In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.

Pollen identification is a critical task across various scientific disciplines, including geology, ecology, evolutionary biology and botany. However, existing methods for pollen identification are often labour-intensive, time-consuming and dependent on highly skilled experts, highlighting the need for an automated and precise system. This study introduces an innovative approach that combines Gabor Filters (GF) with Convolutional Neural Networks (CNN) to enhance the accuracy of pollen classification. The Gabor filters are applied to high-resolution images of diverse pollen species, accentuating texture-specific details essential for differentiation. These pre-processed images are subsequently analysed using a CNN architecture with multiple layers designed to discern hierarchical features critical for precise classification. The proposed GF-CNN model demonstrates exceptional proficiency, achieving remarkable accuracy rates of 99.85% for the Malaysian Pollen Dataset (MPD) and 99.43% for the New Zealand Pollen Dataset (NPD). These results underscore the model's ability to balance precision and recall effectively. Additionally, the model exhibits high sensitivity, indicating an increased true-positive rate, which is essential for detailed ecological studies. Furthermore, the model's improved specificity scores highlight its success in minimizing false positives, emphasizing its relevance for precision-focused research.