UID:
kobvindex_INTbi00005180
Format:
32 pages :
,
illustrations ;
,
21 × 29.7 cm.
Content:
AI-GENERATED ABSTRACT: Abstract: The COVID-19 crisis has had a profound impact on various industries worldwide, including the airline and movie sectors. Machine learning models, which play a crucial role in predicting outcomes and optimizing operations in these industries, have also been affected by the pandemic. This comparative literature review aims to explore and analyze the performance of machine learning models in the airline and movie industries during and after the COVID-19 crisis. By conducting a comprehensive analysis of relevant scholarly articles, conference papers, and industry reports, this review aims to provide insights into the challenges, adaptations, and advancements made in machine learning models pre- and post-pandemic. The relevant papers and sources were selected based on being pre- and post-pandemic. These two categories of sources were then compared for both industries to illustrate the effects of the pandemic on machine learning models in both industries, and how they have developed since this global event. The findings demonstrated that machine learning has been in use for decades in both industries. In the movie industry, the algorithms were mainly used for forecasting revenue or predicting movie success pre-pandemic, while in the airline industry, machine learning models predicted flight patterns/delays or ticket prices. While the algorithms and models in both industries struggled initially in the new dynamic environments, key differences can be synthesized between the developments since. While the airline industry continues to grow and utilizes ML as a globally demanded and necessary industry, the movie industry has still not fully recovered since COVID-19 as many consumers move to digital alternatives like streaming platforms. The findings of this review will contribute to a deeper understanding of the implications of the COVID-19 crisis on machine learning applications and provide insights for researchers, practitioners, and decision-makers in these industries. Keywords: COVID-19 Impact, Machine Learning Models, Airline Industry, Movie Industry, Predictive Analytics, Pandemic Adaptations, Comparative Analysis, Algorithm Performance, Industry Recovery, Digital Transformation.
Note:
DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business & Management, Berlin International University of Applied Sciences, 2023.
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MACHINE-GENERATED CONTENTS NOTE: Table of Contents Abstract..................................................................................................iii Introduction 1.1 Background and Limitations of ML in a Non-Static Environment.....................................4 1.2 Effects of COVID-19 on ML Models in the Airline and Movie Industries........................5 1.3 Academic Contribution.......................................................................................................5 Methodology 2.1 Research Design and Approach..........................................................................................6 2.2 Selection Criteria................................................................................................................6 2.3 Data Collection and Analysis.............................................................................................7 Literature Review 3.1 Machine learning in static and dynamic environments......................................................7 3.2 Types of Machine Learning: Predictive and Descriptive Learning....................................8 3.3 Machine Learning Applications in the Movie Industry: Past Developments...................12 3.4 Advancements in Natural Language Processing Algorithms for Movie Industry Analysis...................................................................................................................................13 3.5 Machine Learning in the Movie Industry: Post-COVID Shifts and Trends.....................15 3.6 Machine Learning Applications in the Airline Industry: Pre-COVID Insights................16 3.7 ML’s Applications in the Airline Industry Post-COVID........................................18 3.8 Implications of Machine Learning Techniques in Dynamic Environments..................19 3.9 Impact of COVID-19 on Machine Learning Applications in the Movie and Airline Industries............................................................................................................21 Results and Discussion 4.1 Impact of COVID-19 on the Movie Industry: Shifting Trends and Environments........22 4.2 Challenges of ML Models in the Movie Industry................................................23 4.3 Airline Industry during the COVID-19 Crisis: Challenges and Adaptations...............25 4.4 Impact of COVID-19 on Flight Operations Post-Covid.......................................25 Conclusion 5.1 Summary of the main findings of the study and outlook..................................................26 References..............................................................................................................................28
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