Abstract
Aviation recommendation and delay prediction (ARDP) systems are data filtering strategies that use algorithms and data to recommend the most favorable aircraft for specific customers. User reviews, comments, and shared experience of aeronautical advice official information about user preferences on recommended systems. Due to the experience of computational models and small data, controlled decisions do not fall within a specific range. This proposal addresses data recommendation and parallel processing issues using supervised machine learning techniques. Large-scale decision-making techniques are used to find alternatives to implement different types of computing structures. It recommends operating systems such as variables or data reduction, data switch cleaning, and operation clustering.
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Sirisati, R.S., Prasanthi, K.G., Latha, A.G. (2021). An Aviation Delay Prediction and Recommendation System Using Machine Learning Techniques. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_25
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DOI: https://doi.org/10.1007/978-981-33-6307-6_25
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