Open Access
Issue |
E3S Web Conf.
Volume 505, 2024
3rd International Conference on Applied Research and Engineering (ICARAE2023)
|
|
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Article Number | 03012 | |
Number of page(s) | 11 | |
Section | Modelling and Numerical Analysis | |
DOI | https://doi.org/10.1051/e3sconf/202450503012 | |
Published online | 25 March 2024 |
- Ortiz-Lopez, C., Bouchard, C., & Rodriguez, M. (2022). Machine learning models with potential application to predict source water quality for treatment purposes: a critical review. Environmental Technology Reviews, 11(1), 118–147. [CrossRef] [Google Scholar]
- Gunasekaran, K., & Boopathi, S. (2023). Artificial Intelligence in Water Treatments and Water Resource Assessments. In Artificial Intelligence Applications in Water Treatment and Water Resource Management (pp. 71–98). IGI Global. [Google Scholar]
- Ray, S. S., Verma, R. K., Singh, A., Ganesapillai, M., & Kwon, Y. N. (2023). A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes. Desalination, 546, 116221. [CrossRef] [Google Scholar]
- Matheri, A. N., Mohamed, B., Ntuli, F., Nabadda, E., & Ngila, J. C. (2022). Sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant. Physics and Chemistry of the Earth, Parts A/B/C, 126, 103152 [CrossRef] [Google Scholar]
- Egbemhenghe, A., Ojeyemi, T., Iwuozor, K. O., Emenike, E. C., Ogunsanya, T. I., Anidiobi, S. U., & Adeniyi, A. G. (2023). Revolutionizing water treatment, conservation, and management: Harnessing the power of AI-driven ChatGPT solutions. Environmental Challenges, 100782. [CrossRef] [Google Scholar]
- Sipokazi Mabuwa, Velaphi Msomi, 2020. Comparative analysis between normal and submerged friction stir processed friction stir welded dissimilar aluminium alloy joints, Journal of Materials Research and Technology, 9(5), 9632–9644, ISSN 2238-7854, https://doi.org/10.1016/j.jmrt.2020.06.024. [CrossRef] [Google Scholar]
- Velaphi Msomi, Sipokazi Mabuwa, 2020. Analysis of material positioning towards microstructure of the friction stir processed AA1050/AA6082 dissimilar joint, Advances in Industrial and Manufacturing Engineering, 1, 100002, ISSN 2666-9129, https://doi.org/10.1016/j.aime.2020.100002. [CrossRef] [Google Scholar]
- Saxena, K. K., & Lal, A. (2012). Comparative Molecular Dynamics simulation study of mechanical properties of carbon nanotubes with number of stone-wales and vacancy defects. Procedia Engineering, 38, 2347–2355. [CrossRef] [Google Scholar]
- Joy, C., Sundar, G. N., & Narmadha, D. (2021 May) AI Driven Automatic Detection of Bacterial Contamination in Water: A Review. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1281–1285). IEEE. [Google Scholar]
- Godavarthi, B., Nalajala, P., & Ganapuram, V. (2017 August) Design and implementation of vehicle navigation system in urban environments using internet of things (IoT). In IOP Conference Series: Materials Science and Engineering (Vol. 225, No. 1, p. 012262). IOP Publishing. [CrossRef] [Google Scholar]
- Kumari, C. U., Murthy, A. S. D., Prasanna, B. L., Reddy, M. P. P., & Panigrahy, A. K. (2021). An automated detection of heart arrhythmias using machine learning technique: SVM. Materials Today: Proceedings, 45, 1393–1398. [CrossRef] [Google Scholar]
- Saxena, K. K., Srivastava, V., & Sharma, K. (2012). Calculation of Fundamental Mechanical Properties of Single Walled Carbon Nanotube using Non-local Elasticity. Advanced Materials Research, 383, 3840–3844. [Google Scholar]
- Tripathi, G. P., Agarwal, S., Awasthi, A., & Arun, V. (2022 August) Artificial Hip Prostheses Design and Its Evaluation by Using Ansys Under Static Loading Condition. In Biennial International Conference on Future Learning Aspects of Mechanical Engineering (pp. 815–828). Singapore: Springer Nature Singapore. [Google Scholar]
- Sudhakar, M. (2023). Artificial Intelligence Applications in Water Treatment and Water Resource Assessment: Challenges, Innovations, and Future Directions. In Intelligent Engineering Applications and Applied Sciences for Sustainability (pp. 248–269). IGI Global. [CrossRef] [Google Scholar]
- Reddy, K. S. P., Roopa, Y. M., Ln, K. R., & Nandan, N. S. (2020 July) IoT based smart agriculture using machine learning. In 2020 Second international conference on inventive research in computing applications (ICIRCA) (pp. 130–134). IEEE [Google Scholar]
- Agrawal, R., Singh, S., Saxena, K. K., & Buddhi, D. (2023). A role of biomaterials in tissue engineering and drug encapsulation. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 09544089221150740. [Google Scholar]
- Arun, V., Shukla, N. K., Singh, A. K., & Upadhyay, K. K. (2015 September) Design of all optical line selector based on SOA for data communication. In Proceedings of the Sixth International Conference on Computer and Communication Technology 2015 (pp. 281–285). [Google Scholar]
- SudhirSastry, Y. B., Krishna, Y., & Budarapu, P. R. (2015). Parametric studies on buckling of thin walled channel beams. Computational Materials Science, 96, 416–424. [CrossRef] [Google Scholar]
- Ramadugu, S., Ledella, S. R. K., Gaduturi, J. N. J., Pinninti, R. R., Sriram, V., & Saxena, K. K. (2023). Environmental life cycle assessment of an automobile component fabricated by additive and conventional manufacturing. International Journal on Interactive Design and Manufacturing (IJIDeM), 1–12. [Google Scholar]
- Geetha, M., Bonthula, S., Al-Maadeed, S., Al-Lohedan, H., Rajabathar, J. R., Arokiyaraj, S., & Sadasivuni, K. K. (2023). Research Trends in Smart Cost-Effective Water Quality Monitoring and Modeling: Special Focus on Artificial Intelligence. Water, 15(18), 3293. [CrossRef] [Google Scholar]
- Awasthi, A., Saxena, K. K., & Arun, V. (2021). Sustainable and smart metal forming manufacturing process. Materials Today: Proceedings, 44, 2069–2079. [CrossRef] [Google Scholar]
- Balguri, P. K., Samuel, D. H., & Thumu, U. (2021). A review on mechanical properties of epoxy nanocomposites. Materials Today: Proceedings, 44, 346–355. [CrossRef] [Google Scholar]
- Ajith, J. B., Manimegalai, R., & Ilayaraja, V. (2020 February) An IoT based smart water quality monitoring system using cloud. In 2020 International conference on emerging trends in information technology and engineering (ic-ETITE) (pp. 1–7). IEEE. [Google Scholar]
- Swapna Sri, M. N., Anusha, P., Madhav, V. V., Saxena, K. K., Chaitanya, C. S., Haranath, R., & Singh, B. (2023). Influence of Cu particulates on a356mmc using frequency response function and damping ratio. Advances in Materials and Processing Technologies, 1–9. [CrossRef] [Google Scholar]
- Telagam, N., Kandasamy, N., & Nanjundan, M. (2017). Smart sensor network based high quality air pollution monitoring system using labview. International Journal of Online Engineering (iJOE), 13(08), 79–87. [CrossRef] [Google Scholar]
- Arora, G. S., & Saxena, K. K. (2023). A review study on the influence of hybridization on mechanical behaviour of hybrid Mg matrix composites through powder metallurgy. Materials Today: Proceedings. [Google Scholar]
- Korpi, A. G., Țălu, Ş., Bramowicz, M., Arman, A., Kulesza, S., Pszczolkowski, B., … & Gopikishan, S. (2019). Minkowski functional characterization and fractal analysis of surfaces of titanium nitride films. Materials Research Express, 6(8), 086463. [CrossRef] [Google Scholar]
- Arun, V., Singh, A. K., Shukla, N. K., & Tripathi, D. K. (2016). Design and performance analysis of SOA - MZI based reversible toffoli and irreversible AND logic gates in a single photonic circuit. Optical and quantum electronics, 48, 1–15. [CrossRef] [Google Scholar]
- Awasthi, A., Saxena, K. K., Dwivedi, R. K., Buddhi, D., & Mohammed, K. A. (2022). Design and analysis of ECAP Processing for Al6061 Alloy: a microstructure and mechanical property study. International Journal on Interactive Design and Manufacturing (IJIDeM), 1–13. [Google Scholar]
- Basavapoornima, C., Kesavulu, C. R., Maheswari, T., Pecharapa, W., Depuru, S. R., & Jayasankar, C. K. (2020). Spectral characteristics of Pr3+-doped lead based phosphate glasses for optical display device applications. Journal of Luminescence, 228, 117585. [CrossRef] [Google Scholar]
- Awasthi, A., Saxena, K. K., & Arun, V. (2020). Sustainability and survivability in manufacturing sector. In Modern Manufacturing Processes (pp. 205–219). Woodhead Publishing. [CrossRef] [Google Scholar]
- Sheikh Khozani, Z., Iranmehr, M., & Wan Mohtar, W. H. M. (2022). Improving Water Quality Index prediction for water resources management plans in Malaysia: application of machine learning techniques. Geocarto International, 37(25), 10058–10075. [CrossRef] [Google Scholar]
- Nova, K. (2023). AI-enabled water management systems: an analysis of system components and interdependencies for water conservation. Eigenpub Review of Science and Technology, 7(1), 105–124. [Google Scholar]
- Singh, B., Saxena, K. K., Dagwa, I. M., Singhal, P., & Malik, V. (2023). Optimization Of Machining Characteristics of Titanium-Based Biomaterials: Approach to Optimize Surface Integrity for Implants Applications. Surface Review and Letters, 2340008. [CrossRef] [Google Scholar]
- Ashish Kumar, Ravindra Singh Rana, Rajesh Purohit, Anurag Namdev, Kuldeep K. Saxena, Atul Kumar, 2022. Optimization of dry sliding wear behavior of Si3N4 and Gr reinforced Al-Zn-Mg-Cu composites using taguchi method, Journal of Materials Research and Technology, 19, 4793–4803, ISSN 2238-7854, https://doi.org/10.1016/j.jmrt.2022.06.172. [CrossRef] [Google Scholar]
- Kumar, A., Rana, R.S., Purohit, R. et al. Investigation of Tensile behaviour, Seizure Conditions and Frictional Characteristics of Al-Zn-Cu-Mg Alloy based Composites. Silicon 15, 7903–7915 (2023). https://doi.org/10.1007/s12633-023-02627-9. [CrossRef] [Google Scholar]
- Kulkarni, A., Yardimci, M., Kabir Sikder, M. N., & Batarseh, F. A. (2023). P2O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants. Journal of Environmental Engineering, 149(9), 04023045. [CrossRef] [Google Scholar]
- Gupta, T. K., Budarapu, P. R., Chappidi, S. R., Yb, S. S., Paggi, M., & Bordas, S. P. (2019). Advances in carbon based nanomaterials for bio-medical applications. Current Medicinal Chemistry, 26(38), 6851–6877. [CrossRef] [PubMed] [Google Scholar]
- Dogo, E. M., Nwulu, N. I., Twala, B., & Aigbavboa, C. (2019). A survey of machine learning methods applied to anomaly detection on drinking-water quality data. Urban Water Journal, 16(3), 235–248. [CrossRef] [Google Scholar]
- AlZubi, A. A. (2022). IoT-based automated water pollution treatment using machine learning classifiers. Environmental Technology, 1–9. [Google Scholar]
- Kim, Y. H., Im, J., Ha, H. K., Choi, J. K., & Ha, S. (2014). Machine learning approaches to coastal water quality monitoring using GOCI satellite data. GIScience & Remote Sensing, 51(2), 158–174. [CrossRef] [Google Scholar]
- Kamyab, H., Khademi, T., Chelliapan, S., SaberiKamarposhti, M., Rezania, S., Yusuf, M., … & Ahn, Y. (2023). The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management. Results in Engineering, 101566. [CrossRef] [Google Scholar]
- Sipokazi Mabuwa and Velaphi Msomi, 2020. The impact of submerged friction stir processing on the friction stir welded dissimilar joints. Materials Research Express, 7, 096513, DOI: 10.1088/2053-1591/abb6b6. [CrossRef] [Google Scholar]
- Guo, H., Huang, J. J., Chen, B., Guo, X., & Singh, V. P. (2021). A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery. International Journal of Remote Sensing, 42(5), 1841–1866. [CrossRef] [Google Scholar]
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