Projects per year
Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
External positions
Kuwait College of Science and Technology (KCST)
… → 2024
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Dive into the research topics where Abolfazl Mehbodniya is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
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Projects
- 1 Finished
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Human Healthcare Prediction with Human Activity Patterns Recognized Using Radio Waves and Machine Learning Approach
Mehbodniya, A. (PI)
1/09/20 → 23/01/22
Project: General Research › General Research 2019 Cycle 1
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Bukhari, S. N. H., Webber, J., & Mehbodniya, A. (2022). Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates. Scientific Reports, 12(1), Article 7810. https://doi.org/10.1038/s41598-022-11731-6Open Access63 Scopus citations
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Webber, J., Mehbodniya, A., Arafa, A., & Alwakeel, A. (2022). Gesture Recognition Using Machine Learning for Light Communication Systems. In 2022 International Mobile and Embedded Technology Conference, MECON 2022 (pp. 52-56). (2022 International Mobile and Embedded Technology Conference, MECON 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MECON53876.2022.97522113 Scopus citations
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Webber, J., Mehbodniya, A., Teng, R., & Arafa, A. (2022). Human–Machine Interaction Using Probabilistic Neural Network for Light Communication Systems. Electronics (Switzerland), 11(6), Article 932. https://doi.org/10.3390/electronics11060932Open Access11 Scopus citations
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Webber, J., Mehbodniya, A., Arafa, A., & Alwakeel, A. (2022). Improved Human Activity Recognition Using Majority Combining of Reduced-Complexity Sensor Branch Classifiers. Electronics (Switzerland), 11(3), Article 392. https://doi.org/10.3390/electronics11030392Open Access13 Scopus citations
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Bukhari, S. N. H., Jain, A., Haq, E., Mehbodniya, A., & Webber, J. (2022). Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens, 11(2), Article 146. https://doi.org/10.3390/pathogens11020146Open Access47 Scopus citations