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Soft Computing Methods and Applications for Decision Making
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Soft Computing Methods and Applications for Decision Making

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 14724

Special Issue Editors

Dr. Camilo Franco

E-Mail Website
Guest Editor
EC-JRC, Ispra, Italy
Interests: decision modeling; machine learning; soft computing; energy; environmental sciences
Prof. Dr. Jens Leth Hougaard

E-Mail Website
Guest Editor
Department of Food and Resource Economics (IFRO), University of Copenhagen, Copenhagen, Denmark
Interests: applied microeconomics; efficiency analysis and benchmarking; allocation rules; health economics; network economics; cooperative game theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedro Melo-Pinto

E-Mail Website
Guest Editor
1. CITAB, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
2. Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal
Interests: computer vision; machine learning; hyperspectral imaging; image classification; object detection
Special Issues, Collections and Topics in MDPI journals
Dr. Tinguaro Rodriguez

E-Mail Website
Guest Editor
1. Faculty of Mathematics, Complutense University of Madrid, 28040 Madrid, Spain
2. Interdisciplinary Mathematics Institute, Complutense University of Madrid, 28040 Madrid, Spain
Interests: fuzzy logic; machine learning; social network analysis; aggregation operators; decision theory; bipolar knowledge representation; humanitarian logistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daniel Gómez Gonzalez

E-Mail Website
Guest Editor
Facultad de Estudios Estadísticos, Universidad Complutense, Avenida Puerta de Hierro s/n, 28040 Madrid, Spain
Interests: data Science; fuzzy sets; aggregation, decision making problems; cooperative game theory; social network analysis; machine learning and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft computing techniques, such as neural networks and fuzzy sets, are able to represent the foundations of decision-making processes, and hence, can solve general decision problems. Neural networks provide powerful architectures for solving complex problems, while fuzzy sets offer general means for preference modeling under uncertainty. This Special Issue focuses on “Soft Computing Methods and Applications for Decision Making”, with an emphasis on knowledge representation, and reliable, interpretable and replicable algorithms. It will be a collection of review papers, research articles and communications on theoretical aspects and real-world applications. Models based on nonclassical assumptions are particularly welcome, presenting theoretical results and applications of soft computing, machine and deep learning models that aim to describe learning processes and decision systems. Special attention will be given to the theory and application of statistical data modeling and machine learning in diverse challenging areas such as energy, environmental sciences and medicine.

Dr. Camilo Franco
Prof. Dr. Jens Leth Hougaard
Prof. Dr. Pedro Melo-Pinto
Dr. Tinguaro Rodriguez
Prof. Dr. Daniel Gómez Gonzalez
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. Applied Sciences 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 modeling
  • machine learning
  • soft computing

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Published Papers (7 papers)

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Research

38 pages, 1722 KiB  
Article
Robust Cross-Validation of Predictive Models Used in Credit Default Risk
by Jose Vicente Alonso and Lorenzo Escot
Appl. Sci. 2025, 15(10), 5495; https://doi.org/10.3390/app15105495 - 14 May 2025
Viewed by 174
Abstract
Model validation is a challenging Machine Learning task, usually more difficult for consumer credit default models because of the availability of small datasets, the modeling of low-frequency events (imbalanced data), and the bias in the explanatory variables induced by the train/test sets split [...] Read more.
Model validation is a challenging Machine Learning task, usually more difficult for consumer credit default models because of the availability of small datasets, the modeling of low-frequency events (imbalanced data), and the bias in the explanatory variables induced by the train/test sets split of the validation techniques (covariate shift). While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. This experimental research focuses on evaluating existing robust cross-validation variants to address the issues of validating credit default models. In addition, some improvements to those methods are proposed and compared with a wide range of validation techniques, including fuzzy methods. To reach solid and practical conclusions, this work limits its scope to logistic regression, as it is the best-practice modeling technique in real-world applications of this context. It is shown that robust cross-validation algorithms lead to more stable estimates, as expected due to the more homogeneous partitions, which have a positive impact on the selection of credit default models. In addition, the enhancements proposed to existing robust techniques lead to improved results when there are data restrictions. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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30 pages, 2499 KiB  
Article
Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
by Ufuk Cebeci, Ugur Simsir and Onur Dogan
Appl. Sci. 2025, 15(1), 425; https://doi.org/10.3390/app15010425 - 5 Jan 2025
Viewed by 873
Abstract
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine [...] Read more.
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP&CODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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31 pages, 11047 KiB  
Article
Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
by Dimitris C. Gkikas and Prokopis K. Theodoridis
Appl. Sci. 2024, 14(23), 11403; https://doi.org/10.3390/app142311403 - 7 Dec 2024
Cited by 1 | Viewed by 4730
Abstract
User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships [...] Read more.
User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships among key metrics including event count, sessions, purchase revenue, transactions, and bounce rate, were examined using descriptive statistics, revealing factors affecting user engagement. Machine learning classifiers, such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), were assessed for their effectiveness in classifying engagement levels. DTs achieved a classification accuracy of 97.98%, outperforming NB (65.00%) and k-NN (97.90%). Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. This study highlights the significance of artificial intelligence (AI) integration in digital marketing as a best practice for optimizing decision-making processes. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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27 pages, 893 KiB  
Article
Stochastic Extensions of the Elo Rating System
by Gonzalo Gómez-Abejón and J. Tinguaro Rodríguez
Appl. Sci. 2024, 14(17), 8023; https://doi.org/10.3390/app14178023 - 8 Sep 2024
Viewed by 1913
Abstract
This work studies how the Elo rating system can be applied to score-based sports, where it is gaining popularity, and in particular for predicting the result at any point of a game, extending its statistical basis to stochastic processes. We derive some new [...] Read more.
This work studies how the Elo rating system can be applied to score-based sports, where it is gaining popularity, and in particular for predicting the result at any point of a game, extending its statistical basis to stochastic processes. We derive some new theoretical results for this model and use them to implement Elo ratings for basketball and soccer leagues, where the assumptions of our model are tested and found to be mostly accurate. We showcase several metrics for comparing the performance of different rating systems and determine whether adding a feature has a statistically significant impact. Finally, we propose an Elo model based on a discrete process for the score that allows us to obtain draw probabilities for soccer matches and has a performance competitive with alternatives like SPI ratings. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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28 pages, 3224 KiB  
Article
Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training
by Alexandre M. Nascimento, Gabriel Kenji G. Shimanuki and Luiz Alberto V. Dias
Appl. Sci. 2024, 14(11), 4880; https://doi.org/10.3390/app14114880 - 4 Jun 2024
Viewed by 1911
Abstract
As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of [...] Read more.
As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models’ in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach’s applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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19 pages, 3565 KiB  
Article
Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
by Dimitris C. Gkikas, Marios C. Gkikas and John A. Theodorou
Appl. Sci. 2024, 14(5), 2129; https://doi.org/10.3390/app14052129 - 4 Mar 2024
Cited by 5 | Viewed by 2084
Abstract
A proposal has been put forward advocating a data-driven strategy that employs classifiers from data mining to foresee and categorize instances of fish mortality. This addresses the increasing concerns regarding the death rates in caged fish environments because of the unsustainable fish farming [...] Read more.
A proposal has been put forward advocating a data-driven strategy that employs classifiers from data mining to foresee and categorize instances of fish mortality. This addresses the increasing concerns regarding the death rates in caged fish environments because of the unsustainable fish farming techniques employed and environmental variables involved. The aim of this research is to enhance the competitiveness of Greek fish farming through the development of an intelligent system that is able to diagnose fish diseases in farms. This system concurrently addresses medication and dosage issues. To achieve this, a comprehensive dataset derived from various aquaculture sources was used, including various factors such as the geographic locations, farming techniques, and indicative parameters such as the water quality, climatic conditions, and fish biological characteristics. The main objective of the research was to categorize fish mortality cases through predictive models. Advanced data mining classification methods, specifically decision trees (DTs), were used for the comparison, aiming to recognize the most appropriate method with high precision and recall rates in predicting fish death rates. To ensure the reliability of the results, a methodical evaluation process was adopted, including cross-validation and a classification performance assessment. In addition, a statistical analysis was performed to gain insights into the factors that identify the correlations between the various factors affecting fish mortality. This analysis contributes to the development of targeted conservation and restoration action strategies. The research results have important implications for sustainable management actions, enabling stakeholders to proactively address issues and monitor aquaculture practices. This proactive approach ensures the protection of farmed fish quantities while meeting global seafood requirements. The data mining using a classification approach coincides with the general context of the UN sustainability goals, reducing the losses in seafood management and production when dealing with the consequences of climate change. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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17 pages, 300 KiB  
Article
Dynamic Cloud Resource Allocation: A Broker-Based Multi-Criteria Approach for Optimal Task Assignment
by Abdulmajeed Aljuhani and Abdulaziz Alhubaishy
Appl. Sci. 2024, 14(1), 302; https://doi.org/10.3390/app14010302 - 29 Dec 2023
Cited by 3 | Viewed by 1182
Abstract
Cloud brokers and service providers are concerned with utilizing available resources to maximize their profits. On the other hand, customers seek the best service provider/resource to provide them with maximum satisfaction. One of the main concerns is the variability of available service providers [...] Read more.
Cloud brokers and service providers are concerned with utilizing available resources to maximize their profits. On the other hand, customers seek the best service provider/resource to provide them with maximum satisfaction. One of the main concerns is the variability of available service providers on the cloud, their capabilities, and the availability of their resources. Furthermore, various criteria influence the effective assignment of a task to a virtual machine (VM) before it is, in turn, submitted to the physical machine (PM). To bring cloud service providers (CSPs) and customers together, this study proposes a broker-based mechanism that measures the tendency of each customer’s task. Then, the proposed mechanism assigns all tasks—in prioritized order of importance—to the best available service provider/resource. The model acquires the importance of each task, CSP, or resource by extracting and manipulating the evaluations provided by decision makers and by adopting a multi-criteria decision-making (MCDM) method. Thus, a partial result of the proposed mechanism is a defined and prioritized pool for each of the tasks, CSPs, and resources. Various MCDM methods are examined and compared to validate the proposed model, and experiments show the applicability of the various methods within the model. Furthermore, the results of the experiments verify the suitability and applicability of the proposed model within the cloud environment. Full article
(This article belongs to the Special Issue Soft Computing Methods and Applications for Decision Making)
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