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Electronics
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New Challenges of Decision Support Systems
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New Challenges of Decision Support Systems

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 5800

Special Issue Editors

Dr. Daniele Spoladore

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Guest Editor
1. Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900 Lecco, Italy
2. Department of Pure and Applied Sciences, Computer Science Division, Insubria University, 21100 Varese, Italy
Interests: ontology development and engineering; decision support systems; semantic web application; applications for rehabilitation and continuity of care; smart home and environments
Special Issues, Collections and Topics in MDPI journals
Dr. Atieh Mahroo

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Guest Editor
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), 23900 Lecco, Italy
Interests: artificial intelligence; smart home; Internet of Things; decision support systems; AI-powered XR; eXtended reality in health, rehabilitation and continuity of care

Special Issue Information

Dear Colleagues,

Decision Support Systems (DSSs) are adopted in several research and industry fields to support decision-making processes with high-quality decisions. In the past decades, the widespread adoption of data-driven technologies introduced elements of novelty in the study and development of DSSs, enabling such systems to face the complexity and dynamic nature of information. Artificial intelligence techniques enabled decision-making solutions capable of supporting end users in dynamic and information-dense areas such as healthcare, industry, finance, environment, and knowledge management. Moreover, as the world is experiencing the huge advancement in integration of eXtended Reality (XR) and Metaverse into our lives, the significance of the data-driven AI and inferences accuracy and reliability is remarkably observable.

Today, DSSs must be able to provide solid recommendations and suggestions by integrating expert knowledge, data-driven inferences, uncertain and incomplete information usually transmitted in massive streams of data in real time without data pre-processing and cleaning. Moreover, theoretical and technological AI-based advances can smoothly integrate the decision-making processes in the digital experience, supporting the generation of tailored and intelligent recommendations.

This Special Issue aims to attract high-value articles and literature reviews exploring the research issues, challenges, solutions, and applications pertaining to DSSs in the modern era.

Topics relevant for this Special Issue include (but are not limited to):

  • Integration of data-driven and ontology-based solutions in DSSs
  • AI techniques and Smart Autonomous Agents for decision-making
  • Explainable AI (xAI) DSSs
  • Robust methodologies for DSSs’ development
  • AI-powered metaverse, opportunities & challenges
  • Challenges of DSSs and data-driven approaches in real time XR environments
  • Applications of DSSs in different research areas (healthcare, personalized medicine, Ambient Assisted Living and Ambient Assisted Working, learning environments, finance, manufacturing and industry, circular economy, environmental management, knowledge management).

Examples of articles describing Decision Support Systems in different fields [1–11].

References

  1. Spoladore, D.; Pessot, E. An Ontology-Based Decision Support System to Foster Innovation and Competitiveness Opportunities of Health Tourism Destinations. In Digital and Strategic Innovation for Alpine Health Tourism. SpringerBriefs in Applied Sciences and Technology; Spoladore, D., Pessot, E., Sacco, M., Eds.; Springer: Cham, Switzerland, 2023. https://doi.org/10.1007/978-3-031-15457-7_4.
  2. Mahroo, A.; Spoladore, D.; Ferrandi, P.; Lovato, I. A Digital Application for Strategic Development of Health Tourism Destinations. In Digital and Strategic Innovation for Alpine Health Tourism; Spoladore, D., Pessot, E., Sacco, M., Eds.; SpringerBriefs in Applied Sciences and Technology; Springer: Cham, Switzerland, 2023. https://doi.org/10.1007/978-3-031-15457-7_5.
  3. Spoladore, D.; Mahroo, A.; Trombetta, A.; Sacco, M. DOMUS: A domestic ontology managed ubiquitous system. J. Ambient. Intell. Hum. Comput. 2022, 13, 3037–3052. https://doi.org/10.1007/s12652-021-03138-4.
  4. Spoladore, D.; Colombo, V.; Arlati, S.; Mahroo, A.; Trombetta, A.; Sacco, M. An Ontology-Based Framework for a Telehealthcare System to Foster Healthy Nutrition and Active Lifestyle in Older Adults. Electronics 2021, 10, 2129. https://doi.org/10.3390/electronics10172129.
  5. Spoladore, D.; Sacco, M. Semantic and Dweller-Based Decision Support System for the Reconfiguration of Domestic Environments: RecAAL. Electronics 2018, 7, 179. https://doi.org/10.3390/electronics7090179.
  6. Zhai, Z.; Martínez, J.F.; Beltran, V.; Martínez, N.L. Decision support systems for agriculture 4.0: Survey and challenges. Comput. Electron. Agric. 2020, 170, 105256.
  7. Juan, Y.K.; Chi, H.Y.; Chen, H.H. Virtual reality-based decision support model for interior design and decoration of an office building. Eng. Constr. Archit. Manag. 2021, 28, 229–245.
  8. Deveci, M.; Mishra, A.R.; Gokasar, I.; Rani, P.; Pamucar, D.; Özcan, E. A decision support system for assessing and prioritizing sustainable urban transportation in metaverse. IEEE Trans. Fuzzy Syst. 2022, 31, 475–484.
  9. Gonzales, R.M.D.; Hargreaves, C.A. How can we use artificial intelligence for stock recommendation and risk management? A proposed decision support system. Int. J. Inf. Manag. Data Insights 2022, 2, 100130.
  10. Naqvi, S.M.R.; Ghufran, M.; Meraghni, S.; Varnier, C.; Nicod, J.M.; Zerhouni, N. Human knowledge centered maintenance decision support in digital twin environment. J. Manuf. Syst. 2022, 65, 528–537.
  11. Cantini, A.; Peron, M.; De Carlo, F.; Sgarbossa, F. A decision support system for configuring spare parts supply chains considering different manufacturing technologies. Int. J. Prod. Res. 2022, 1–21.

Dr. Daniele Spoladore
Dr. Atieh Mahroo
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

  • decision support systems
  • Artificial Intelligence
  • data-driven AI
  • ontology
  • knowledge inference
  • explainable AI
  • extended reality
  • AI-powered metaverse

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Published Papers (4 papers)

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Research

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20 pages, 6690 KiB  
Article
Reconfiguring Vehicles for Drivers with Disability: A Knowledge-Based Decision Support System
by Daniele Spoladore, Atieh Mahroo, Angelo Davalli and Marco Sacco
Electronics 2024, 13(21), 4147; https://doi.org/10.3390/electronics13214147 - 22 Oct 2024
Viewed by 693
Abstract
Driving a car is pivotal to supporting Persons with Disabilities (PwDs) independence and quality of life. The problem of reconfiguring a vehicle to meet both the PwD’s needs and the (local or supranational) regulations is far from trivial since it requires the identification [...] Read more.
Driving a car is pivotal to supporting Persons with Disabilities (PwDs) independence and quality of life. The problem of reconfiguring a vehicle to meet both the PwD’s needs and the (local or supranational) regulations is far from trivial since it requires the identification of the appropriate modifications and adaptations to be installed on the driver’s car. However, PwDs may not be acquainted with the mechanical modification, aids, and devices installed on their cars to allow them to drive, nor may they be aware of the possible configurations available. In the Italian context, this knowledge is strictly regulated by local and European regulations, which—according to the type(s) of impairments a driver has—indicate the possible configurations for the vehicles and the aids and mechanical modifications that need to be implemented. Therefore, to support PwDs in understanding the possible modification(s) their cars could undergo, a novel knowledge-based Decision Support System (DSS) was developed with the support of the Italian National Institute for Insurance against Accidents at Work (INAIL). The DSS exploits ontological engineering to formalize the relevant information on cars’ modifications, PwDs’ impairments, and a rule engine to match candidate drivers with the (sets of) car configurations that can be installed on their vehicles. Thus, the proposed DSS can enable the drivers to acquire more insights on the types and functionalities of the driving aids they will use. It also supports INAIL in administering the “special driving license”. Full article
(This article belongs to the Special Issue New Challenges of Decision Support Systems)
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14 pages, 3298 KiB  
Article
Parkinson’s Disease Severity Index Based on Non-Motor Symptoms by Self-Organizing Maps
by Sabrina B. M. Nery, Suellen M. Araújo, Bianca G. Magalhães, Kelson J. S. de Almeida and Pedro D. Gaspar
Electronics 2024, 13(8), 1523; https://doi.org/10.3390/electronics13081523 - 17 Apr 2024
Viewed by 857
Abstract
Parkinson’s disease, a progressive neurodegenerative disorder of the motor system, shows non-motor symptoms up to 10 years before classic motor signs, highlighting the importance of early detection for effective treatment. This study proposes a severity index using an Artificial Neural Network (ANN) trained [...] Read more.
Parkinson’s disease, a progressive neurodegenerative disorder of the motor system, shows non-motor symptoms up to 10 years before classic motor signs, highlighting the importance of early detection for effective treatment. This study proposes a severity index using an Artificial Neural Network (ANN) trained by the Self-Organizing Maps (SOM) algorithm, with data from the FOX Insight database. After pre-processing, 41,892 questionnaires were selected, covering 25 questions about non-motor symptoms, defined by a neurologist, and divided into four classes representing stages of the disease. The goal is to offer a tool to classify patients based on these symptoms, allowing for accurate monitoring and personalized interventions. Validation was carried out with data from patients responding to the questionnaire at spaced moments, simulating medical consultations. The study was successful in developing the severity index, highlighting the importance of gastrointestinal and urinary symptoms at different stages. The persistence of difficulty sleeping in group 3 indicates special attention must be paid to this symptom in the initial stages. These results highlight the clinical and practical relevance of the index, although more studies with real patients are needed for validation. Full article
(This article belongs to the Special Issue New Challenges of Decision Support Systems)
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Review

Jump to: Research

17 pages, 4969 KiB  
Review
Concept of the Intelligent Support of Decision Making for Manufacturing a 3D-Printed Hand Exoskeleton within Industry 4.0 and Industry 5.0 Paradigms
by Izabela Rojek, Jakub Kopowski, Piotr Kotlarz, Janusz Dorożyński and Dariusz Mikołajewski
Electronics 2024, 13(11), 2091; https://doi.org/10.3390/electronics13112091 - 28 May 2024
Viewed by 1209
Abstract
Supporting the decision-making process for the production of a 3D-printed hand exoskeleton within the Industry 4.0 and Industry 5.0 paradigms brings new concepts of manufacturing procedures for 3D-printed medical devices, including hand exoskeletons for clinical applications. The article focuses on current developments in [...] Read more.
Supporting the decision-making process for the production of a 3D-printed hand exoskeleton within the Industry 4.0 and Industry 5.0 paradigms brings new concepts of manufacturing procedures for 3D-printed medical devices, including hand exoskeletons for clinical applications. The article focuses on current developments in the design and manufacturing of hand exoskeletons and their future directions from the point of view of implementation within the Industry 4.0 and Industry 5.0 paradigms and applications in practice. Despite numerous publications on the subject of hand exoskeletons, many have not yet entered production and clinical application. The results of research on hand exoskeletons to date indicate that they achieve good therapeutic effects not only in terms of motor control, but also in a broader context: ensuring independence and preventing secondary motor changes. This makes interdisciplinary research on hand exoskeletons a key study influencing the future lives of patients with hand function deficits and the further work of physiotherapists. The main aim of this article is to check in what direction hand exoskeletons can be developed from a modern economic perspective and how decision support systems can accelerate these processes based on a literature review, expert opinions, and a case study. Full article
(This article belongs to the Special Issue New Challenges of Decision Support Systems)
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21 pages, 1469 KiB  
Review
Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems
by Andreas Soularidis, Konstantinos Ι. Kotis and George A. Vouros
Electronics 2024, 13(3), 526; https://doi.org/10.3390/electronics13030526 - 28 Jan 2024
Viewed by 1570
Abstract
Natural disasters such as earthquakes, floods, and forest fires involve critical situations in which human lives and infrastructures are in jeopardy. People are often injured and/or trapped without being able to be assisted by first responders on time. Moreover, in most cases, the [...] Read more.
Natural disasters such as earthquakes, floods, and forest fires involve critical situations in which human lives and infrastructures are in jeopardy. People are often injured and/or trapped without being able to be assisted by first responders on time. Moreover, in most cases, the harsh environment jeopardizes first responders by significantly increasing the difficulty of their mission. In such scenarios, time is crucial and often of vital importance. First responders must have a clear and complete view of the current situation every few seconds/minutes to efficiently and timely tackle emerging challenges, ensuring the safety of both victims and personnel. Advances in related technology including robots, drones, and Internet of Things (IoT)-enabled equipment have increased their usability and importance in life- and time-critical decision support systems such as the ones designed and developed for Search and Rescue (SAR) missions. Such systems depend on efficiency in their ability to integrate large volumes of heterogeneous and streaming data and reason with this data in (near) real time. In addition, real-time critical data integration and reasoning need to be performed on edge devices that reside near the missions, instead of using cloud infrastructure. The aim of this paper is twofold: (a) to review technologies and approaches related to real-time semantic data integration and reasoning on IoT-enabled collaborative entities and edge devices in life- and time-critical decision support systems, with a focus on systems designed for SAR missions and (b) to identify open issues and challenges focusing on the specific topic. In addition, this paper proposes a novel approach that will go beyond the state-of-the-art in efficiently recognizing time-critical high-level events, supporting commanders and first responders with meaningful and life-critical insights about the current and predicted state of the environment in which they operate. Full article
(This article belongs to the Special Issue New Challenges of Decision Support Systems)
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