Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, th...
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Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm (BID-DMSFNN-COA), for classifying product color brand images as "Stylish" and "Natural". By leveraging deep learning techniques and optimizationalgorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as "Stylish" and "Natural". Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both "Natural" and "Stylish" classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for "Stylish" and 65 %-70 % for "Natural" product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.
Detecting spina bifida defects in an early parental stage and providing proper remedial measures is the main objective. There it demands effective learning approaches to automatically detect and classify fetal disabil...
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Detecting spina bifida defects in an early parental stage and providing proper remedial measures is the main objective. There it demands effective learning approaches to automatically detect and classify fetal disability. However, the existing approaches face several complicated issues like time complexity, cost, and misclassification. This paper proposes a novel Modified Faster Region Convolutional neural Network based cheetah Optimizer (Modified FRCNN-CO) model to accurately predict whether the child is defective with spina bifida disease or not. The ultrasound scanning images of the fetus acquired during the second trimester are considered an efficient screening tool for detecting fetus abnormalities. The ultrasound scan image of the fetal spine obtained during the 18th week of pregnancy is taken as input. The poor quality and noise factors present in ultrasound images are enhanced and removed respectively using preprocessing pipelines namely contrast enhancement, intensity adjustment, and denoising. The enhanced images are segmented through the generative adversarial network (GAN) model. With the capability to capture data distribution, the GAN model segments and emphasizes defective regions of images. This procedure makes the proposed modified FRCNN-CO model accurately identify the images with spina bifida disease. Based on the features extracted, the proposed modified FRCNN-CO model accurately classifies and labels the ultrasound images into two classes normal '0 & PRIME;and defective '1 & PRIME;. The experimental outcomes illustrate the supremacy of the proposed modified FRCNN-CO model over other compared methods, especially achieved detection accuracy of about 97.8%.
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