Aircraft Aviation System Environment Impact Factors Prediction using Machine Learning
S. Krishna Mohan Rao1, B. V. Rama Krishna2, V. G. Sai Krishna Desharaju3
1Dr. B. V. Rama Krishna*, Associate Professor, CSE-Department, Kakinada, A.P., India.
2Dr. S. Krishna Mohan Rao, Associate Professor, GIFT, Bhubaneswar, India.
3V. G. Sai Krishna Desharaju, Ph.D. in Computer Science from Rayalaseema University, Pasupula, Andhra Pradesh, India
Manuscript received on March 16, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 2526-2530 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8503038620/2020©BEIESP | DOI: 10.35940/ijrte.F8503.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Aircraft aviation system modules are considered for eco friendly oriented service estimation by global organizations. The emissions and aerodrome infrastructures effects the environment and citizen areas surrounding to aerodromes. An interest in researching to identify substantial environmental impact factors by authorities to support Eco-systems increased. In this paper Machine-Learning techniques applied over various training data sets related to aircraft aviation systems to generate interesting patterns related to environmental effects by aircrafts. Probabilistic prediction algorithms applied to support decision systems in generating guidelines to enhance the Eco-friendly architectures of aerodromes as well as aircrafts. The factors identification and territorial based environment precautions deviation observed for locating Eco-system regulation needed zones. The classifications performed in this paper over aircraft systems generate interesting measures to classify environmental scalable aircrafts in future with better eco-friendly technology. Rule miners identify the zones attributes associations among various countries. The work projected in this paper supports aircraft organizations to accurately estimate the environmental effect scores for aviation systems.
Keywords: ARM, Classification, Impact Analysis, Decision Support, Prediction.
Scope of the Article: Machine Learning.