Open Access
Issue
E3S Web Conf.
Volume 505, 2024
3rd International Conference on Applied Research and Engineering (ICARAE2023)
Article Number 01037
Number of page(s) 11
Section Materials Science
DOI https://doi.org/10.1051/e3sconf/202450501037
Published online 25 March 2024
  1. Suh, J. S., Kim, Y. M., Yim, C. D., Suh, B. C., Bae, J. H., & Lee, H. W. (2023). Interpretable machine learning-based analysis of mechanical properties of extruded Mg-Al-Zn-Mn-Ca-Y alloys. Journal of Alloys and Compounds, 968, 172007. [Google Scholar]
  2. Nakkeeran, G., Krishnaraj, L., Bahrami, A., Almujibah, H., Panchal, H., & Zahra, M. M. A. (2023). Machine learning application to predict the Mechanical properties of Glass Fiber mortar. Advances in Engineering Software, 180, 103454. [CrossRef] [Google Scholar]
  3. Pan, H., Peng, J., Geng, X., Gao, M., & Miao, X. (2023). Prediction of mechanical properties for typical pressure vessel steels by small punch test combined with machine learning. International Journal of Pressure Vessels and Piping, 206, 105060. [Google Scholar]
  4. Fei, Z., Liang, S., Cai, Y., & Shen, Y. (2023). Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar. Materials, 16(2), 583. [CrossRef] [PubMed] [Google Scholar]
  5. 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]
  6. 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]
  7. Pashmforoush, F. (2023). Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 237(4), 1214–1223. [Google Scholar]
  8. 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]
  9. Moein, M. M., Saradar, A., Rahmati, K., Mousavinejad, S. H. G., Bristow, J., Aramali, V., & Karakouzian, M. (2023). Predictive models for concrete properties using machine learning and deep learning approaches: A review. Journal of Building Engineering, 63, 105444. [Google Scholar]
  10. 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]
  11. 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]
  12. Kolesnikov, V. I., Belyak, O. A., Suvorova, T. V., Guda, A. A., & Pashkov, D. M. (2022, October). Machine Learning-Based Predictive Modeling of Mechanical Properties of Coatings. In International Conference on Intelligent Information Technologies for Industry (pp. 162–171). Cham: Springer International Publishing. [Google Scholar]
  13. 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]
  14. 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]
  15. 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]
  16. SudhirSastry, Y. B., Krishna, Y., & Budarapu, P. R. (2015). Parametric studies on buckling of thin walled channel beams. Computational Materials Science, 96, 416–424. [Google Scholar]
  17. 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]
  18. Stoll, A., & Benner, P. (2021). Machine learning for material characterization with an application for predicting mechanical properties. GAMM-Mitteilungen, 44(1), e202100003. [Google Scholar]
  19. Cilla, M., Pérez-Rey, I., Martínez, M. A., Pena, E., & Martínez, J. (2018). On the use of machine learning techniques for the mechanical characterization of soft biological tissues. International journal for numerical methods in biomedical engineering, 34(10), e3121. [Google Scholar]
  20. Nasiri, S., & Khosravani, M. R. (2021). Machine learning in predicting mechanical behavior of additively manufactured parts. Journal of materials research and technology, 14, 1137–1153. [Google Scholar]
  21. 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]
  22. 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]
  23. Guo, K., Yang, Z., Yu, C. H., & Buehler, M. J. (2021). Artificial intelligence and machine learning in design of mechanical materials. Materials Horizons, 8(4), 1153–1172. [CrossRef] [PubMed] [Google Scholar]
  24. Vergara, D., Rubio, M. P., Prieto, F., & Lorenzo, M. (2016). Enhancing the teaching/learning of materials mechanical characterization by using virtual reality. J. Mater. Educ, 38(3-4), 63–74. [Google Scholar]
  25. 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]
  26. 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]
  27. Misra, S., Li, H., & He, J. (2019). Machine learning for subsurface characterization. Gulf Professional Publishing. [Google Scholar]
  28. Hoerig, C. L. (2015). Mechanical Characterization of Tissue-like Materials Using Information Based Machine Learning (Doctoral dissertation, University of Illinois at Urbana-Champaign). [Google Scholar]
  29. Awasthi, A., Saxena, K. K., & Arun, V. (2020). Sustainability and survivability in manufacturing sector. In Modern Manufacturing Processes (pp. 205–219). Woodhead Publishing. [Google Scholar]
  30. 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]
  31. Balguri, P. K., Samuel, D. H., & Thumu, U. (2021). A review on mechanical properties of epoxy nanocomposites. Materials Today: Proceedings, 44, 346–355. [Google Scholar]
  32. 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]
  33. 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]
  34. Awasthi, A., Saxena, K. K., & Arun, V. (2021). Sustainable and smart metal forming manufacturing process. Materials Today: Proceedings, 44, 2069–2079. [CrossRef] [Google Scholar]
  35. 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]
  36. 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. [Google Scholar]
  37. 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. [Google Scholar]
  38. Jayanthi, N., Babu, B. V., & Rao, N. S. (2017). Survey on clinical prediction models for diabetes prediction. Journal of Big Data, 4, 1–15. [CrossRef] [Google Scholar]
  39. Sastry, Y. S., Budarapu, P. R., Krishna, Y., & Devaraj, S. (2014). Studies on ballistic impact of the composite panels. Theoretical and Applied Fracture Mechanics, 72, 2–12. [Google Scholar]
  40. Kota, V. R., & Bhukya, M. N. (2019). A novel global MPP tracking scheme based on shading pattern identification using artificial neural networks for photovoltaic power generation during partial shaded condition. IET Renewable Power Generation, 13(10), 1647–1659. [Google Scholar]
  41. Dhanalaxmi, B., Naidu, G. A., & Anuradha, K. (2015). Adaptive PSO based association rule mining technique for software defect classification using ANN. Procedia Computer Science, 46, 432–442. [Google Scholar]
  42. 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]
  43. 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]
  44. Yadav, S., Sharma, P., Yamasani, P., Minaev, S., & Kumar, S. (2014). A prototype micro-thermoelectric power generator for micro-electromechanical systems. Applied Physics Letters, 104(12). [CrossRef] [Google Scholar]
  45. Numan, A., Gill, A. A., Rafique, S., Guduri, M., Zhan, Y., Maddiboyina, B., … & Dang, N. N. (2021). Rationally engineered nanosensors: A novel strategy for the detection of heavy metal ions in the environment. Journal of Hazardous Materials, 409, 124493. [Google Scholar]
  46. Bhukya, M. N., Kota, V. R., & Depuru, S. R. (2019). A simple, efficient, and novel standalone photovoltaic inverter configuration with reduced harmonic distortion. IEEE access, 7, 43831–43845. [Google Scholar]
  47. Peddakrishna, S., & Khan, T. (2018). Design of UWB monopole antenna with dual notched band characteristics by using n-shaped slot and EBG resonator. AEU-International Journal of Electronics and Communications, 96, 107–112. [Google Scholar]
  48. Vijayakumar, Y., Nagaraju, P., Yaragani, V., Parne, S. R., Awwad, N. S., & Reddy, M. R. (2020). Nanostructured Al and Fe co-doped ZnO thin films for enhanced ammonia detection. Physica B: Condensed Matter, 581, 411976. [Google Scholar]
  49. 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]
  50. SudhirSastry, Y. B., Krishna, Y., & Budarapu, P. R. (2015). Parametric studies on buckling of thin walled channel beams. Computational Materials Science, 96, 416–424. [Google Scholar]
  51. Naresh, M., & Munaswamy, P. (2019). Smart agriculture system using IoT technology. International journal of recent technology and engineering, 7(5), 98–102. [Google Scholar]
  52. Raji, A., Nesakumar, J. I. E. T., Mani, S., Perumal, S., Rajangam, V., Thirunavukkarasu, S., & Lee, Y. R. (2021). Biowaste-originated heteroatom-doped porous carbonaceous material for electrochemical energy storage application. Journal of Industrial and Engineering Chemistry, 98, 308–317. [Google Scholar]
  53. Singh, B., Kumar, I., Saxena, K. K., Mohammed, K. A., Khan, M. I., Moussa, S. B., & Abdullaev, S. S. (2023). A future prospects and current scenario of aluminium metal matrix composites characteristics. Alexandria Engineering Journal, 76, 1–17. [CrossRef] [Google Scholar]
  54. Ramprasad, P., Basavapoornima, C., Depuru, S. R., & Jayasankar, C. K. (2022). Spectral investigations of Nd3+: Ba (PO3) 2+ La2O3 glasses for infrared laser gain media applications. Optical Materials, 129, 112482. [Google Scholar]
  55. Yue, L., Jayapal, M., Cheng, X., Zhang, T., Chen, J., Ma, X., … & Zhang, W. (2020). Highly dispersed ultra- small nano Sn-SnSb nanoparticles anchored on N-doped graphene sheets as high performance anode for sodium ion batteries. Applied Surface Science, 512, 145686. [CrossRef] [Google Scholar]
  56. Jaidass, N., Moorthi, C. K., Babu, A. M., & Babu, M. R. (2018). Luminescence properties of Dy3+ doped lithium zinc borosilicate glasses for photonic applications. Heliyon, 4(3). [Google Scholar]
  57. Spandana, K., & Rao, V. S. (2018). Internet of things (Iot) based smart water quality monitoring system. International Journal of Engineering & Technology, 7(3.6), 259–262. [Google Scholar]
  58. Goud, J. S., Srilatha, P., Kumar, R. V., Kumar, K. T., Khan, U., Raizah, Z., … & Galal, A. M. (2022). Role of ternary hybrid nanofluid in the thermal distribution of a dovetail fin with the internal generation of heat. Case Studies in Thermal Engineering, 35, 102113. [Google Scholar]
  59. Indira, D. N. V. S. L.S., Ganiya, R. K., Ashok Babu, P., Xavier, A., Kavisankar, L., Hemalatha, S., … & Yeshitla, A. (2022). Improved artificial neural network with state order dataset estimation for brain cancer cell diagnosis. BioMed Research International, 2022. [Google Scholar]
  60. Kalyani, G., Janakiramaiah, B., Karuna, A., & Prasad, L. N. (2023). Diabetic retinopathy detection and classification using capsule networks. Complex & Intelligent Systems, 9(3), 2651–2664. [Google Scholar]
  61. Ramu, G. (2018). A secure cloud framework to share EHRs using modified CP-ABE and the attribute bloom filter. Education and Information Technologies, 23(5), 2213–2233. [Google Scholar]
  62. Kumar, K. U., Babu, P., Basavapoornima, C., Praveena, R., Rani, D. S., & Jayasankar, C. K. (2022). Spectroscopic properties of Nd3+-doped boro-bismuth glasses for laser applications. Physica B: Condensed Matter, 646, 414327. [Google Scholar]
  63. 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]
  64. Chaudhury, S., Krishna, A. N., Gupta, S., Sankaran, K. S., Khan, S., Sau, K., … & Sammy, F. (2022). Effective image processing and segmentation-based machine learning techniques for diagnosis of breast cancer. Computational and Mathematical Methods in Medicine, 2022. [Google Scholar]
  65. Lakshmi, L., Reddy, M. P., Santhaiah, C., & Reddy, U. J. (2021). Smart phishing detection in web pages using supervised deep learning classification and optimization technique adam. Wireless Personal Communications, 118(4), 3549–3564. [Google Scholar]
  66. Cheruvu, A., Radhakrishna, V., & Rajasekhar, N. (2017, May). Using normal distribution to retrieve temporal associations by Euclidean distance. In 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1–3). IEEE. [Google Scholar]
  67. Radhakrishna, V., Kumar, P. V., Janaki, V., & Rajasekhar, N. (2016, June). Estimating prevalence bounds of temporal association patterns to discover temporally similar patterns. In International Conference on Soft Computing-MENDEL (pp. 209–220). Cham: Springer International Publishing. [Google Scholar]
  68. Vijaykumar, G., Gantala, A., Gade, M. S. L., Anjaneyulu, P., & Ahammad, S. H. (2017). Microcontroller based heartbeat monitoring and display on PC. Journal of Advanced Research in Dynamical and Control Systems, 9(4), 250–260. [Google Scholar]
  69. Devi, M. D., Juliet, A. V., Hariprasad, K., Ganesh, V., Ali, H. E., Algarni, H., & Yahia, I. S. (2021). Improved UV Photodetection of Terbium-doped NiO thin films prepared by cost-effective nebulizer spray technique. Materials Science in Semiconductor Processing, 127, 105673. [CrossRef] [Google Scholar]
  70. Vallabhuni, R. R., Lakshmanachari, S., Avanthi, G., & Vijay, V. (2020, December). Smart cart shopping system with an RFID interface for human assistance. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (pp. 165–169). IEEE. [CrossRef] [Google Scholar]
  71. Padmaja, B., Prasad, V. R., & Sunitha, K. V. N. (2018). A machine learning approach for stress detection using a wireless physical activity tracker. International Journal of Machine Learning and Computing, 8(1), 33–38. [Google Scholar]
  72. Reddy, P. V., Reddy, B. V., & Rao, P. S. (2018). A numerical study on tube hydroforming process to optimize the process parameters by Taguchi method. Materials Today: Proceedings, 5(11), 25376–25381. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.