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All issues Volume 172 (2025) BIO Web Conf., 172 (2025) 02001 References
Open Access
Issue
BIO Web Conf.
Volume 172, 2025
International Conference on Nurturing Innovative Technological Trends in Engineering – BIOscience (NITTE-BIO 2025)
Article Number 02001
Number of page(s) 16
Section Bioinformatics / Computational Biology
DOI https://doi.org/10.1051/bioconf/202517202001
Published online 10 April 2025
  • Opara, U.L., Prospects for Agricultural, Biosystems, and Biological Engineering Education and Research for Knowledge-Intensive, Data-Driven, Climate-Smart, and Sustainable Agriculture. In Agricultural, Biosystems, and Biological Engineering Education (pp. 560-568). CRC Press. [Google Scholar]
  • National Academies of Sciences, Division of Behavioral, Social Sciences, Board on Environmental Change, Medicine Division, Nutrition Board, Division on Earth, Life Studies, Water Science, Technology Board and Board on Life Sciences, 2019. Science breakthroughs to advance food and agricultural research by 2030. National Academies Press. [Google Scholar]
  • Neethirajan, S., 2024. From Predictive Analytics to Emotional Recognition Evolving Landscape of Cognitive Computing in Animal Welfare. International Journal of Cognitive Computing in Engineering. [Google Scholar]
  • Dhivya, S., Areche, F.O., Kumar, B.S., Hariprabhu, M. and Mutha, S., (2023). The Role of Bioengineering in Healthcare. In Handbook of Research on Advanced Functional Materials for Orthopedic Applications (pp. 279-298). IGI Global. [Google Scholar]
  • M. Shah, P. Padhiyar, K. Parmar, J. Patel, S. Panesar. (2023). Recent Challenges in Medical Science using Machine Learning Techniques: A Review, International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, pp. 516-523, doi: 10.1109/I-SMAC58438.2023.10290424. [Google Scholar]
  • A. Hua et al., “Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data,” in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 9, pp. 2452-2460, Sept. 2020, doi: 10.1109/JBHI.2020.2999902. [CrossRef] [PubMed] [Google Scholar]
  • M. -P. Hosseini, A. Hosseini. (2021). A Review on Machine Learning for EEG Signal Processing in Bioengineering, IEEE Reviews in Biomedical Engineering, vol. 14, pp. 204-218, doi: 10.1109/RBME.2020.2969915. [CrossRef] [PubMed] [Google Scholar]
  • Y. Prakash, P. Rama and S. Jagadeesh, (2024). Machine Learning-based Soil Fertility Analysis to Maintain Environmental Sustainability,2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India, pp. 1732-1739, doi: 10.1109/ICACRS58579.2023.10404822. [Google Scholar]
  • A. -M. Ştefan, E. -A. Paraschiv, S. Ovreiu. (2020). A Review of Glaucoma Detection from Digital Fundus Images using Machine Learning Techniques, International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, pp. 1-4, doi:10.1109/EHB50910.2020.9280218. [Google Scholar]
  • Sun, A.Y. and Scanlon, B.R., (2019). How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions. Environmental Research Letters, 14(7), p.073001. [CrossRef] [Google Scholar]
  • Senoo, E.E.K., Anggraini, L., Kumi, J.A., Luna, B.K., (2024). IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities. Electronics, 13(10), p.1894. [CrossRef] [Google Scholar]
  • Pfisterer, K., (2021). Collaborative design and feasibility assessment of computational nutrient-sensing for simulated food-intake tracking in a healthcare environment. [Google Scholar]
  • Kong, X., Zeng, Q., Guo, X. and Kong, F., (2024). Sustainable Cultivation of Discipline Competition Programs for Innovation and Entrepreneurship Education: An Example of the Food Science and Engineering Major. Sustainability, 16(14), p.5846. [CrossRef] [Google Scholar]
  • Holland, C., McCarthy, A., Ferri, P. and Shapira, P., (2024). Innovation intermediaries at the convergence of digital technologies, sustainability, and governance: A case study of AI-enabled engineering biology. Technovation, 129, p.102875. [CrossRef] [Google Scholar]
  • Rezaei, N., Saghazadeh, A., (2022). Thinking 2050: Bioengineering of Science and Art. In Thinking: Bioengineering of Science and Art (pp. 713-752). Cham: Springer International Publishing. [CrossRef] [Google Scholar]
  • Nicholson, A., Pavlin, J., Buckley, G., Amponsah, E., & National Academies of Sciences, Engineering, and Medicine. (2020, May). Nurturing Innovations Through Novel Ecosystems to Accelerate Research and Development. In Exploring the Frontiers of Innovation to Tackle Microbial Threats: Proceedings of a Workshop. National Academies Press (US). [Google Scholar]
  • Bala, M., Sharma, R. and Gupta, S., 2024. Integration of Hybrid Nanomaterials and Artificial Intelligence for Sustainable Agriculture. In Technological Applications of Nano-Hybrid Composites (pp. 97-118). IGI Global. [Google Scholar]
  • S. Geerthik, K. J. Oliviya and R. Keerthana. (2024). A System and Method for Fruit Ripeness Prediction Using Transfer Learning and CNN, International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, pp. 1-6, doi: 10.1109/IC3IoT60841.2024.10550209. [Google Scholar]
  • R., Nithyashri, J., Revathi, S., Mohana Priya, R. (2024). An Intelligent System for Plant Disease Diagnosis and Analysis Based on Deep Learning and Augmented Reality. In: Jacob, I.J., Piramuthu, S., Falkowski-Gilski, P. (eds) Data Intelligence and Cognitive Informatics. ICDICI 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-7962-2_27. [Google Scholar]
  • M. Razmah, T. Veeramakali, S. S and Y. R. (2022). Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm, International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, pp. 1-5, doi:10.1109/ICPECTS56089.2022.10047501. [Google Scholar]
  • T. S. (2024). AgriBot : An Integrated Chatbot Platform for Precision Agriculture and Farmer Support using Deep Learning Techniques, International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, doi: 10.1109/ICPECTS62210.2024.10780432. [Google Scholar]
  • S. Prabu. (2024). A Novel Predictive Analysis and Classification of Land Subsidence Vulnerability Mapping based on GIS using Hybrid Optimized Machine Learning Techniques and Computer Vision, Procedia Computer Science, Volume 233, pp 343-352, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2024.03.224. [CrossRef] [Google Scholar]
  • R. Sowmiya and J. Nithish. (2024). Dynamic Water Quality Monitoring via IoT Sensor Networks and Machine Learning Technique, International Conference on Communication, Computing and Internet of Things (IC3IoT), Chennai, India, pp. 1-6, doi: 10.1109/IC3IoT60841.2024.10550224. [Google Scholar]

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