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Decision Science Applications and Models (DSAM)
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IN3—Computer Science, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
CIGIP, Universitat Politècnica de València, 46022 Valencia, Spain
Computer Science, Multimedia and Telecommunication Studies, Universitat Oberta de Catalunya, Barcelona, 08018, Spain
Dr. Laura Calvet
Department of Telecommunication and Systems Engineering, Autonomous University of Barcelona, Sabadell, 08202, Spain
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Decision Science Applications and Models (DSAM)

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
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Topic Information

Dear Colleagues,

The theme “Decision Science Applications and Models” aims at providing cutting-edge methodologies, models, and case studies in the area of applied decision science, thus contributing to economic, technological, environmental, and social progress. This theme seeks to explore innovative advancements and practical applications that bridge theory and practice in decision-making methodologies across various domains.

Decision Science is an interdisciplinary field that merges principles from mathematics, statistics, computer science, artificial intelligence, economics, and behavioral science to enhance decision-making processes. The topics of interest include, but are not limited to, the following ones:

  • Decision-making methodologies in the digital era: Exploring novel methodologies and frameworks for effective decision-making using technological advancements.
  • Mathematical models for complex decision problems: Developing and applying mathematical models to address multifaceted decision challenges across diverse domains.
  • Machine learning applications in decision science: Utilizing machine learning techniques to extract insights and optimize decision-making processes.
  • Data analytics and statistics for informed decision-making: Exploring the use of data analytics and statistical methods to support informed and robust decision-making.
  • AI-driven decision-making advancements: Investigating the role of artificial intelligence in shaping decision strategies and outcomes.
  • Economical and behavioral aspects in decision science: Understanding customers’ behavior and biases to improve decision-making models and strategies.

We welcome original research articles, reviews, case studies, and methodological papers that provide innovative applications, theoretical advancements, and practical implementations in the field of decision science. Submissions should contribute to the thematic focus of this theme and present new insights or methodologies. We also welcome original and high-quality full papers derived from extended abstracts selected in peer-review international conferences on decision science, such as the 2024 DSA Int. Summer Conference: https://decisionsciencealliance.org/ISC-2024/ 

Prof. Dr. Daniel Riera Terrén
Prof. Dr. Angel A. Juan
Dr. Majsa Ammuriova
Dr. Laura Calvet
Topic Editors

Keywords

  • decision science
  • business analytics
  • optimization models
  • artificial intelligence
  • operations research

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Computers
computers 		2.8 4.7 2012 17.7 Days CHF 1800 Submit
Informatics
informatics 		3.1 4.8 2014 30.3 Days CHF 1800 Submit
Information
information 		3.1 5.8 2010 18 Days CHF 1600 Submit
Logistics
logistics 		3.8 5.1 2017 25.4 Days CHF 1400 Submit
Mathematics
mathematics 		2.4 3.5 2013 16.9 Days CHF 2600 Submit
Algorithms
algorithms 		2.3 3.7 2008 15 Days CHF 1600 Submit

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Published Papers (1 paper)

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23 pages, 1989 KiB  
Article
Optimization of Obstructive Sleep Apnea Management: Novel Decision Support via Unsupervised Machine Learning
by Arthur Pinheiro de Araújo Costa, Adilson Vilarinho Terra, Claudio de Souza Rocha Junior, Igor Pinheiro de Araújo Costa, Miguel Ângelo Lellis Moreira, Marcos dos Santos, Carlos Francisco Simões Gomes and Antonio Sergio da Silva
Informatics 2024, 11(2), 22; https://doi.org/10.3390/informatics11020022 - 19 Apr 2024
Viewed by 456
Abstract
This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria [...] Read more.
This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria Decision Analysis (MCDA) to calculate performance criteria weights of Continuous Positive Airway Pressure (CPAP—key in managing OSA) and to evaluate these devices. Uniquely, the CROWM incorporates non-beneficial criteria in PCA and employs communalities to accurately represent the performance evaluation of alternatives within each resulting principal factor, allowing for a more accurate and robust analysis of alternatives and variables. This article aims to employ CROWM to evaluate CPAP for effectiveness in combating OSA, considering six performance criteria: resources, warranty, noise, weight, cost, and maintenance. Validated by established tests and sensitivity analysis against traditional methods, CROWM proves its consistency, efficiency, and superiority in decision-making support. This method is poised to influence assertive decision-making significantly, aiding healthcare professionals, researchers, and patients in selecting optimal CPAP solutions, thereby advancing patient care in an interdisciplinary research context. Full article
(This article belongs to the Topic Decision Science Applications and Models (DSAM))
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