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Applied Sciences
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Applications of Data Science and Artificial Intelligence
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Applications of Data Science and Artificial Intelligence

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

Deadline for manuscript submissions: 30 May 2024 | Viewed by 4529

Special Issue Editors

Dr. Carlos J. Costa

E-Mail Website
Guest Editor
ISEG – Lisbon School of Economics and Management, Universidade de Lisboa, 1200-781 Lisboa, Portugal
Interests: data science; data science and management; machine learning in finance; gamification; information systems
Special Issues, Collections and Topics in MDPI journals
Dr. Manuela Aparicio

E-Mail Website
Guest Editor
NOVA IMS Information Management School, Universidade Nova de Lisboa Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: data science; artificial intelligence; information systems; e-learning; digital transformation; gamification; e-commerce
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Significant advances in artificial intelligence (AI) have led to new challenges and opportunities in the field. Data science is a rapidly growing area of study and professional discipline. It is thus critical to investigate this new reality from a social and corporate standpoint. Abundant information about data science and AI and how they may be used to solve economic and societal problems exists. However, in order to realize the widespread use of data science and AI in business and everyday life, their efficacy must be objectively assessed. This Special Issue aims to gather contributions from academics investigating a variety of subjects and viewpoints, including AI-related management, social sciences, and engineering. Given the present level of AI and data science, three forms are of particular interest: machine learning, natural language processing, and robotics. Submissions considering other relevant topics will also be considered.

Dr. Carlos J. Costa
Dr. Manuela Aparicio
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

  • data science applications
  • AI applications
  • machine learning applications
  • NLP applications
  • AI trends
  • data science trends

Published Papers (4 papers)

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Editorial

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3 pages, 196 KiB  
Editorial
Applications of Data Science and Artificial Intelligence
by Carlos J. Costa and Manuela Aparicio
Appl. Sci. 2023, 13(15), 9015; https://0-doi-org.brum.beds.ac.uk/10.3390/app13159015 - 07 Aug 2023
Cited by 2 | Viewed by 1296
Abstract
A series of waves have marked the history of artificial intelligence (AI) [...] Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

Research

Jump to: Editorial

16 pages, 15347 KiB  
Article
Transforming Customer Digital Footprints into Decision Enablers in Hospitality
by Achini Adikari, Su Nguyen, Rashmika Nawaratne, Daswin De Silva and Damminda Alahakoon
Appl. Sci. 2024, 14(7), 3114; https://0-doi-org.brum.beds.ac.uk/10.3390/app14073114 - 08 Apr 2024
Viewed by 400
Abstract
The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative [...] Read more.
The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making. Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)
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40 pages, 5710 KiB  
Article
The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
by Fátima Trindade Neves, Manuela Aparicio and Miguel de Castro Neto
Appl. Sci. 2024, 14(5), 2209; https://0-doi-org.brum.beds.ac.uk/10.3390/app14052209 - 06 Mar 2024
Viewed by 1239
Abstract
In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. [...] Read more.
In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities. Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)
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16 pages, 541 KiB  
Article
Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
by Da Huang, Zhaoguo Liu and Dan Wu
Appl. Sci. 2024, 14(1), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010040 - 20 Dec 2023
Cited by 1 | Viewed by 866
Abstract
Feature selection has perennially stood as a pivotal concern in the realm of time-series forecasting due to its direct influence on the efficacy of predictive models. Conventional approaches to feature selection predominantly rely on domain knowledge and experiential insights and are, therefore, susceptible [...] Read more.
Feature selection has perennially stood as a pivotal concern in the realm of time-series forecasting due to its direct influence on the efficacy of predictive models. Conventional approaches to feature selection predominantly rely on domain knowledge and experiential insights and are, therefore, susceptible to individual subjectivity and the resultant inconsistencies in the outcomes. Particularly in domains such as financial markets, and within datasets comprising time-series information, an abundance of features adds complexity, necessitating adept handling of high-dimensional data. The computational expenses associated with traditional methodologies in managing such data dimensions, coupled with vulnerability to the curse of dimensionality, further compound the challenges at hand. In response to these challenges, this paper advocates for an innovative approach—a feature selection method grounded in ensemble learning. The paper explicitly delineates the formal integration of ensemble learning into feature selection, guided by the overarching principle of “good but different”. To operationalize this concept, five feature selection methods that are well suited to ensemble learning were identified, and their respective weights were determined through K-fold cross-validation when applied to specific datasets. This ensemble method amalgamates the outcomes of diverse feature selection techniques into a numeric composite, thereby mitigating potential biases inherent in traditional methods and elevating the precision and comprehensiveness of feature selection. Consequently, this method improves the performance of time-series prediction models. Full article
(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)
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