Facial expression recognition plays a crucial role in various applications, including human-computer interaction and affective computing. This study proposes a novel approach to enhance automatic facial expression recognition using a combination of Symbolic Aggregate approXimation (SAX) and Local Binary Pattern (LBP) techniques. SAX transforms time series data into symbolic sequences, simplifying the complexity of facial expression dynamics. LBP extracts local texture features from facial images, capturing subtle variations in expression. By integrating SAX with LBP, this approach achieves robust representation of both temporal and spatial facial dynamics, enhancing the accuracy and efficiency of facial expression recognition systems. Experimental results demonstrate the effectiveness of the proposed method in recognizing diverse facial expressions across different datasets.

This study investigates the application of Low-Density Parity-Check (LDPC) codes in enhancing error-free cryptographic communication, leveraging the Stopping Set Algorithm for improved reliability. LDPC codes are known for their capacity to achieve near-Shannon limit performance in error correction, making them suitable for secure data transmission. The Stopping Set Algorithm plays a crucial role in identifying critical error patterns that could compromise communication integrity. This research explores the synergy between LDPC codes and the Stopping Set Algorithm, evaluating their effectiveness in mitigating errors and enhancing the security and efficiency of cryptographic communication systems. Results demonstrate significant improvements in error detection and correction capabilities, highlighting the potential of LDPC-based approaches to advance secure communication protocols.