TY  - JOUR
AV  - public
IS  - 1
UR  - http://doi.org/10.1038/s41598-025-90616-w
SN  - 2045-2322
JF  - Scientific Reports
KW  - Features fusion; Composite structure; Architectural bonding; Algorithmic channelizing; Deep learning
N2  - The process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance.
VL  - 15
A1  - Kanwal, Khadija
A1  - Ahmad, Khawaja Tehseen
A1  - Shabir, Aiza
A1  - Jing, Li
A1  - Garay, Helena
A1  - Prado González, Luis Eduardo
A1  - Karamti, Hanen
A1  - Ashraf, Imran
ID  - uneatlantico17140
Y1  - 2025/03//
TI  - Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization
ER  -