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3 pages, 196 KiB

Open AccessEditorial

Applications of Data Science and Artificial Intelligence

by Carlos J. Costa and Manuela Aparicio

Appl. Sci. 2023, 13(15), 9015; https://doi.org/10.3390/app13159015 - 7 Aug 2023

Cited by 4 | Viewed by 2624

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

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19 pages, 3351 KiB

Open AccessArticle

Automatizing Automatic Controller Design Process: Designing Robust Automatic Controller under High-Amplitude Disturbances Using Particle Swarm Optimized Neural Network Controller

by Celal Onur Gökçe

Appl. Sci. 2024, 14(17), 7859; https://doi.org/10.3390/app14177859 - 4 Sep 2024

Viewed by 490

Abstract

In this study, a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, the design of an optimal neural network-based controller is performed automatically. Several common reference types are [...] Read more.

In this study, a novel approach of designing automatic control systems with the help of AI tools is proposed. Given plant dynamics, expected references, and expected disturbances, the design of an optimal neural network-based controller is performed automatically. Several common reference types are studied including step, square, sine, sawtooth, and trapezoid functions. Expected reference–disturbance pairs are used to train the system for finding optimal neural network controller parameters. A separate test set is used to test the system for unexpected reference–disturbance pairs to show the generalization performance of the proposed system. Parameters of a real DC motor are used to test the proposed approach. The real DC motor’s parameters are estimated using a particle swarm optimization (PSO) algorithm. Initially, a proportional–integral (PI) controller is designed using a PSO algorithm to find the simple controller’s parameters optimally and automatically. Starting with the neural network equivalent of the optimal PI controller, the optimal neural network controller is designed using a PSO algorithm for training again. Simulations are conducted with estimated parameters for a diverse set of training and test patterns. The results are compared with the optimal PI controller’s performance and reported in the corresponding section. Encouraging results are obtained, suggesting further research in the proposed direction. For low-disturbance scenarios, even simple controllers can have acceptable performance, but the real quality of a proposed controller should be shown under high-amplitude and difficult disturbances, which is the case in this study. The proposed controller shows higher performance, especially under high disturbances, with an 8.6% reduction in error rate on average compared with the optimal PI controller, and under high-amplitude disturbances, the performance difference is of more than 2.5 folds. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

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14 pages, 766 KiB

Open AccessArticle

ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS

by Giannis Vassiliou, Georgia Eirini Trouli, Georgia Troullinou, Nikolaos Spyridakis, George Bitzarakis, Fotini Droumalia, Antonis Karagiannakis, Georgia Skouteli, Nikolaos Oikonomou, Dimitra Deka, Emmanouil Makaronas, Georgios Pronoitis, Konstantinos Alexandris, Stamatios Kostopoulos, Yiannis Kazantzakis, Nikolaos Vlassis, Eleftheria Sfinarolaki, Vardis Daskalakis, Iakovos Giannakos, Argyro Stamatoukou, Nikolaos Papadakis and Haridimos Kondylakis Show full author list Hide full author list

Appl. Sci. 2024, 14(17), 7640; https://doi.org/10.3390/app14177640 - 29 Aug 2024

Viewed by 747

Abstract

The exponential growth of Knowledge Graphs necessitates effective and efficient methods for their exploration and understanding. Frequently Asked Questions (FAQ) is a service that typically presents a list of questions and answers related to a specific topic, and which is intended to help [...] Read more.

The exponential growth of Knowledge Graphs necessitates effective and efficient methods for their exploration and understanding. Frequently Asked Questions (FAQ) is a service that typically presents a list of questions and answers related to a specific topic, and which is intended to help people understand that topic. Although FAQ has already shown its value on large websites and is widely used, to the best of our knowledge it has not yet been exploited for Knowledge Graphs. In this paper, we present ULYSSES, the first system for automatically constructing FAQ lists for large Knowledge Graphs. Our method consists of three key steps. First, we select the most frequent queries by exploiting the available query logs. Next, we answer the selected queries, using the original graph. Finally, we construct textual descriptions of both the queries and the corresponding answers, exploring state-of-the-art transformer models, i.e., ChatGPT 3.5 and Gemini 1.5 Pro. We evaluate the results of each model, using a human-constructed FAQ list, contributing a unique dataset to the domain and showing the benefits of our approach. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

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16 pages, 1570 KiB

Open AccessArticle

A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection

by Gengsong Li, Qibin Zheng, Yi Liu, Xiang Li, Wei Qin and Xingchun Diao

Appl. Sci. 2024, 14(14), 5993; https://doi.org/10.3390/app14145993 - 9 Jul 2024

Viewed by 970

Abstract

Data missing is a ubiquitous problem in real-world systems that adversely affects the performance of machine learning algorithms. Although many useful imputation methods are available to address this issue, they often fail to consider the information provided by both features and labels. As [...] Read more.

Data missing is a ubiquitous problem in real-world systems that adversely affects the performance of machine learning algorithms. Although many useful imputation methods are available to address this issue, they often fail to consider the information provided by both features and labels. As a result, the performance of these methods might be constrained. Furthermore, feature selection as a data quality improvement technique has been widely used and has demonstrated its efficiency. To overcome the limitation of imputation methods, we propose a novel algorithm that combines data imputation and feature selection to tackle classification problems for mixed data. Based on the mean and standard deviation of quantitative features and the selecting probabilities of unique values of categorical features, our algorithm constructs different imputation models for quantitative and categorical features. Particle swarm optimization is used to optimize the parameters of the imputation models and select feature subsets simultaneously. Additionally, we introduce a legacy learning mechanism to enhance the optimization capability of our method. To evaluate the performance of the proposed method, seven algorithms and twelve datasets are used for comparison. The results show that our algorithm outperforms other algorithms in terms of accuracy and

F_{1}

score and has reasonable time overhead. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

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24 pages, 3487 KiB

Open AccessArticle

A New Hybrid Approach for Product Management in E-Commerce

by Hacire Oya Yüregir, Metin Özşahin and Serap Akcan Yetgin

Appl. Sci. 2024, 14(13), 5735; https://doi.org/10.3390/app14135735 - 1 Jul 2024

Viewed by 827

Abstract

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to [...] Read more.

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based–genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

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16 pages, 15347 KiB

Open AccessArticle

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://doi.org/10.3390/app14073114 - 8 Apr 2024

Viewed by 949

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

Open AccessArticle

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://doi.org/10.3390/app14052209 - 6 Mar 2024

Cited by 1 | Viewed by 4710

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

Open AccessArticle

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://doi.org/10.3390/app14010040 - 20 Dec 2023

Cited by 1 | Viewed by 1709

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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Review

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18 pages, 1246 KiB

Open AccessReview

Artificial Intelligence-Powered Recommender Systems for Promoting Healthy Habits and Active Aging: A Systematic Review

by Juan Lopez-Barreiro, Jose Luis Garcia-Soidan, Luis Alvarez-Sabucedo and Juan M. Santos-Gago

Appl. Sci. 2024, 14(22), 10220; https://doi.org/10.3390/app142210220 - 7 Nov 2024

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Abstract

(1) Background: Increasing life expectancy allows for more age-related health issues. Enhancing physical, cognitive, mental, and social health is crucial. Promoting healthy habits combats stress and diseases. Recommendation systems, like collaborative filtering, tailor suggestions but face challenges. Techniques such as artificial intelligence and [...] Read more.

(1) Background: Increasing life expectancy allows for more age-related health issues. Enhancing physical, cognitive, mental, and social health is crucial. Promoting healthy habits combats stress and diseases. Recommendation systems, like collaborative filtering, tailor suggestions but face challenges. Techniques such as artificial intelligence and machine learning are vital. Personalized health recommendations improve lifestyles and mitigate issues. (2) Methods: A systematic review adhering to the general principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses was conducted with the aim of identifying articles on innovative research about using recommendation algorithms, machine learning, or artificial intelligence to promote healthy habits and active aging. (3) Results: A total of 34 articles were included in this work. They address the topic of recommendation systems that use machine learning or artificial intelligence in the promotion of healthy habits. (4) Conclusions: This article reviews health-related activity recommendation techniques for the general population. With rising life expectancy and common health issues, effective recommendations are crucial for future public health. Limitations include excluding simpler models. Despite many proposals, systematic adherence mechanisms are lacking. Developing traceable, verifiable systems for healthy activity recommendations is vital for aging populations in developed countries. Full article

(This article belongs to the Special Issue Applications of Data Science and Artificial Intelligence)

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