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Applied Sciences
Special Issues
Decision Support Systems and Their Applications
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Decision Support Systems and Their Applications

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 (10 August 2021) | Viewed by 10726

Special Issue Editor

Prof. Sean Eom

E-Mail Website
Guest Editor
The Department of Management Harrison College of Business, Southeast Missouri State University, Cape Girardeau, MO 63701, USA
Interests: decision support systems; business intelligence; distance learning

Special Issue Information

Dear Colleagues,

You are invited to submit original papers to address topics including, but not limited to, the following areas: operations research (OR)/management science (MS) model-based decision support systems (DSS), business intelligence (BI), and big data analytics (DA).

First, we welcome DSS research based on the data-dialogue-model (DDM) paradigm, organizational perspectives (design, implementation, and evaluation of DSS), and application development research in business, agriculture, education, engineering, animal biosciences, nursing, sports medicine, defense, childcare, climatic changes, forensic science, etc.

Second, we welcome BI research that focuses on design, implementation, and use to achieve business goals. Specifically, it may focus on identifying critical success factors, and justification of BI adoption, implementation, and utilization for sensing opportunities for organizational innovation.

Finally, we welcome any research examining the development pattern and trends of DA research in corporate functional management fields and other non-business areas such as education, environment, government, and city management.

Prof. Sean Eom
Guest Editor

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 support systems (DSS)
  • decision support system applications
  • business intelligence (BI)
  • big data analytics (DA)
  • advanced data analytics
  • big data
  • data warehouses
  • data mining
  • intelligent agents (IA)
  • learning and engagement analytics

Published Papers (4 papers)

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Research

15 pages, 1260 KiB  
Article
A Novel Stacked Ensemble for Hate Speech Recognition
by Mona Khalifa A. Aljero and Nazife Dimililer
Appl. Sci. 2021, 11(24), 11684; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411684 - 09 Dec 2021
Cited by 12 | Viewed by 1915
Abstract
Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a [...] Read more.
Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines the outputs of the three base classifiers and the features employed by the base classifiers to produce the final output. It is shown that the proposed architecture improves the performance of the widely used single classifiers as well as the standard stacking and classifier ensemble using majority voting. We also present results on the use of various combinations of machine learning classifiers as base classifiers. The experimental results from the proposed architecture indicated an improvement in the performance on all four datasets compared with the standard stacking, base classifiers, and majority voting. Furthermore, on three of these datasets, the proposed architecture outperformed all state-of-the-art systems. Full article
(This article belongs to the Special Issue Decision Support Systems and Their Applications)
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22 pages, 1780 KiB  
Article
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
by Simone Monaco, Salvatore Greco, Alessandro Farasin, Luca Colomba, Daniele Apiletti, Paolo Garza, Tania Cerquitelli and Elena Baralis
Appl. Sci. 2021, 11(22), 11060; https://0-doi-org.brum.beds.ac.uk/10.3390/app112211060 - 22 Nov 2021
Cited by 11 | Viewed by 2886
Abstract
Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, [...] Read more.
Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem. Full article
(This article belongs to the Special Issue Decision Support Systems and Their Applications)
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26 pages, 1243 KiB  
Article
A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems
by Marco Cipriano, Luca Colomba and Paolo Garza
Appl. Sci. 2021, 11(15), 6967; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156967 - 28 Jul 2021
Cited by 9 | Viewed by 2065
Abstract
Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist [...] Read more.
Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Decision Support Systems and Their Applications)
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14 pages, 2991 KiB  
Article
Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming
by Luís Cicero Bezerra da Silva, Bruna Daniela Mendes Lopes, Isidro Manuel Blanquet and Carlos Alberto Ferreira Marques
Appl. Sci. 2021, 11(13), 5875; https://0-doi-org.brum.beds.ac.uk/10.3390/app11135875 - 24 Jun 2021
Cited by 4 | Viewed by 2386
Abstract
The development of better monitoring technologies, the early combat of outbreaks, massive mortality, and promoting sustainability are challenges that the aquaculture industry still faces, and the development of solutions for this is an open problem. In this paper, focusing our attention on monitoring [...] Read more.
The development of better monitoring technologies, the early combat of outbreaks, massive mortality, and promoting sustainability are challenges that the aquaculture industry still faces, and the development of solutions for this is an open problem. In this paper, focusing our attention on monitoring technologies as a promising solution to these issues, we report a Gaussian distribution model for detecting dangerous operating conditions in industrial fish farming. This approach allows us to indicate through a 2D image visualization when fish production is under normal, warning, or dangerous operating conditions. Furthermore, our proposed method has promising possibilities for application in the most varied fields of science, given that the mathematical procedure described allows us to discover the fundamental statistical structure of physical, chemical, and biological systems governed by laws of a probabilistic nature. Full article
(This article belongs to the Special Issue Decision Support Systems and Their Applications)
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