Multiple model visual feature embedding and selection method for an efficient oncular disease classification.

Early detection of ocular diseases is vital to preventing severe complications, yet it remains challenging due to the need for skilled specialists, complex imaging processes, and limited resources. Automated solutions are essential to enhance diagnostic precision and support clinical workflows. This study presents a deep learning-based system for automated classification of ocular diseases using the Ocular Disease Intelligent Recognition (ODIR) dataset.

Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm.

Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system.

Clinical and Microbiological Profile of Melioidosis in a Tertiary Care Hospital in Kerala, India.

Melioidosis is a neglected tropical infection caused by the Gram-negative bacterium Burkholderia pseudomallei, which is found in soil and water across tropical countries. The infection spectrum ranges from mild localized lesions to severe sepsis. The clinical presentation, severity, and outcome are influenced by the route of infection, bacterial load, strain virulence, and specific virulence genes of B.

Reinforcement and characterization of biopolymer-rhamnolipid nanoparticles: Biocompatibility, in-vitro stability, physico-chemical, rheological and bioactive properties.

In the present study, biopolymer (chitosan and alginate)-reinforced rhamnolipid nanoparticles were prepared and represented as 'ALG-RHLP-NPs' and 'CHI-RHLP-NPs'. The sizes of the nanoparticles ranged from 150 to 300 nm. The encapsulation efficiencies of ALG-RHLP-NPs and CHI-RHLP-NPs were found to be 81.