• Suche: Machine Learning with TensorFlow

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101

TensorFlow on State-of-the-Art HPC Clusters: A Machine Learning use Case
Ramirez-Gargallo, Guillem ; Garcia-Gasulla, Marta ; Mantovani, Filippo ; et al.
Recercat. Dipósit de la Recerca de Catalunya
instname
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Image Recognition TensorFlow High Performance Computi... x86 02 engineering and techn... 01 natural sciences …
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102

Project repositories for machine learning with TensorFlow
Janardhanan, PS
In Procedia Computer Science 2020 171:188-196

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103

Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework
Quoc Toan Nguyen
Journal of Computer Sciences Institute, Vol 23 (2022)

Electronic computers. Co... deep learning computer vision YOLO embedded system 0202 electrical engineer... …
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104

DICKT—Deep Learning-Based Image Captioning using Keras and TensorFlow
Phung Thao ; Satyam Mishra ; Le Anh Ngoc ; et al.
Annals of computer science and information systems, Vol 38, Pp 105-110 (2022)

Artificial intelligence Information technology Speech recognition Visual Question Answerin... Semantic Analysis Deep Learning …
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105

Deep learning model for predicting daily IGS zenith tropospheric delays in West Africa using TensorFlow and Keras
Osah, Samuel ; Acheampong, Akwasi A. ; Fosu, Collins ; et al.
In Advances in Space Research 1 August 2021 68(3):1243-1262

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106

Implementation of Different Machine Learning Projects Using Scikit-learn and Tensorflow Frameworks
Durmuş Özkan Şahin
Advances in Computational Intelligence and Robotics ISBN: 9798369310625

Buch
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107

TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications
Heber, Frederik ; Trstanova, Zofia ; Leimkuhler, Benedict

Mathematics - Statistics... Computer Science - Machi... Statistics - Machine Lea...
Report
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108

Comparison of distributed Machine Learning frameworks in a fog environment: Conceptual and Performance analysis
Sanyadanam, Anusri ; Srirama, Satish Narayana
In Internet of Things November 2025 34

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109

Detection of Back Cover Material Defects Based on Convolutional Neural Network and TensorFlow
Sasa Ani Arnomo ; Siti Fairuz Nurr Sadikan
Ilkom Jurnal Ilmiah, Vol 17, Iss 1, Pp 20-26 (2025)

defect quality control Electronic computers. Co... deep learning QA75.5-76.95 back cover material …
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110

Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow
Marcucci, Giulia ; Pierangeli, Davide ; Pinkse, Pepijn ; et al.

Quantum Physics Computer Science - Machi... Physics - Optics
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111

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
Agrawal, Akshay ; Modi, Akshay Naresh ; Passos, Alexandre ; et al.
Proc. of the 2nd SysML Conference, 2019

Computer Science - Progr... Computer Science - Machi...
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112

Efficient Graph Deep Learning in TensorFlow with tf_geometric
Changsheng Xu ; Jun Hu ; Quan Zhao ; et al.
Proceedings of the 29th ACM International Conference on Multimedia. :3775-3778

FOS: Computer and inform... Computer Science - Machi... Statistics - Machine Lea... 0202 electrical engineer... Machine Learning (stat.M... 02 engineering and techn... …
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114

Traffic Lights Detection and Recognition with New Benchmark Datasets Using Deep Learning and TensorFlow Object Detection API
Irfan Kilic ; Galip Aydin
Traitement du Signal. 39:1673-1683

0502 economics and busin... 05 social sciences
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116

1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras
Okunola, Abiodun

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117

Optimal distributed parallel algorithms for deep learning framework Tensorflow
Xie, Yuanlun ; He, Majun ; Ma, Tingsong ; et al.
Applied Intelligence: The International Journal of Research on Intelligent Systems for Real Life Complex Problems. :1-21

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118

An Ingenious Deep Learning Approach for Home Automation using Tensorflow Computational Framework
P. Ilampiray ; A. Thilagavathy ; Challa Sai Hari Uma Sahith ; et al.
2023 Second International Conference on Electronics and Renewable Systems (ICEARS). :1142-1146

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119

Autoencoder for Image Retrieval System using Deep Learning Technique with Tensorflow and Kears
Kanchan Wangi ; Aziz Makandar
2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS). :1-5

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120

Implementation of a convolutional neural network using Tensorflow machine learning platform
Yu.Ya. Tomka ; M.V. Talakh ; V.V. Dvorzhak ; et al.
Optoelectronic Information-Power Technologies. 44:55-65

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