تطوير Pd-Maas القائم على IIOT باستخدام نموذج RNN-LSTM مع تحسين جيلي فيش في صناعة بناء السفن الهندية

Mourougane A. Present Scenario of Ship Building Industry in Indian. J Dev Econ Manag Res. 2020; 6: 29-38.

Khare A, Kakandikar GM, Kulkarni OK. An Insight Review on Jellyfish Optimization Algorithm and Its Application in Engineering. Rev Comput Eng Stud. 2022; 9: 31-40. https://doi.org/10.18280/rces.090103

Rao PN, Jayasree P. Predictive Maintenance-as-a-Service (PdMaaS) Using Industrial Internet of Things (IIoT) and machine learning for Mechanical Equipment Used into Indian Ship Building Industry. Int J Mech Eng. 2022; 7: 674-687.

Agarwala N. Shipbuilding legacy in India under the Wadia family. Aust J Marit Ocean Aff. 2021; 30: 1-20. https://doi.org/10.1080/18366503.2021.1961353

Chehri A, Jeon G. The industrial internet of things: examining how the IIoT will improve the predictive maintenance. InInnovation in Medicine and Healthcare Systems, and Multimedia: Proceedings of KES-InMed-19 and KES-IIMSS-19 Conferences, 2019; 145: 517-527. Springer Singapore. https://doi.org/10.1007/978-981-13-8566-7_47

Achouch M, Dimitrova M, Ziane K, Sattarpanah Karganroudi S, Dhouib R, Ibrahim H, et al. On predictive maintenance in industry 4.0: Overview, models, and challenges. Appl Sci. 2022; 12: 1-22. https://doi.org/10.3390/app12168081

Iqbal J, Omar M, Yasin A. The impact of agile methodologies and cost management success factors: An empirical study. Baghdad Sci J. 2019; 16: 496-504. https://doi.org/10.21123/bsj.2019.16.2(SI).0496

Zonta T, Da Costa CA, da Rosa Righi R, de Lima MJ, da Trindade ES, Li GP. Predictive maintenance in the Industry 4.0: A systematic literature review. Comput Ind Eng. 2020; 150: 1-17. https://doi.org/10.1016/j.cie.2020.106889

Sang GM. Predictive maintenance for industry 4.0, a holistic approach to performing predictive maintenance as a service (Doctoral dissertation, Bournemouth University). Predictive Maintenance for Industry 4.0. 2021; 1: 1-274.

Ijiga OE, Malekian R, Chude-Okonkwo UA. Enabling emergent configurations in the industrial Internet of Things for oil and gas explorations: A survey. Electronics. 2020; 9: 1-34. https://doi.org/10.3390/electronics9081306

Yang B, Pang Z, Wang S, Mo F, Gao Y. A coupling optimization method of production scheduling and computation offloading for intelligent workshops with cloud-edge-terminal architecture. J Manuf Syst. 2022; 65: 421-438. https://doi.org/10.1016/j.jmsy.2022.10.002

Faheem M, Butt R A, Raza B, Ashraf M W, Ngadi M A, Gungor V C. Energy efficient and reliable data gathering using internet of software-defined mobile sinks for WSNs-based smart grid applications. Comput Stand Interfaces. 2019; 66: 1-18. https://doi.org/10.1016/j.csi.2019.03.009

Faheem M, Fizza G, Ashraf M W, Butt R A, Ngadi M A, Gungor V C. Big Data acquired by Internet of Things-enabled industrial multichannel wireless sensors networks for active monitoring and control in the smart grid Industry 4.0. Data Brief. 2021; 35: 1-12. https://doi.org/10.1016/j.dib.2021.106854

Faheem M, Butt R A, Raza B, Ashraf M W, Ngadi M A, Gungor V C. A multi-channel distributed routing scheme for smart grid real-time critical event monitoring applications in the perspective of Industry 4.0. Int J Ad Hoc Ubiquitous Comput. 2019; 32: 236-256. https://dx.doi.org/10.1504/IJAHUC.2019.103264

Toma RN, Prosvirin AE, Kim JM. Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors. 2020; 20: 1-19. https://doi.org/10.3390/s20071884

Akter S, Shamrat FJ, Chakraborty S, Karim A, Azam S. COVID-19 detection using deep learning algorithm on chest X-ray images. Biology. 2021; 10: 1-23. https://doi.org/10.3390/biology10111174

Jin XB, Zheng WZ, Kong JL, Wang XY, Bai YT, Su TL, et al. Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization. Energies. 2021; 14: 1-18. https://doi.org/10.3390/en14061596

Mosavi A, Ozturk P, Chau KW. Flood prediction using machine learning models: Literature review. Water. 2018; 10: 1-40. https://doi.org/10.3390/w10111536

Khan MA, Sharif M, Akram T, Damaševičius R, Maskeliūnas R. Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics. 2021; 11: 1-26. https://doi.org/10.3390/diagnostics11050811

Chitradevi D, Prabha S. Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease. Appl. Soft Comput. 2020; 86: 1-34. https://doi.org/10.1016/j.asoc.2019.105857

Khare N, Devan P, Chowdhary CL, Bhattacharya S, Singh G, Singh S, et al. SMO-DNN: Spider monkey optimization and deep neural network hybrid classifier model for intrusion detection. Electronics. 2020; 9: 1-18. https://doi.org/10.3390/electronics9040692