Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing
Abstract
:1. Introduction
2. Literature Review
3. Work Methodology
4. Summary of Curricula Review
- Knows contemporary tools of AI, including artificial neural networks and genetic algorithms, and can use them to solve complex tasks and problems occurring in management and production engineering;
- Knows modern information technologies, such as Online Analytical Processing (OLAP), data warehouses, methods and tools of AI, and can use them to create intelligent decision systems;
- Knows the methods and tools of AI and can use them to create knowledge bases to support the knowledge management process;
- Knows the basic statistical methods and advanced methods of AI, necessary for the analysis of engineering, business, or production data, and is able to use them to solve tasks;
- Has the ability to use appropriate software to solve specific decision problems, both single-criteria and multi-criteria, and to create advisory systems using the MATLAB Fuzzy Logic Toolbox software for problems occurring in conditions of uncertainty.
- Fundamentals of neural networks. Biological bases of neurocomputing, basic model of neuron and neural network. Basic rules for teaching neural networks (supervised―the delta rule, and unsupervised―the Hebb rule), the concept of error function, the problem of generalization, the role of training and test set. Basic neural network learning algorithm―back propagation method, types of back propagation algorithms. Self-organizing neural networks (SONN): basics, neighborhood function, practical aspects of calculations using Self Organizing Maps (SOM). Neural networks with feedback: Hopfield and Hamming networks. Practical applications of neural networks for solving tasks: classification, clustering, forecasting, image processing, and recognition in automation.
- Application of AI methods: hybrid systems. Decision support system (DSS) based on the knowledge base―intelligent DSS. Design and implementation of intelligent DSS with the use of AI tools (neural networks, genetic algorithms, and fuzzy logic).
- Preparation of training data sets for modeling and simulating artificial neural networks in the Statistica Neural Networks software. Solving practical tasks of classification, forecasting, and grouping with the use of neural networks, including a multi-layer perceptron, radial basis function network and Kohonen neural network.
5. Questionnaire Development
- General questions covering: the country or countries where the student is or was learning, level of study, and field of study;
- AI-related questions;
- IoT-related questions;
- EC-related questions.
- Topics that are connected with AI/IoT/EC;
- Degree of students’ knowledge about tools/software/environment that can be used in AI/IoT/EC;
- Applications and contexts of using AI/IoT/EC;
- Learning techniques used in the education process;
- Difficulties in learning AI/IoT/EC;
- Students’ needs associated with the learning process.
6. Conducting a Survey
7. Results of the Survey
7.1. General Overview
7.2. Artificial Intelligence
- We take into account 65 responses to close-ended questions covering only AI-related issues;
- We assign the following values to answer variants: 0 “not at all” and empty answer, 1 “to a small extent”, 2 “to some extent”, 3 “to a moderate extent”, 4 “to a great extent”, 5 “to a very great extent”;
- We sum up the values from all 65 responses for a given student.
- Understanding the concept of AI issues and how AI works—the complexity of issues causes a barrier that is difficult to overcome for people who want to start learning AI;
- Mathematical issues behind AI;
- High complexity of algorithms;
- Programming languages and coding;
- Finding the right resources for tutorials and other materials to study.
- Too few practical issues and applications of AI in education;
- Lack of real-life case studies drawn primarily from business and companies;
- Too few laboratories, projects, and workshops in AI education;
- Insufficient mathematical preparation of students to learn AI;
- Poor access to good-quality study materials and modern AI tools, which will be constantly updated in line with emerging novelties;
- Too little emphasis on a good understanding of the basics of AI during education.
- Participation in internships, joint international projects, and competitions;
- Infrastructure for testing AI applications;
- Appropriate equipment (more computing resources);
- Contact with companies and professionals;
- Online base of educational materials and AI tools and more programming lessons.
7.3. Internet of Things
- Teaching the theory behind IoT to understand when and why IoT is useful;
- More project-based learning and workshops (e.g., small weekly projects or projects combining all IoT techniques) including examples and practical activities (real scenarios and cases);
- More information on sensors, energy consumption, circuitry;
- Increasing the availability of IoT devices (digital twins, simulators) and easy-to-use technologies;
- More accessible information, materials, and online courses;
- Teaching C++, Python, JavaScript;
- Introducing subjects at universities entirely dedicated to IoT.
7.4. Edge Computing
- Data replication algorithms, exclusion algorithms, fault tolerance algorithms;
- Data replication, shared memory vs. shared nothing, distributed exclusion algorithm (Ricart–Agrawala and Lamport).
- Learning about it a little bit sooner;
- Online seminars.
7.5. Additional Comments
8. Analysis and Discussion
- RQ1: What knowledge and skills in the field of AI, IoT, and EC do the students possess and at what level?
- RQ2: What knowledge and skills in the field of AI, IoT, and EC are missing compared to the topics presented in literature?
- RQ3: To what extent do the students know how to apply AI, IoT, and EC in industrial problems solving?
- RQ4: How useful are different learning techniques for teaching AI, IoT, and EC?
8.1. RQ1: What Knowledge and Skills in the Field of AI, IoT, and EC Do the Students Possess and at What Level?
8.2. RQ2: What Knowledge and Skills in the Field of AI, IoT, and EC Are Missing Compared to the Topics Presented in Literature?
8.3. RQ3: To What Extent Do the Students Know How to Apply AI, IoT, and EC in Industrial Problems Solving?
8.4. RQ4: How Useful Are Different Learning Techniques for Teaching AI, IoT, and EC?
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Universities | University of Pisa (Italy), University of Ioannina (Greece), Rzeszów University of Technology (Poland), University Ramon Llull (Spain) |
Courses | Aeronautics and Space Technology; Artificial Intelligence; Artificial Intelligence Fundamentals; Big Data Engineering; Big Data Mining; Computational Intelligence; Computational Intelligence and Deep Learning; Computational Mathematics for Learning and Data Analysis; Computer engineering (2); Data Analysis with Classic Techniques and Machine Learning Techniques; Data Mining; Data Mining and Machine Learning; Data Science; Electronic Engineering; Human Language Technologies; Internet of Things; Internet of Things and Edge Computing; Machine Learning; Management and Production Engineering; Material Engineering; Technologies for the Digital Transformation of the Companies (MTTD); Mechanics and Mechanical Engineering; Mechatronics; Optimization Methods and Game Theory; Statistical Machine Learning; Supply Chain Management and Technology; Transportation; University Expert in Digital Transformation |
Learning modules | Advanced robot controls; AI (2); Automatics and domotics; Basics of AI; Bigdata, Analytics and AI; Computer networks in materials engineering; Computer vision; Computing infrastructures; Data analysis and visualization; Data analysis with R and Python languages; Data analytics; Data center technologies; Data mining (2); Decision support systems; Diagnostics and supervision of machining systems; Diagnostics of mechatronic systems; Digital transformation of the company; Distributed architecture projects; Embedded systems; Engineering of exploitation of road transportation means; Enterprise management support; Expert systems in aviation; Information technologies for I4.0; Intelligent computer systems; Intelligent measuring systems (2); Interconnection of data networks; Knowledge based systems; Knowledge discovery and data mining; Knowledge management; Local area networks; Methods of AI; IoT, Mobility and process automation; Modeling of production processes; Monographic lecture; NC controlled machines; Networking and IT security; Numerical simulation of technological processes; On-board control systems; Optimization methods; Projects in robotics; Statistics; Storage technologies; Supply chain technology; Technical robotics; Technology optimization; Telematics in transportation |
Education level | BSc, MSc |
ILO | 153 Intended Learning Outcomes (ILO) connected with AI, IoT and EC were reviewed |
TLA | Descriptions of 255 Teaching and Learning Activities (TLA) connected with the mentioned ILOs were reviewed |
Infrastructure indicated in curricula | Amazon Web Services; Cloudera Hadoop (CDH); Google Colab; IBM university platform; Microsoft Azure; KNX; Robots (wheeled and manipulation): Fanuc, ABB, Kawasaki, UR3; Students’ laptop; Virtual machine |
Software indicated in curricula | Aitech Sphinx (DeTreex, DSS, PC-Shell); Apache Spark; Arduino SDK; Azure Databricks; Azure IoT Hub; Azure Machine Learning; Azure Stack Edge; BI Beans; BotSociety; BotEngine; C programming language; C++ programming language; CISCO Packet Tracer; CLOS; Code Composer Studio; Comarch BI; DialogFlow; Docker; Flume; Hadoop Hbase; Hadoop HDFS; Hadoop Map/Reduce; IBM Cloud Watson; Java; JESS; Kafka; Kali Linux; Maple; Matlab; Matlab Fuzzy Logic Toolbox; MATLAB Neural Network Toolbox; Metasploit; Microsoft Bot Framework; Microsoft Dynamics AX Business Intelligence; Microsoft SQL Server Analysis Services; MPI Software; Octave; Prolog; Python programming language; PyTorch; QlikSense; QlikView; R programming language; Robot Operating System; Sci-kit learn; Siemens Simatic; Sqoop; Spark; SPSS; Statistica Data Miner; Statistica Neural Networks; Tableau; Tanagra; TensorFlow; TwinCAT; WEKA |
Teaching Methods and Techniques | Case Method (through real cases and knowledge pills, the students learn about the subject), lecture, lab, individual project, team project, problem-based learning, Master class |
Country | Number of Questionnaires | Country | Number of Questionnaires | Country | Number of Questionnaires |
---|---|---|---|---|---|
Afghanistan | 1 | Greece | 54 | Pakistan | 3 |
Azerbaijan | 2 | India | 4 | Poland | 97 |
Belgium | 2 | Iran | 3 | Portugal | 29 |
Bulgaria | 1 | Ireland | 1 | Romania | 149 |
Canada | 4 | Italy | 90 | Russia | 1 |
China | 1 | Kenya | 3 | Serbia | 2 |
Croatia | 2 | Latvia | 1 | Spain | 64 |
Czechia | 1 | Liechtenstein | 1 | Sri Lanka | 1 |
Denmark | 1 | Lithuania | 8 | Sweden | 6 |
Egypt | 1 | Macedonia | 1 | Turkey | 5 |
Finland | 1 | Netherlands | 4 | United Kingdom | 8 |
France | 20 | Nigeria | 2 | United States | 2 |
Germany | 15 | Norway | 1 | Venezuela | 1 |
TOTAL | 593 |
Have You Ever Learned About? | AI | IoT | EC |
---|---|---|---|
Yes | 305 | 139 | 23 |
54.37% | 24.78% | 4.10% | |
No | 256 | 409 | 509 |
45.63% | 72.91% | 90.73% | |
No answer | 0 | 13 | 29 |
0.00% | 2.32% | 5.17% |
AI Area | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Machine learning | 9 | 59 | 93 | 68 | 54 | 21 | 1 |
2.95% | 19.34% | 30.49% | 22.30% | 17.70% | 6.89% | 0.33% | |
Deep learning | 34 | 88 | 74 | 46 | 40 | 14 | 9 |
11.15% | 28.85% | 24.26% | 15.08% | 13.11% | 4.59% | 2.95% | |
Data mining | 51 | 85 | 67 | 47 | 33 | 11 | 11 |
16.72% | 27.87% | 21.97% | 15.41% | 10.82% | 3.61% | 3.61% | |
Computation intelligence | 56 | 77 | 70 | 52 | 31 | 7 | 12 |
18.36% | 25.25% | 22.95% | 17.05% | 10.16% | 2.30% | 3.93% | |
Natural language processing | 82 | 85 | 51 | 35 | 32 | 9 | 11 |
26.89% | 27.87% | 16.72% | 11.48% | 10.49% | 2.95% | 3.61% | |
Computer vision | 77 | 86 | 62 | 37 | 24 | 9 | 10 |
25.25% | 28.20% | 20.33% | 12.13% | 7.87% | 2.95% | 3.28% | |
Cognitive computing | 126 | 93 | 42 | 16 | 14 | 3 | 11 |
41.31% | 30.49% | 13.77% | 5.25% | 4.59% | 0.98% | 3.61% |
ML Technique | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Supervised learning | 33 | 75 | 52 | 62 | 48 | 32 | 3 |
10.82% | 24.59% | 17.05% | 20.33% | 15.74% | 10.49% | 0.98% | |
Semi-supervised learning | 52 | 92 | 58 | 51 | 31 | 8 | 13 |
17.05% | 30.16% | 19.02% | 16.72% | 10.16% | 2.62% | 4.26% | |
Unsupervised learning | 54 | 86 | 47 | 46 | 40 | 18 | 14 |
17.70% | 28.20% | 15.41% | 15.08% | 13.11% | 5.90% | 4.59% | |
Reinforcement learning | 85 | 90 | 48 | 36 | 25 | 6 | 15 |
27.87% | 29.51% | 15.74% | 11.80% | 8.20% | 1.97% | 4.92% |
Deep Learning Model | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Convolutional neural network | 68 | 70 | 53 | 50 | 38 | 22 | 4 |
22.30% | 22.95% | 17.38% | 16.39% | 12.46% | 7.21% | 1.31% | |
Recurrent neural network | 81 | 81 | 49 | 44 | 26 | 10 | 14 |
26.56% | 26.56% | 16.07% | 14.43% | 8.52% | 3.28% | 4.59% | |
Transformer | 145 | 70 | 38 | 23 | 11 | 4 | 14 |
47.54% | 22.95% | 12.46% | 7.54% | 3.61% | 1.31% | 4.59% | |
Generative adversarial network (GAN) | 152 | 72 | 25 | 21 | 16 | 4 | 15 |
49.84% | 23.61% | 8.20% | 6.89% | 5.25% | 1.31% | 4.92% |
Data Mining Phase | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Business understanding | 115 | 72 | 60 | 23 | 19 | 8 | 8 |
37.70% | 23.61% | 19.67% | 7.54% | 6.23% | 2.62% | 2.62% | |
Data understanding | 76 | 65 | 52 | 48 | 36 | 10 | 18 |
24.92% | 21.31% | 17.05% | 15.74% | 11.80% | 3.28% | 5.90% | |
Data preparation | 77 | 59 | 56 | 41 | 31 | 21 | 20 |
25.25% | 19.34% | 18.36% | 13.44% | 10.16% | 6.89% | 6.56% | |
Modeling | 85 | 55 | 57 | 43 | 29 | 21 | 15 |
27.87% | 18.03% | 18.69% | 14.10% | 9.51% | 6.89% | 4.92% | |
Evaluation | 89 | 66 | 40 | 45 | 28 | 21 | 16 |
29.18% | 21.64% | 13.11% | 14.75% | 9.18% | 6.89% | 5.25% | |
Deployment | 105 | 81 | 43 | 35 | 16 | 10 | 15 |
34.43% | 26.56% | 14.10% | 11.48% | 5.25% | 3.28% | 4.92% |
Computation Intelligence Aspect | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Fuzzy systems | 106 | 70 | 57 | 42 | 18 | 3 | 9 |
34.75% | 22.95% | 18.69% | 13.77% | 5.90% | 0.98% | 2.95% | |
Neural networks | 34 | 65 | 69 | 57 | 42 | 23 | 15 |
11.15% | 21.31% | 22.62% | 18.69% | 13.77% | 7.54% | 4.92% | |
Genetic algorithms | 76 | 83 | 49 | 51 | 20 | 9 | 17 |
24.92% | 27.21% | 16.07% | 16.72% | 6.56% | 2.95% | 5.57% |
Natural Language Processing Aspect | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Speech recognition | 105 | 93 | 44 | 31 | 16 | 5 | 11 |
34.43% | 30.49% | 14.43% | 10.16% | 5.25% | 1.64% | 3.61% | |
Natural language generation | 121 | 67 | 48 | 27 | 12 | 7 | 23 |
39.67% | 21.97% | 15.74% | 8.85% | 3.93% | 2.30% | 7.54% | |
Natural language translation | 122 | 72 | 38 | 28 | 16 | 6 | 23 |
40.00% | 23.61% | 12.46% | 9.18% | 5.25% | 1.97% | 7.54% |
Computer Vision Aspect | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Image classification | 63 | 70 | 63 | 42 | 36 | 20 | 11 |
20.66% | 22.95% | 20.66% | 13.77% | 11.80% | 6.56% | 3.61% | |
Object localization and detection | 70 | 84 | 56 | 42 | 21 | 13 | 19 |
22.95% | 27.54% | 18.36% | 13.77% | 6.89% | 4.26% | 6.23% | |
Image segmentation | 79 | 88 | 48 | 34 | 22 | 16 | 18 |
25.90% | 28.85% | 15.74% | 11.15% | 7.21% | 5.25% | 5.90% | |
Domain adaptation | 142 | 75 | 42 | 14 | 7 | 6 | 19 |
46.56% | 24.59% | 13.77% | 4.59% | 2.30% | 1.97% | 6.23% | |
Neural style transfer | 149 | 68 | 34 | 18 | 12 | 6 | 18 |
48.85% | 22.30% | 11.15% | 5.90% | 3.93% | 1.97% | 5.90% |
Cognitive Computing Aspect | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To A Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Interactive task learning | 147 | 84 | 38 | 15 | 6 | 5 | 10 |
48.20% | 27.54% | 12.46% | 4.92% | 1.97% | 1.64% | 3.28% | |
Game playing agents | 135 | 66 | 51 | 20 | 7 | 6 | 20 |
44.26% | 21.64% | 16.72% | 6.56% | 2.30% | 1.97% | 6.56% | |
Meta-algorithms in cognitive computing | 177 | 59 | 27 | 11 | 5 | 6 | 20 |
58.03% | 19.34% | 8.85% | 3.61% | 1.64% | 1.97% | 6.56% |
Programming Language | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
C/C++ | 83 | 44 | 61 | 57 | 39 | 12 | 9 |
27.21% | 14.43% | 20.00% | 18.69% | 12.79% | 3.93% | 2.95% | |
Python | 49 | 48 | 47 | 54 | 58 | 35 | 14 |
16.07% | 15.74% | 15.41% | 17.70% | 19.02% | 11.48% | 4.59% | |
Lisp | 244 | 23 | 7 | 5 | 2 | 2 | 22 |
80.00% | 7.54% | 2.30% | 1.64% | 0.66% | 0.66% | 7.21% | |
Java | 118 | 57 | 35 | 33 | 27 | 17 | 18 |
38.69% | 18.69% | 11.48% | 10.82% | 8.85% | 5.57% | 5.90% | |
MATLAB | 59 | 60 | 72 | 53 | 35 | 12 | 14 |
19.34% | 19.67% | 23.61% | 17.38% | 11.48% | 3.93% | 4.59% | |
Prolog | 229 | 33 | 13 | 4 | 4 | 4 | 18 |
75.08% | 10.82% | 4.26% | 1.31% | 1.31% | 1.31% | 5.90% | |
R | 181 | 40 | 27 | 18 | 15 | 7 | 17 |
59.34% | 13.11% | 8.85% | 5.90% | 4.92% | 2.30% | 5.57% |
Software/Environment | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
AITECH SPHINX | 254 | 19 | 5 | 1 | 3 | 1 | 22 |
83.28% | 6.23% | 1.64% | 0.33% | 0.98% | 0.33% | 7.21% | |
Statistica | 211 | 36 | 15 | 7 | 5 | 3 | 28 |
69.18% | 11.80% | 4.92% | 2.30% | 1.64% | 0.98% | 9.18% | |
MATLAB | 72 | 64 | 57 | 50 | 28 | 15 | 19 |
23.61% | 20.98% | 18.69% | 16.39% | 9.18% | 4.92% | 6.23% | |
MS Excel | 118 | 44 | 31 | 40 | 27 | 18 | 27 |
38.69% | 14.43% | 10.16% | 13.11% | 8.85% | 5.90% | 8.85% | |
Scilab | 235 | 27 | 4 | 7 | 2 | 0 | 30 |
77.05% | 8.85% | 1.31% | 2.30% | 0.66% | 0.00% | 9.84% | |
RStudio | 186 | 42 | 17 | 13 | 12 | 7 | 28 |
60.98% | 13.77% | 5.57% | 4.26% | 3.93% | 2.30% | 9.18% | |
SWI Prolog | 234 | 20 | 8 | 6 | 6 | 1 | 30 |
76.72% | 6.56% | 2.62% | 1.97% | 1.97% | 0.33% | 9.84% | |
PyCharm | 130 | 40 | 25 | 35 | 33 | 18 | 24 |
42.62% | 13.11% | 8.20% | 11.48% | 10.82% | 5.90% | 7.87% | |
Spyder | 181 | 25 | 21 | 19 | 21 | 9 | 29 |
59.34% | 8.20% | 6.89% | 6.23% | 6.89% | 2.95% | 9.51% | |
Visual Studio Code | 101 | 55 | 30 | 40 | 28 | 31 | 20 |
33.11% | 18.03% | 9.84% | 13.11% | 9.18% | 10.16% | 6.56% | |
Anaconda | 122 | 35 | 21 | 33 | 37 | 35 | 22 |
40.00% | 11.48% | 6.89% | 10.82% | 12.13% | 11.48% | 7.21% |
AI Applications | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Quality problems | 104 | 73 | 53 | 29 | 17 | 7 | 22 |
34.10% | 23.93% | 17.38% | 9.51% | 5.57% | 2.30% | 7.21% | |
Predictive maintenance | 116 | 68 | 35 | 27 | 21 | 7 | 31 |
38.03% | 22.30% | 11.48% | 8.85% | 6.89% | 2.30% | 10.16% | |
Deliveries | 138 | 68 | 34 | 21 | 6 | 4 | 34 |
45.25% | 22.30% | 11.15% | 6.89% | 1.97% | 1.31% | 11.15% | |
Supply chains management | 153 | 50 | 40 | 18 | 9 | 3 | 32 |
50.16% | 16.39% | 13.11% | 5.90% | 2.95% | 0.98% | 10.49% | |
Scheduling problems | 128 | 52 | 52 | 28 | 6 | 6 | 33 |
41.97% | 17.05% | 17.05% | 9.18% | 1.97% | 1.97% | 10.82% | |
Manufacturing processes monitoring | 151 | 59 | 34 | 21 | 7 | 4 | 29 |
49.51% | 19.34% | 11.15% | 6.89% | 2.30% | 1.31% | 9.51% | |
Anomaly detection | 119 | 58 | 37 | 23 | 29 | 8 | 31 |
39.02% | 19.02% | 12.13% | 7.54% | 9.51% | 2.62% | 10.16% | |
Computer vision | 108 | 61 | 43 | 27 | 26 | 13 | 27 |
35.41% | 20.00% | 14.10% | 8.85% | 8.52% | 4.26% | 8.85% | |
Optimization | 77 | 63 | 45 | 41 | 32 | 15 | 32 |
25.25% | 20.66% | 14.75% | 13.44% | 10.49% | 4.92% | 10.49% | |
Cognitive systems | 136 | 63 | 34 | 25 | 10 | 3 | 34 |
44.59% | 20.66% | 11.15% | 8.20% | 3.28% | 0.98% | 11.15% | |
Autonomous systems | 117 | 64 | 43 | 31 | 18 | 4 | 28 |
38.36% | 20.98% | 14.10% | 10.16% | 5.90% | 1.31% | 9.18% | |
Robots | 124 | 60 | 36 | 34 | 17 | 6 | 28 |
40.66% | 19.67% | 11.80% | 11.15% | 5.57% | 1.97% | 9.18% |
AI Applications | ||
---|---|---|
Ad-click prediction Agriculture (2), e.g., farm fields observation Application development Automated data exploration Chatbots/Conversational agents (2) Complex networks Computer vision Cross-silo federated learning Cybersecurity Digital culture Digital marketing Expert systems Finite element modeling | Finance (2) Game development (4), e.g., tic-tac-toe Hair region segmentation Image classification Inverse problems solving IoT Healthcare/Medicine (7), e.g., Bioinformatics, Biomedical image analysis, Calorie counter, Disease prediction, Drug repurposing, Medical data classification, Survival prediction Minimal tasks Network monitoring and security | Natural language processing OCR systems Process optimization and research Recommendation systems (2) Research on creativity Risk stratification Scheduling problem with quality aspects Sequence models Solving mathematical puzzles Spam/ham classification Sports analytics Telecom Text mining |
Group of Students | No. | Min | Mode | Median | Arithmetic Mean | Max |
---|---|---|---|---|---|---|
Not involved in any AI project | 207 | 0 | 26 | 59 | 68.74 | 264 |
Involved in at least one AI project | 98 | 7 | 110 | 110 | 112.69 | 294 |
Sum of Ranks for the “Project” Group | Sum of Ranks for the “Non-Project” Group | U Statistic | Z Statistic | p-Value |
---|---|---|---|---|
19,579 | 27,086 | 5558 | 6.374 | 0.000 |
Learning Technique | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Lectures | 24 | 31 | 77 | 64 | 55 | 32 | 22 |
7.87% | 10.16% | 25.25% | 20.98% | 18.03% | 10.49% | 7.21% | |
Labs | 7 | 18 | 37 | 55 | 70 | 92 | 26 |
2.30% | 5.90% | 12.13% | 18.03% | 22.95% | 30.16% | 8.52% | |
Workshops | 11 | 20 | 48 | 60 | 61 | 75 | 30 |
3.61% | 6.56% | 15.74% | 19.67% | 20.00% | 24.59% | 9.84% | |
Project-based learning (individual work) | 11 | 18 | 36 | 51 | 74 | 83 | 32 |
3.61% | 5.90% | 11.80% | 16.72% | 24.26% | 27.21% | 10.49% | |
Project-based learning (teamwork) | 13 | 19 | 34 | 44 | 86 | 79 | 30 |
4.26% | 6.23% | 11.15% | 14.43% | 28.20% | 25.90% | 9.84% | |
Problem-based Learning | 16 | 21 | 40 | 53 | 78 | 65 | 32 |
5.25% | 6.89% | 13.11% | 17.38% | 25.57% | 21.31% | 10.49% | |
E-learning | 23 | 43 | 75 | 64 | 39 | 31 | 30 |
7.54% | 14.10% | 24.59% | 20.98% | 12.79% | 10.16% | 9.84% | |
General review of an issue | 32 | 49 | 70 | 55 | 41 | 29 | 29 |
10.49% | 16.07% | 22.95% | 18.03% | 13.44% | 9.51% | 9.51% |
IoT Topic | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
General information about IoT | 1 | 19 | 29 | 53 | 23 | 7 | 7 |
0.72% | 13.67% | 20.86% | 38.13% | 16.55% | 5.04% | 5.04% | |
Application scenarios of IoT | 7 | 18 | 32 | 39 | 23 | 6 | 14 |
5.04% | 12.95% | 23.02% | 28.06% | 16.55% | 4.32% | 10.07% | |
IoT Architecture | 15 | 30 | 40 | 27 | 10 | 5 | 12 |
10.79% | 21.58% | 28.78% | 19.42% | 7.19% | 3.60% | 8.63% | |
IoT deployment | 27 | 29 | 35 | 21 | 10 | 4 | 13 |
19.42% | 20.86% | 25.18% | 15.11% | 7.19% | 2.88% | 9.35% | |
IoT components | 20 | 31 | 33 | 24 | 16 | 4 | 11 |
14.39% | 22.30% | 23.74% | 17.27% | 11.51% | 2.88% | 7.91% | |
M2M industrial IoT protocols | 54 | 34 | 18 | 11 | 7 | 2 | 13 |
38.85% | 24.46% | 12.95% | 7.91% | 5.04% | 1.44% | 9.35% | |
Sensors | 17 | 26 | 28 | 29 | 19 | 8 | 12 |
12.23% | 18.71% | 20.14% | 20.86% | 13.67% | 5.76% | 8.63% | |
IoT devices programming | 29 | 29 | 25 | 21 | 14 | 8 | 13 |
20.86% | 20.86% | 17.99% | 15.11% | 10.07% | 5.76% | 9.35% | |
IoT maintenance | 43 | 40 | 23 | 13 | 4 | 2 | 14 |
30.94% | 28.78% | 16.55% | 9.35% | 2.88% | 1.44% | 10.07% | |
Distribution of computing processes in IoT nets | 44 | 37 | 24 | 13 | 5 | 2 | 14 |
31.65% | 26.62% | 17.27% | 9.35% | 3.60% | 1.44% | 10.07% | |
Computer Networking | 11 | 32 | 36 | 31 | 15 | 6 | 8 |
7.91% | 23.02% | 25.90% | 22.30% | 10.79% | 4.32% | 5.76% | |
Data analytics | 23 | 28 | 33 | 28 | 10 | 6 | 11 |
16.55% | 20.14% | 23.74% | 20.14% | 7.19% | 4.32% | 7.91% | |
Cloud computing | 21 | 37 | 30 | 24 | 9 | 5 | 13 |
15.11% | 26.62% | 21.58% | 17.27% | 6.47% | 3.60% | 9.35% | |
Databases development | 21 | 28 | 30 | 30 | 11 | 6 | 13 |
15.11% | 20.14% | 21.58% | 21.58% | 7.91% | 4.32% | 9.35% | |
Data transfer protocols | 21 | 25 | 32 | 31 | 11 | 6 | 13 |
15.11% | 17.99% | 23.02% | 22.30% | 7.91% | 4.32% | 9.35% | |
IoT Communication Terminals and Gateways | 33 | 33 | 33 | 13 | 12 | 2 | 13 |
23.74% | 23.74% | 23.74% | 9.35% | 8.63% | 1.44% | 9.35% | |
Knowledge management | 32 | 41 | 26 | 19 | 6 | 1 | 14 |
23.02% | 29.50% | 18.71% | 13.67% | 4.32% | 0.72% | 10.07% | |
Cybersecurity | 33 | 35 | 20 | 27 | 8 | 5 | 11 |
23.74% | 25.18% | 14.39% | 19.42% | 5.76% | 3.60% | 7.91% | |
Cryptography | 42 | 25 | 17 | 28 | 9 | 5 | 13 |
30.22% | 17.99% | 12.23% | 20.14% | 6.47% | 3.60% | 9.35% | |
Basic Network Attacks | 39 | 22 | 27 | 21 | 13 | 4 | 13 |
28.06% | 15.83% | 19.42% | 15.11% | 9.35% | 2.88% | 9.35% | |
Real Time Operating Systems | 31 | 30 | 27 | 22 | 10 | 6 | 13 |
22.30% | 21.58% | 19.42% | 15.83% | 7.19% | 4.32% | 9.35% | |
Searching for Vulnerabilities | 51 | 31 | 18 | 13 | 9 | 2 | 15 |
36.69% | 22.30% | 12.95% | 9.35% | 6.47% | 1.44% | 10.79% |
Context of Using IoT | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Quality problems | 42 | 28 | 25 | 21 | 6 | 4 | 13 |
30.22% | 20.14% | 17.99% | 15.11% | 4.32% | 2.88% | 9.35% | |
Machine condition monitoring | 39 | 32 | 23 | 17 | 8 | 6 | 14 |
28.06% | 23.02% | 16.55% | 12.23% | 5.76% | 4.32% | 10.07% | |
Robotics | 32 | 35 | 27 | 16 | 10 | 6 | 13 |
23.02% | 25.18% | 19.42% | 11.51% | 7.19% | 4.32% | 9.35% | |
Deliveries | 53 | 34 | 17 | 11 | 3 | 4 | 17 |
38.13% | 24.46% | 12.23% | 7.91% | 2.16% | 2.88% | 12.23% | |
Market behavior | 57 | 34 | 14 | 13 | 3 | 2 | 16 |
41.01% | 24.46% | 10.07% | 9.35% | 2.16% | 1.44% | 11.51% | |
Data management | 33 | 34 | 29 | 15 | 11 | 3 | 14 |
23.74% | 24.46% | 20.86% | 10.79% | 7.91% | 2.16% | 10.07% | |
Support decision-making | 44 | 34 | 18 | 15 | 10 | 3 | 15 |
31.65% | 24.46% | 12.95% | 10.79% | 7.19% | 2.16% | 10.79% | |
Process parameters monitoring | 43 | 36 | 22 | 13 | 6 | 5 | 14 |
30.94% | 25.90% | 15.83% | 9.35% | 4.32% | 3.60% | 10.07% | |
Logistics | 54 | 34 | 18 | 12 | 3 | 4 | 14 |
38.85% | 24.46% | 12.95% | 8.63% | 2.16% | 2.88% | 10.07% |
Context of Using IoT | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Digital twins | 24 | 17 | 32 | 29 | 6 | 10 | 21 |
17.27% | 12.23% | 23.02% | 20.86% | 4.32% | 7.19% | 15.11% | |
Big data management | 10 | 14 | 31 | 27 | 19 | 18 | 20 |
7.19% | 10.07% | 22.30% | 19.42% | 13.67% | 12.95% | 14.39% | |
Data processing and transformation | 8 | 12 | 27 | 29 | 25 | 17 | 21 |
5.76% | 8.63% | 19.42% | 20.86% | 17.99% | 12.23% | 15.11% | |
Data display | 11 | 15 | 32 | 34 | 13 | 11 | 23 |
7.91% | 10.79% | 23.02% | 24.46% | 9.35% | 7.91% | 16.55% | |
Industrial Automations | 12 | 10 | 30 | 24 | 22 | 17 | 24 |
8.63% | 7.19% | 21.58% | 17.27% | 15.83% | 12.23% | 17.27% | |
Anomaly detection | 16 | 8 | 26 | 24 | 28 | 15 | 22 |
11.51% | 5.76% | 18.71% | 17.27% | 20.14% | 10.79% | 15.83% | |
IaaS (Infrastructure as a Service) | 19 | 15 | 21 | 34 | 18 | 8 | 24 |
13.67% | 10.79% | 15.11% | 24.46% | 12.95% | 5.76% | 17.27% | |
PaaS (Platform as a Service) | 20 | 16 | 29 | 24 | 19 | 8 | 23 |
14.39% | 11.51% | 20.86% | 17.27% | 13.67% | 5.76% | 16.55% | |
SaaS (Software as a Service) | 17 | 15 | 30 | 18 | 29 | 8 | 22 |
12.23% | 10.79% | 21.58% | 12.95% | 20.86% | 5.76% | 15.83% | |
Containers and orchestrators | 15 | 13 | 24 | 36 | 19 | 10 | 22 |
10.79% | 9.35% | 17.27% | 25.90% | 13.67% | 7.19% | 15.83% | |
Application Programming Interface (API) | 8 | 14 | 17 | 25 | 35 | 19 | 21 |
5.76% | 10.07% | 12.23% | 17.99% | 25.18% | 13.67% | 15.11% |
Learning Technique to Teach IoT | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Lectures | 6 | 18 | 31 | 32 | 23 | 16 | 13 |
4.32% | 12.95% | 22.30% | 23.02% | 16.55% | 11.51% | 9.35% | |
Labs | 2 | 4 | 18 | 31 | 33 | 36 | 15 |
1.44% | 2.88% | 12.95% | 22.30% | 23.74% | 25.90% | 10.79% | |
Workshops | 4 | 7 | 18 | 29 | 33 | 34 | 14 |
2.88% | 5.04% | 12.95% | 20.86% | 23.74% | 24.46% | 10.07% | |
Project-based learning (individual work) | 1 | 6 | 11 | 25 | 38 | 42 | 16 |
0.72% | 4.32% | 7.91% | 17.99% | 27.34% | 30.22% | 11.51% | |
Project-based learning (teamwork) | 2 | 3 | 14 | 24 | 37 | 45 | 14 |
1.44% | 2.16% | 10.07% | 17.27% | 26.62% | 32.37% | 10.07% | |
Problem-based learning | 5 | 3 | 14 | 29 | 39 | 34 | 15 |
3.60% | 2.16% | 10.07% | 20.86% | 28.06% | 24.46% | 10.79% | |
E-learning | 12 | 17 | 32 | 31 | 16 | 18 | 13 |
8.63% | 12.23% | 23.02% | 22.30% | 11.51% | 12.95% | 9.35% | |
General review of an issue | 10 | 10 | 30 | 33 | 25 | 17 | 14 |
7.19% | 7.19% | 21.58% | 23.74% | 17.99% | 12.23% | 10.07% |
Software/Environment | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
MapReduce | 81 | 12 | 9 | 10 | 6 | 3 | 18 |
58.27% | 8.63% | 6.47% | 7.19% | 4.32% | 2.16% | 12.95% | |
Cloud Services & Serverless Technologies (AWS, GCP, DigitalOcean, Linode) | 60 | 19 | 13 | 14 | 9 | 5 | 19 |
43.17% | 13.67% | 9.35% | 10.07% | 6.47% | 3.60% | 13.67% | |
AWS Lambda | 88 | 13 | 5 | 12 | 3 | 0 | 18 |
63.31% | 9.35% | 3.60% | 8.63% | 2.16% | 0.00% | 12.95% | |
Azure functions | 71 | 12 | 13 | 16 | 8 | 1 | 18 |
51.08% | 8.63% | 9.35% | 11.51% | 5.76% | 0.72% | 12.95% | |
Arduino IoT | 36 | 24 | 19 | 21 | 15 | 9 | 15 |
25.90% | 17.27% | 13.67% | 15.11% | 10.79% | 6.47% | 10.79% |
EC Topic | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
General concept | 0 | 6 | 4 | 7 | 2 | 1 | 3 |
0.00% | 26.09% | 17.39% | 30.43% | 8.70% | 4.35% | 13.04% | |
Privacy and security | 3 | 4 | 6 | 4 | 2 | 0 | 4 |
13.04% | 17.39% | 26.09% | 17.39% | 8.70% | 0.00% | 17.39% | |
Scalability and reliability | 2 | 7 | 4 | 3 | 3 | 0 | 4 |
8.70% | 30.43% | 17.39% | 13.04% | 13.04% | 0.00% | 17.39% | |
Speed and efficiency | 1 | 6 | 6 | 4 | 2 | 0 | 4 |
4.35% | 26.09% | 26.09% | 17.39% | 8.70% | 0.00% | 17.39% | |
Applications | 0 | 7 | 3 | 6 | 3 | 0 | 4 |
0.00% | 30.43% | 13.04% | 26.09% | 13.04% | 0.00% | 17.39% |
Technology in EC Implementation | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Mobile Edge Computing | 4 | 7 | 1 | 8 | 0 | 0 | 3 |
17.39% | 30.43% | 4.35% | 34.78% | 0.00% | 0.00% | 13.04% | |
Fog computing | 3 | 6 | 4 | 5 | 0 | 0 | 5 |
13.04% | 26.09% | 17.39% | 21.74% | 0.00% | 0.00% | 21.74% | |
Service composition and service-oriented computing | 5 | 5 | 2 | 7 | 0 | 0 | 4 |
21.74% | 21.74% | 8.70% | 30.43% | 0.00% | 0.00% | 17.39% | |
Micro data centers | 3 | 8 | 6 | 1 | 1 | 0 | 4 |
13.04% | 34.78% | 26.09% | 4.35% | 4.35% | 0.00% | 17.39% | |
Container technology | 2 | 7 | 3 | 4 | 2 | 0 | 5 |
8.70% | 30.43% | 13.04% | 17.39% | 8.70% | 0.00% | 21.74% | |
Azure edge | 7 | 5 | 1 | 4 | 1 | 0 | 5 |
30.43% | 21.74% | 4.35% | 17.39% | 4.35% | 0.00% | 21.74% |
Algorithm/Technique in EC Implementation | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Distributed computing | 3 | 7 | 4 | 2 | 2 | 1 | 4 |
13.04% | 30.43% | 17.39% | 8.70% | 8.70% | 4.35% | 17.39% | |
Distributed storage | 5 | 6 | 4 | 3 | 0 | 1 | 4 |
21.74% | 26.09% | 17.39% | 13.04% | 0.00% | 4.35% | 17.39% | |
Reliability and fault tolerance | 5 | 2 | 3 | 7 | 1 | 0 | 5 |
21.74% | 8.70% | 13.04% | 30.43% | 4.35% | 0.00% | 21.74% | |
Containerization | 6 | 3 | 6 | 3 | 1 | 0 | 4 |
26.09% | 13.04% | 26.09% | 13.04% | 4.35% | 0.00% | 17.39% | |
Energy efficiency | 3 | 7 | 5 | 3 | 1 | 0 | 4 |
13.04% | 30.43% | 21.74% | 13.04% | 4.35% | 0.00% | 17.39% | |
Data replication | 5 | 3 | 4 | 5 | 2 | 0 | 4 |
21.74% | 13.04% | 17.39% | 21.74% | 8.70% | 0.00% | 17.39% | |
Efficiently collecting, aggregating, and moving data | 3 | 5 | 6 | 4 | 1 | 0 | 4 |
13.04% | 21.74% | 26.09% | 17.39% | 4.35% | 0.00% | 17.39% |
To What Extent Do You… | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
identify the challenges of EC? | 3 | 5 | 6 | 3 | 1 | 0 | 5 |
13.04% | 21.74% | 26.09% | 13.04% | 4.35% | 0.00% | 21.74% | |
design an EC architecture? | 6 | 4 | 5 | 2 | 1 | 0 | 5 |
26.09% | 17.39% | 21.74% | 8.70% | 4.35% | 0.00% | 21.74% | |
describe the differences between edge, fog, cloud, and pervasive computing? | 3 | 7 | 5 | 2 | 1 | 0 | 5 |
13.04% | 30.43% | 21.74% | 8.70% | 4.35% | 0.00% | 21.74% | |
implement software solutions using EC middlewares? | 6 | 5 | 4 | 2 | 1 | 0 | 5 |
26.09% | 21.74% | 17.39% | 8.70% | 4.35% | 0.00% | 21.74% | |
understand the strengths and weaknesses of an EC architecture? | 3 | 7 | 4 | 3 | 1 | 0 | 5 |
13.04% | 30.43% | 17.39% | 13.04% | 4.35% | 0.00% | 21.74% | |
develop an edge computing project? | 5 | 8 | 1 | 3 | 1 | 0 | 5 |
21.74% | 34.78% | 4.35% | 13.04% | 4.35% | 0.00% | 21.74% | |
read papers related to EC? | 4 | 6 | 1 | 4 | 2 | 1 | 5 |
17.39% | 26.09% | 4.35% | 17.39% | 8.70% | 4.35% | 21.74% | |
do data analytics in EC environments? | 6 | 9 | 0 | 2 | 1 | 0 | 5 |
26.09% | 39.13% | 0.00% | 8.70% | 4.35% | 0.00% | 21.74% |
Hardware/Software | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
FPGAs | 12 | 5 | 1 | 0 | 1 | 0 | 4 |
52.17% | 21.74% | 4.35% | 0.00% | 4.35% | 0.00% | 17.39% | |
Edge accelerators | 13 | 5 | 0 | 0 | 1 | 0 | 4 |
56.52% | 21.74% | 0.00% | 0.00% | 4.35% | 0.00% | 17.39% | |
Azure IoT Edge | 11 | 7 | 0 | 0 | 1 | 0 | 4 |
47.83% | 30.43% | 0.00% | 0.00% | 4.35% | 0.00% | 17.39% | |
AWS IoT Greengrass | 15 | 2 | 1 | 0 | 1 | 0 | 4 |
65.22% | 8.70% | 4.35% | 0.00% | 4.35% | 0.00% | 17.39% | |
RTOS | 15 | 2 | 1 | 0 | 1 | 0 | 4 |
65.22% | 8.70% | 4.35% | 0.00% | 4.35% | 0.00% | 17.39% |
EC Application | Not at All | To a Small Extent | To Some Extent | To a Moderate Extent | To a Great Extent | To a Very Great Extent | ND |
---|---|---|---|---|---|---|---|
Autonomous machines | 6 | 8 | 3 | 0 | 2 | 0 | 4 |
26.09% | 34.78% | 13.04% | 0.00% | 8.70% | 0.00% | 17.39% | |
Autonomous production planning system | 8 | 8 | 1 | 2 | 0 | 0 | 4 |
34.78% | 34.78% | 4.35% | 8.70% | 0.00% | 0.00% | 17.39% | |
Augmented reality | 8 | 7 | 3 | 1 | 0 | 0 | 4 |
34.78% | 30.43% | 13.04% | 4.35% | 0.00% | 0.00% | 17.39% | |
Mobile agents (e.g., drones) | 5 | 8 | 4 | 1 | 1 | 0 | 4 |
21.74% | 34.78% | 17.39% | 4.35% | 4.35% | 0.00% | 17.39% | |
Autonomous products | 9 | 6 | 3 | 1 | 0 | 0 | 4 |
39.13% | 26.09% | 13.04% | 4.35% | 0.00% | 0.00% | 17.39% | |
Autonomy in energy networks | 8 | 7 | 1 | 2 | 1 | 0 | 4 |
34.78% | 30.43% | 4.35% | 8.70% | 4.35% | 0.00% | 17.39% | |
Facial recognition algorithms | 6 | 5 | 4 | 2 | 1 | 0 | 5 |
26.09% | 21.74% | 17.39% | 8.70% | 4.35% | 0.00% | 21.74% | |
Smart cities | 6 | 5 | 6 | 1 | 1 | 0 | 4 |
26.09% | 21.74% | 26.09% | 4.35% | 4.35% | 0.00% | 17.39% | |
Industry 4.0 | 4 | 10 | 3 | 1 | 1 | 0 | 4 |
17.39% | 43.48% | 13.04% | 4.35% | 4.35% | 0.00% | 17.39% |
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Paśko, Ł.; Mądziel, M.; Stadnicka, D.; Dec, G.; Carreras-Coch, A.; Solé-Beteta, X.; Pappa, L.; Stylios, C.; Mazzei, D.; Atzeni, D. Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing. Sustainability 2022, 14, 3312. https://doi.org/10.3390/su14063312
Paśko Ł, Mądziel M, Stadnicka D, Dec G, Carreras-Coch A, Solé-Beteta X, Pappa L, Stylios C, Mazzei D, Atzeni D. Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing. Sustainability. 2022; 14(6):3312. https://doi.org/10.3390/su14063312
Chicago/Turabian StylePaśko, Łukasz, Maksymilian Mądziel, Dorota Stadnicka, Grzegorz Dec, Anna Carreras-Coch, Xavier Solé-Beteta, Lamprini Pappa, Chrysostomos Stylios, Daniele Mazzei, and Daniele Atzeni. 2022. "Plan and Develop Advanced Knowledge and Skills for Future Industrial Employees in the Field of Artificial Intelligence, Internet of Things and Edge Computing" Sustainability 14, no. 6: 3312. https://doi.org/10.3390/su14063312