A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

Xiong, Honglin; Fan, Chao; Chen, Hongmin; Yang, Y. Tony; Antwi, Collins Opoku; Fan, Xiaomao

doi:10.1177/21582440211071102

Cited by 11 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is necessary to create a strategy for the airports operation with a satisfactory level of passenger service quality and the organization of operational activities of transport means. Similar questions and approaches to their solution have already been covered in the works of contemporaries [430][431][432][433]. The authors of the paper [433] proposed an approach to determine the airport capacity and passenger flow fluctuations using the air passenger index introduced by them.…”

mentioning

confidence: 89%

See 1 more Smart Citation

Improvement of Scientific Approaches to the Development of Engineering

Vladlenov¹,

Babyak²,

Gladilin³

et al. 2022

View full text Add to dashboard Cite

All rights reserved. Printed in the United States of America. No part of this publication may be reproduced, distributed, or transmitted, in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. The content and reliability of the articles are the responsibility of the authors. When using and borrowing materials reference to the publication is required. Collection of scientific articles published is the scientific and practical publication, which contains scientific articles of students, graduate students, Candidates and Doctors of Sciences, research workers and practitioners from Europe and Ukraine. The articles contain the study, reflecting the processes and changes in the structure of modern science.

show abstract

mentioning

confidence: 89%

“…Similar questions and approaches to their solution have already been covered in the works of contemporaries [430][431][432][433]. The authors of the paper [433] proposed an approach to determine the airport capacity and passenger flow fluctuations using the air passenger index introduced by them.…”

mentioning

confidence: 89%

Improvement of Scientific Approaches to the Development of Engineering

Vladlenov¹,

Babyak²,

Gladilin³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Deep learning is computationally very complex, showing the model's tendency to overfit. A recent study found that hybrid models worked well and attempted to combine the best features of various models [115]. Complex deep neural networks are the future when time-series data contain uncertainty.…”

Section: Hybrid Modelsmentioning

confidence: 99%

Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.

show abstract

“…The global “digital divide” status quo is quickly changing with the progress in artificial intelligence (AI) technologies and their application area expansion. Nowadays, AI has been widely researched and achieves great success in recent years, and the heart of AI technologies is machine learning (ML) algorithms ( Xiong et al, 2022 ). With the development of the digital economy, Internet, Internet of things (IoT), mobile Internet, and cloud technologies, the application of AI based on health big data presents an explosive increase in recent years ( Gokmen and Vlasov, 2016 ; Dolley, 2018 ; Ngiam and Khor, 2019 ; Ye et al, 2021 ; Weerasinghe et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

A survey of data element perspective: Application of artificial intelligence in health big data

Xiong¹,

Chen²,

Xu³

et al. 2022

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

Artificial intelligence (AI) based on the perspective of data elements is widely used in the healthcare informatics domain. Large amounts of clinical data from electronic medical records (EMRs), electronic health records (EHRs), and electroencephalography records (EEGs) have been generated and collected at an unprecedented speed and scale. For instance, the new generation of wearable technologies enables easy-collecting peoples’ daily health data such as blood pressure, blood glucose, and physiological data, as well as the application of EHRs documenting large amounts of patient data. The cost of acquiring and processing health big data is expected to reduce dramatically with the help of AI technologies and open-source big data platforms such as Hadoop and Spark. The application of AI technologies in health big data presents new opportunities to discover the relationship among living habits, sports, inheritances, diseases, symptoms, and drugs. Meanwhile, with the development of fast-growing AI technologies, many promising methodologies are proposed in the healthcare field recently. In this paper, we review and discuss the application of machine learning (ML) methods in health big data in two major aspects: (1) Special features of health big data including multimodal, incompletion, time validation, redundancy, and privacy. (2) ML methodologies in the healthcare field including classification, regression, clustering, and association. Furthermore, we review the recent progress and breakthroughs of automatic diagnosis in health big data and summarize the challenges, gaps, and opportunities to improve and advance automatic diagnosis in the health big data field.

show abstract

A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

Cited by 11 publications

References 33 publications

Improvement of Scientific Approaches to the Development of Engineering

Improvement of Scientific Approaches to the Development of Engineering

Systematic review of passenger demand forecasting in aviation industry

A survey of data element perspective: Application of artificial intelligence in health big data

Contact Info

Product

Resources

About