FPGA-based automatic Pill Dispenser using Decision Tree Classifier
Main Article Content
Keywords
Pill Separator, Decision Tree Algorithm, Hardware Description Language, Field Programmable Gate Array
Abstract
The proposed method involves the classification of Pills and dispenses at the required time to the elderly persons and patients. The decision tree machine learning methodology is included for the classification of Pills based on features namely color, shape, and size. The main objective of this proposed method is to help elderly people and patients in dispensing accurate pills in the prescribed time. Though there are several machine algorithms are used for classification, the Decision Tree algorithm classifies the features with high accuracy and can handle complex datasets. In this work, the Decision Tree algorithm is developed for the given sample dataset of the pill dispensed based on the time. The developed decision tree-based classification is HDL coded and verified with the real-time Xilinx Artix-7 FPGA device. The performance analysis of the proposed method is evaluated for power and area using the EDA tools.
References
2. Jinghua Zhang, Li Liu, Kai Gao, and Dewen Hu, "Few-shot Class-incremental Pill Recognition", IEEE Explorer, 2023, DOI: https://arxiv.org/abs/2304.11959.
3. Hyuk-Ju Kwon,Hwi-Gang Kim and Sung-Hak Lee, "Pill Detection Model for Medicine Inspection Based on Deep Learning", Chemosensors, MDPI, Vol.no.10, Issue no. 4, pp:1-17, 2022, DOI: https://doi.org/10.3390/chemosensors10010004.
4. Aditi Govindu, Sushila Palwe, "Early detection of Parkinson's disease using machine learning", Procedia Computer Science, Vol. no. 218, pp:249-261, 2023, DOI:10.1016/j.procs.2023.01.007
5. Wan-Jung Chang, Liang-Bi Chen, Chia-Hao Hsu, Jhen-Hao Chen, Tzu-Chin Yang, and Cheng-Pei Lin, "MedGlasses: A Wearable Smart-Glasses-Based Drug Pill Recognition System Using Deep Learning for Visually Impaired Chronic Patients", IEEE Access, Vol. no. 8, pp: 17013 - 17024, 2020, DOI: 10.1109/ACCESS.2020.2967400.
6. Lahir Marni, Morteza Hosseini, Jennifer Hopp, Pedram Mohseni and Tinoosh Mohsenin, "A Real-Time Wearable FPGA-based Seizure Detection Processor Using MCMC", 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018, DOI: 10.1109/ISCAS.2018.8351525
7. Heba Askr, Enas Elgeldawi, Heba Aboul Ella, Yaseen A. M. M. Elshaier, Mamdouh M. Gomaa, Aboul Ella Hassanien, "Deep learning in drug discovery: an integrative review and future challenges", Artificial Intelligence Review, 2022, DOI: https://doi.org/10.1007/s10462-022-10306-1
8. Adrienne Kline, Hanyin Wang, Yikuan Li, Saya Dennis, Meghan Hutch, Zhenxing Xu, Fei Wang, Feixiong Cheng and Yuan Luo, "Multimodal machine learning in precision health: A scoping review", Digital Medicine, 2022, DOI: https://doi.org/10.1038/s41746-022-00712-8
9. Bor-Shing Lin, and Tian-Shiue Yen, "An FPGA-Based Rapid Wheezing Detection System" International Journal of Environment Research Public Health, Vol.11, pp: 1573-1593; 2014, DOI: 10.3390/ijerph110201573.
10. Kritika Malhotra, Amit Prakash Singh, "Implementation of decision tree algorithm on FPGA devices", IAES International Journal of Artificial Intelligence, Vol. 10, No.1, pp: 131-138, 2021, DOI: 10.11591/ijai.v10.i1.pp131-138.
11. Rituparna Choudhury, S. R. Ahamed, Prithwijit Guha, "Efficient Hardware Implementation of Decision Tree Training Accelerator", IEEE Explorer, 2020,doi:10.1109/iSES50453.2020.00055
12. Da Tong, Yun R. Qu, and Viktor K. Prasanna, "Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms", IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 11, pp:3046 - 3059, 2017,doi: 10.1109/TPDS.2017.2714661
13. Nithya Ramalingam, Anitha Thiagarajan, "FPGA-based fault analysis for 7-level switched ladder multi-level inverter using decision tree algorithm", International Journal of Reconfigurable and Embedded Systems, Vol. 12, No. 2, pp: 157-164, 2023, DOI: 10.11591/ijres.v12.i2.pp157-164
14. RominaMolina, FernandoLoor, VeronicaGil-Costa, FrancoMariaNardini, RaffaelePerego, SalvatoreTrani, "Efficient traversal of decision tree ensembles with FPGAs", Journal of Parallel and Distributed Computing, Vol. 155, pp:38-49, 2021, DOI: https://doi.org/10.1016/j.jpdc.2021.04.008
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.