Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
New Challenges of Decision Support Systems
share announcement

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: 31 October 2024 | Viewed by 2108

Special Issue Editors

Dr. Daniele Spoladore

E-Mail
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

E-Mail
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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

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 417
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

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 963
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop