The Academic Perspective Procedia publishes Academic Platform symposiums papers as three volumes in a year. DOI number is given to all of our papers.
Publisher : Academic Perspective
Journal DOI : 10.33793/acperpro
Journal eISSN : 2667-5862
 H. Ceylan, “Türkiye’de İnşaat Sektöründe Meydana Gelen İş Kazalarının Analizi,” Uluslararası Muhendis. Arastirma ve Gelistirme Derg., vol. 6, no. 1, pp. 1–6, Jan. 2014, doi: 10.29137/umagd.346068.
 S. Karahan and Y. S. Akgul, “Eye detection by using deep learning,” in 2016 24th Signal Processing and Communication Application Conference (SIU), May 2016, pp. 2145–2148, doi: 10.1109/SIU.2016.7496197.
 M. F. ADAK, “Identification of Plant Species by Deep Learning and Providing as A Mobile Application,” Sak. Univ. J. Comput. Inf. Sci., vol. 3, no. 3, pp. 231–237, Dec. 2020, doi: 10.35377/saucis.03.03.773465.
 C. Cao et al., “An Improved Faster R-CNN for Small Object Detection,” IEEE Access, vol. 7, pp. 106838–106846, 2019, doi: 10.1109/ACCESS.2019.2932731.
 F. Sultana, A. Sufian, and P. Dutta, “A Review of Object Detection Models Based on Convolutional Neural Network,” 2020, pp. 1–16.
 M. YU, Y. LIN, and X. WANG, “An efficient hybrid eye detection method,” TURKISH J. Electr. Eng. Comput. Sci., vol. 24, pp. 1586–1603, 2016, doi: 10.3906/elk-1312-150.
 Y. Jia et al., “Caffe: Convolutional Architecture for Fast Feature Embedding,” Jun. 2014, [Online]. Available: http://arxiv.org/abs/1408.5093.
 V. Jain and E. Learned-Miller, “FDDB: A Benchmark for Face Detection in Unconstrained Settings.”
 B.-C. Chen, C.-S. Chen, and W. H. Hsu, “Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,” 2014, pp. 768–783.
 E. Civik and U. Yuzgec, “Deep Learning Based Continuous Real-Time Driver Fatigue Detection for Embedded System,” in 2020 28th Signal Processing and Communications Applications Conference (SIU), Oct. 2020, pp. 1–4, doi: 10.1109/SIU49456.2020.9302035.
 H. Aung, A. V. Bobkov, and N. L. Tun, “Face Detection in Real Time Live Video Using Yolo Algorithm Based on Vgg16 Convolutional Neural Network,” in 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), May 2021, pp. 697–702, doi: 10.1109/ICIEAM51226.2021.9446291.
 D. Garg, P. Goel, S. Pandya, A. Ganatra, and K. Kotecha, “A Deep Learning Approach for Face Detection using YOLO,” in 2018 IEEE Punecon, Nov. 2018, pp. 1–4, doi: 10.1109/PUNECON.2018.8745376.
 A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Apr. 2020, [Online]. Available: http://arxiv.org/abs/2004.10934.
 A. M. Roy, R. Bose, and J. Bhaduri, “A fast accurate fine-grain object detection model based on YOLOv4 deep neural network,” Oct. 2021, [Online]. Available: http://arxiv.org/abs/2111.00298.
 X. He, J. Wang, C. Chen, and X. Yang, “Detection of the Floating Objects on the Water Surface Based on Improved YOLOv5,” in 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Dec. 2021, pp. 772–777, doi: 10.1109/ICIBA52610.2021.9688111.
 J. G. Shanahan and L. Dai, “Introduction to Computer Vision and Real Time Deep Learning-based Object Detection,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2020, pp. 3523–3524, doi: 10.1145/3394486.3406713.
 S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path Aggregation Network for Instance Segmentation,” Mar. 2018, [Online]. Available: http://arxiv.org/abs/1803.01534.