Systems Engineering and Knowledge Management

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 29815

Special Issue Editor


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Guest Editor
Faculty of Informatics and Management, University of Hradec Králové, Rokitanského 62, 50003 Hradec Králové, Czech Republic
Interests: system dynamics; systems engineering; modelling; simulation
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Special Issue Information

Dear Colleagues,

The International Council on Systems Engineering, the leading authority in the realm of Systems Engineering (SE), defines this field of study as a transdisciplinary and integrative approach enabling the realization of the whole life cycle of any engineered system. However, the shift to the transdisciplinary view was based on intradisciplinary and multidisciplinary perspectives on SE. The intradisciplinary point of view is more or less traditional. It is closely associated with the design, development and implementation of information systems. These systems require the cooperation of two stakeholders: a business one (demand) and a technical one (supply). However, this setting can be applied to any domain in which someone needs a technical system and someone is capable of delivering it. Later, a multidisciplinary perspective was shaped. This perspective highlights the necessity of the cooperation of experts from various specialities to develop and deliver required complex systems. The necessity of the coordination, synchronization and orchestration of processes and resources is crucial. The role of a system engineer transforms a little bit as technical knowledge and expertise have to be complemented by the mastering of soft skills such as leadership, motivation or decision making. Finally, a transdisciplinary point of view stresses that engineering activities, regardless of the domain or the type of system developed, can be generalized and successfully applied during the development of any type of system. In this way, transdisciplinary SE focuses on basic concepts, their relationships, procedures, activities, best practices or fundamental principles of SE. It considers SE as a generic structured development process that proceeds from concept to production and operation.

Similarly, Knowledge Management (KM) can be understood from two perspectives. The first one is based on the technical perspective, in which KM is characterized by research in fields such as expert or knowledge-based systems. This perspective is mainly associated with the intradisciplinary approach to SE as a specific type of computer-based system is designed, developed and implemented. It operates with specific procedural or declarative knowledge in the form of rules, classes with their attributes, ontologies or different types of networks. It is an established technological discipline that embodies the lowest and the most basic level in which proper attention to knowledge is exercised. The second one is tied to soft systems, in which KM is considered an approach to organizations' improved performance. KM encompasses a knowledge-based and knowledge-orientated organizational management irrespective of organizational mandate or nature. Therefore, KM can be introduced in business organizations, educational institutions or even civil administration. In doing so, prominence to knowledge resources and knowledge processes is highlighted.

This Special Issue intends to publish a novel and original work focused on the mutual connection of SE and KM as outlined above. Prospective authors are anticipated to deal with various aspects of SE as an integrative approach, ranging from project management issues and requirements gathering to technical development and coordination of experts during the development of knowledge systems regardless of the level of their hardness or softness. In this way, an engineered system is considered a system, a technological or organizational one, engineered for work with knowledge in the broad sense of its understanding.

Prof. Dr. Vladimír Bureš
Guest Editor

Manuscript Submission Information

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • systems engineering methodologies
  • systems engineer
  • knowledge engineering
  • knowledge management process
  • knowledge and expert systems
  • system life cycle
  • systems design and development
  • systems engineering and project management
  • validation and verification processes
  • requirement management
  • modeling and simulation

Published Papers (12 papers)

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Research

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40 pages, 7427 KiB  
Article
Success Factors in Management of IT Service Projects: Regression, Confirmatory Factor Analysis, and Structural Equation Models
by Rafał Michalski and Szymon Zaleski
Information 2024, 15(2), 105; https://doi.org/10.3390/info15020105 - 09 Feb 2024
Viewed by 1272
Abstract
Although there have been some studies on the success factors for IT software projects, there is still a lack of coherent research on the success factors for IT service projects. Therefore, this study aimed to identify and understand the factors and their relationships [...] Read more.
Although there have been some studies on the success factors for IT software projects, there is still a lack of coherent research on the success factors for IT service projects. Therefore, this study aimed to identify and understand the factors and their relationships that contribute to the success of IT service projects. For this purpose, multivariate regressions and structural equation models (SEMs) were developed and analyzed. The regression models included six project management success criteria used as dependent variables (quality of the delivered product, scope realization and requirements, timeliness of delivery, delivery within budget, customer satisfaction, and provider satisfaction) and four independent variables (agile techniques and change management, organization and people, stakeholders and risk analysis, work environment), which had been identified through exploratory factor analysis. The results showed that not all success factors were relevant to all success criteria, and there were differences in their importance. An additional series of exploratory and confirmatory factor analyses along with appropriate statistical measures were employed to evaluate the quality of these four factors. The SEM approach was based on five latent constructs with a total of twenty components. The study suggests that investing in improving people’s knowledge and skills, using agile methodologies, creating a supportive work environment, and involving stakeholders in regular risk analysis are important for project management success. The results also suggest that the success factors for IT service projects depend on both traditional and agile approaches. The study extensively compared its findings with similar research and discussed common issues and differences in both the model structures and methodologies applied. The investigation utilized mathematical methods and techniques that are not commonly applied in the field of project management success modeling. The comprehensive methodology that was applied may be helpful to other researchers who are interested in this topic. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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17 pages, 413 KiB  
Article
A Study and Application Analysis Exploring Pythagorean Fuzzy Set Distance Metrics in Decision Making
by Palvinder Thakur, Bartosz Paradowski, Neeraj Gandotra, Parul Thakur, Namita Saini and Wojciech Sałabun
Information 2024, 15(1), 28; https://doi.org/10.3390/info15010028 - 02 Jan 2024
Viewed by 1139
Abstract
The ever-increasing demand for high-quality solutions drives research toward more sophisticated decision-making solutions. In the field of decision making, the ability to solve complex real-world problems is of paramount importance. To this end, fuzzy sets are used, which offer the possibility of incorporating [...] Read more.
The ever-increasing demand for high-quality solutions drives research toward more sophisticated decision-making solutions. In the field of decision making, the ability to solve complex real-world problems is of paramount importance. To this end, fuzzy sets are used, which offer the possibility of incorporating uncertainty into the values describing decision options. This study focuses on Pythagorean fuzzy sets, an extension of classical fuzzy sets, providing even more tools for modeling real-world problems by presenting a distance measure for these specific sets. A verification of the characteristics of the proposed distance measure has been carried out, proving its validity. The proposed measure is characterized by a more straightforward formula and thus simplifies the calculations. Furthermore, to confirm its usability, a multi-criteria decision-making methodology is presented, the results of which are compared with two multi-criteria decision-making methods, namely, PF-TOPSIS and PF-VIKOR, and another distance measure previously presented in the literature. The comparative analysis highlights lower variability in terms of preference values calculated using the proposed distance measure, which confirms the stability and reliability of the newly proposed distance measure while maintaining low computational complexity. Moreover, a high correlation with rankings calculated using PF-TOPSIS ensures its utility in terms of decision making. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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22 pages, 5215 KiB  
Article
Enhancing Strategic Planning of Projects: Selecting the Right Product Development Methodology
by Itai Lishner and Avraham Shtub
Information 2023, 14(12), 632; https://doi.org/10.3390/info14120632 - 25 Nov 2023
Viewed by 1497
Abstract
The selection of an appropriate development methodology is a critical strategic decision when managing a New Product Development (NPD) project. However, accurately estimating project duration based on the chosen methodology remains a challenge. This paper addresses the limitations of existing models and proposes [...] Read more.
The selection of an appropriate development methodology is a critical strategic decision when managing a New Product Development (NPD) project. However, accurately estimating project duration based on the chosen methodology remains a challenge. This paper addresses the limitations of existing models and proposes a novel NPD project model that allows for testing and evaluation of different product development strategies. The model considers Waterfall, Spiral, Agile, and Hybrid methodologies and provides system engineers and project managers with decision-making tools to determine the optimal strategy and understand associated tradeoffs. The model is validated using real projects from various organizations and methodologies. It incorporates stochastic variables, risk management, and dynamic resource allocation, while addressing both Waterfall and Agile methodologies. The study contributes to the body of knowledge by offering practical tools for system engineers and project managers for choosing development methodology, improving project duration estimation, and identifying critical processes and risks in NPD projects. The research results also provide a basis for further studies and can benefit researchers interested in systems engineering methodologies. The proposed model fills a gap in the literature by providing a validated NPD model to evaluate the impact of different product development methodologies on project duration. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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39 pages, 1887 KiB  
Article
Efficient Resource Utilization in IoT and Cloud Computing
by Vivek Kumar Prasad, Debabrata Dansana, Madhuri D. Bhavsar, Biswaranjan Acharya, Vassilis C. Gerogiannis and Andreas Kanavos
Information 2023, 14(11), 619; https://doi.org/10.3390/info14110619 - 19 Nov 2023
Viewed by 2288
Abstract
With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the [...] Read more.
With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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28 pages, 5708 KiB  
Article
A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems
by Bilal Naji Alhasnawi, Basil H. Jasim, Ali M. Jasim, Vladimír Bureš, Arshad Naji Alhasnawi, Raad Z. Homod, Majid Razaq Mohamed Alsemawai, Rabeh Abbassi and Bishoy E. Sedhom
Information 2023, 14(10), 521; https://doi.org/10.3390/info14100521 - 25 Sep 2023
Cited by 9 | Viewed by 1023
Abstract
The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. [...] Read more.
The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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35 pages, 9764 KiB  
Article
Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling
by Manolis Remountakis, Konstantinos Kotis, Babis Kourtzis and George E. Tsekouras
Information 2023, 14(9), 504; https://doi.org/10.3390/info14090504 - 13 Sep 2023
Cited by 2 | Viewed by 3254
Abstract
Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This [...] Read more.
Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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12 pages, 4141 KiB  
Article
A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications
by Viet Q. Vu, Minh-Quang Tran, Mohammed Amer, Mahesh Khatiwada, Sherif S. M. Ghoneim and Mahmoud Elsisi
Information 2023, 14(7), 379; https://doi.org/10.3390/info14070379 - 03 Jul 2023
Cited by 1 | Viewed by 2118
Abstract
Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) [...] Read more.
Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA’s Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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29 pages, 4153 KiB  
Article
Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline
by Konstantinos Filippou, George Aifantis, George A. Papakostas and George E. Tsekouras
Information 2023, 14(4), 232; https://doi.org/10.3390/info14040232 - 09 Apr 2023
Cited by 5 | Viewed by 2586
Abstract
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the [...] Read more.
In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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26 pages, 6000 KiB  
Article
A Virtual Reality Lab for Automotive Service Specialists: A Knowledge Transfer System in the Digital Age
by Irina Makarova, Jamila Mustafina, Aleksey Boyko, Larisa Fatikhova, Gleb Parsin, Polina Buyvol and Vladimir Shepelev
Information 2023, 14(3), 163; https://doi.org/10.3390/info14030163 - 03 Mar 2023
Cited by 7 | Viewed by 2097
Abstract
Under the influence of the COVID-19 pandemic, there is an accelerated transition from the traditional form of knowledge transfer to online learning. Our study of 344 automotive students showed that the success of this transition depends on the readiness to introduce special digital [...] Read more.
Under the influence of the COVID-19 pandemic, there is an accelerated transition from the traditional form of knowledge transfer to online learning. Our study of 344 automotive students showed that the success of this transition depends on the readiness to introduce special digital tools for organizing knowledge and conducting practical forms of classes. In this regard, a modern digital form of organizing and transferring knowledge to automotive service engineers in the form of virtual laboratories was developed and presented in the article. The work scenarios, functionality, and minimum technical requirements of virtual laboratories as software systems are described and reviewed in the paper. The rationale for the effectiveness of the application, based on the results of using 109 university students in training practice, is presented as a result of the research. An analysis of the distributions of the student survey results and their training progress revealed differences at the p = 0.05 significance level. This confirmed the hypothesis that the use of methods for teaching engineers special disciplines and language skills using VR technologies is much more effective than the traditional one. An increase in students’ interest in learning was revealed, and their performance improved markedly. This proves that the immersive nature of VR technology makes it possible to better assimilate the studied material, increase the level of motivation of future car service specialists, and also allow the organization of the transfer of knowledge online. The very process of knowledge transfer becomes the point of acquiring new digital competencies necessary for high-tech industries. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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23 pages, 6320 KiB  
Article
A Framework for User-Focused Electronic Health Record System Leveraging Hyperledger Fabric
by Mandla Ndzimakhwe, Arnesh Telukdarie, Inderasan Munien, Andre Vermeulen, Uche K. Chude-Okonkwo and Simon P. Philbin
Information 2023, 14(1), 51; https://doi.org/10.3390/info14010051 - 16 Jan 2023
Cited by 6 | Viewed by 3696
Abstract
This research study aims to examine the possibilities of Hyperledger Fabric (HLF) in the healthcare sector. The study addresses the gap in the knowledge base through developing customization techniques to enable the simplicity and efficacy of Electronic Medical Records (EMR) adoption for healthcare [...] Read more.
This research study aims to examine the possibilities of Hyperledger Fabric (HLF) in the healthcare sector. The study addresses the gap in the knowledge base through developing customization techniques to enable the simplicity and efficacy of Electronic Medical Records (EMR) adoption for healthcare industry applications. The focus of this research explores methods of using blockchain technology that prioritise users. The investigation of several concepts used in developing web applications has been determined. The study identified that an open-source project, known as Hyperledger Fabric, can be utilised to construct a novel method of storing EMRs. The framework provides a test network that can be customised to satisfy the need of several projects, including storing medical records. This research additionally outlines the difficulties encountered and problems that need to be resolved before Hyperledger Fabric can be successfully implemented in healthcare systems. Considering all types of blockchains available, the needs are met by Hyperledger Fabric, which offers a distributed and secure environment for healthcare systems. Blockchain has the potential to transform healthcare by putting the patient at the centre of the system and enhancing health data protection and interoperability. Also, by using grant and revoke access mechanisms, patients have complete control over their medical information as well as authorized doctors who are allowed to view records. This functionality is made possible by the chaincode defined in the blockchain platform. The research study has both practitioner and research implications for the development of secure blockchain-based EMRs. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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Review

Jump to: Research

23 pages, 4520 KiB  
Review
Quo Vadis Business Simulation Games in the 21st Century?
by Mirjana Pejić Bach, Tamara Ćurlin, Ana Marija Stjepić and Maja Meško
Information 2023, 14(3), 178; https://doi.org/10.3390/info14030178 - 13 Mar 2023
Cited by 3 | Viewed by 2448
Abstract
Business simulation games have become popular in higher education and business environments. The paper aims to identify the primary research trends and topics of business simulation games research using a systematic and automated literature review with the motivation of research (learning driven and [...] Read more.
Business simulation games have become popular in higher education and business environments. The paper aims to identify the primary research trends and topics of business simulation games research using a systematic and automated literature review with the motivation of research (learning driven and domain driven). Based on these findings, the future development of business simulation games research projected papers that research business simulation games were extracted from Scopus. Second, the research timeline, main publication venues and citation trends have been analysed. Third, the most frequent words, phrases, and topics were extracted using text mining. Results indicate that the research on business simulation games has stagnated, with the most cited papers published in the 2000s. There is a balance between learning-driven and domain driven-research, while technology-driven research is scarce, indicating that the technology used for business simulation games is mature. We project that the research on business simulation games needs to be directed in the area of new technologies that could improve communication with and among the users (virtual reality, augmented reality, simulation games) and technologies that could improve the reasoning and decision-making complexity in business simulation games (artificial intelligence). Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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15 pages, 2442 KiB  
Review
Optimisation of Knowledge Management (KM) with Machine Learning (ML) Enabled
by Muhammad Anshari, Muhammad Syafrudin, Abby Tan, Norma Latif Fitriyani and Yabit Alas
Information 2023, 14(1), 35; https://doi.org/10.3390/info14010035 - 06 Jan 2023
Cited by 4 | Viewed by 4348
Abstract
The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and discovery. KM necessitates ML for improved organisational experiences, particularly in making knowledge management more [...] Read more.
The emergence of artificial intelligence (AI) and its derivative technologies, such as machine learning (ML) and deep learning (DL), heralds a new era of knowledge management (KM) presentation and discovery. KM necessitates ML for improved organisational experiences, particularly in making knowledge management more discoverable and shareable. Machine learning (ML) is a type of artificial intelligence (AI) that requires new tools and techniques to acquire, store, and analyse data and is used to improve decision-making and to make more accurate predictions of future outcomes. ML demands big data be used to develop a method of data analysis that automates the construction of analytical models for the purpose of improving the organisational knowledge. Knowledge, as an organisation’s most valuable asset, must be managed in automation to support decision-making, which can only be accomplished by activating ML in knowledge management systems (KMS). The main objective of this study is to investigate the extent to which machine learning applications are used in knowledge management applications. This is very important because ML with AI capabilities will become the future of managing knowledge for business survival. This research used a literature review and theme analysis of recent studies to acquire its data. The results of this research provide an overview of the relationship between big data, machine learning, and knowledge management. This research also shows that only 10% of the research that has been published is about machine learning and knowledge management in business and management applications. Therefore, this study gives an overview of the knowledge gap in investigating how ML can be used in KM for business applications in organisations. Full article
(This article belongs to the Special Issue Systems Engineering and Knowledge Management)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The application of symbolic classifier in cervical cancer diagnosis
Authors: Nikola Anđelić; Ariana Lorencin; Matko Glučina; Ivan Lorencin
Affiliation: Istrian University of Applied Sciences
Abstract: Objectives: Cervical cancer is present in most cases of squamous cell carcinoma. In most cases, it is the result of an infection with human papillomavirus or adenocarcinoma. This type of cancer is the third most common cancer of the female reproductive organs. The risk groups for cervical cancer are mostly younger women who frequently change partners, have early sexual intercourse, are infected with human papillomavirus (HPV), and who are nicotine addicts. In most cases, the cancer is asymptomatic until it has progressed to the later stages. Cervical cancer screening rates are low, especially in developing countries and in some minority groups. Due to these facts, the introduction of a tentative cervical cancer screening based on a questionnaire can enable more diagnoses of cervical cancer in the initial stages of the disease. Methods: In this research, publicly available cervical cancer data collected on 859 female patients are used. Each sample consists of 36 input attributes and four different outputs Hinselmann, Schiller, cytology, and biopsy. Results: From the achieved results, it can be seen that the by utilization of symbolic classifier, high classification performances are achieved.

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