Watermarking is one of the most common techniques used to protect data’s authenticity, integrity, and security. The obfuscation in the frequency domain used in the watermarking method makes the watermarking stronger than the obfuscation in the spatial domain. It occupies an important place in watermarking works in imperceptibility, capacity, and robustness. Finding the optimal location to hide the watermarking is one of the most challenging tasks in these methods and affects the method’s performance. In this article, sample identification information is processed with the method of watermaking on the hiding environment created by using a chaos‐based random number generator on biomedical data to provide solutions to problems such as visual attack, identity theft, and information confusion. In order to obtain biomedical data, a lensless digital in‐line holographic microscopy (DIHM) setup was designed, and holographic data of human blood and cancer cell lines, which are widely used in the laboratory environment, were obtained. The standard USAF 1951 target was used to evaluate the resolution of our imaging setup. Various QR codes were generated for medical sample identification, and the captured medical data were processed by watermarking it with chaos‐based random number generators. A new method using chaos‐based discrete wavelet transform (DWT) and singular value decomposition (SVD) has been developed and applied to high‐resolution data to eliminate the problem of encrypted data being directly targeted by third‐party attacks. The performance of the proposed new watermarking method has been demonstrated by various robustness and invisibility tests. Experimental results showed that the proposed scheme reached an average PSNR value of 564588 dB and SSIM value of 0.9972 against several geometric and destructive attacks, which means that the proposed method does not affect the image quality and also ensures the security of the watermarking information. The results of the proposed method have shown that it can be used efficiently in various fields.

Predicting molecular properties with high accuracy is essential across scientific fields, from drug discovery and biotechnology to materials science and environmental research. In biomedical sciences, accurate molecular property prediction is crucial for elucidating disease mechanisms, identifying potential drug candidates, and optimising various processes. However, existing approaches, often based on low-dimensional representations, fail to capture the intricate spatial and structural complexities of molecular data. This study introduces a novel hybrid deep learning model, the Graph-Aware AURA-LSTM (Attentive Unified Representation Architecture—Long Short-Term Memory), designed to determine molecular properties with unprecedented accuracy using advanced graphical representations. AURA-LSTM combines multiple Graph Neural Network (GNN) architectures, specifically Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Isomorphism Networks (GINs), in a parallel structure to comprehensively capture the multidimensional structural features of molecules. Within this architecture, GCNs incorporate local structural relationships, GATs apply attention mechanisms to highlight critical structural elements, and GINs capture intricate molecular details through isomorphic distinction, resulting in a richly detailed feature matrix. The feature layer then processes this BiLSTM matrix, which evaluates temporal relationships to enhance molecular feature classification. Evaluated on eight benchmark datasets, AURA-LSTM demonstrated superior performance, consistently achieving over 90% accuracy and outperforming state-of-the-art methods. These results position AURA-LSTM as a robust tool for molecular feature classification, uniquely capable of integrating temporally aware insights from distinct GNN architectures.

Classification and counting of cells in the blood is crucial for diagnosing and treating diseases in the clinic. A peripheral blood smear method is a fast, reliable, robust diagnostic tool for examining blood samples. However, cell overlap during the peripheral smear process may cause incorrectly predicted results in counting blood cells and classifying cell types. The overlapping problem can occur in automated systems and manual inspections by experts. Convolutional neural networks (CNN) provide reliable results for the segmentation and classification of many problems in the medical field. However, creating ground truth labels in the data during the segmentation process is time-consuming and error-prone. This study proposes a new CNN-based strategy to eliminate the overlap-induced counting problem in peripheral smear blood samples and accurately determine the blood cell type. In the proposed method, images of the peripheral blood were divided into sub-images, block by block, using adaptive image processing techniques to identify the overlapping cells and cell types. CNN was used to classify cell types and overlapping cell numbers in sub-images. The proposed method successfully counts overlapping erythrocytes and determines the cell type with an accuracy rate of 99.73\%. The results of the proposed method have shown that it can be used efficiently in various fields.