Annotated Biblography
You will provide an annotated bibliography for each article in one page and discuss how these articles identify a gap in the literature that you wish to address in your dissertation
Computer Automation and Artificial Intelligence :
https://inis.iaea.org/collection/NCLCollectionStore/_Public/25/029/25029353.pdf?r=1&r=1#:~:text=Recent%20development%20in%20computer%20technology,techniques%20with%20an%20improved%20efficiency.&text=Automation%20refers%20to%20the%20act,process%20more%20automatic%20than%20before.&text=Automation%20involves%20the%20integration%20of%20tour%20types%20of%20devices.
Journal of machine learning research :
Provided attachment
Reference
https://owl.purdue.edu/owl/general_writing/common_writing_assignments/annotated_bibliographies/annotated_bibliography_samples.html
https://guides.library.cornell.edu/annotatedbibliography
sustainability
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;
[emailprotected] (R.C.); [emailprotected] (M.T.);
[emailprotected] (G.P.)
2 Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Via G. Di
Biasio, 43, 03043 Cassino FR, Italy; [emailprotected]
* Correspondence: [emailprotected]
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 [1].
In this regard, it is interesting to remember that the father of artificial intelligence, John McCarthy [2],
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 [3].
Sustainability 2020, 12, 492; doi:10.3390/su12020492 www.mdpi.com/journal/sustainability
http://www.mdpi.com/journal/sustainability
http://www.mdpi.com
https://orcid.org/0000-0002-5154-5428
http://dx.doi.org/10.3390/su12020492
http://www.mdpi.com/journal/sustainability
https://www.mdpi.com/2071-1050/12/2/492?type=check_update&version=2
Sustainability 2020, 12, 492 2 of 26
On a very broad account, the areas of artificial intelligence are classified into 16 categories [48].
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 [1218]. 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 [19].
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 [20]. 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 [21].
Thus, subfields of AI, such as machine learning, natural language processing, image processing,
and data mining, have also become an important topic for todays 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 [2224].
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 26
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 [25]. 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 26
Sustainability 2020, 12, x FOR PEER REVIEW 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.
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 26
Table 1. Keywords and time period.
Keywords Time Period
Artificial Intelligence
19992019Machine Learning
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 [26]: Despite some great successes,
such as IBMs 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
Sustainability 2020, 12, 492 6 of 26
Wahlster, Director and CEO of the German Research Center for Artificial Intelligence, addressed the
opening ceremony audience.
Sustainability 2020, 12, x FOR PEER REVIEW 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 [27].
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
Science
Chemical
Engineering
Computer Science
Information Systems
Computer Science Artificial
Intelligence
Automation Control
Systems
Engineering Energy
Materials Science
Multidisciplinary
Environmental Sciences
Environmental
Studies
Materials
Science
Decision Science
Engineering Electrical
Electronic
Computer Science
Hardware Architecture
Operations Research
Management Science
Environmental
Science
Business
Management
and accounting
Telecommunications Industrial Relations Labor Robotics
Engineering Environmental Engineering Manufacturing Thermodynamics
Engineering Industrial
Computer Science Theory
Methods
Energy Fuels
Engineering Civil Engineering Mechanical
Computer Science
Cybernetics
Computer Science Software
Engineering
Multidisciplinary Sciences
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.
Figure 2. Research growth on Scopus and Web of Science.
In fact, this research indicates that over the time period considered (19992019), the number of
published articles remains almost constant until 2013, from which it undergoes an increase.
Subsequently, the increase in the adoption of these ones has led researchers to keep pace with the
growth of I4.0 [27].
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 Science ChemicalEngineering
Computer Science
Information Systems
Computer Science
Artificial Intelligence
Automation Control
Systems
Engineering Energy Materials ScienceMultidisciplinary
Environmental
Sciences Environmental Studies
Materials Science Decision Science Engineering ElectricalElectronic
Computer Science
Hardware Architecture
Operations Research
Management Science
Environmental
Science
Business
Management and
accounting
Telecommunications Industrial RelationsLabor Robotics
Engineering
Environmental
Engineering
Manufacturing Thermodynamics
Engineering Industrial Computer ScienceTheory Methods Energy Fuels
Engineering Civil EngineeringMechanical
Computer Science
Cybernetics
Computer Science
Software Engineering
Multidisciplinary
Sciences
Sustainability 2020, 12, 492 7 of 26
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.
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 Larraaga, Calvo, Santana et al. in 2006 [28] 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 [29], 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. [30] 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 20012008 and 20082011 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 26Sustainability 2020, 12, x FOR PEER REVIEW 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.
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.
Sustainability 2020, 12, x FOR PEER REVIEW 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. Figure 4. Collaborative groups.
Sustainability 2020, 12, 492 9 of 26
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.
Sustainability 2020, 12, x FOR PEER REVIEW 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.
Figure 5. Top 20 research areas contributions.
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.
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
