Computing with Words 2.0: A Machine Learning Driven Fuzzy Linguistic Framework for Human-Centric AI
DOI:
https://doi.org/10.46610/JoFSFLD.2025.v02i02.005Keywords:
Black-box AI, Computing with Words, Fuzzy linguistic modeling, Fuzzy logic, Machine LearningAbstract
To address that, this paper sets out an extended Computing with Words 2.0 (CWW 2.0) system that represents combination of fuzzy linguistic modeling and machine learning to improve the capacity of AI systems to comprehend and reason with the vagueness and ambiguity of human language. Using machine learning, the framework is capable of dynamically learning membership functions, linguistic term definitions, and fuzzy inference rules by adapting to a domain-specific data, allowing higher adaptation than in traditional static fuzzy models. The ability to model uncertainty provided by fuzzy logic can be combined with data-driven learning, which provides the proposed approach with applicability in the real world with context-specific and interpretable decision support. Its architecture combines a hybrid fuzzy-statistical engine, natural language linguistic term extraction and a human-in-the-loop contextual refinement mechanism. Experimentation on the healthcare and learning datasets shows that accuracy, versatility and explainability are significantly better than that found in both traditional fuzzy systems and pure statistical AI models. The solution to the black-box AI drawbacks consists in the fact that the output given is not only precise but also clear and correlating with the patterns of human logic. Finally, the ML-augmented CWW 2.0 framework constitutes an important contribution to the development of human-centric AI systems that will efficiently work in uncertain environments without losing trust and interpretability.
References
J. Liu, Y. Fang, Z. Yu, and T. Wu, “Design and Construction of a Knowledge Database for Learning Japanese Grammar Using Natural Language Processing and Machine Learning Techniques,” 2022 4th International Conference on Natural Language Processing (ICNLP), Mar. 2022, doi: https://doi.org/10.1109/icnlp55136.2022.00068.
H. Yang, Z. Xiong, J. Zhang, K. Qin, and G. Lu, “Cascade Morphological n-Gram Can Improve Chinese Words Representation Learning,” 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), vol. 13, pp. 842–847, Jul. 2019, doi: https://doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00152.
W. Hongwei and F. Penglong, “Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method,” Journal of Systems Engineering and Electronics, vol. 31, no. 4, pp. 761–769, Aug. 2020, doi: https://doi.org/10.23919/jsee.2020.000051.
R. Seth and A. Sharaff, “Sentiment-Aware Detection Method of Fake News Based on Linguistic Fuzzy Bi-LSTM,” 2023 OITS International Conference on Information Technology (OCIT), pp. 628–633, Dec. 2023, doi: https://doi.org/10.1109/ocit59427.2023.10430669.
Y. Zheng, Z. Xu, and X. Wang, “The fusion of deep learning and fuzzy systems: A state-of-the-art survey,” IEEE Transactions on Fuzzy Systems, pp. 1–1, 2021, doi: https://doi.org/10.1109/tfuzz.2021.3062899.
K. Chrysafiadi, M. Virvou, and G. A. Tsihrintzis, “A fuzzy-based mechanism for automatic personalized assessment in an e-learning system for computer programming,” Intelligent Decision Technologies, vol. 16, no. 4, pp. 699–714, Nov. 2022, doi: https://doi.org/10.3233/idt-220227.
M. Ivanova, P. Petkova, and N. Petkov, “Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice,” Electronics, vol. 10, no. 22, pp. 2878–2878, Nov. 2021, doi: https://doi.org/10.3390/electronics10222878.
A. Bressane, A. Júlia, M. Vinícius, G. Ruas, and R. G. Negri, “Fuzzy Machine Learning Applications in Environmental Engineering: Does the Ability to Deal with Uncertainty Really Matter?,” Sustainability, vol. 16, no. 11, pp. 4525–4525, May 2024, doi: https://doi.org/10.3390/su16114525.
Z. Wang and X. Sun, “Design of Online Translation Recognition System Based On Natural Language Processing,” 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON), pp. 1–5, Aug. 2024, doi: https://doi.org/10.1109/nmitcon62075.2024.10698882.
R. C. Palsaniya, “Artificial Intelligence (AI) Standards and QA in Semiconductor Manufacturing,” 2025 IEEE Conference on Artificial Intelligence (CAI), pp. 1475–1479, May 2025, doi: https://doi.org/10.1109/cai64502.2025.00252.
H. Wang, K. Guo, and Z. Liu, “Mixed Word Embedding Method Based on Knowledge Graph Augment for Text Classification,” 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 1618–1623, Dec. 2019, doi: https://doi.org/10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00238.
K. Chen, “Research on dynamic motion pattern recognition and analysis algorithm based on artificial intelligence,” 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM), pp. 755–759, Nov. 2024, doi: https://doi.org/10.1109/ispcem64498.2024.00135.
S. Zhou, P. Liu, L. Liu, W. Song, and M. Cheng, “Analysis of The Characteristics of Similar Words Computed by Word Embeddings,” 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 327–330, Jul. 2020, doi: https://doi.org/10.1109/iceiec49280.2020.9152307.
M. Sam’an, Y. Dasril, and M. A. Muslim, “The New Fuzzy Analytical Hierarchy Process with Interval Type-2 Trapezoidal Fuzzy Sets and Its Application,” Fuzzy Information and Engineering, vol. 13, no. 3, pp. 391–419, Jul. 2021, doi: https://doi.org/10.1080/16168658.2021.1952760.
A. Mieszkowicz-Rolka and L. Rolka, “Variable Precision Fuzzy Rough Set Model with Linguistic Labels,” 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8, Jul. 2020, doi: https://doi.org/10.1109/fuzz48607.2020.9177649.
M. Chen, H.-K. Lam, B. Xiao, and H. Zhou, “Membership-Function-Dependent Control Design of Interval Type-2 Sampled-Data Fuzzy-Model-Based Output-Feedback Tracking Control System,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 9, pp. 3823–3832, Nov. 2021, doi: https://doi.org/10.1109/tfuzz.2021.3130303.
M. Jiang, W. Zhang, and H. Zhang, “Broad Learning System Based on Triangular Fuzzy Membership Function,” 2023 8th International Conference on Image, Vision and Computing (ICIVC), pp. 842–846, Jul. 2023, doi: https://doi.org/10.1109/icivc58118.2023.10270212.
Y. Kerlooza and A. Ramadhani, “The Determination of the Fuzzy Membership Sets for a Hybrid Coffee Bean Dryer Temperature Controller,” 2023 International Conference on Informatics Engineering, Science & Technology (INCITEST), pp. 1–5, Oct. 2023, doi: https://doi.org/10.1109/incitest59455.2023.10396975.
S. Keskin and A. Yazıcı, “From Data to Insights: Using Fuzzy Logic in Spatial Data Summarization with Fuzzy Spatial OLAP,” 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8, Jun. 2024, doi: https://doi.org/10.1109/fuzz-ieee60900.2024.10611789.
S. A. Shanthi and V. Valarmathi, “Image Processing on Fuzzy Near Sets,” 2021 IEEE Madras Section Conference (MASCON), pp. 1–5, Aug. 2021, doi: https://doi.org/10.1109/mascon51689.2021.9563367.
J. Lu, G. Ma, and G. Zhang, “Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review,” IEEE transactions on Fuzzy Systems, pp. 1–18, Jan. 2024, doi: https://doi.org/10.1109/tfuzz.2024.3387429.
P. K. Gupta and P. K. Muhuri, “Multi-objective linguistic optimization: Extensions and new directions using 2-tuple fuzzy linguistic representation model,” 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), vol. 8, pp. 1–6, Jul. 2017, doi: https://doi.org/10.1109/fuzz-ieee.2017.8015772
S. Ashraf and P. C. Shill, “Linguistic Fuzzy Modeling for High Dimensional Regression Problem Using Multi-Objective Genetic Algorithm,” 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pp. 1–4, Feb. 2018, doi: https://doi.org/10.1109/ic4me2.2018.8465603.
