Frontiers in Ocean Engineering, 2022, 3(3); doi: 10.38007/FOE.2022.030307.
Saravanan Kavita
University of Garden City, Sudan
With the development of various marine exploration means, the ability to obtain marine environmental information has also been unprecedentedly improved. Marine data presents the characteristics of strong spatial-temporal correlation and diverse formats, but it brings problems such as complex relationships between data, difficult analysis of heterogeneous data, and low processing efficiency. Therefore, the marine project planning management technology has become the focus of research. This paper studies and analyzes the marine project planning management technology based on machine learning(ML) algorithm. The planning, classification, organizational structure and control principle of offshore engineering project(OEP) are discussed; This paper proposes a ML algorithm, optimizes the project schedule of marine engineering project(MEP) planning management technology through the training algorithm of smooth support vector regression model, and finally introduces each module of the system briefly in combination with the planning management of MEPs, which verifies the feasibility of the ML algorithm, improves the production efficiency of marine engineering, and has a certain reference significance for future development.
Machine Learning, Ocean Engineering, Project Planning, Management Technology
Saravanan Kavita. Marine Engineering Project Planning Management Technology based on Machine Learning. Frontiers in Ocean Engineering (2022), Vol. 3, Issue 3: 55-64. https://doi.org/10.38007/FOE.2022.030307.
[1] Pandey M , Litoriya R , Pandey P . Applicability of Machine Learning Methods on Mobile App Effort Estimation: Validation and Performance Evaluation. International Journal of Software Engineering and Knowledge Engineering, 2020, 30(1):23-41. https://doi.org/10.1142/S0218194020500023
[2] Kalathas I , Papoutsidakis M , Drosos C . Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization. Advances in Science Technology and Engineering Systems Journal, 2020, 5(4):287-295. https://doi.org/10.25046/aj050435
[3] Kim H S , Chung J H , Baek W K . A Study on A Motor Noise Diagnosis Method Using Voice Recognition and Machine Learning Techniques. Transactions of the Korean Society for Noise and Vibration Engineering, 2021, 31(1):40-46. https://doi.org/10.5050/KSNVE.2021.31.1.040
[4] Meghana P , Akhila R , Sandeep P , et al. Machine Learning Algorithms Based Cognitive Services For Securing Data With Blockchain. Complexity International, 2021, 25(2):1602-1612.
[5] Naeem S , Mashwani W K , Ali A , et al. Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data. Computers, Materials and Continua, 2021, 67(3):3451-3461. https://doi.org/10.32604/cmc.2021.015872
[6] Alrahis L , Patnaik S , Knechtel J , et al. UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking. IEEE Transactions on Information Forensics and Security, 2021, PP(99):1-1. https://doi.org/10.1109/TIFS.2021.3057576
[7] Moon J , Jung S , Park S , et al. Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting. Computers, Materials and Continua, 2021, 68(3):2945-2959. https://doi.org/10.32604/cmc.2021.017435
[8] Klein S , Rashedi N , Sun Y , et al. 1292: A Multivariate Machine Learning Algorithm for Occult Hemorrhage in a Porcine Model. Critical Care Medicine, 2021, 49(1):652-652. https://doi.org/10.1097/01.ccm.0000731056.53582.4c
[9] Levine M , Hartsig A . Modernizing Management of Offshore Oil and Gas in Federal Waters. The Environmental Law Reporter, 2019, 49(5):10452-10472.
[10] Yavuz A A , Ergl B , Aik E G . Evaluation of Traffic Accidents Using Machine Learning Methods. Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi, 2021, 13(1):66-73. https://doi.org/10.29137/umagd.705156
[11] Shetty S C . Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(6):2565-2580. https://doi.org/10.17762/turcomat.v12i6.5703
[12] Sridevi M , Arun K . A framework for performance evaluation of machine learning techniques to predict the decision to choose palliative care in advanced stages of Alzheimer's disease. Indian Journal of Computer Science and Engineering, 2021, 12(1):35-46. https://doi.org/10.21817/indjcse/2021/v12i1/211201140
[13] Al-AttarReem Tareqr.t.alattar@gmail.comAL-KhafajiMahmoud SalehAL-AniFaris H.University of Technology,Baghdad,Iraq&Al-Esraa University College,Baghdad,Iraq.Al-Nahrain University,Baghdad,Iraq.University of Technology,Baghdad,Iraq. Fuzzy - Based Multi - Criteria Decision Support System for Maintenance Management of Wastewater Treatment Plants. Civil and Environmental Engineering, 2021, 17(2):654-672.
[14] Pomeroy R S , Garces L R , MD Pido, et al. The role of scale within an Ecosystem Approach to fisheries management: Policy and practice in Southeast Asian seas. Marine Policy, 2019, 106(AUG.):103531.1-103531.10. https://doi.org/10.1016/j.marpol.2019.103531
[15] Tien H V , Tan P N . Marine algal species and marine protected area management: A case study in Phu Quoc, Kien Giang, Vietnam. Ocean & Coastal Management, 2019, 178(AUG.):104816.1-104816.11. https://doi.org/10.1016/j.ocecoaman.2019.104816
[16] Macedo H S , Medeiros R P , Mcconney P . Are multiple-use marine protected areas meeting fishers' proposals? Strengths and constraints in fisheries' management in Brazil. Marine Policy, 2019, 99(JAN.):351-358. https://doi.org/10.1016/j.marpol.2018.11.007
[17] Kim J K . A Study on the Marine Traffic Density and Management Plan for the Route Congestion Area. Journal of Fisheries and Marine Sciences Education, 2019, 31(2):449-458. https://doi.org/10.13000/JFMSE.2019.4.31.2.449
[18] Levine M , Hartsig A . Modernizing Management of Offshore Oil and Gas in Federal Waters. The Environmental Law Reporter, 2019, 49(5):10452-10472.