Despite the promising capabilities of transformer models, current research in FND has several limitations that affect both theoretical understanding and practical deployment. Various studies have observed that these models are treated as black boxes: evaluating their performance without understanding which architectural components contribute the most significantly to their effectiveness [8]. The lack of systematic ablation studies means that researchers cannot determine whether improvements come from attention mechanisms, positional encodings, layer normalization, or other architectural elements. In addition, existing evaluations often lack rigorous statistical validation, reproducibility protocols, and cross-dataset generalization analysis, making it difficult to assess the true robustness and reliability of proposed solutions. It is also important to find the optimal balance between model complexity, computational efficiency, and performance stability in various datasets. To address these fundamental challenges, this research introduces a comprehensive framework for transformer-based FND that combines rigorous empirical analysis with principled architectural optimization. This approach recognizes that effective FND requires not only accurate models, but also a deep understanding of why these models work, which components are essential, and how they can be optimized for both performance and practical deployment. The research contributions of this study are presented as follows:  
1. Component-Level Analysis: We present a comprehensive component-level analysis of transformer architectures for FND: systematically evaluating BERT, the Robustly Optimized BERT Pretraining approach (RoBERTa), Decoding-Enhanced BERT with Disentangled attention (DeBERTa), and the distilled version of BERT (DistilBERT). Through rigorous ablation studies that isolate attention mechanisms, layer normalization, residual connections, feedforward networks, dropout regularization, positional encoding, and model-specific components, we quantify the individual contribution of each element to overall performance. Through this analysis, we found that attention mechanisms contribute 19-22% to detection accuracy, layer normalization provides consistent 2-6% improvements, and feedforward networks show context-dependent effectiveness. This establishes empirical benchmarks that have been absent in the existing literature. 2. Evidence-Based Synthesis: We introduce an enhanced optimal methodology that combines the most effective architectural components of different transformer models (based on rigorous empirical evidence rather than intuitive design choices). Our principled approach integrates BERT's foundational attention mechanisms, RoBERTa's GELU activation optimizations, DeBERTa's enhanced classification capabilities, and DistilBERT's efficiency innovations into a unified optimal architecture. This synthesis framework uses three quantitative criteria: component criticality, consistency in the cross-dataset, and computational efficiency. It establishes a replicable methodology for the optimization of evidence-based architectures. * Scalable Transformer Variants: We develop a scalable architecture framework that offers three performance variants (0.3M, 1.2M, and 3.1M parameters) while maintaining architectural consistency between different computational constraints. Our optimal base model achieves a reduction in parameters of 85% compared to the BERT base while improving performance. In addition, it demonstrates 70% faster training convergence, requires 60% less memory from the GPU, and provides 3\(\times\) faster inference speed. This contribution addresses the critical need for deployable FND systems in resource-constrained environments with minimal trade-offs. * Rigorous Evaluation Framework: We establish new standards for experimental rigor in FND research through comprehensive reproducibility protocols, statistical validation frameworks, and multidataset consistency assessment.