e829b7a0-d0ad-47ad-afc3-42f3241fac6d beie director@blueeyesintelligence.org 10.35940/ijrte.C8140.13030924 10.1016/j.rser.2020.110433Levasseur, A., Mercies-Blais, S., Prairie, Y. T., Treblay, A. and Turpin, A.(2021) Improving the accuracy of electricity carbon footprint: Estimation of hydroelectric reservoir greenhouse gas emissions, Renewable and Sustainable Energy Reviews, (vol.136, pp.1-20). : http://www.elsevier.com/locate/rser https://doi.org/10.1016/j.rser.2020.11043310.1016/j.energy.2020.117197Wang, J. Q., Du, Y., Wang, J. (2020) LSTM based long-term energy consumption prediction with periodicity." Energy, (vol.197, pp.117197) https://doi.org/10.1016/j.energy.2020.11719710.1007/s12525-020-00441-4Thiebes, S., Lins, S., & Sunyaev, A. (2021). Trustworthy artificial intelligence. Electronic Markets, (vol.31, issue.2, pp.447-464). https://doi.org/10.1007/s12525-020-00441-410.1007/s00330-020-06946-yStrohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P., & Moors, E. H. (2020). Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. European radiology, (vol.30, pp.5525-5532). https://doi.org/10.1007/s00330-020-06946-y10.1016/j.ijhcs.2020.102551Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, Article 102551. https://doi.org/10.1016/j.ijhcs.2020.10255110.1016/j.ijinfomgt.2022.102538Herm, L. V., Heinrich, K., Wanner, J. and Janiesch, C.(2023), Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability, International Journal of Information Management, (vol.69, pp.1-20). https://doi.org/10.1016/j.ijinfomgt.2022.102538Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D..(2019) Grad-CAM: Visual explanations from deep networks via gradient-based localization Computer vision and pattern recognition, pp.618-626. https://arxiv.org/abs/1610.0239110.1029/2021MS002464Labe, Z. M. and Barnes, E. A.(2021). Detecting Climate Signals Using Explainable AI With Single-Forcing Large Ensembles, Journal of Advances in Modeling Earth Systems(JAMES), 13, e2021MS002464, https://doi. org/10.1029/2021MS002464., https://doi.org/10.1029/2021MS00246410.1175/MWR-D-18-0316.1Gagne, D. J., Haupt, S. E., Nychka, D. W., & Thompson, G. (2019). Interpretable deep learning for spatial analysis of severe hailstorms. American Meteorological Society (vol.147, issue.8, pp.2827-2845). https://doi.org/10.1175/MWR-D-18-0316.110.1016/j.jclepro.2022.134793Heo, S., Ko, J., Kim, S. Y., Jeong, C., Hwangbo, S. and Yoo, C. K.(2022). Explainable AI-driven net-zero carbon roadmap for petrochemical industry considering stochastic scenarios of remotely sensed offshore wind energy, Journal of Cleaner Production, (vol.379, issue.2, pp.1-12). https://doi.org/10.1016/j.jclepro.2022.13479310.1109/TCDS.2016.2628365Krening, S., Harrison, B., Feigh, K. M., Isbell, C. L., Riedl, M. and Thomaz, A. '(2016)Learning from explanations using sentiment and advice in RL,'' IEEE Trans. Cogn. Develop. Syst., (vol.9, issue.1,pp. 44-55). https://doi.org/10.1109/TCDS.2016.262836510.1145/2783258.2788613Caruana, R., Lou, Y., Gehrke, J., Koch, R., Sturm,M. and Elhadad, N.(2015) Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission,' in Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp.1721-1730. https://doi.org/10.1145/2783258.278861310.1145/3313831.3376590Liao, V., Gruen, D. and Miller, S.(2020). Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI '20). Association for Computing Machinery, New York, NY, USA, pp.1-15. https://doi.org/10.1145/3313831.3376590Xu, W.(2023) A User experience 3.0(UX 3.0)" paradigm framework: User experience design for human-centered AI systems, pp.1-11, https://arxiv.org/abs/2403.01609,10.2196/53402Sharma, N.,Grotenhuijs, K., Gemert-Pijnen, J. E. W. C. V.,Oinas-Kukkonen, H. . and Braakman-Jansen, L.M.A.(2023), Low-Fidelity Prototype of a Sensor-Dependent Interaction Platform: Formative Evaluation With Informal Caregivers of Older Adults With Cognitive Impairment, JMIR XR and Spatial computing, 8,1-20, https://preprints.jmir.org/preprint/53402 https://doi.org/10.2196/5340210.1007/978-3-319-46454-1_24Anderson, P., Fernando, B., Johnson, M. and SGould, S.(2016) Spice: Semantic propositional image caption evaluation." In European Conference on Computer Vision,, Springer 382-398. https://doi.org/10.1007/978-3-319-46454-1_2410.1137/080716542Beck, A. and Teboulle, M.(2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM journal on imaging sciences, (vol. 2, issue. 1, pp.183-202). https://doi.org/10.1137/080716542Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Con, J., Haug, M., Hussmann, H. (2020) Bringing Transparency Design into Practice, Explainable IUIs, ACM, pp.211-223.Fox, M., Long, D. and Magazzeni, D.(2017) Explainable planning,"' in Proc. IJCAI Workshop XAI,, pp.24-30.10.1109/TKDE.2007.190734Robnik_Sikonja, M., Kononenko, I..(2008) Explaining classi_cations for individual in- stances, IEEE Transactions on Knowledge and Data Engineering, (vol.20, issue.5, pp.589). https://doi.org/10.1109/TKDE.2007.19073410.3390/electronics8080832Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, (vol.8, issue.832, pp.1-34) https://doi.org/10.3390/electronics8080832Du, M., Liu, N., & Hu, X. (2018). Techniques for interpretable machine learning. arXiv preprint arXiv:1808.00033.10.1016/j.artint.2018.07.007Miller, T. (2019). Explanation in Artificial Intelligence: Insights from the Social Sciences. Artificial Intelligence, (vol.267, pp.1-38). https://doi.org/10.1016/j.artint.2018.07.007Ying, R., Bourgeois, D., You, J., Zitnik, M., and Leskovec, J. (2019). GNN Explainer: A Tool for Posthoc Explanation of Graph Neural Networks. arXiv preprint arXiv:1903.03894.Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018). How powerful are graph neural networks?. arXiv preprint arXiv:1810.00826.10.1073/pnas.1900654116Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R. and Yu, B. (2018). Interpretable machine learning: definitions, methods, and applications. Proceedings of the National Academy of Sciences, (vol.116, issue.44,pp. 22071-22080) https://doi.org/10.1073/pnas.190065411610.1109/ICHI.2018.00095Ahmad, A. M., Eckert, C., Teredesai, A., and McKelvey, G. (2018). Interpretable Machine Learning in Healthcare. In IEEE Intelligent Informatics Bulletin. New York, NY: IEEE, pp.1-7. https://doi.org/10.1109/ICHI.2018.0009510.1145/3306618.3314229Lakkaraju, H., Kamar, E., Caruana, R., and Leskovec, J. (2019). Faithful and Customizable Explanations of Black Box Models. In AIES '19 Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. ACM New York, NY, USA, (pp. 131-138). https://doi.org/10.1145/3306618.3314229Kolasani, S.(2023). Innovations in digital, enterprise, cloud, data transformation, and organizational change management using agile, lean, and data-driven methodologies. International Journal of Machine Learning and Artificial Intelligence, (vol.4, issiue.4, pp.1-18).10.1109/TPAMI.2023.3331846Rong, Y., Leemann, T., Nguyen, T.T., Fiedler, L., Qian, P., Unhelkar, V., Seidel, T., Kasneci, G.; Kasneci, E.(2024) Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations. IEEE Trans. Pattern Anal. Mach. Intell. (vol.46, pp.2104-2122). https://doi.org/10.1109/TPAMI.2023.333184610.1145/2939672.2939778Ribeiro, M. T., Singh, S. and Guestrin, C.(2016) "Why Should I Trust You?" Explaining the Predictions of Any Classifier, KDD 2016 San Francisco, CA, USA , pp.1-10. https://doi.org/10.1145/2939672.293977810.1007/s10822-020-00314-0Rodriguez-Perez R, Bajorath J (2020) Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. Journal of Computer Aided Mol Des, (vol.34, pp.1013-1026) https://doi.org/10.1007/s10822-020-00314-0Doshi-Velez, F. and Kim, B. (2017), Towards a Rigorous Science of Interpretation Learning, arXIV:1702.08608V2[stat.ML], 1-14.10.5772/intechopen.92172Evren, D. (2020). Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models, Advances in Deep Learning Publisher: InTechOpen, pp.1-19, DOI: 10.5772/intechopen.92172.10.1007/s12599-021-00683-2Bauer, K., Hinz, O., Aalat, W. V. D., Weinhardt, C. (2021). Expl(AI)n It to Me - Explainable AI and Information Systems Research, Business Information System Engineering, pp.1-4, https://doi.org/10.1007/s12599-021-00683-210.1016/j.energy.2022.125468Zhang, Y., Teoh, B. K., Wu, M., Chen, J., Zhang, L.(2023), Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence, Siencedirect, (vol.262, pp.1-15)10.35940/ijitee.L3758.1081219Joshi, A. M., & Prabhune, S. (2019). Random Forest: A Hybrid Implementation for Sarcasm Detection in Public Opinion Mining. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 12, pp. 5022-5025). https://doi.org/10.35940/ijitee.l3758.108121910.35940/ijeat.E1049.0585S19S, Kamalalochana., & Guptha, Dr. N. (2019). Optimizing Random Forest to Detect Disease in Apple Leaf. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 5s, pp. 244-249). https://doi.org/10.35940/ijeat.e1049.0585s1910.35940/ijrte.F7879.038620T., G., M., V. Y., M., U., D., R., & K., R. B. (2020). Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 1623-1630). https://doi.org/10.35940/ijrte.f7879.038620