Machine Learning to Predict Recommendation by Tourists in a Spanish Province

Castillo, Santiago; Basurto, Nuño; Val, Pablo Arranz; Maraña, Paula Antón; Cosío, Alvaro Herrero

doi:10.1142/s021962202250016x

“…Imbalanced datasets occur when the distribution of classes in the dataset is highly skewed, with one class being significantly more prevalent than the other(s). This is a common issue in various domains, including bioinformatics [47], healthcare [48], speech signal analysis [49], tourism [50], cybersecurity [51], and many others sectors.…”

Section: ) Missing Value Handlingmentioning

confidence: 99%

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

Hossain,

Khatun,

Ahmed

2023

Preprint

0

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<p>Industry 4.0, also known as the Fourth Industrial Revolution, is characterized by the incorporation of advanced manufacturing technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and automation. With the increasing adoption of Industry 4.0 technologies, it becomes crucial to implement effective security measures to safeguard these systems from cyber attacks. The development of intrusion detection systems (IDS) that can detect and respond to cyber threats in real-time is crucial for securing Industry 4.0 systems. This research topic seeks to investigate the various techniques and methodologies employed in developing IDS for Industry 4.0 systems, with a particular concentration on identifying the most effective solutions for protecting these systems from cyber attacks. In this study, we compared supervised and unsupervised intrusion detection algorithms. We utilized data collected from heterogeneous sources, including Telemetry datasets of IoT and The industrial Internet of things (IIoT) sensors, Operating systems (OS) datasets of Windows 7 and 10, as well as Ubuntu 14 and 18 TLS and Network traffic datasets simulated by the School of Engineering and Information Technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). The preliminary results of IDS accuracy are extremely encouraging on the selected data for this study (Windows OS and Ubuntu OS), which motivates the continuance of this line of inquiry using a variety of other data sources to formulate a general recommendation of IDS for Industry 4.0.</p>

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“…Imbalanced datasets occur when the distribution of classes in the dataset is highly skewed, with one class being significantly more prevalent than the other(s). This is a common issue in various domains, including bioinformatics [47], healthcare [48], speech signal analysis [49], tourism [50], cybersecurity [51], and many others sectors.…”

Section: ) Missing Value Handlingmentioning

confidence: 99%

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

Hossain,

Khatun,

Ahmed

2023

Preprint

0

View full text Add to dashboard Cite

<p>Industry 4.0, also known as the Fourth Industrial Revolution, is characterized by the incorporation of advanced manufacturing technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and automation. With the increasing adoption of Industry 4.0 technologies, it becomes crucial to implement effective security measures to safeguard these systems from cyber attacks. The development of intrusion detection systems (IDS) that can detect and respond to cyber threats in real-time is crucial for securing Industry 4.0 systems. This research topic seeks to investigate the various techniques and methodologies employed in developing IDS for Industry 4.0 systems, with a particular concentration on identifying the most effective solutions for protecting these systems from cyber attacks. In this study, we compared supervised and unsupervised intrusion detection algorithms. We utilized data collected from heterogeneous sources, including Telemetry datasets of IoT and The industrial Internet of things (IIoT) sensors, Operating systems (OS) datasets of Windows 7 and 10, as well as Ubuntu 14 and 18 TLS and Network traffic datasets simulated by the School of Engineering and Information Technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). The preliminary results of IDS accuracy are extremely encouraging on the selected data for this study (Windows OS and Ubuntu OS), which motivates the continuance of this line of inquiry using a variety of other data sources to formulate a general recommendation of IDS for Industry 4.0.</p>

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

1

0

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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Machine Learning to Predict Recommendation by Tourists in a Spanish Province

Cited by 6 publications

References 74 publications

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

Machine learning applied to tourism: A systematic review

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