eprintid: 17140 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/71/40 datestamp: 2025-03-14 23:30:10 lastmod: 2025-03-14 23:30:11 status_changed: 2025-03-14 23:30:10 type: article metadata_visibility: show creators_name: Kanwal, Khadija creators_name: Ahmad, Khawaja Tehseen creators_name: Shabir, Aiza creators_name: Jing, Li creators_name: Garay, Helena creators_name: Prado González, Luis Eduardo creators_name: Karamti, Hanen creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: helena.garay@uneatlantico.es creators_id: uis.prado@uneatlantico.es creators_id: creators_id: title: Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Features fusion; Composite structure; Architectural bonding; Algorithmic channelizing; Deep learning abstract: 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. date: 2025-03 publication: Scientific Reports volume: 15 number: 1 id_number: doi:10.1038/s41598-025-90616-w refereed: TRUE issn: 2045-2322 official_url: http://doi.org/10.1038/s41598-025-90616-w access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés 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. metadata Kanwal, Khadija; Ahmad, Khawaja Tehseen; Shabir, Aiza; Jing, Li; Garay, Helena; Prado González, Luis Eduardo; Karamti, Hanen y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, helena.garay@uneatlantico.es, uis.prado@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2025) Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization. Scientific Reports, 15 (1). ISSN 2045-2322 document_url: http://repositorio.uneatlantico.es/id/eprint/17140/1/s41598-025-90616-w.pdf