[1] J. B. West and J. B West, The Original Presentation of Boyle’s Law. Essays on the History of Respiratory Physiology, (2015) 55-60.
[2] R. Menikoff, Empirical equations of state for solids, in ShockWave Science and Technology Reference Library, Springer (2007) 143-188.
[3] E. -H. Benmekki, Fluid phase equilibria with theoretical and semi-empirical equation of state models, University of Illinois at Chicago (1988).
[4] E. U. Akpan, G. C. Enyi, G. Nasr, A. A. Yahaya, A. A. Ahmadu, and B. Saidu, Water-based drilling fluids for high-temperature applications and water-sensitive and dispersible shale formations, J. Pet. Eng., 175 (2019) 1028-1038.
[5] R. Beckmüller, M. Thol, I. Bell, E. Lemmon, and R. Span, New equations of state for binary hydrogen mixtures containing methane, nitrogen, carbon monoxide, and carbon dioxide, J. Phys. Chem. Ref. Data, 50 (2021).
[6] M. R. Dobbelaere, P. P. Plehiers, R. Van de Vijver, C. V. Stevens, and K.M. Van Geem, Machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats, Engr., 7 (2021) 1201-1211.
[7] B. Mahesh, Machine learning algorithms-a review, IJSR, 9 (2020) 381-386.
[8] S. Ray, A quick review of machine learning algorithms. in 2019 International conference on machine learning, big data, cloud and parallel computing, COMITCon 2019. IEEE.
[9] A. Shrestha and A. Mahmood, Review of deep learning algorithms and architectures, IEEE access, 7 (2019) 53040-53065.
[10] B. M. S. Hasan and A. M. Abdulazeez, A review of principal component analysis algorithm for dimensionality reduction, JSCDM 2 (2021) 20-30.
[11] D. Karaboga and E. Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, Artif. Intell. Rev., 52 (2019) 2263-2293.
[12] M. Sadighi, B. Motamedvaziri, H. Ahmadi, and A. Moeini, Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA. Environ. Earth Sci., 79 (2020) 1-14.
[13] S. Walczak, Artificial neural networks, in Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction. IGI global (2019) 40-53.
[14] M. Islam, G. Chen, and S. Jin, An overview of neural network. AJNNA, 5 (2019) 7-11.
[15] S.-J. Wu, C.-T. Hsu, and C.-H. Chang, Stochastic modeling of artificial neural networks for real-time hydrological forecasts based on uncertainties in transfer functions and ANN weights. Hydrol. Res., 52 (2021) 1490-1525.
[16] B. B. Bezabeh and A. D. Mengistu, The effects of multiple layers feed-forward neural network transfer function in digital based ethiopian soil classification and moisture prediction, Int. Jr. Electr. Comput. Eng., 10 (2020) 4073-9.
[17] M. Tamulionis and A. Serackis, Comparison of multi-layer perceptron and cascade feed-forward neural network for head-related transfer function interpolation, in 2019 Open Conference of Electrical, Electronic and Information Sciences (eStream). 2019. IEEE.
[18] S. Zhao, W. Xu, and L. Chen, The modeling and products prediction for biomass oxidative pyrolysis based on PSO-ANN method: An artificial intelligence algorithm approach, Fuel, 312 (2022) 122966.
[19] H. Soltani, A. Karimi, and S. Falahatpisheh, The optimization of biodiesel production from transesterification of sesame oil via applying ultrasound-assisted techniques: comparison of RSM and ANN–PSO hybrid model, CPPM, 17 (2022) 55-67.
[20] S. Nazari, H. R. Momtaz, and M. Servati, Modeling cation exchange capacity in gypsiferous soils using hybrid approach involving the artificial neural networks and ant colony optimization (ANN–ACO), MESE, (2022) 1-10.
[21] L. A. Zadeh, Fuzzy sets, Information and control, 8 (1965) 338-353.
[22] N. Sabri, S. Aljunid, M. Salim, R. Badlishah, R. Kamaruddin, and M. Malek, Fuzzy inference system: Short review and design, Int. Rev. Autom. Control, 6 (2013) 441-449.
[23] P. Mitra, S. Maulik, S. Chowdhury, and S. Chowdhury, ANFIS based automatic voltage regulator with hybrid learning algorithm, in 2007 42nd International Universities Power Engineering Conference. 2007. IEEE.
[24] A. H. S. Dehaghani and M. H. Badizad, A soft computing approach for prediction of P-ρ-T behavior of natural gas using adaptive neuro-fuzzy inference system, Pet., 3 (2017) 447-453.
[25] A. Baghban, M. A. Ahmadi, and B. H. Shahraki, Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches, J. Supercrit. Fluids, 98 (2015) 50-64.
[26] C. Jhin and K.T. Hwang, Prediction of radical scavenging activities of anthocyanins applying adaptive neuro-fuzzy inference system (ANFIS) with quantum chemical descriptors, Int. J. Mol. Sci., 15 (2014) 14715-14727.
[27] C. E. Onu, C. N. Nweke, and J. T. Nwabanne, Modeling of thermo-chemical pretreatment of yam peel substrate for biogas energy production: RSM, ANN, and ANFIS comparative approach, Appl. Surf. Sci. Adv., 11 (2022) 100299.
[28] M. Dolatabadi, M. Mehrabpour, M. Esfandyari, H. Alidadi, and M. Davoudi, Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS, Chemom. Intell. Lab. Syst., 181 (2018) 72-78.
[29] S. Areerachakul, Comparison of ANFIS and ANN for estimation of biochemical oxygen demand parameter in surface water, Int. J. Chem. Eng., 6 (2012) 286-290.
[30] G. C. Straty, M. J. Ball, and T. J. Bruno, PVT measurements on benzene at temperatures to 723 K. JCED, 32 (1987) 163-166.
[31] J. S. Lopez-Echeverry, S. Reif-Acherman, and E. Araujo-Lopez, Peng-Robinson equation of state: 40 years through cubics, Fluid Ph. Equilibria, 447 (2017) 39-71.
[32] R. Behl, V. Kanwar, and Y. I. Kim, Higher-order families of multiple root finding methods suitable for non-convergent cases and their dynamics, Math. Model. Anal., 24 (2019) 422-444.