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221

Disease Detection in Bombyx Mori Silkworm Using Deep Learning Algorithm CNN
Singla, Sanjay ; Garg, Stuti ; Garg, Ishika ; et al.
2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech) ICACCTECH Advanced Computing & Communication Technologies (ICACCTech), 2023 International Conference on. :316-320 Dec, 2023

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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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223

Mood Melody Matchmaker System Using Deep Learning Model
Mishra, Susmita ; Pradhan, Chittaranjan ; V, Jananee
2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) Science Technology Engineering and Mathematics (ICONSTEM), 2024 Ninth International Conference on. :1-4 Apr, 2024

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224

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras
Kensert, Alexander ; Desmet, Gert ; Cabooter, Deirdre
Journal of Computer-Aided Molecular Design: Incorporating Perspectives in Drug Discovery and Design. 39(1)

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225

Bayesian Neural Networks via MCMC: A Python-Based Tutorial
Chandra, R. ; Simmons, J.
IEEE Access Access, IEEE. 12:70519-70549 2024

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226

A Static Analysis Approach for Detecting Array Shape Errors in Python.
YUNG YU ZHUANG ; CHIEN-WEN KAO ; WEI-HSIN YEN
Journal of Information Science & Engineering. Jan2025, Vol. 41 Issue 1, p97-119. 23p.

Computational linguistic... Human error Machine learning Labor costs Pythons Python programming langu... …
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228

FACE MASK DETECTION USING PYTHON AND DEEP LEARNING
J. Tanvi ; K. Sreeja ; K. Likhitha ; et al.
International Journal of Computer Science and Mobile Computing. 11:55-65

0502 economics and busin... 05 social sciences 3. Good health
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229

SeisAug: A data augmentation python toolkit
Pragnath, D. ; Srijayanthi, G. ; Kumar, Santosh ; et al.
In Applied Computing and Geosciences February 2025 25

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230

Fixing Broken Graphs: LLM-Powered Automatic Code Optimization for DNN Programs
Wang, Haotian ; Sui, Yicheng ; Xie, Yudong ; et al.
2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE) ASE Automated Software Engineering (ASE), 2025 40th IEEE/ACM International Conference on. :1718-1730 Nov, 2025

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231

Automated Detection of Nonhelmeted Motor Cyclists and License Plate Recognition - YOLOv8 With Paddle OCR
D, Saravanaprabhu ; V, Naresh Prasad ; P, Gowtham Kumar ; et al.
2025 Second International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM) Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), 2025 Second International Conference on. :1-6 Oct, 2025

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232

Transformer-Based Semantic Embeddings and Hybrid Neural Networks for Robust Software Vulnerability Detection
Gunda, Brahma Sagar ; Krishna, G Bala ; Rawat, Sandeep Singh
2025 Innovations in Power and Advanced Computing Technologies (i-PACT) Innovations in Power and Advanced Computing Technologies (i-PACT), 2025. :1-9 Sep, 2025

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233

Stability and Anomaly Analysis of RC Circuits under Disturbance Conditions Based on Deep Learning
Lai, Binyu ; Li, Xinyun ; Qin, Nuoyi ; et al.
2024 6th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT) Electronics and Communication, Network and Computer Technology (ECNCT), 2024 6th International Conference on. :177-181 Jul, 2024

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234

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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235

NVIDIA Jetson Nano and Python-based Economical Human Fall Detection and Analysis System
Sudthongkhong, Chudanat ; Suksri, Siwat ; Ratanaubol, Chanate ; et al.
2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) SITIS Signal-Image Technology & Internet-Based Systems (SITIS), 2023 17th International Conference on. :463-467 Nov, 2023

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236

Scalable Deep Learning for Categorization of Satellite Images
Swapna, B ; Venkatessan, Radhika ; Taskeen, Fathima ; et al.
2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2023 7th International Conference on. :773-778 Oct, 2023

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237

MRA-Based Digital Filter Using Daubechies 12 and Deep Learning for Exoplanet Light Curves
Toledo-Mercado, Esteban ; Soto, Ismael
2024 43rd International Conference of the Chilean Computer Science Society (SCCC) Chilean Computer Science Society (SCCC), 2024 43rd International Conference of the. :1-8 Oct, 2024

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