Comparative analysis of machine learning models for predicting dielectric properties in MoS2 nanofiller-reinforced epoxy composites
Authors:
- Atul D WatpadeAWAtul D Watpade
- Sanketsinh ThakorSTSanketsinh Thakor
- Prince JainPrince Jain
- Prajna P. MohapatraPMPrajna P. Mohapatra
- Chandan R. VajaCVChandan R. Vaja
- Anand JoshiAnand Joshi
- Dimple V. ShahDSDimple V. Shah
- Mohammad Tariqul IslamMIMohammad Tariqul Islam
Open Access
- 14 March 2024
- journal article
- research article
- Published by Elsevier in Ain Shams Engineering Journal
- Vol. 15 (6)
Abstract
No abstract availableKeywords
Funding Information
- Universiti Kebangsaan Malaysia (DPK-2022-006)
This publication has 33 references indexed in Scilit:
- Thermal conductivity of epoxy composites modified by microspheric molybdenum disulfidePolymer Bulletin, 2022
- Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concreteNeural Computing and Applications, 2022
- Design of an ultra-thin hepta-band metamaterial absorber for sensing applicationsOptical and Quantum Electronics, 2022
- Magnetic, dielectric and structural properties of CoxZn(0.90-x)Al0.10Fe2O4 synthesized by sol–gel method with application as flexible microwave substrates for microstrip patch antennaJournal of Materials Research and Technology, 2022
- An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock massJournal of Rock Mechanics and Geotechnical Engineering, 2021
- Accelerated search for ABO3-type the electronic contribution of polycrystalline dielectric constants by machine learningComputational Materials Science, 2021
- A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young’s modulus and unconfined compressive strength of rockEngineering with Computers, 2021
- Significance of artificial neural network analytical models in materials’ performance predictionBulletin of Materials Science, 2020
- Influence of Fe3O4 nanoparticles on the optical, magnetic and electrical properties of PMMA/PEO composites: Combined FT-IR/DFT for electrochemical applicationsJournal of Organometallic Chemistry, 2020
- Quantitative structure-property relationships of electroluminescent materials: Artificial neural networks and support vector machines to predict electroluminescence of organic moleculesBulletin of Materials Science, 2013