Open Access
Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
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Article Number | 01005 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/epjconf/202532801005 | |
Published online | 18 June 2025 |
- Z.L. Wang, L. Yuan, W. Li, and J.Y. Li, "Ferroptosis in Parkinson's disease: Glia-neuron crosstalk," Trends Mol. Med., vol. 28, pp. 258–269, (2022). [CrossRef] [Google Scholar]
- S. Yousefvand and F. Hamidi, "Role of lateral hypothalamus area in the central regulation of feeding," Int. J. Pept. Res. Ther., vol. 28, p. 83, (2020). [Google Scholar]
- A. Panda and P. Bhuyan, "Machine learning-based framework for early detection of distinguishing different stages of Parkinson's disease," Spec. Ugdym., vol. 2, pp. 30–42, (2022). [Google Scholar]
- K.M. Alalayah, E.M. Senan, H.F. Atlam, I.A. Ahmed, and H.S.A. Shatnawi, "Automatic and early detection of Parkinson's disease by analyzing acoustic signals using classification algorithms based on recursive feature elimination method," Diagnostics, vol. 13, p. 1924, (2023). [CrossRef] [PubMed] [Google Scholar]
- R. Khaskhoussy and Y.B. Ayed, "An I-vector-based approach for discriminating between patients with Parkinson's disease and healthy people," in Proc. Fourteenth Int. Conf. Mach. Vision, Rome, Italy, Nov. 2021, vol. 12084, pp. 69–77 (2021). [Google Scholar]
- S. Saravanan, K. Ramkumar, K. Adalarasu, V. Sivanandam, S.R. Kumar, S. Stalin, and R. Amirtharajan, "A systematic review of artificial intelligence (AI) based approaches for the diagnosis of Parkinson's disease," Arch. Comput. Methods Eng., vol. 29, pp. 3639–3653, (2022). [CrossRef] [Google Scholar]
- R. Maskeliünas, R. Damasevicius, A. Kulikajevas, E. Padervinskis, K. Pribusis, and V. Uloza, "A hybrid U-Lossian deep learning network for screening and evaluating Parkinson's disease," Appl. Sci., vol. 12, p. 11601, (2022). [CrossRef] [Google Scholar]
- A. Ahmadi, H. Bazregarzadeh, and K. Kazemi, "Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity," Biocybern. Biomed. Eng., vol. 41, pp. 316–332, (2021). [CrossRef] [Google Scholar]
- S.E. Sânchez-Hernândez, R.A. Salido-Ruiz, S. Torres-Ramos, and I. Român-Godinez, "Evaluation of feature selection methods for classification of epileptic seizure EEG signals," Sensors, vol. 22, p. 3066, (2022). [CrossRef] [PubMed] [Google Scholar]
- H.W. Loh, C.P. Ooi, E. Palmer, P.D. Barua, S. Dogan, T. Tuncer, M. Baygin, and U.R. Acharya, "GaborPDNet: Gabor transformation and deep neural network for Parkinson's disease detection using EEG signals," Electronics, vol. 10, p. 1740, (2021). [CrossRef] [Google Scholar]
- M. Aljalal, S.A. Aldosari, K. AlSharabi, A.M. Abdurraqeeb, and F.A. Alturki, "Parkinson's disease detection from resting-state EEG signals using common spatial pattern, entropy, and machine learning techniques," Diagnostics, vol. 12, p. 1033, (2022). [CrossRef] [PubMed] [Google Scholar]
- L. Borzi, I. Mazzetta, A. Zampogna, A. Suppa, G. Olmo, and F. Irrera, "Prediction of freezing of gait in Parkinson's disease using wearables and machine learning," Sensors, vol. 21, p. 614, (2021). [CrossRef] [PubMed] [Google Scholar]
- N. Chintalapudi, G. Battineni, M.A. Hossain, and F. Amenta, "Cascaded deep learning frameworks in contribution to the detection of Parkinson's disease," Bioengineering, vol. 9, p. 116, (2022). [CrossRef] [PubMed] [Google Scholar]
- A. Rana, A. Dumka, R. Singh, M. Rashid, N. Ahmad, and M.K. Panda, "An efficient machine learning approach for diagnosing Parkinson's disease by utilizing voice features," Electronics, vol. 11, p. 3782, (2022). [CrossRef] [Google Scholar]
- H. Khachnaoui, N. Khlifa, and R. Mabrouk, "Machine learning for early Parkinson's disease identification within SWEDD group using clinical and DaTSCAN SPECT imaging features," J. Imaging, vol. 8, p. 97, (2022). [CrossRef] [Google Scholar]
- M. Pramanik, R. Pradhan, P. Nandy, A.K. Bhoi, and P. Barsocchi, "Machine learning methods with decision forests for Parkinson's detection," Appl. Sci., vol. 11, p. 581, (2021). [CrossRef] [Google Scholar]
- B.E. Sakar, M.E. Isenkul, C.O. Sakar, A. Sertbas, F. Gurgen, S. Delil, and O. Kursun, "Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings," IEEE J. Biomed. Health Inform., vol. 17, pp. 828–834, (2013). [CrossRef] [PubMed] [Google Scholar]
- Du, Q., Shen, J., Wen, P. et al. Parkinson's Disease Detection by Using Machine Learning Method based on Local Classification on Class Boundary. Discov Appl Sci 6, 576 (2024). https://doi.org/10.1007/s42452-024-06295-1. [CrossRef] [Google Scholar]
- Srinivasan, S., Ramadass, P., Mathivanan, S. et al. "Detection of Parkinson disease using multiclass machine learning approach". Sci Rep 14, 13813 (2024). https://doi.org/10.1038/s41598-024-64004-9 [CrossRef] [PubMed] [Google Scholar]
- Alshammri R, Alharbi G, Alharbi, E. and Almubark, I., "Machine learning approaches to identify Parkinson's disease using voice signal features", Front. Artif. Intell. 6:1084001. (2023). doi: 10.3389/frai.2023.1084001 [CrossRef] [Google Scholar]
- Wankhede, D.S., Shelke, C.J., Shrivastava, V.K., Achary, R., & Mohanty, S.N.. Brain tumor detection and classification using adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN model. EAI Endorsed Transactions on Pervasive Health and Technology, 10 (2024) [Google Scholar]
- Wankhede, D.S., Kalra, N., Dhabliya, R., Khetani, V., Waykole, T., & Shirkande, A.S. Enhancing Alzheimer's Disease Prediction with Bayesian Optimization and Ensemble Methods. Proceedings of the 5th International Conference on Information Management & Machine Intelligence, 1–6. (2023). https://doi.org/10.1145/3647444.3647935 [Google Scholar]
- Wankhede, D.S., Shelke, C.J., & George, A. An enhanced algorithm for predicting IDH1 mutations and 1p19q mitigation in glioma tumor. AIP Conference Proceedings, 3217(1), 020025 (2024). https://doi.org/10.1063/5.0237441 [CrossRef] [Google Scholar]
- Wankhede, D.S., & Shelke, C.J. Improving glioblastoma multiforme recurrence prediction through integrated radiomics and deep learning techniques. Panamerican Mathematical Journal, 34 (1), 25–35 (2024). https://doi.org/10.52783/pmj.v34.i1.903 [CrossRef] [Google Scholar]
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