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122

AIADA: Accuracy Impact Assessment of Deprecated Python API Usages on Deep Learning Models
Haochen Zou
Journal of Software. :269-281

0202 electrical engineer... 02 engineering and techn...
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123

Enhancing Python Code Smell Detection with Heterogeneous Ensembles.
Sandouka, Rana ; Aljamaan, Hamoud
International Journal of Software Engineering & Knowledge Engineering. Jul2025, Vol. 35 Issue 7, p963-986. 24p.

RELIABILITY in engineeri... COMPUTER software qualit... PYTHON programming langu... ENSEMBLE learning MACHINE learning DEEP learning …
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124

Gampy: a fast plugin for integration of Python-based deep-learning models to the GAMA platform
Dang, Huu-Tu ; Gaudou, Benoit ; Verstaevel, Nicolas ; et al.
2nd conference GAMA Days 2022, Jun 2022, Online, France

Online, France Simulation deep learning Gama plugin Python integration intelligent behavior …
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125

Real-Time Image Processing Using Deep Learning With Opencv And Python
null Ujjwal Sharma , Tanya Goel , Dr. Jagbeer Singh
Journal of Pharmaceutical Negative Results. :1905-1908

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127

DeepDiveR—A software for deep learning estimation of palaeodiversity from fossil occurrences
Rebecca B. Cooper ; Bethany J. Allen ; Daniele Silvestro
Methods in Ecology and Evolution, Vol 16, Iss 9, Pp 1923-1934 (2025)

biodiversity computational palaeobiol... deep learning macroevolution python programming R programming …
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128

VEHICLE DETECTION FROM VIDEO SEQUENCE USING DEEP LEARNING TECHNIQUE
Dr. D. Sri Hari ; P.Iswarya ; P.Sudharsan ; et al.

My SQL connector Neural Networks Thony python vehicle detection python programming Deep Learning technique …
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129

CLAVE: A deep learning model for source code authorship verification with contrastive learning and transformer encoders
Álvarez-Fidalgo, David ; Ortin, Francisco
In Information Processing and Management May 2025 62(3)

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130

Demand Forecasting in Python: Deep Learning Model Based on LSTM Architecture versus Statistical Models
Kolková, Andrea ; Navrátil, Miroslav
Acta Polytechnica Hungarica. 18:123-141

demand forecasting SARIMA 03 medical and health sc... 0302 clinical medicine TBATS Prophet …
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131

TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning
Aguilera Martos, Ignacio ; García Vico, Ángel Miguel ; Luengo Martín, Julián ; et al.

Time series Deep learning Python
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132

Image segmentation techniques using python and deep learning
Mayank Pandey ; Anuwanshi Sharma
International Journal of Communication and Information Technology. 4:18-25

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133

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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134

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
Jamasb A. R. ; Vinas R. ; Ma E. J. ; et al.

Bioinformatics Deep learning Geometry Graph theory High level languages Learning systems …
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135

VERONA: A python library for benchmarking deep learning in business process monitoring
Pedro Gamallo-Fernandez ; Efrén Rama-Maneiro ; Juan C. Vidal ; et al.
SoftwareX, Vol 26, Iss , Pp 101734- (2024)

Process mining Predictive process monit... Benchmarking Deep learning Computer software QA76.75-76.765
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136

Diagnosis of Glioma, Menigioma and Pituitary brain tumor using MRI images recognition by Deep learning in Python
Seyed Masoud Ghoreishi Mokri ; Newsha Valadbeygi ; Vera Grigoryeva
EAI Endorsed Transactions on Intelligent Systems and Machine Learning Applications. 1

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137

Automatic feature extraction using deep learning for automatic modulation classification implemented with Python
Nakul Kishor Pathak ; Varun Bajaj
Signal Processing with Python ISBN: 9780750359290

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138

Automatic Photo Enhancer Using Machine Learning and Deep Learning with Python
S. Saravanan ; Hemal Shingloo ; Nameera Sajid ; et al.
Signals and Communication Technology ISBN: 9783031479410

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139

Python for Deep Learning
A. Lakshmi Muddana ; Sandhya Vinayakam
Python for Data Science ISBN: 9783031524721

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140

Research and Application Implementation of Deep Learning Algorithms Based on Python
Sha Jin
Lecture Notes in Electrical Engineering ISBN: 9789819741205

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