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: 20 July 2024 | Viewed by 1479

Special Issue Editors

EC-JRC, Ispra, Italy
Interests: decision modeling; machine learning; soft computing; energy; environmental sciences
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
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
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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
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

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Keywords

  • decision modeling
  • machine learning
  • soft computing

Published Papers (2 papers)

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Research

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 - 04 Mar 2024
Viewed by 475
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
Viewed by 397
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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