An intelligent framework combining deep learning and fuzzy logic for accurate remote language translation

In the era of globalized communication, the need for accurate and context-sensitive language translation has become increasingly vital-especially in remote, real-time applications such as telemedicine, virtual collaboration, and cross-border communication. While transformer-based neural machine translation (NMT) models have significantly advanced translation quality by leveraging deep contextual representations, they often lack interpretability and struggle with idiomatic expressions, low-resource languages, and semantic ambiguity. To address these limitations, this study proposes an intelligent hybrid translation framework that integrates a transformer-based NMT backbone with a fuzzy logic inference module. The neural model generates baseline translations, while the fuzzy component enhances output quality by applying linguistic rules and evaluating syntactic, semantic, and discourse-level features. The proposed system incorporates contextual feature extraction, fuzzification, rule-based evaluation, and defuzzification stages to produce translations that are not only fluent but also contextually accurate and explainable. Comprehensive experiments were conducted on benchmark datasets across both high-resource and low-resource language pairs. Results show that the hybrid framework consistently outperforms rule-based and transformer-only models, achieving higher Bilingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and F1-scores, while reducing translation errors and improving user-perceived readability. Additionally, the system demonstrates strong real-time performance across edge and cloud-based deployments, maintaining sub-second latency with minimal computational overhead. An ablation study confirms the individual contributions of the fuzzy module and contextual features to translation quality. Qualitative case studies further illustrate the system's ability to resolve ambiguity and refine idiomatic content. This research highlights the effectiveness of integrating symbolic reasoning with deep learning for building scalable, interpretable, and accurate translation systems in modern multilingual environments.