Region-based Network for Yoga Pose Estimation with Discriminative Fine-Tuning Optimization
Keywords:Yoga pose estimation, region-based network, CNN RCNN deep-learning, optimization
Pose estimation of human activity recognition has been a keen area of interest in augmented reality experiences, gaming and robotics, animations, behavioral analysis, and more. One such exciting variant of pose estimation in the field of health and science is yoga pose estimation. This paper explores yoga pose estimation using deep learning networks. The research aims to build a system for estimating 45 different complex yoga asanas from 11,000 images using deep learning algorithms. This system is built using a Region-based Convolutional Neural Network (RCNN) to estimate the joints in the body, followed by a Convolutional Neural Network (CNN) for classifying the poses. The model is trained using the Yoga-82 (hierarchically labeled) dataset, a new dataset with complex pose variations mainly designed for hierarchical labeling. Next, it highlights the pose estimation task through ResNet models followed by an optimization algorithm, which increases the accuracy by 10%. The resultant accuracy is 90.5% for the ResNet50 model. Finally, it provides a solution for overlapping yoga poses, multi-person, in-air, and non-conventional poses using a dense network of 17 critical points for analysis and prediction.
R. M. Haralick, H. Joo, C. Lee, X. Zhuang, V. G. Vaidya, and M. B. Kim, “Pose estimation from corresponding point data,” IEEE Transactions Syst Man Cybern, vol. 19, no. 6, pp. 1426–1446, 1989, doi: 10.1109/21.44063.
P. Vyas, “Pose estimation and action recognition in sports and fitness,” 2019, doi: 10.31979/etd.w8ug-4v5c.
S. Chen and R. R. Yang, “Pose Trainer: Correcting Exercise Posture using Pose Estimation,” Arxiv, 2020.
Y. Li, C. Wang, Y. Cao, B. Liu, J. Tan, and Y. Luo, “Human pose estimation based in-home lower body rehabilitation system,” 2020 Int Jt Conf Neural Networks Ijcnn, vol. 00, pp. 1–8, 2020, doi: 10.1109/ijcnn48605.2020.9207296.
S. R. Rick, S. Bhaskaran, Y. Sun, S. McEwen, and N. Weibel, “NeuroPose,” Proc 24th Int Conf Intelligent User Interfaces Companion, pp. 105–106, 2019, doi: 10.1145/3308557.3308682.
J. Segen and S. Kumar, “Shadow gestures: 3D hand pose estimation using a single camera,” Proc 1999 IEEE Comput Soc Conf Comput Vis Pattern Recognit Cat Pr00149, vol. 1, pp. 479-485 Vol. 1, 1999, doi: 10.1109/cvpr.1999.786981.
H. Kang, C. W. Lee, and K. Jung, “Recognition-based gesture spotting in video games,” Pattern Recogn Lett, vol. 25, no. 15, pp. 1701–1714, 2004, doi: 10.1016/j.patrec.2004.06.016.
H. Xie, A. Watatani, and K. Miyata, “Visual Feedback for Core Training with 3D Human Shape and Pose,” 2019 Nicograph Int Nicoint, vol. 00, pp. 49–56, 2019, doi: 10.1109/nicoint.2019.00017.
G. S. Birdee, G. Y. Yeh, P. M. Wayne, R. S. Phillips, R. B. Davis, and P. Gardiner, “Clinical Applications of Yoga for the Pediatric Population: A Systematic Review,” Acad Pediatr, vol. 9, no. 4, pp. 212-220.e9, 2009, doi: 10.1016/j.acap.2009.04.002.
M. Eichner and V. Ferrari, “Human Pose Co-Estimation and Applications,” IEEE T Pattern Anal, vol. 34, no. 11, pp. 2282–2288, 2012, doi: 10.1109/tpami.2012.85.
Z. Yang, X. Yu, and Y. Yang, “DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency,” 2021 IEEE Cvf Conf Comput Vis Pattern Recognit Cvpr, vol. 00, pp. 3906–3915, 2021, doi: 10.1109/cvpr46437.2021.00390.
R. Divya and J. D. Peter, “Smart healthcare system-a brain-like computing approach for analyzing the performance of detectron2 and PoseNet models for anomalous action detection in aged people with movement impairments,” Complex Intelligent Syst, pp. 1–20, 2021, doi: 10.1007/s40747-021-00319-8.
Y. Wu et al., “A Computer Vision-Based Yoga Pose Grading Approach Using Contrastive Skeleton Feature Representations,” Healthc, vol. 10, no. 1, p. 36, 2021, doi: 10.3390/healthcare10010036.
S. P, K. Manik, and S. K, “Role of yoga in attention, concentration, and memory of medical students,” National J Physiology Pharm Pharmacol, vol. 8, no. 9, p. 1526, 2018, doi: 10.5455/njppp.2018.8.0723521082018.
I. Stephens, “Case report: The Use of Medical Yoga for Adolescent Mental Health,” Complement Ther Med, vol. 43, pp. 60–65, 2019, doi: 10.1016/j.ctim.2019.01.006.
S. Goyal and A. Jain, “Yoga Pose Perfection using Deep Learning: An Algorithm to Estimate the Error in Yogic Poses,” J Student Res, vol. 10, no. 3, 2021, doi: 10.47611/jsrhs.v10i3.2140.
J. Palanimeera and K. Ponmozhi, “Classification of yoga pose using machine learning techniques,” Mater Today Proc, vol. 37, pp. 2930–2933, 2021, doi: 10.1016/j.matpr.2020.08.700.
G. Balakrishnan, A. Zhao, A. V. Dalca, F. Durand, and J. Guttag, “Synthesizing Images of Humans in Unseen Poses,” 2018 IEEE Cvf Conf Comput Vis Pattern Recognit, pp. 8340–8348, 2018, doi: 10.1109/cvpr.2018.00870.
A. Rajšp and I. Fister, “A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training,” Appl Sci, vol. 10, no. 9, p. 3013, 2020, doi: 10.3390/app10093013.
A. Weitz, L. Colucci, S. Primas, and B. Bent, “InfiniteForm: A synthetic, minimal bias dataset for fitness applications,” Arxiv, 2021.
A. Kortylewski, Q. Liu, H. Wang, Z. Zhang, and A. Yuille, “Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion,” 2020 IEEE Winter Conf Appl Comput Vis Wacv, vol. 00, pp. 1322–1330, 2020, doi: 10.1109/wacv45572.2020.9093560.
V. Sharma, M. Gupta, A. Kumar, and D. Mishra, “Video Processing Using Deep Learning Techniques: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 139489–139507, 2021, doi: 10.1109/access.2021.3118541.
Y.-H. Byeon, J.-Y. Lee, D.-H. Kim, and K.-C. Kwak, “Posture Recognition Using Ensemble Deep Models under Various Home Environments,” Appl Sci, vol. 10, no. 4, p. 1287, 2020, doi: 10.3390/app10041287.
A. Badiola-Bengoa and A. Mendez-Zorrilla, “A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise,” Sensors Basel Switz, vol. 21, no. 18, p. 5996, 2021, doi: 10.3390/s21185996.
A. Ross and S. Thomas, “The Health Benefits of Yoga and Exercise: A Review of Comparison Studies,” J Altern Complementary Medicine, vol. 16, no. 1, pp. 3–12, 2010, doi: 10.1089/acm.2009.0044.
S. Jain, A. Rustagi, S. Saurav, R. Saini, and S. Singh, “Three-dimensional CNN-inspired deep learning architecture for Yoga pose recognition in the real-world environment,” Neural Comput Appl, vol. 33, no. 12, pp. 6427–6441, 2021, doi: 10.1007/s00521-020-05405-5.
K. A. P. D. PF and N. P. E. Partini, “The Implementation of Yoga Teaching in Improving Elementary School Students’ Learning Concentration.”
A. Büssing, A. Michalsen, S. B. S. Khalsa, S. Telles, and K. J. Sherman, “Effects of Yoga on Mental and Physical Health: A Short Summary of Reviews,” Evidence-based Complementary Altern Medicine Ecam, vol. 2012, p. 165410, 2012, doi: 10.1155/2012/165410.
M. D. Tran, R. G. Holly, J. Lashbrook, and E. A. Amsterdam, “Effects of Hatha Yoga Practice on the Health‐Related Aspects of Physical Fitness,” Prev Cardiol, vol. 4, no. 4, pp. 165–170, 2001, doi: 10.1111/j.1520-037x.2001.00542.x.
G. G. Chiddarwar, A. Ranjane, M. Chindhe, R. Deodhar, and P. Gangamwar, “AI-Based Yoga Pose Estimation for Android Application,” Int J Innovative Sci Res Technology, vol. 5, no. 9, pp. 1070–1073, 2020, doi: 10.38124/ijisrt20sep704.
P. Plantard, H. P. H. Shum, and F. Multon, “Filtered pose graph for efficient kinect pose reconstruction,” Multimed Tools Appl, vol. 76, no. 3, pp. 4291–4312, 2017, doi: 10.1007/s11042-016-3546-4.
T. Ou, Y. Hoshino, H. Ohsuga, M. Yamada, and T. Miyamoto, “The Humanoid Robot/Camera System for Teaching YOGA Exercise Motions,” 2020 IEEE 9th Global Conf Consumer Electron Gcce, vol. 00, pp. 826–830, 2020, doi: 10.1109/gcce50665.2020.9292075.
C. Buizza and Y. Demiris, “Rotational Adjoint Methods for Learning-Free 3D Human Pose Estimation from IMU Data,” 2020 25th Int Conf Pattern Recognit Icpr, vol. 00, pp. 7868–7875, 2021, doi: 10.1109/icpr48806.2021.9413050.
Y. Chen, Y. Tian, and M. He, “Monocular human pose estimation: A survey of deep learning-based methods,” Comput Vis Image Und, vol. 192, p. 102897, 2020, doi: 10.1016/j.cviu.2019.102897.
S. Jin et al., “Whole-Body Human Pose Estimation in the Wild,” Arxiv, 2020.
W. Deng, L. Bertoni, S. Kreiss, and A. Alahi, “Joint Human Pose Estimation and Stereo 3D Localization,” 2020 IEEE Int Conf Robotics Automation Icra, vol. 00, pp. 2324–2330, 2020, doi: 10.1109/icra40945.2020.9197069.
X. Chen, Z. Zhou, Y. Ying, and D. Qi, “Real-time Human Segmentation using Pose Skeleton Map,” 2019 Chin Control Conf Ccc, vol. 00, pp. 8472–8477, 2019, doi: 10.23919/chicc.2019.8865151.
G. Papandreou et al., “Towards Accurate Multi-Person Pose Estimation in the Wild,” 2017 IEEE Conf Comput Vis Pattern Recognit Cvpr, pp. 3711–3719, 2017, doi: 10.1109/cvpr.2017.395.
A. Jain, J. Tompson, Y. LeCun, and C. Bregler, “MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation,” Arxiv, 2014.
H. Altwaijry, A. Veit, and S. Belongie, “Learning to Detect and Match Keypoints with Deep Architectures,” Procedings Br Mach Vis Conf 2016, p. 49.1-49.12, 2016, doi: 10.5244/c.30.49.
R. Mitra, N. B. Gundavarapu, A. Sharma, and A. Jain, “Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation,” 2020 IEEE Cvf Conf Comput Vis Pattern Recognit Cvpr, vol. 00, pp. 6906–6915, 2020, doi: 10.1109/cvpr42600.2020.00694.
K. Zhou, X. Han, N. Jiang, K. Jia, and J. Lu, “HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation,” IEEE T Pattern Anal, vol. PP, no. 99, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051173.
A. Lai, B. Reddy, and and B. van Vlijmen, “Yog.ai: Deep Learning for Yoga,” CS230:Deep Learning, Winter 2019. Stanford University, CA.(LateX template borrowed from NIPS 2017.). p. 6, 2019, 2019.
J. Jose and S. Shailesh, “Yoga Asana Identification: A Deep Learning Approach,” Iop Conf Ser Mater Sci Eng, vol. 1110, no. 1, p. 012002, 2021, doi: 10.1088/1757-899x/1110/1/012002.
E. W. Trejo and P. Yuan, “Recognition of Yoga Poses Through an Interactive System with Kinect Device,” 2018 2nd Int Conf Robotics Automation Sci Icras, vol. 00, pp. 1–5, 2018, doi: 10.1109/icras.2018.8443267.
M. U. Islam, H. Mahmud, F. B. Ashraf, I. Hossain, and Md. K. Hasan, “Yoga Posture Recognition by Detecting Human Joint Points in Real Time Using Microsoft Kinect,” 2017 IEEE Region 10 Humanit Technology Conf R10-htc, pp. 668–673, 2017, doi: 10.1109/r10-htc.2017.8289047.
S. Patil, A. Pawar, A. Peshave, A. N. Ansari, and A. Navada, “Yoga Tutor Visualization and Analysis Using SURF Algorithm,” 2011 IEEE Control Syst Graduate Res Colloquium, vol. 1, pp. 43–46, 2011, doi: 10.1109/icsgrc.2011.5991827.
W. wu, W. Yin, and F. Guo, “Learning and Self-instruction Expert System For Yoga,” 2010 2nd Int Work Intelligent Syst Appl, pp. 1–4, 2010, doi: 10.1109/iwisa.2010.5473592.
R. Huang, J. Wang, H. Lou, H. Lu, and B. Wang, “Miss Yoga: A Yoga Assistant Mobile Application Based on Keypoint Detection,” 2020 Digital Image Comput Techniques Appl Dicta, vol. 00, pp. 1–3, 2020, doi: 10.1109/dicta51227.2020.9363384.
R. A. Güler, N. Neverova, and I. Kokkinos, “DensePose: Dense Human Pose Estimation in the Wild,” 2018 IEEE Cvf Conf Comput Vis Pattern Recognit, pp. 7297–7306, 2018, doi: 10.1109/cvpr.2018.00762.
Z. Wang, J. Chen, and S. C. H. Hoi, “Deep Learning for Image Super-resolution: A Survey,” Arxiv, 2019.
S. K. Yadav, A. Singh, A. Gupta, and J. L. Raheja, “Real-time Yoga recognition using deep learning,” Neural Comput Appl, vol. 31, no. 12, pp. 9349–9361, 2019, doi: 10.1007/s00521-019-04232-7.
V. Bazarevsky, I. Grishchenko, K. Raveendran, T. Zhu, F. Zhang, and M. Grundmann, “BlazePose: On-device Real-time Body Pose tracking,” Arxiv, 2020.
M. Verma, S. Kumawat, Y. Nakashima, and S. Raman, “Yoga-82: A New Dataset for Fine-grained Classification of Human Poses,” 2020 IEEE Cvf Conf Comput Vis Pattern Recognit Work Cvprw, vol. 00, pp. 4472–4479, 2020, doi: 10.1109/cvprw50498.2020.00527.
X. Lin, C. Zhao, and W. Pan, “Towards Accurate Binary Convolutional Neural Network,” Arxiv, 2017.
F. Cao, K. Yao, and J. Liang, “Deconvolutional neural network for image super-resolution,” Neural Networks, vol. 132, pp. 394–404, 2020, doi: 10.1016/j.neunet.2020.09.017.
C. Affonso, A. L. D. Rossi, F. H. A. Vieira, and A. C. P. de L. F. de Carvalho, “Deep learning for biological image classification,” Expert Syst Appl, vol. 85, pp. 114–122, 2017, doi: 10.1016/j.eswa.2017.05.039.
S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. A. Benediktsson, “Deep Learning for Hyperspectral Image Classification: An Overview,” IEEE T Geosci Remote, vol. 57, no. 9, pp. 6690–6709, 2019, doi: 10.1109/tgrs.2019.2907932.
X. Yang, Y. Ye, X. Li, R. Y. K. Lau, X. Zhang, and X. Huang, “Hyperspectral Image Classification With Deep Learning Models,” IEEE T Geosci Remote, vol. 56, no. 9, pp. 5408–5423, 2018, doi: 10.1109/tgrs.2018.2815613.
Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks,” IEEE T Geosci Remote, vol. 54, no. 10, pp. 6232–6251, 2016, doi: 10.1109/tgrs.2016.2584107.
K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” Arxiv, 2015.
M. Jogin, Mohana, M. S. Madhulika, G. D. Divya, R. K. Meghana, and S. Apoorva, “Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning,” 2018 3rd IEEE Int Conf Recent Trends Electron Information Commun Technology Rteict, vol. 00, pp. 2319–2323, 2018, doi: 10.1109/rteict42901.2018.9012507.
T. Otsuzuki, H. Hayashi, Y. Zheng, and S. Uchida, “Regularized Pooling,” Arxiv, 2020.
H. Zhang and J. Ma, “Hartley Spectral Pooling for Deep Learning,” Arxiv, 2018, doi: 10.4208/csiam-am.2020.
J. Jin and A. D. & E. Culurciello, “flattened convolutional neural networks for feed forward acceleration1412.5474.pdf,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Work. Track Proc., no. 2014, pp. 1–11, 2015.
A. Novikov, D. Podoprikhin, A. Osokin, and D. Vetrov, “Tensorizing Neural Networks,” Arxiv, 2015.
T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, “Convolutional Long Short-Term Memory, Fully Connected Deep Neural Networks,” 2015 IEEE Int Conf Acoust Speech Signal Process Icassp, pp. 4580–4584, 2015, doi: 10.1109/icassp.2015.7178838.
N. B. Nordsborg, H. G. Espinosa, and D. V. Thiel, “Estimating Energy Expenditure During Front Crawl Swimming Using Accelerometers,” Procedia Engineer, vol. 72, pp. 132–137, 2014, doi: 10.1016/j.proeng.2014.06.024.
S. Haque, A. S. A. Rabby, M. A. Laboni, N. Neehal, and S. A. Hossain, “Recent Trends in Image Processing and Pattern Recognition, Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part I,” Comm Com Inf Sc, pp. 186–193, 2019, doi: 10.1007/978-981-13-9181-1_17.
J. Hosang, R. Benenson, P. Dollar, and B. Schiele, “What Makes for Effective Detection Proposals?,” IEEE T Pattern Anal, vol. 38, no. 4, pp. 814–830, 2015, doi: 10.1109/tpami.2015.2465908.
A. Chaudhari, O. Dalvi, O. Ramade, and D. Ambawade, “Yog-Guru: Real-Time Yoga Pose Correction System Using Deep Learning Methods,” 2021 Int Conf Commun Information Comput Technology Iccict, vol. 00, pp. 1–6, 2021, doi: 10.1109/iccict50803.2021.9509937.
G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik, “R-CNNs for Pose Estimation and Action Detection,” Arxiv, 2014.
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri, and D. Tran, “Detect-and-Track: Efficient Pose Estimation in Videos,” 2018 IEEE Cvf Conf Comput Vis Pattern Recognit, pp. 350–359, 2018, doi: 10.1109/cvpr.2018.00044.
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, “Selective Search for Object Recognition,” Int J Comput Vision, vol. 104, no. 2, pp. 154–171, 2013, doi: 10.1007/s11263-013-0620-5.
Y. Zhao, G. Karypis, and U. Fayyad, “Hierarchical Clustering Algorithms for Document Datasets,” Data Min Knowl Disc, vol. 10, no. 2, pp. 141–168, 2005, doi: 10.1007/s10618-005-0361-3.
C. Briggs, Z. Fan, and P. Andras, “Federated learning with hierarchical clustering of local updates to improve training on non-IID data,” 2020 Int Jt Conf Neural Networks Ijcnn, vol. 00, pp. 1–9, 2020, doi: 10.1109/ijcnn48605.2020.9207469.
A. Nibali, Z. He, S. Morgan, and L. Prendergast, “3D Human Pose Estimation with 2D Marginal Heatmaps,” Arxiv, 2018.
M. Oberweger, M. Rad, and V. Lepetit, “Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation,” Arxiv, 2018.
A. Eklund, “Cascade Mask R-CNN and Keypoint Detection used in Floorplan Parsing,” Examensarbete 30 hp Juli 2020, 2020.
N. Liu, T. Celik, and H.-C. Li, “Gated Ladder-Shaped Feature Pyramid Network for Object Detection in Optical Remote Sensing Images,” IEEE Geosci Remote S, vol. 19, pp. 1–5, 2022, doi: 10.1109/lgrs.2020.3046137.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature Pyramid Networks for Object Detection,” 2017 IEEE Conf Comput Vis Pattern Recognit Cvpr, pp. 936–944, 2017, doi: 10.1109/cvpr.2017.106.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Arxiv, 2014.
D. Yu, H. Wang, P. Chen, and Z. Wei, “Rough Sets and Knowledge Technology, 9th International Conference, RSKT 2014, Shanghai, China, October 24-26, 2014, Proceedings,” Lect Notes Comput Sc, pp. 364–375, 2014, doi: 10.1007/978-3-319-11740-9_34.
N. D. Reddy, “Classification of Dermoscopy Images using Deep Learning,” Arxiv, 2018.
J. Howard and S. Gugger, “Fastai: A Layered API for Deep Learning,” Information, vol. 11, no. 2, p. 108, 2020, doi: 10.3390/info11020108.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” 2014 IEEE Conf Comput Vis Pattern Recognit, pp. 580–587, 2014, doi: 10.1109/cvpr.2014.81.
J. Howard and S. Ruder, “Universal Language Model Fine-tuning for Text Classification,” Arxiv, 2018.
H.-T. Chen, Y.-Z. He, C.-C. Hsu, C.-L. Chou, S.-Y. Lee, and B.-S. P. Lin, “MultiMedia Modeling, 20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part I,” Lect Notes Comput Sc, pp. 496–505, 2014, doi: 10.1007/978-3-319-04114-8_42.
Hassan, Hussein Ayman. “Automatic Feedback For Physiotherapy Exercises Based On PoseNet.” (2020).
H.-T. Chen, Y.-Z. He, C.-L. Chou, S.-Y. Lee, B.-S. P. Lin, and J.-Y. Yu, “Computer-assisted self-training system for sports exercise using kinects,” 2013 IEEE Int Conf Multimedia Expo Work Icmew, pp. 1–4, 2013, doi: 10.1109/icmew.2013.6618307.
H. Wang, “Neural Network-Oriented Big Data Model for Yoga Movement Recognition,” Comput Intel Neurosc, vol. 2021, p. 4334024, 2021, doi: 10.1155/2021/4334024.
S. Kothari, “Yoga Pose Classification Using Deep Learning,” 2020, doi: 10.31979/etd.rkgu-pc9k.
H.-T. Chen, Y.-Z. He, and C.-C. Hsu, “Computer-assisted yoga training system,” Multimed Tools Appl, vol. 77, no. 18, pp. 23969–23991, 2018, doi: 10.1007/s11042-018-5721-2.
Mr. A. Kingsly Jabakumar. (2019). Enhanced QoS and QoE Support through Energy Efficient Handover Algorithm for UMTS Architectures. International Journal of New Practices in Management and Engineering, 8(01), 01 - 07. https://doi.org/10.17762/ijnpme.v8i01.73
Omondi, P., Ji-hoon, P., Cohen, D., Silva, C., & Tanaka, A. Deep Learning-Based Object Detection for Autonomous Vehicles. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/149
Kawale, S., Dhabliya, D., & Yenurkar, G. (2022). Analysis and simulation of sound classification system using machine learning techniques. Paper presented at the 2022 International Conference on Emerging Trends in Engineering and Medical Sciences, ICETEMS 2022, 407-412. doi:10.1109/ICETEMS56252.2022.10093281 Retrieved from www.scopus.com
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.