{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:22:33Z","timestamp":1753881753650,"version":"3.41.2"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p> The field of hexagonal image processing is concerned with the creation of image processing systems that combine the advantages of biological model-based evolutionary motivated frameworks. The structure and functionality of artificial neural networks were inspired by biological processes. The fundamental framework of recording and output devices limits their present state of the art. Prior neural networks have used square or hexagonal style input to completely connected layers, which resulted in a high coherence problem between two adjacent hexagonal kernel layers due to pooling. Previous research does not design the self-data structure to support convolution to increase computational efficiency, so it violates the convolution and pooling operator, which greatly degrades the image process performance. This paper introduces a novel paradigm Proficient Deep Learning-based Hexrep Neural Network that overcomes major significant problems in image operations structure constraint, coherence problem, and violation of convolution and pooling operator and achieves hexagonal image processing. <\/jats:p>","DOI":"10.1142\/s0219467823500328","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T03:49:42Z","timestamp":1653623382000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Hexrep Neural Network for Convergence Free with Operator\u2019s Efficacy in Hexagonal Image Processing"],"prefix":"10.1142","volume":"22","author":[{"given":"Prathibha","family":"Varghese","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.\u00a0Arockia Selva","family":"Saroja","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"S0219467823500328BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00188"},{"key":"S0219467823500328BIB003","volume":"12","author":"Ke J.","year":"2018","journal-title":"IEEE Trans. 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