Garment defect detection has been successfully implemented in the quality quick response system for textile manufacturing automation. Defects in the production of textiles waste a lot of resources and reduce the quality of the finished goods. It is challenging to detect garment defects automatically because of the complexity of images and variety of patterns in textiles. This study presented a novel deep learning-based Garment defect detection framework named as Deep-GD model for sequentially identifying image defects in patterned garments and classify the defect types. Initially, the images are gathered from the HKBU database and bilateral filters are used in the pre-processing of images to remove distortions. A squeeze-and-excitation network (SE-net) combined with Random Decision Forest Classifier with Bayesian Optimization (RDF-BO) algorithm is used to detect and classify garment defects. By analyzing the differences among the original and reconstruction images, the unsupervised technique trains to rebuild the fabric pattern. The SE-net is used to identify certain fabric flaws in the garments of the pre-processed images. Then, the defects-related garments are processed using RDF-BO algorithm for classifying the garment defect from these regions. Finally, the proposed Deep-GD model is used for classifying defected fabric into 12 classes such as Defect free, soil stain, oil stain, Double end, Snarls, Miss, Horizontal stripes, Lumpy, Dye spot, Fall out, Hairiness, tiny hole. The proposed Deep-GD model achieves the overall classification accuracy of 97.16%, which is comparatively superior to the existing techniques.

The proliferation of healthcare data and the increasing reliance on cloud infrastructures have intensified concerns about security, data privacy, and accessibility. Addressing the critical challenge of safeguarding sensitive patient information, this study proposes a new hybrid encryption architecture that integrates Principal Component Analysis (PCA)-based compression, a fall webworm optimization (FWW)-driven S-box encryption mechanism (FWW-S), and blockchain enabled secure data transmission. The model capitalizes on the dimensionality reduction capabilities of PCA to efficiently compress medical data while preserving critical diagnostic features. The bio-inspired FWW algorithm is employed to dynamically generate high-entropy cryptographic keys through an optimized S-box structure, enhancing encryption robustness and key diversity. Encrypted and compressed data is subsequently transmitted via a blockchain protocol to ensure tamper-proof, traceable, and decentralized data sharing. The suggested method overcomes the drawbacks of traditional hybrid encryption approaches, especially with regard to computational effectiveness and key generation flexibility. Simulation results confirm the effectiveness of the FWW-S framework in maintaining data confidentiality, integrity, and authentication within distributed healthcare systems. The scheme demonstrates significant improvements in encryption/decryption time, storage efficiency, energy consumption, and resistance to unauthorized access. By successfully balancing data sharing with privacy protection, this work offers a scalable and secure solution for next-generation medical cloud computing environments.

Diabetic Macular Edema (DME) causes fluid accumulation within retinal layers, leading to irreversible vision loss if not detected early. Although Optical C