Special Issues
Table of Content

Advancements in Natural Language Processing (NLP) and Fuzzy Logic

Submission Deadline: 31 May 2025 View: 946 Submit to Special Issue

Guest Editors

Prof. Kuei-Hu Chang, Department of Management Sciences, ROC Military Academy, Taiwan
Prof. Yung-Chia Chang, Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Taiwan
Dr. Hsiang-Yu Chung, Department of Management Sciences, ROC Military Academy, Taiwan

Summary

This special issue is dedicated to exploring the latest innovations and developments in the fields of Natural Language Processing (NLP) and Fuzzy Logic. These cutting-edge domains are pivotal in the advancement of artificial intelligence (AI), enabling machines to understand and interact with human languages and make decisions in uncertain environments. The significance of NLP is highlighted by its applications in various technologies such as chatbots, translation services, sentiment analysis, and more, which enhance user experience and accessibility. Fuzzy Logic, on the other hand, plays a crucial role in handling uncertainties and modeling complex systems, finding applications in control systems, pattern recognition, and consumer electronics.


Despite their advancements, the fields of NLP and Fuzzy Logic face several challenges. In NLP, major issues include the need for more sophisticated language models that can understand context better, the handling of multilingual data, and addressing biases in AI systems. Fuzzy Logic must overcome challenges in creating more accurate and interpretable models, integrating with other AI techniques, and improving computational efficiency.


This special issue aims to showcase the current trends, breakthroughs, and innovative applications of NLP and Fuzzy Logic. We invite researchers and practitioners to contribute their original research articles and review studies, shedding light on both theoretical and practical aspects of these technologies.

 

Relevant Topics:

1. Advances in language models and contextual understanding in NLP

2. Techniques for multilingual and cross-lingual NLP applications

3. Addressing biases and ethical considerations in AI and NLP

4. Innovative applications of Fuzzy Logic in control systems and robotics

5. Integration of Fuzzy Logic with other AI techniques, such as neural networks

6. Enhancements in speech recognition and synthesis

7. Developments in sentiment analysis and emotion detection

8. Applications of NLP in healthcare and biomedical fields

9. Efficient computational methods for Fuzzy Logic systems

10. Trends in AI-driven decision-making and uncertainty management systems 


Keywords

natural language processing (NLP); fuzzy logic; AI and machine learning; semantic analysis; speech recognition; text mining; decision-making systems; uncertainty management; intelligent systems; pattern recognition 

Published Papers


  • Open Access

    ARTICLE

    A Hybrid Framework Combining Rule-Based and Deep Learning Approaches for Data-Driven Verdict Recommendations

    Muhammad Hameed Siddiqi, Menwa Alshammeri, Jawad Khan, Muhammad Faheem Khan, Asfandyar Khan, Madallah Alruwaili, Yousef Alhwaiti, Saad Alanazi, Irshad Ahmad
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062340
    (This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
    Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >

  • Open Access

    ARTICLE

    AdaptForever: Elastic and Mutual Learning for Continuous NLP Task Mastery

    Ke Chen, Cheng Peng, Xinyang He, Jiakang Sun, Xu Liu, Xiaolin Qin, Yong Zhong
    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4003-4019, 2025, DOI:10.32604/cmc.2025.057443
    (This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
    Abstract In natural language processing (NLP), managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models, leading to practical inefficiencies. To address this challenge, we introduce AdaptForever, a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism. Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities, facilitating effective knowledge transfer from previous tasks to new ones. By combining Elastic Weight Consolidation (EWC) for knowledge preservation with specialized regularization terms, AdaptForever successfully maintains More >

  • Open Access

    ARTICLE

    LKMT: Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

    Muhammad Naeem Ul Hassan, Zhengtao Yu, Jian Wang, Ying Li, Shengxiang Gao, Shuwan Yang, Cunli Mao
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 951-969, 2024, DOI:10.32604/cmc.2024.054673
    (This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
    Abstract Thanks to the strong representation capability of pre-trained language models, supervised machine translation models have achieved outstanding performance. However, the performances of these models drop sharply when the scale of the parallel training corpus is limited. Considering the pre-trained language model has a strong ability for monolingual representation, it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models. To alleviate the dependence on the parallel corpus, we propose a Linguistics Knowledge-Driven Multi-Task (LKMT) approach to… More >

  • Open Access

    ARTICLE

    Leveraging Uncertainty for Depth-Aware Hierarchical Text Classification

    Zixuan Wu, Ye Wang, Lifeng Shen, Feng Hu, Hong Yu
    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581
    (This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
    Abstract Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to More >

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