[1] Akimova E. M., Kisel T. N.: Integrated application of strategic analysis methods. Kant 39(2), 2021, 10–16 [https://doi.org/10.24923/2222-243x.2021-39.2].
DOI: https://doi.org/10.24923/2222-243X.2021-39.2
[2] Butler M., Petre L., Sere K.: Integrated formal methods. Springer Berlin, Heidelberg 2002 [https://doi.org/10.1007/3-540-47884-1].
DOI: https://doi.org/10.1007/3-540-47884-1
[3] Dheeraj K. N., et al.: Crop quality prediction using ml and neural networks. International Journal on Cybernetics & Informatics 10(2), 2021, 7–11 [https://doi.org/10.5121/ijci.2021.100202].
DOI: https://doi.org/10.5121/ijci.2021.100202
[4] Evgeni P.: Testability analysis methods. Evgeni P.: Digital integrated circuits. Design-for-Test Using Simulink and Stateflow. CRC Press, Boca Raton 2007 [https://doi.org/10.1201/9781315222110-4].
DOI: https://doi.org/10.1201/9781315222110-4
[5] Fumagalli F., et al.: Incremental permutation feature importance (iPFI): Towards online explanations on data streams. Machine Learning 112, 2023, 4863–4903 [https://doi.org/10.1007/s10994-023-06385-y].
DOI: https://doi.org/10.1007/s10994-023-06385-y
[6] Gelperin D.: The power of integrated methods. ACM SIGSOFT Software Engineering Notes 19(4), 1994, 77–78 [https://doi.org/10.1145/190679.190687].
DOI: https://doi.org/10.1145/190679.190687
[7] Gleirscher M., Foster S., Woodcock J.: New opportunities for integrated formal methods. ACM Computing Surveys 52(6), 2020, 1–36 [https://doi.org/10.1145/3357231].
DOI: https://doi.org/10.1145/3357231
[8] Goldina A. A.: Methods of information disclosure in integrated reporting. Models, Systems, Networks in Economics, Technology, Nature and Society 3, 2021 [https://doi.org/10.21685/2227-8486-2021-3-2].
DOI: https://doi.org/10.21685/2227-8486-2021-3-2
[9] Hahn S.-J., Lee B.-H.: Quality evaluation to small scaled software implementation result. The Journal of Korean Institute of Information Technology 21(1), 2023, 1–10 [https://doi.org/10.14801/jkiit.2023.21.1.1].
DOI: https://doi.org/10.14801/jkiit.2023.21.1.1
[10] Hooker J. N.: Optimization basics. Integrated methods for optimization. Springer US, 2011 [https://doi.org/10.1007/978-1-4614-1900-6_3].
DOI: https://doi.org/10.1007/978-1-4614-1900-6_3
[11] Indi M. M., et al.: Evaluation of the effectiveness of technology-based project management systems for software development. Global International Journal of Innovative Research 1(2), 2023, 175–181 [https://doi.org/10.59613/global.v1i2.30].
DOI: https://doi.org/10.59613/global.v1i2.30
[12] Jha K. N., Chockalingam C. T.: Prediction of quality performance using artificial neural networks. Journal of Advances in Management Research 6(1), 2009, 70–86 [https://doi.org/10.1108/09727980910972172].
DOI: https://doi.org/10.1108/09727980910972172
[13] Kamaletdinov S., et al.: Evaluation of data quality based on Bayesian networks in railway rolling stock monitoring systems. E3S Web of Conferences 460, 2023, 04014 [https://doi.org/10.1051/e3sconf/202346004014].
DOI: https://doi.org/10.1051/e3sconf/202346004014
[14] Kaneko H.: Cross‐validated permutation feature importance considering correlation between features. Analytical Science Advances 3(9-10), 2022, 278–287 [https://doi.org/10.1002/ansa.202200018].
DOI: https://doi.org/10.1002/ansa.202200018
[15] Kim S.-H.: Integrated development environment for java native methods. The Journal of the Korea Contents Association 10(7), 2010, 122–132 [https://doi.org/10.5392/jkca.2010.10.7.122].
DOI: https://doi.org/10.5392/JKCA.2010.10.7.122
[16] Movsessian A., Cava D. G., Tcherniak D.: Interpretable machine learning in damage detection using shapley additive explanations. ASME J. Risk Uncertainty Part B. 8(2), 2022 [https://doi.org/10.1115/1.4053304].
DOI: https://doi.org/10.1115/1.4053304
[17] Ni H., Yin H.: Exchange rate prediction using hybrid neural networks and trading indicators. Neurocomputing 72(13-15), 2009, 2815–2823 [https://doi.org/10.1016/j.neucom.2008.09.023].
DOI: https://doi.org/10.1016/j.neucom.2008.09.023
[18] Oden J. T., Robinson J.: Integrated theory of finite element methods. Mathematics of Computation 29(132), 1975, 1157 [https://doi.org/10.2307/2005763].
DOI: https://doi.org/10.2307/2005763
[19] Pankov P. S., Tagaeva S. B.: Systems of differential equations and computer phenomena. Herald of Institute Mathematics of the National Academy of Sciences of the Kyrgyz Republic 2, 2020, 86–93 [https://doi.org/10.52448/16948173_2020_2_86].
DOI: https://doi.org/10.52448/16948173_2020_2_86
[20] Roser M. E., et al.: Investigating reasoning with multiple integrated neuroscientific methods. Frontiers in Human Neuroscience 9, 2015, [https://doi.org/10.3389/fnhum.2015.00041].
DOI: https://doi.org/10.3389/fnhum.2015.00041
[21] Smaling A.: Mixed, integrated, merged or hybrid methods? Kwalon 22(3), 2017. [https://doi.org/10.5117/2017.022.003.001].
DOI: https://doi.org/10.5117/2017.022.003.001
[22] ter Beek M. H., Monahan R.: Integrated formal methods. Springer International Publishing. 2022 [https://doi.org/10.1007/978-3-031-07727-2].
DOI: https://doi.org/10.1007/978-3-031-07727-2
[23] Wang S., Zhang Y.: Grad-CAM: Understanding AI models. Computers, Materials & Continua 76(2), 2023, 1321–1324 [https://doi.org/10.32604/cmc.2023.041419].
DOI: https://doi.org/10.32604/cmc.2023.041419
[24] Yoon H.: A quantitative evaluation for usability under software quality models. International Journal on Recent and Innovation Trends in Computing and Communication 11(3), 2023, 24–29 [https://doi.org/10.17762/ijritcc.v11i3.6194].
DOI: https://doi.org/10.17762/ijritcc.v11i3.6194
[25] Yue C., et al.: An entropy-based group decision-making approach for software quality evaluation. Expert Systems With Applications 238C, 2023, 121979 [https://doi.org/10.1016/j.eswa.2023.121979].
DOI: https://doi.org/10.1016/j.eswa.2023.121979
[26] Zhang X., et al.: Informative data selection with uncertainty for multimodal object detection. IEEE Transactions on Neural Networks and Learning Systems 35(10), 2023, 13561-13573 [https://doi.org/10.1109/tnnls.2023.3270159].
DOI: https://doi.org/10.1109/TNNLS.2023.3270159
[27] Zhao J., et al.: Evaluating the impact of uncertainty visualization on model reliance. IEEE Transactions on Visualization and Computer Graphics 30(7), 2023, 4093–4107 [https://doi.org/10.1109/tvcg.2023.3251950].
DOI: https://doi.org/10.1109/TVCG.2023.3251950
[28] Zhao Z., Severini T. A.: Integrated likelihood computation methods. Computational Statistics 32(1), 2016, 281–313 [https://doi.org/10.1007/s00180-016-0677-z].
DOI: https://doi.org/10.1007/s00180-016-0677-z
