Developing An Innovative Image Processing Model For Computer Networks through Optimized K-Nearest Neighbour Algorithm
Keywords:
Computer Networks, Image Processing, Machine Learning and Image Data, Red Deer Optimized Adaptive K-Nearest Neighbour (RD-AKNN)Abstract
Image processing is the process of enhancing or extracting information from images. It includes a wide range of methods, including segmentation and pattern recognition. In the context of computer networks, image processing is critical for enhancing data transfer and communication efficiency. The integration of image processing with computer networks improves the overall efficiency of visual information collaboration, which leads to innovations in various domains. In this research, we developed a novel machine learning-based image data processing model for computer networks named Red Deer optimized Adaptive K-Nearest Neighbour (RD-AKNN). We gathered a dataset that includes various types of image data to train our proposed approach for image processing. The data cleaning process is performed to reduce the redundancy, Global Contrast Normalization (GCN) algorithm is utilized to pre-process the gathered raw data. Red Deer Optimization (RDO) is employed to enhance the crucial characteristics of the suggested AKNN architecture for developing an innovative mage data processing model in computer networks. We implemented our proposed methodology in Python software. The finding analysis phase is performed with various metrics such as recall (97.5%), accuracy (97.2%), F1-score (98.3%) and precision (98.1%) to evaluate the proposed algorithm with other conventional methodologies. The experimental results demonstrate that the proposed RD-AKNN approach performed better than other conventional approaches for enhanced image data processing in computer networks.
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