A P J Abdul Kalam Technological University

This study explores how brain-computer interfaces (BCIs) and neurotechnology could transform methods of corporate learning and development (L&D). It looks at how employee-tailored learning experiences, knowledge retention, and skill development could be accelerated by modern technologies. This chapter investigates important elements including the organizational and cultural changes needed for the integration of neurotechnology, employee attitudes towards BCI-enhanced learning, and the strategic decision-making processes linked with the employment of cognitive-enhancing technologies. Moreover, it emphasizes on the ethical problems and policy guidelines necessary for responsible application, therefore addressing various challenges and resistance elements. Examined additionally are ideal approaches for creating neural-enhanced training programs and leadership opinions on the consequences of cognitive development. By combining concepts from past studies and real-world implementations, this chapter offers a complete awareness of the sociocultural consequences of emerging technology in professional learning contexts. This chapter provides useful information for companies using neurotechnology, therefore improving the dialog on the junction of transhumanism and human resource development.

Abstract Traffic sign recognition (TSR) plays an important role in autonomous driving. The main challenge for implementing such models is that they require accurate interpretation of traffic signs, under real-time conditions like adverse weather and illumination variations. Also, the application should be able to run on a standalone embedded system. Recent solutions using CNNs and standard ViTs either lack robustness under degradation or are too computationally heavy for embedded real-time use. To address these challenges, the paper proposes a novel compact TSR pipeline combining (i) optimized invariant color transforms either C1C2C3 or I1I2I3 (ii) selective Lucy–Richardson deblurring, (iii) a simplified Retinex illumination correction, (iv) an improved lightweight Vision Transformer (ViT-Lite) with Performer linear attention (v) Improved Grey Wolf Optimizer IGWO hyper-parameter tuning. The resulting architecture is optimized for real-time embedded deployment. To evaluate the model’s perferomance, a publically available German Traffic Sign Recognition Benchmark (GTSRB) dataset has been used. The model was able to classify the images, achieving a 99.0% accuracy with 2.1 M parameters and 0.45 GFLOPs, running at ~20 ms per frame (≈50 FPS). This performance scores outperforms recent CNN and transformer-based models while maintaining a low computational requirement, validating the the model’s effectiveness for real-time driving applications.