نموذج الكشف عن الأشياء بالاعتماد على الواقع المعزز للآثار العراقي

Ghasemi Y, Jeong H, Choi SH, Park K-B, Lee JYJCiI. Deep learning-based object detection in augmented reality: A systematic review. Comput Ind. 2022; 139: 103661. http://dx.doi.org/10.1016/j.compind.2022.103661

Khan MA, Israr SS, Almogren A, Din IU, Almogren A, Rodrigues JJ. Using augmented reality and deep learning to enhance Taxila Museum experience. J Real-Time Image Process 18 (2): 321–32. https://doi.org/10.1007/s11554-020-01038-y

Sweeney SK, Newbill P, Ogle T, Terry KJT. Using augmented reality and virtual environments in historic places to scaffold historical empathy.TechTrends 2018; 62: 114-8. https://doi.org/10.1007/s11528-017-0234-9

Blanco-Fernández Y, López-Nores M, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M, García-Duque J. REENACT: A step forward in immersive learning about Human History by augmented reality, role playing and social networking. Expert Syst Appl. 2014; 41(10): 4811-28. https://doi.org/10.1016/j.eswa.2014.02.018

Oleksy T, Wnuk A. Augmented places: An impact of embodied historical experience on attitudes towards places. Comput. Hum. Behav. 2016 Apr 1;57:11-6. https://doi.org/10.1016/j.chb.2015.12.014

Çakiroğlu Ü, Aydin M, Köroğlu Y, Ayvaz Kina MJILE. Looking past seeing present: teaching historical empathy skills via augmented realit.nteract. Interact Learn Environ. 2023:1-13. https://doi.org/10.1080/10494820.2023.2174142

Carmigniani J, Furht B, Anisetti M, Ceravolo P, Damiani E, Ivkovic MJMt, et al. Augmented reality technologies, systems and applications. Multimed. Tools Appl. 2011; 51: 341-77. https://doi.org/10.1007/s11042-010-0660-6

Fenais AS, Ariaratnam ST, Ayer SK, Smilovsky NJJoITiC. A review of augmented reality applied to underground construction. J Inf echnol Constr. 2020; 25: 308-24. https://doi.org/10.36680/j.itcon.2020.018

Ponnusamy V, Natarajan, Solutions, Applications. Precision agriculture using advanced technology of IoT, unmanned aerial vehicle, augmented reality, and machine learning. IIOT. 2021: 207-29. https://doi.org/10.1007/978-3-030-52624-5_14

Lalonde J-F, editor Deep learning for augmented reality. 2018 17th Workshop on Information Optics (WIO); 2018: IEEE. https://doi.org/10.1109/WIO.2018.8643463

Park K-B, Kim M, Choi SH, Lee JYJR, Manufacturing C-I. Deep learning-based smart task assistance in wearable augmented reality. Robot Comput Integr Manuf. 2020; 63: 101887. https://doi.org/10.1016/j.rcim.2019.101887

Alsaedi EM, Farhan Ak. Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption. Baghdad Sci J. 2023; 20(1): 0206. https://dx.doi.org/10.21123/bsj.2022.6550

He Y, Ren J, Yu G, Cai YJIToWC. Optimizing the learning performance in mobile augmented reality systems with CNN. IEEE Trans Wirel Commun. 2020; 19(8): 5333-44. https://doi.org/10.1109/TWC.2020.2992329

Ababsa F-e, Mallem M, editors. Robust camera pose estimation using 2d fiducials tracking for real-time augmented reality systems. Proceedings of the 2004 ACM SIGGRAPH international conference on Virtual Reality continuum and its applications in industry; 2004. https://doi.org/10.1145/1044588.1044682

Abdullah TH, Alizadeh F, Abdullah BH. COVID-19 Diagnosis System using SimpNet Deep Model. Baghdad Sci J. 2022; 19(5): 1078. https://doi.org/10.21123/bsj.2022.6074

Sprute D, Viertel P, Tönnies K, König M, editors. Learning virtual borders through semantic scene understanding and augmented reality. IEEE Int Conf Intell Robots Sys.; 2019: https://doi.org/10.1109/IROS40897.2019.8967576

Soroush M, Mehrtash A, Khazraee E, Ur JAJRS. Deep learning in archaeological remote sensing: Automated qanat detection in the Kurdistan region of Iraq. Remote Sens. 2020; 12(3): 500. https://doi.org/10.3390/rs12030500

Yer A, Franklin M. AI-Powered Archaeology: Determining the Origin Culture of Various Ancient Artifacts Using Machine Learning. JSR. 2022; 11(1). https://doi.org/10.47611/jsrhs.v11i1.2465

Verschoof-Van der Vaart WB, Lambers K. Learning to look at LiDAR: The use of R-CNN in the automated detection of archaeological objects in LiDAR data from the Netherlands. J Comput Appl Archaeol. 2019; 2(1). https://doi.org/10.5334/jcaa.32

. Combined detection and segmentation of archeological structures from LiDAR data using a deep learning approach. Comput Appl Archaeol. 2021; 4(1): 1. https://dx.doi.org/10.5334/jcaa.64

Rahman MA, Wang Y, editors. Optimizing intersection-over-union in deep neural networks for image segmentation. ISVC; 2016: Springer. https://doi.org/10.1007/978-3-319-50835-1_22

Lin T-Y, Goyal P, Girshick R, He K, Dollár P, editors. Focal loss for dense object detection. Proc IEEE Int Conf Comput Vis. ; 2017. https://doi.org/10.48550/arXiv.1708.02002