2024
Sachan, S., Almaghrabi, F., Yang, J. -B., & Xu, D. -L. (2024). Human-AI Collaboration to Mitigate Decision Noise in Financial Underwriting: A Study on FinTech Innovation in a Lending Firm. International Review of Financial Analysis. doi:10.1016/j.irfa.2024.103149DOI: 10.1016/j.irfa.2024.103149
Sachan, S., & Liu (Lisa), X. (2024). Blockchain-based auditing of legal decisions supported by explainable AI and generative AI tools. Engineering Applications of Artificial Intelligence, 129, 107666. doi:10.1016/j.engappai.2023.107666DOI: 10.1016/j.engappai.2023.107666
Secure and Transparent Lawyer-in-the-Loop Medico-Legal Insurance Decisions by Explainable AI and Blockchain Technology (Conference Paper)
Sachan, S., & Fairclough, G. (2024, March 8). Secure and Transparent Lawyer-in-the-Loop Medico-Legal Insurance Decisions by Explainable AI and Blockchain Technology. In 10th International Conference on Information Management. University of Cambridge.
2023
Sachan, S., Fickett, D. S., Kyaw, N. E. E., Purkayastha, R. S., & Renimol, S. (2023). A Blockchain Framework in Compliance with Data Protection Law to Manage and Integrate Human Knowledge by Fuzzy Cognitive Maps: Small Business Loans. In 2023 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN AND CRYPTOCURRENCY, ICBC. doi:10.1109/ICBC56567.2023.10174925DOI: 10.1109/ICBC56567.2023.10174925
Sachan, S., & Muwanga, J. (2023). Integration of Explainable Deep Neural Network with Blockchain Technology: Medical Indemnity Insurance. In CEUR Workshop Proceedings Vol. 3554 (pp. 123-128).
2022
Fintech Lending Decisions: An Interpretable Knowledge-Base System for Retail and Commercial Loans (Chapter)
Sachan, S. (2022). Fintech Lending Decisions: An Interpretable Knowledge-Base System for Retail and Commercial Loans. In Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 128-140). Springer International Publishing. doi:10.1007/978-3-031-08974-9_10DOI: 10.1007/978-3-031-08974-9_10
2021
Sachan, S., Almaghrabi, F., Yang, J. -B., & Xu, D. -L. (2021). Evidential reasoning for preprocessing uncertain categorical data for trustworthy decisions: An application on healthcare and finance. EXPERT SYSTEMS WITH APPLICATIONS, 185. doi:10.1016/j.eswa.2021.115597DOI: 10.1016/j.eswa.2021.115597
2020
Global and local interpretability of belief rule base (Chapter)
Sachan, S., Yang, J. B., & Xu, D. L. (2020). Global and local interpretability of belief rule base. In Unknown Book (Vol. 12, pp. 68-75). Retrieved from https://www.webofscience.com/DOI: 10.1142/9789811223334_0009
Explainable Artificial Intelligence for Digital Forensics: Opportunities, Challenges and a Drug Testing Case Study (Chapter)
Kelly, L., Sachan, S., Ni, L., Almaghrabi, F., Allmendinger, R., & Chen, Y. -W. (n.d.). Explainable Artificial Intelligence for Digital Forensics: Opportunities, Challenges and a Drug Testing Case Study. In Digital Forensic Science. IntechOpen. doi:10.5772/intechopen.93310DOI: 10.5772/intechopen.93310
Generalized Stochastic Petri-Net Algorithm with Fuzzy Parameters to Evaluate Infrastructure Asset Management Policy (Conference Paper)
Sachan, S., & Donchak, N. (2020). Generalized Stochastic Petri-Net Algorithm with Fuzzy Parameters to Evaluate Infrastructure Asset Management Policy. In 2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE). doi:10.1109/fuzz48607.2020.9177562DOI: 10.1109/fuzz48607.2020.9177562
An explainable AI decision-support-system to automate loan underwriting (Journal article)
Sachan, S., Yang, J. -B., Xu, D. -L., Benavides, D. E., & Li, Y. (2020). An explainable AI decision-support-system to automate loan underwriting. EXPERT SYSTEMS WITH APPLICATIONS, 144. doi:10.1016/j.eswa.2019.113100DOI: 10.1016/j.eswa.2019.113100
2019
Maximum likelihood evidential reasoning-based hierarchical inference with incomplete data (Conference Paper)
Liu, X., Sachan, S., Yang, J. -B., & Xu, D. -L. (2019). Maximum Likelihood Evidential Reasoning-Based Hierarchical Inference with Incomplete Data. In 2019 25TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC) (pp. 42-47). doi:10.23919/iconac.2019.8895062DOI: 10.23919/IConAC.2019.8895062
Multi-segment deep convolution neural networks for classification of faults in sensors at railway point systems (Conference Paper)
Sachan, S., & Donchak, N. (2019). Multi-Segment Deep Convolution Neural Networks for Classification of Faults in Sensors at Railway Point Systems. In 2019 25TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND COMPUTING (ICAC) (pp. 147-152). doi:10.23919/iconac.2019.8895081DOI: 10.23919/IConAC.2019.8895081
Probabilistic dynamic programming algorithm: a solution for optimal maintenance policy for power cables (Journal article)
Sachan, S., & Zhou, C. (2019). Probabilistic dynamic programming algorithm: a solution for optimal maintenance policy for power cables. Life Cycle Reliability and Safety Engineering, 8(2), 117-127. doi:10.1007/s41872-019-00074-3DOI: 10.1007/s41872-019-00074-3
2018
A hybrid belief rule based decision support system for assessing credit risk for mortgage lending (Conference Paper)
Sachan, S., Yang, J. -B., & Xu, D. -L. (2018). A hybrid belief rule based decision support system for assessing credit risk for mortgage lending. In https://www.euro-online.org/media_site/reports/EURO29_AB.pdf.
Comments from young scholars: Can machines completely replace humans in manufacturing processes? (Journal article)
Yang, S. (2018). Comments from young scholars: Can machines completely replace humans in manufacturing processes?. FRONTIERS OF ENGINEERING MANAGEMENT, 5(4), 541-547. doi:10.15302/J-FEM-2018207DOI: 10.15302/j-fem-2018207
2017
Multiple Correspondence Analysis to Study Failures in a Diverse Population of a Cable (Journal article)
Sachan, S., Zhou, C., Wen, R., Sun, W., & Song, C. (2017). Multiple Correspondence Analysis to Study Failures in a Diverse Population of a Cable. IEEE TRANSACTIONS ON POWER DELIVERY, 32(4), 1696-1704. doi:10.1109/TPWRD.2016.2615470DOI: 10.1109/TPWRD.2016.2615470
2016
Cost Effective Replacement of Power Cables by Stochastic Dynamic Programming Approach (Conference Paper)
Sachan, S., Zhou, C., Bevan, G., & Alkali, B. (2016). Cost Effective Replacement of Power Cables by Stochastic Dynamic Programming Approach. In 2016 INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS (CMD) (pp. 299-302). Retrieved from https://www.webofscience.com/
Stochastic dynamic programming approach for proactive replacement of power cables (Conference Paper)
Sachan, S., Chengke Zhou, C. Z., Bevan, G., & Alkali, B. (2016). Stochastic dynamic programming approach for proactive replacement of power cables. In CIRED Workshop 2016. Institution of Engineering and Technology. doi:10.1049/cp.2016.0680DOI: 10.1049/cp.2016.0680
2015
Failure prediction of power cables using failure history and operational conditions (Conference Paper)
Sachan, S., Zhou, C., Bevan, G., & Alkali, B. (2015). Failure prediction of power cables using failure history and operational conditions. In 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials (ICPADM). IEEE. doi:10.1109/icpadm.2015.7295288DOI: 10.1109/icpadm.2015.7295288
Prediction of power cable failure rate based on failure history and operational conditions (Conference Paper)
Sachan, S., Zhou, C., Bevan, G., & Alkali, B. (2015). Prediction of power cable failure rate based on failure history and operational conditions. In http://www.jicable.org/2015/prg_sessions_A1_E10.php. Versailles, France.
A stochastic electrothermal degradation model of power cables (Journal article)
Sachan, S., Wen, R., Xiang, Y., Yao, L., & Zhou, C. (2015). A stochastic electrothermal degradation model of power cables. Gaodianya Jishu/High Voltage Engineering, 41(4), 1178-1187. doi:10.13336/j.1003-6520.hve.2015.04.015DOI: 10.13336/j.1003-6520.hve.2015.04.015