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Efficient Pattern Recognition Resource Utilization Neural Network

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Efficient Pattern Recognition Resource Utilization Neural Network

Volume 11, Issue 1, Page No 44–50, 2026

Author’s Name: Hanan Hassan Ali Adlan* 1EmailORCID, Elsadig Ahmed Mohamed Babiker* 2EmailORCID
1 Department of Computer Science, Princess Nourah Bint Abdulrahman University, Riyadh, 11761, Saudi Arabia
2 Department of Computer Science, Prince Sattam Bin Abdulaziz University, Kharj, 16273, Saudi Arabia
*whom correspondence should be addressed. E-mail: hahassan@pnu.edu.sa, e.babiker@psau.edu.sa

Adv. Sci. Technol. Eng. Syst. J. 11(1), 44–50 (2026); crossref symbol DOI: 10.25046/aj110105

Keywords: Neural Networks, Backpropagation, Kernel, Feature Map, Receptive Fields, Euclidean Distance, K-Means

Received: 15 November 2025, Revised: 6 January 2026, Accepted: 8 January 2026, Published Online: 16 January 2026
(This article belongs to the SP19 (Special Issue on Innovation in Computing, Engineering Science & Technology 2025-26) & Section Artificial Intelligence in Computer Science (CAI))
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Abstract

Neural Networks derives intelligent systems and modern autonomous applications. With the complexity introduced in today’s systems, designing architectures remains open research problem in all fields. Despite many efforts to generalize, the resources required by neural architectures remain challenge. Robots design, Smart cities, IoTs …etc. become the leading industry and driving the fourth industrial revolutions. All these challenges dictate the search for efficient utilization of resources. This paper demonstrates details of the architecture ElHa Net. The architecture finds the minimum resources required for pattern recognition domains. Composed of two stages, Extraction stage followed by classification stage. The extraction stage is a self-extraction, performed by convolutions like processes. The classification stage receives the extracted patterns and associates them through weighting to defined classes. The architecture is found to compete with many reported in literature.

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