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AI Ready – Analysis Towards a Standardized Readiness Framework



                   [44] Traffic accidents still No. 1 killer in KSA. (2016, January 26). Arab News. https:// www
                   .arabnews .com/ saudi -arabia/ news/ 870636

                   [45] Vaneikemahommes, Q. (VOLPE). (n.d.). Functional Safety Assessment of a Generic,
                   Conventional, Hydraulic Braking System with Antilock Brakes, Traction Control, and Electronic
                   Stability Control.

                   [46] Weather Based Agro Advisory Services. (n.d.). Retrieved May 9, 2024, from https:// ccari
                   .icar .gov .in/ agroadvisory .html #: ~: text = Agro %2Dadvisory %20services %20are %20the ,disease
                   %2C %20water %20and %20input %20management.

                   [47] Fang, J., Xu, R., Yang, Y., Li, X., Zhang, S., Peng, X., & Liu, X. (2017). Introduction and
                   simulation of dedicated short range communication. 2017 IEEE 5th International Symposium
                   on Electromagnetic Compatibility (EMC-Beijing), 1-10. https:// doi .org/ 10 .1109/ EMC -B .2017
                   .8260392
                   [48] Li, L., Sali, A., Noordin, N. K., Ismail, A., & Hashim, F. (2023). Prediction of Peatlands Forest
                   Fires in Malaysia Using Machine Learning. Forests, 14(7), Article 7. https:// doi .org/ 10 .3390/
                   f14071472

                   [49] Vanitha, V., Rajathi, N., & Prakash Kumar, K. (2023). AI-Based Agriculture Recommendation
                   System for Farmers. In J. C. Bansal & M. S. Uddin (Eds.), Computer Vision and Machine Learning
                   in Agriculture, Volume 3 (pp. 91-103). Springer Nature. https:// doi .org/ 10 .1007/ 978 -981 -99
                   -3754 -7 _7

                   [50] “AI-PROTECT-IMEC: AI-powered Protection & Resilience Optimization for IMEC”, Asian
                   Disaster Preparedness Center (ADPC).

                   [51] Rubí, J. N. S., de Carvalho, P. H. P., & Gondim, P. R. L. (2023). Application of machine
                   learning models in the behavioral study of forest fires in the Brazilian Federal District region.
                   Engineering Applications of Artificial Intelligence, 118, 105649.  https:// doi .org/ 10 .1016/ j
                   .engappai .2022 .105649

                   [52] Khan, A., Gupta, S., & Gupta, S. K. (2022). Emerging UAV technology for disaster detection,
                   mitigation, response, and preparedness. Journal of Field Robotics, 39(6), 905-955. https:// doi
                   .org/ 10 .1002/ rob .22075

                   [53] Hu, X., Li, S., Huang, T., Tang, B., Huai, R., & Chen, L. (2023). How Simulation Helps Autonomous
                   Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence (arXiv:2305.01263). arXiv.
                   http:// arxiv .org/ abs/ 2305 .01263

                   [54] OpenCV, Detection of ArUco Markers https:// docs .opencv .org/ 4 .x/ d5/ dae/ tutorial _aruco
                   _detection .html

                   [55] robotflow, Everything you need to build and deploy computer vision models, https://
                   roboflow .com/

                   [56] “MQTT: The Standard for IoT Messaging”, https:// mqtt .org/

                   [57] Aarvik, P. (2019). Artificial Intelligence–a promising anti-corruption tool in development
                   settings. U4Anti-Corruption Resource Centre.






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