keywords were extracted directly, which are those that always appear in association with each document. Starting from this classification, the graphic representation, a word cloud shape, of the keywords (Figure 9) was extracted. It can be noted that the most used term is precisely “machine”, “learning”, and “intelligence”, which the software represents with greater characters than all the other terms. Figure 9. Top 20 keywords cloud contribution by NVivo 12. The font size describes how much the keyword is indexed. Another mode of representation is the tree words (Figure 10). Also, in this case, the most indexed words are those represented in the larger boxes. Sustainability 2020, 12, 492 13 of 24 Figure 10. Top 20 keywords tree contribution by Nvivo 12. As expected, the most indexed words are obviously “learning”, “machine”, and “intelligence”, with high numbers. It is logical that among the first results, words that recall the technology itself were obtained, but it is interesting to note that words referring to other fields of AI applications are also indexed. The reason is to be found in the fact that AI and ML are technologies that cross all the sectors involved in I4.0 and that, therefore, do not remain circumscribed. Specifically, words, such as “data”, “neural”, “decision”, and “management”, are very or average indexed, demonstrating the fact that AI also extends to many other sectors. Another tool for the analysis for keywords is the UCINET software, through which social networks analysis is carried out. Social network analysis (SNA), which is also often called social network theory, is a modern technology of social relations. SNA finds application in various social sciences, and has recently been used in the study of various phenomena, such as international trade, information dissemination, the study of institutions, and the functioning of organizations. The analysis of the use of the term SNA in the scientific literature shows that in the last five years, there has been exponential growth of the use of this mode of computable representation of complex and interdependent phenomena. The software returns a graph representing a socio‐metric network (Figure 11), which draws the relationships that exist within the class. Each relationship is represented by an oriented arrow. Sustainability 2020, 12, 492 14 of 24 Figure 11. Keywords Network by UCINET Software. In Figure 11, nodes and leaves can be identified. The nodes are represented by red circles and are correspond to the most common keywords, where the words “machine”, “learning”, “artificial”, and “intelligence” have been united to form the key words “machine learning” and “artificial intelligence”. The leaves, on the other hand, are represented by blue squares and correspond to the articles. To facilitate reading, the document titles were not inserted, but the (Identification) ID count for each of them is shown in the Appendix A. The first thing that can be noticed is the isolation of many leaves that are not connected to the nodes. This means that the corresponding documents are not described by the keywords represented by the nodes. Really, they are characterized by keywords that have a frequency of the order of units. Another thing that easily jumps to the eye is a density that is larger around the keywords “machine learning”, “decision”, “data”, “algorithm”, “system”, “artificial intelligence”, “method”, and “optimization”. This density is reflected in the cloud and the box chart produced by NVivo 12. Therefore, we can say that those are the words that most often appear in the documents analyzed, emphasizing, once again, that they include terms that do not just refer to the technology object of study but also to other fields of application. 3.3. Phase 3: Discussion 3.3.1. Benefits of Artificial Intelligence and Machine Learning in Industrial Contexts From the analysis of the research carried out, the first information that emerged is that there is a growing importance of innovation and digitalization in products, services, and processes. Consequently, it means that the adoption of advanced manufacturing technologies, such AI and ML, is an emerging issue. In other words, AI/ML algorithms represent an opportunity to handle high dimensional problems and data. The interest in the subject is extended to all scientific sectors, but with a focus on computer science and engineering. The most significant benefits of using AI and ML in industrial sectors include: 1) Greater innovation, 2) process optimization, 3) resources optimization, and 4) improved quality. After all, AI with ML is one of the most important technologies today and is transforming the economy and society, as demonstrated by the over 340,000 patent applications filed since the 1950s. Sustainability 2020, 12, 492 15 of 24 Other information that emerged is about the authors and affiliation. Many of these are in a 1:1 ratio compared to the selected documents and this supports the fact that there is no interest in technological applications in one direction, but that, once again, the interest is very wide in the scientific community. Furthermore, it can be said that the countries most interested in scientific research are the USA, China and European countries. This result is not a surprise. In terms of investment, the effort currently being deployed by the United States and China to acquire dominance in the AI sector is far superior to that of other countries. More specifically, China has clearly stated its ambition to become a world leader in AI by 2030. Among the Chinese plans, of absolute interest is the “Made in China 2025” plan, dedicated to the manufacturing sector; the “Internet +” plan is also dedicated to smart manufacturing and innovation. A direct consequence of the above considerations could be having new generations of researchers who will contribute to future comparisons, accompanied by new questions for investigations. 3.3.2. Emerging Trends of Artificial Intelligence and Machine Learning in Sustainable Manufacturing From the perspective of sustainability, the analysis highlighted that the new paradigm of smart manufacturing has the potential to bring fundamental improvements in the industry by addressing the issue of scarce resources and improving productivity. In fact, the survey pointed out a growing interest on applications related to green manufacturing and sustainable development, proving that AI/ML play an important role in increasing sustainability through the intelligent utilization of materials and energy consumption (i.e., reduction of energy consumption and pollutant emissions, environmental footprint monitoring and evaluation, etc.). Furthermore, it emerged that AI/ML algorithms present a wide array of applications that provide an opportunity for sustainable development, which will involve several stakeholders from different countries and sectors, including inventory and supply chain management, predictive maintenance, and production. In particular, Pérez‐Ortiz, Jiménez‐Fernández, Gutiérrez et al. reviewed the most important classification algorithms applied to renewable energy (RE) problems. The main use of algorithms is as a tool for predictive analysis and consequently for data preprocessing, result interpretation, or evaluation in order to improve energy and resource management. In this context, it also emerged that AI/ML have been successfully utilized in various processes’ optimization, applications in manufacturing, and predictive maintenance in different industries. The work published by Lieber, Stolpe, Konrad et al. represents a good research within steel industry production. It proposes an approach for automatically preprocessing value series data to improve the quality of the process and products. It means that AI/ML techniques were found to provide promising potential for improved quality control optimization in manufacturing systems. Appropriate adoption of AI/ML technologies will promote sustainable manufacturing and the formation of a new generation of intelligent manufacturing, including all areas that characterize a sustainable process, ranging from the supply chain management to quality control, to predictive maintenance, to energy consumption. Table 5 summarizes the main areas in sustainable manufacturing, their respective key objectives, and the main AI/ML applications. Table 5. Main areas in sustainable manufacturing. Main Areas in Key AI/ML Sustainable Objective Applications Manufacturing Improves transparency, accelerates Supply Chain Ready product available in the decision‐making, and produces Management appropriate place at a specific time accurate demand forecasting Sustainability 2020, 12, 492 16 of 24 Recognize the early signs of potential production failures within Improves the response time and Quality Control the shortest terms in order to save allows eliminating possible failures resources and sustain operational efficiency Detects possible production Creates accurate forecasts as to Predictive malfunctions that may cause when the machinery must be Maintenance product quality issues repaired Improves excessive use of certain materials, redundant production Energy Recommendations that will strike a scrap waste, inefficient supply chain consumption balance in energy use management, logistics, and unequal distribution of energy resources. However, the relationship between I.4 technologies, AI/ML, and sustainability demands a more conceptual and empirical investigation. This is corroborated by an article recently published in Nature Sustainability by the director of the Earth Institute at Columbia University, Jeffrey Sachs, and other experts, and the so‐called Fourth Industrial Revolution (made of artificial intelligence and other digital technologies) is even cited as one of the six transformations necessary to achieve the sustainable development goals. 4. Conclusions This research focused on the study of the state of the art of AI and ML applications, selecting literature on what has now become a particularly hot topic in scientific research. The literature available on any subject is now wide and a complete coverage of all the documents published with respect to a particular topic can be challenging or even impossible. Therefore, a systematic selection of the most relevant literature was implemented. This document provides a systematic review of applications in various scientific fields using ML techniques. For the selection of documents, objective and clear methods of investigation were used, independent of the experience of the researchers. Among the objectives of the document, it aimed to not only provide a comprehensive framework on the literature on the research of AI and ML but also a starting point for integrating knowledge through research in this area and to suggest future research paths. It is important to underline that this document was produced using only two databases, i.e., WoS and Scopus, in which only documents with open access were included. There are, therefore, many other documents with restricted access and other indexing databases, such as Google Scholar, that could be integrated for future research. Author Contributions: All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. Funding: This work has been conducted under the framework of the Italian project “Linee Guida per I4.0‐ Campania”—funded by Regione Campania within POR FSE 2014–2020 Asse IV “Capacità istituzionale e amministrativa” objectives 18 (RA) 11.3 and 21 (RA) 11.6. Conflicts of Interest: The authors declare no conflict of interest. Sustainability 2020, 12, 492 17 of 24 Appendix A ID Research ID Year Title Authors Source Title TC Count Source doc Larrañaga, P.; Calvo, B.; Santana, R.; Bielza, C.; 1 SCP 2 2006 Machine learning in bioinformatics Galdiano, J.; Inza, I.; Lozano, J.A.; Armañanzas, Briefings in Bioinformatics 298 R.; Santafé, G.; Pérez, A.; Robles, V. Data‐driven modelling: Some past 2 WoS 62 2008 Solomatine, D.P.; Ostfeld, A. Journal of Hydroinformatics 160 experiences and new approaches Learning from imbalanced data: Open 3 SCP 26 2016 Krawczyk, B. Progress in Artificial Intelligence 119 challenges and future directions 4 WoS 63 2001 Computer go: An AI oriented survey Bouzy, B; Cazenave, T Artificial Intelligence 114 Structured machine learning: The next Dietterich, T.G.; Domingos, P.; Getoor, L.; 5 SCP 6 2008 Machine Learning 75 ten years Muggleton, S.; Tadepalli, P. Machine learning in manufacturing: Production and Manufacturing 6 SCP 28 2016 Advantages, challenges, and Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.D. 52 Research applications Machine learning paradigms for next‐ Jiang, C.; Zhang, H.; Ren, Y.; Han, Z.; Chen, 7 WoS 64 2017 Ieee Wireless Communications 50 generation wireless networks K.C.; Hanzo, L. Machine learning techniques in disease 8 SCP 3 2006 forecasting: A case study on rice blast Kaundal, R.; Kapoor, A.A.; Raghava, G.P.S. BMC Bioinformatics 48 prediction A comparison of machine learning algorithms for chemical toxicity Judson, R.; Elloumi, F.; Woodrow, R.W.; Li, Z.; 9 SCP 4 2008 BMC Bioinformatics 45 classification using a simulated multi‐ Shah, I. scale data model A review of intelligent driving style 10 SCP 19 2015 analysis systems and related artificial Meiring, G.A.M.; Myburgh, H.C. Sensors (Switzerland) 33 intelligence algorithms A machine learning framework for gait classification using inertial sensors: Mannini, A.; Trojaniello, D.; Cereatti, A.; 11 SCP 21 2016 Sensors 31 Application to elderly, post‐stroke and Sabatini, A.M. huntington’s disease patients Application of machine learning in SNP Matukumalli, L.K.; Grefenstette, J.J.; Hyten, D.L.; 12 SCP 1 2006 BMC Bioinformatics 30 discovery Choi, I.Y.; Cregan, P.B.; Van Tassell, C.P. Beam search algorithms for multilabel 13 SCP 10 2013 Kumar, A.; Vembu, S.; Menon, A.K.; Elkan, C. Machine Learning 29 learning Burke, Robin; Felfernig, Alexander; Goeker, 14 WoS 65 2011 Recommender Systems: An Overview Ai Magazine 29 M.H. Biomedical informatics for computer‐ 15 SCP 11 2013 aided decision support systems: A Belle, A.; Kon, M.A.; Najarian, K. The Scientific World Journal 27 survey Sustainability 2020, 12, 492 18 of 24 Application of machine learning to Tixier, A.J.P.; Hallowell, M.R.; Rajagopalan, B.; 16 SCP 23 2016 Automation in Construction 21 construction injury prediction Bowman, D. Quality prediction in interlinked manufacturing processes based on Lieber, D.; Stolpe, M.; Konrad, B.; Deuse, J.; 17 SCP 12 2013 Procedia CIRP 18 supervised & unsupervised machine Morik, K. learning Semantic framework of internet of 18 SCP 29 2016 Zhang, N.; Chen, H.; Chen, X.; Chen, J. Sensors 17 things for smart cities: Case studies Support vector machines in structural Çevik, A.; KURTOĞLU, A.E.; Bilgehan, M.; Journal of Civil Engineering and 19 SCP 20 2015 15 engineering: A review Gülşan, M.E.; Albegmprli, H.M. Management A review of classification problems and Pérez‐Ortiz, M.; Jiménez‐Fernández, S.; 20 SCP 25 2016 algorithms in renewable energy Gutiérrez, P.A.; (...); Hervás‐Martínez, C.; Energies 15 applications Salcedo‐Sanz, S. Artificial intelligence (AI) methods in Mata, J.; de Miguel, I.; Durán, R.J.; (...); Jukan, A.; Optical Switching and 21 SCP 43 2018 optical networks: A comprehensive 15 Chamania, M. Networking survey Fault diagnosis of automobile gearbox Praveenkumar, T.; Saimurugan, M.; 22 SCP 14 2014 Procedia Engineering 14 based on machine learning techniques Krishnakumar, P.; Ramachandran, K.I. Improving active Mealy machine Aarts, F.; Kuppens, H.; Tretmans, J.; Vaandrager, 23 SCP 16 2014 learning for protocol conformance Machine Learning 11 F.; Verwer, S. testing Strategies and Principles of Distributed 24 WoS 66 2016 Xing, E.P.; Ho, Q.; Xie, P.; Wei, D. Engineering 11 Machine Learning on Big Data Recent advances on artificial intelligence Eurasip Journal on Wireless 25 WoS 67 2015 and learning techniques in cognitive Abbas, N.; Nasser, Y.; El Ahmad, K. 11 Communications and Networking radio networks Artificial intelligence (AI) methods in Mata, J.; de Miguel, I.; Duran, R.J.; Merayo, N.; Optical Switching and 26 WoS 68 2018 optical networks: A comprehensive 9 Singh, S.K.; Jukan, A.; Chamania, M. Networking survey A big data driven sustainable Journal of Computational Science 27 SCP 40 2018 manufacturing framework for condition‐ Kumar, A.; Shankar, R.; Thakur, L.S. 8 27, pp. 428–439 based maintenance prediction Research and Application of a Novel Hybrid Model Based on Data Selection 28 WoS 69 2017 Yang, W.; Wang, J.; Wang, R. Entropy 8 and Artificial Intelligence Algorithm for Short Term Load Forecasting Context Aware Process Mining in 29 SCP 33 2017 Becker, T.; Intoyoad, W. Procedia CIRP 7 Logistics Applications of machine learning 30 SCP 24 2016 methods to identifying and predicting Marasco, D.E.; Kontokosta, C.E. Energy and Buildings 6 building retrofit opportunities Sustainability 2020, 12, 492 19 of 24 Operational Demand Forecasting in District Heating Systems Using Johansson, C.; Bergkvist, M.; Geysen, D.; (...); 31 SCP 37 2017 Energy Procedia 6 Ensembles of Online Machine Learning Lavesson, N.; Vanhoudt, D. Algorithms Advances in Multiple Criteria Decision 32 WoS 70 2018 Making for Sustainability: Modeling and Shen, K.Y.; Tzeng, G.H. Sustainability 6 Applications Frontiers of Information Hybrid‐augmented intelligence: Zheng, N.N.; Liu, Z.Y.; Ren, P.J.; Ma, Y.Q.; Chen, 33 WoS 71 2017 Technology & Electronic 6 Collaboration and cognition S.T.; Yu, S.Y.; Xue, J.R.; Chen, B.D.; Wang, F.Y. Engineering Lecture Notes in Computer Science (including subseries Performance evaluation of the NVIDIA Lecture Notes in Artificial 34 SCP 5 2008 GeForce 8800 GTX GPU for machine El Zein, A.; McCreath, E.; Rendell, A.; Smola, A. 5 Intelligence and Lecture Notes in learning Bioinformatics) 5101 LNCS(PART 1), pp. 466–475 A review of artificial intelligence International Journal of Electronics 35 SCP 7 2011 Bilski, A. 5 algorithms in document classification and Telecommunications Proceedings of the ACM SIGKDD An architecture for agile machine International Conference on 36 SCP 18 2015 Schleier‐Smith, J. 4 learning in real‐time applications Knowledge Discovery and Data Mining Machine learning in agriculture: A Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; 37 SCP 52 2018 Sensors 4 review Bochtis, D. Application of Information Processes 38 SCP 22 2016 Applicative Modelling to Virtual Zykov, S.; Shumsky, L. Procedia Computer Science 3 Machines Auto Configuration Geometry‐aware principal component 39 SCP 34 2017 analysis for symmetric positive definite Horev, I.; Yger, F.; Sugiyama, M. Machine Learning 3 matrices A Fuzzy Least Squares Support Tensor International Journal of Emerging 40 SCP 17 2015 Zhang, R.; Zhou, Z. 2 Machines in Machine Learning Technologies in Learning Nuclear energy system’s behavior and Gomez Fernandez, M.; Tokuhiro, A.; Welter, K.; 41 SCP 36 2017 Nuclear Engineering and Design 2 decision making using machine learning Wu, Q. Application study of machine learning 42 SCP 9 2013 Qiu, T.; Zhang, S.; Zhou, H.; Bai, X.; Liu, P. Information Technology Journal 1 in lightning forecasting WOWMON: A machine learning‐based 43 SCP 30 2016 profiler for self‐adaptive Zhang, X.; Abbasi, H.; Huck, K.; Malony, A.D. Procedia Computer Science 1 instrumentation of scientific workflows An event search platform using machine Proceedings of the International 44 SCP 31 2017 Rodrigues, M.A.; Silva, R.R.; Bernardino, J. 1 learning Conference on Software Sustainability 2020, 12, 492 20 of 24 Engineering and Knowledge Engineering, SEKE Automated business process management‐in times of digital 45 SCP 32 2017 Paschek, D.; Luminosu, C.T.; Draghici, A. MATEC Web of Conferences 1 transformation using machine learning or artificial intelligence Application of machine learning methods in big data analytics at 46 SCP 42 2018 Valpeters, M.; Kireev, I.; Ivanov, N. MATEC Web of Conferences 1 management of contracts in the construction industry Wiley Interdisciplinary Reviews: Data mining and machine learning in 47 SCP 48 2018 Yildirim, P.; Birant, D.; Alpyildiz, T. Data Mining and Knowledge 1 textile industry Discovery Big Data Analytics, Machine Learning, Kibria, M.G.; Kien, N.; Villardi, G.P.; Zhao, O.; 48 WoS 72 2018 and Artificial Intelligence in Next‐ Ieee Access 1 Ishizu, K.; Kojima, F. Generation Wireless Networks 8th International Workshop Quantum neuromorphic hardware for 49 WoS 73 2017 Prati, E. Dice2016: Spacetime ‐ Matter ‐ 1 quantum artificial intelligence Quantum Mechanics International Conference on Exploiting Computational intelligence Communications, Management, 50 WoS 74 2015 Paradigms in e‐Technologies and Said, H.M.; Salem, A.M. 1 and Information Technology Activities (Iccmit’2015) International Conference on Sentiment Analysis of Products Using 51 WoS 75 2012 Unnamalai, K. Modelling Optimization and 1 Web Computing Taxonomy development and its impact Faliagka, E.; Karydis, I.; Rigou, M.; (...); IFIP Advances in Information and 52 SCP 8 2012 0 on a self‐learning e‐recruitment system Tsakalidis, A.; Tzimas, G. Communication Technology Research on adaptive multi‐filtering 53 SCP 13 2013 Cao, X.F.; Kang, W.; Shi, Q.; Shi, F.F. Information Technology Journal 0 model of network sensitive information Grade: Machine‐learning support for 54 SCP 15 2014 Waters, A.; Miikkulainen, R. AI Magazine 0 graduate admissions Leveraging linked open data Turkish Journal of Electrical 55 SCP 27 2016 information extraction for data mining Mahule, R.; Vyas, O.P. Engineering and Computer 0 applications Sciences Rapid prototyping IoT solutions based Rizzo, A.; Montefoschi, F.; Caporali, M.; (...); ACM International Conference 56 SCP 38 2017 0 on Machine Learning Burresi, G.; Giorgi, R. Proceeding Series Towards automatic learning of Electronic Proceedings in 57 SCP 39 2017 heuristics for mechanical Vigueras, G.; Carro, M.; Tamarit, S.; Mariño, J. Theoretical Computer Science, 0 transformations of procedural code EPTCS Application of artificial intelligence Zajačko, I.; Gál, T.; Ságová, Z.; Mateichyk, V.; 58 SCP 41 2018 MATEC Web of Conferences 0 principles in mechanical engineering Wiecek, D. Sustainability 2020, 12, 492 21 of 24 Artificial Intelligence in Medical Chan, Y.K.; Chen, Y.F.; Pham, T.; Chang, W.; 59 SCP 44 2018 Journal of Healthcare Engineering 0 Applications Hsieh, M.Y. A semantic internet of things framework ACM International Conference 60 SCP 45 2018 using machine learning approach based Ding, P.W.; Hsu, I.C. 0 Proceeding Series on cloud computing A Survey on Machine Learning‐Based Wireless Communications and 61 SCP 46 2018 Mobile Big Data Analysis: Challenges Xie, J.; Song, Z.; Li, Y.; (...); Zhang, J.; Guo, J. 0 Mobile Computing and Applications Big Data and Machine Learning Based 62 SCP 47 2018 Kaur, P.; Sharma, M.; Mittal, M. Procedia Computer Science 0 Secure Healthcare Framework Discovering discontinuity in big ACM Transactions on 63 SCP 49 2018 Tuarob, S.; Strong, R.; Chandra, A.; Tucker, C.S. 0 financial transaction data Management Information Systems Introducing children to machine IDC 2018 ‐ Proceedings of the 2018 64 SCP 50 2018 learning concepts through hands‐on Hitron, T.; Erel, H.; Wald, I.; Zuckerman, O. ACM Conference on Interaction 0 experience Design and Children Machine learning for software Proceedings ‐ International 65 SCP 51 2018 engineering: Models, methods, and Meinke, K.; Bennaceur, A. Conference on Software 0 applications Engineering Machine Learning in IT Service 66 SCP 53 2018 Zuev, D.; Kalistratov, A.; Zuev, A. Procedia Computer Science 0 Management Research and application of computer 67 SCP 54 2018 control system based on complex neural Yang, R. MATEC Web of Conferences 0 network Interdisciplinary Journal of Text classification techniques: A 68 SCP 55 2018 Thangaraj, M.; Sivakami, M. Information, Knowledge, and 0 literature review Management A Machine Learning Method for Sun, S.; Zhang, J.; Bi, J.; Wang, Y.; Moghaddam, Journal of Advanced 69 SCP 56 2019 Predicting Driving Range of Battery 0 M.H.Y. Transportation Electric Vehicles An empirical comparison of machine‐ Munkhdalai, L.; Munkhdalai, T.; Namsrai, O.E.; 70 SCP 57 2019 learning methods on bank client credit Sustainability 0 Lee, J.Y.; Ryu, K.H. assessments Comparison of multiple linear regression, artificial neural network, Niu, W.J.; Feng, Z.K.; Feng, B.F.; (...); Cheng, 71 SCP 58 2019 extreme learning machine, and support Water 0 C.T.; Zhou, J.Z. vector machine in deriving operation rule of hydropower reservoir Development and evaluation of a low‐ cost and smart technology for precision Computers and Electronics in 72 SCP 59 2019 Partel, V.; Charan Kakarla, S.; Ampatzidis, Y. 0 weed management utilizing artificial Agriculture intelligence Sustainability 2020, 12, 492 22 of 24 Identifying known and unknown mobile Security and Communication 73 SCP 60 2019 application traffic using a multilevel Zhao, S.; Chen, S.; Sun, Y.; (...); Su, J.; Su, C. 0 Networks classifier Optimized Clustering Algorithms for Wohwe Sambo, D.; Yenke, B.O.; Förster, A.; 74 SCP 61 2019 Large Wireless Sensor Networks: A Sensors 0 Dayang, P. Review FPGA‐Based Accelerators of Deep 75 WoS 76 2019 Learning Networks for Learning and Shawahna, A.; Sait, S.M.; El‐Maleh, A. Ieee Access 0 Classification: A Review A quantum machine learning algorithm 76 WoS 77 2018 Gao, X.; Zhang, Z.Y.; Duan, L.M. Science Advances 0 based on generative models Machine Learning for Network Journal of Optical 77 WoS 78 2018 Automation: Overview, Architecture, Rafique, D.; Velasco, L. 0 Communications and Networking and Applications A wireless sensor data‐based coal mine gas monitoring algorithm with least International Journal of 78 WoS 79 2018 squares support vector machines Chen, P.; Xie, Y.; Jin, P.; Zhang, D. 0 Distributed Sensor Networks optimized by swarm intelligence techniques Nuclear energy system’s behavior and Fernandez, M.G.; Tokuhiro, A.; Welter, K.; Wu, 79 WoS 80 2017 Nuclear Engineering and Design 0 decision making using machine learning Q. Automated business process 8th International Conference on management—In times of digital Manufacturing Science and 80 WoS 81 2017 Paschek, D.; Luminosu, C.T.; Draghici, A. 0 transformation using machine learning Education (Mse 2017)—Trends in or artificial intelligence New Industrial Revolution 27Th International Conference on The Evaluation of Resonance Frequency Flexible Automation and 81 WoS 82 2017 for Piezoelectric Transducers by Chang, F.M. 0 Intelligent Manufacturing, Faim Machine Learning Methods 2017 From Extraction to Generation of Design Information Paradigm Shift in Data International Conference on 82 WoS 83 2017 Chiba, K.; Nakata, M. 0 Mining via Evolutionary Learning Computational Science (Iccs 2017) Classifier System Sustainability 2020, 12, 492 23 of 24 References 1. Gupta, N.A. Literature Survey on Artificial Intelligence. 2017. Available online: https://www.ijert.org/research/a‐literature‐survey‐on‐artificial‐intelligence‐IJERTCONV5IS19015.pdf (accessed on 7 January 2020). 2. McCarthy, J.; Minsky, M.L.; Rochester, N.; Shannon, C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. AI Mag. 2006, 27, 12. 3. Moore, A. Carnegie Mellon Dean of Computer Science on the Future of AI. Available online: https://www.forbes.com/sites/peterhigh/2017/10/30/carnegie‐mellon‐dean‐of‐computer‐science‐on‐the‐ future‐of‐ai/#3a283c652197 (accessed on 7 January 2020). 4. Becker, A.; Bar‐Yehuda, R.; Geiger, D. Randomised algorithms for the loop cutset problem. J. Artif. Intell. Res. 2000, 12, 219–234. 5. Singer, J.; Gent, I.P.; Smaill, A. Backbone fragility and the local search cost peak. J. Artif. Intell. Res. 2000, 12, 235–270. 6. Chen, X.; Van Beek, P. Conflict‐directed backjumping revisited. J. Artif. Intell. Res. 2001, 14, 53–81. 7. Hong, J. Goal recognition through goal graph analysis. J. Artif. Intell. Res. 2001, 15, 1–30. 8. Stone, P.; Littman, M.L.; Singh, S.; Kearns, M. ATTAC‐2000: An adaptive autonomous bidding agent. J. Artif. Intell. Res. 2000, 15, 189–206. 9. Peng, Y.; Zhang, X. Integrative data mining in systems biology: from text to network mining. Artif. Intell. Med. 2007, 41, 83–86. 10. Zhou, X.; Liu, B.; Wu, Z.; Feng, Y. Integrative mining of traditional Chines medicine literature and MEDLINE for functional gene networks. Artif. Intell. Med. 2007, 41, 87–104. 11. Wang, S.; Wang, Y.; Du, W.; Sun, F.; Wang, X.; Zhou, C.; Liang, Y. A multi‐approaches‐guided genetic algorithm with application to operon prediction. Artif. Intell. Med. 2007, 41, 151–159. 12. Halal, W.E. Artificial intelligence is almost here. Horizon 2003, 11, 37–38. Available online: https://www.emerald.com/insight/content/doi/10.1108/10748120310486771/full/html (accessed 7 January 2020). 13. Masnikosa, V.P. The fundamental problem of an artificial intelligence realization. Kybernetes 1998, 27, 71– 80 14. Metaxiotis, K.; Ergazakis, K.; Samouilidis, E.; Psarras, J. Decision support through knowledge management: The role of the artificial intelligence. Inf. Manag. Comput. Secur. 2003, 11, 216–221. 15. Raynor, W.J. The international dictionary of artificial intelligence. Ref. Rev. 2000, 14, 1–380. 16. Stefanuk, V.L.; Zhozhikashvili, A.V. Productions and rules in artificial intelligence. Kybernetes 2002, 31, 817– 826. 17. Tay, D.P.H.; Ho, D.K.H., Artificial intelligence and the mass appraisal of residential apartments. J. Prop. Valuat. Invest. 1992, 10, 525–540. 18. Wongpinunwatana, N.; Ferguson, C.; Bowen, P. An experimental investigation of the effects of artificial intelligence systems on the training of novice auditors. Manag. Audit. J. 2000, 15, 306–318. 19. Oke, S.A. A literature review on artificial intelligence. Int. J. Inf. Manag. Sci. 2008, 19, 535–570. 20. Carvalho, T.P.; Soares, F.A.A.M.N.; Vita, R.; da Francisco, P.R.; Basto, J.P.; Alcalá, S.G.S. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019, 1, 1–12. 21. Majorel Deutschland GmbH Artificial Intelligence and Sustainability. Available online: https://www.future‐customer.com/artificial‐intelligence‐and‐sustainability/ (accessed on 8 January 2020). 22. Markham, I.S.; Mathieu, R.G.; Wray, B.A. Kanban setting through artificial intelligence: A comparative study of artificial neural networks and decision trees. Integr. Manuf. Syst. 2000, 11, 239–246. 23. Kotsiantis, S.B.; Zaharakis, I.; Pintelas, P. Supervised machine learning: A review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 2007, 160, 3–24. 24. Cortes, C.; Vapnik, V. Support‐vector networks. Mach. Learn. 1995, 20, 273–297. 25. Kitchenham, B. Procedures for Performing Systematic Reviews. Technical Report TR/SE‐0401. 2004. Available online: https://pdfs.semanticscholar.org/2989/0a936639862f45cb9a987dd599dce9759bf5.pdf?_ga=2.7241591.475223 78.1578382825‐243572483.1578382825 (accessed on 7 January 2020). 26. Duan, Y.; Edwards, J.S.; Dwivedi, Y.K. Artificial intelligence for decision making in the era of Big Data— Evolution, challenges and research agenda. Int. J. Inf. Manag. 2019, 48, 63–71. Sustainability 2020, 12, 492 24 of 24 27. De Felice, F.; Petrillo, A.; Zomparelli, F. Prospective design of smart manufacturing: An Italian pilot case study. Manuf. Lett. 2018, 15, 81–85. 28. Larrañaga, P.; Calvo, B.; Santana, R.; Bielza, C.; Galdiano, J.; Inza, I.; Lozano, J.A.; Armañanzas, R.; Santafé, G.; Pérez, A.; et al. Machine Learning. in Bioinformatics. Brief. Bioinform. 2006, 7, 86–112. 29. Krawczyk, B. Learning from imbalanced data: Open challenges and future directions. Prog. Artif. Intell. 2016, 5, 221–232. 30. Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.D. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016, 4, 23–45. 31. Dutton, T. An Overview of National AI Strategies. Available online: http://www.jaist.ac.jp/~bao/AI/OtherAIstrategies/An%20Overview%20of%20National%20AI%20Strategie s%20%E2%80%93%20Politics%20+%20AI%20%E2%80%93%20Medium.pdf (accessed 8 January 2020). 32. Pérez‐Ortiz, M.; Jiménez‐Fernández, S.; Gutiérrez, P.A.; Alexandre, E.; Hervás‐Martínez, C.; Salcedo‐Sanz, S. A Review of Classification Problems and Algorithms in Renewable Energy Applications. Energies 2016, 9, 607. 33. Lieber, D.; Stolpe, M.; Konrad, B.; Deuse, J.; Morik, K. Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. Procedia CIRP 2013, 7, 193–198. 34. Sachs, J.D.; Schmidt‐Traub, G.; Mazzucato, M.; Messner, D.; Nakicenovic, N.; Rockström, J. Six Transformations to Achieve the Sustainable Development Goals. Nat. Sustain. 2019, 2, 805–814. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).