Titlebook: Computer Vision and Machine Intelligence Paradigms for SDGs; Select Proceedings o R. Jagadeesh Kannan,Sabu M. Thampi,Shyh-Hau Wang Conferen

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书目名称Computer Vision and Machine Intelligence Paradigms for SDGs读者反馈学科排名

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Convergence Perceptual Model for Computing Time Series Data on Fog Environment, the edge devices and cloud storage. Computational by means of fog-node attracts the need by replacing existing cloudlets time complexity in resource management. Hence fog as things operates as a controller in cyber physical systems like controlling transmission lines of high volt system connections

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Localized Super Resolution for Foreground Images Using U-Net and MR-CNN,al Network (CNN) architecture known as U-Net for super resolution combined with Mask Region-Based CNN (MR-CNN) for foreground super resolution is analyzed. This analysis is carried out based on localized super resolution, i.e., we pass the LR Images to a pre-trained image segmentation model (MR-CNN)

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Pest Detection Using Improvised YOLO Architecture,nvolution Neural Network) and YOLO (You Only Look Once) are used to classify the features extracted by deep feature extraction. Improvised YOLO is used which has proven pest prediction of about 95%. The performance of current research is compared, and common datasets are introduced. This paper exami

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Deep Learning Based Recognition of Plant Diseases,es. To get a better performance model, the data augmentation process is used, and we got 18,392 images. In our model, we used a convolutional neural network (CNN) to classify the citrus and tomato leaf diseases. The diseases are Mancha Graxa, Citrus Canker, Tomato Early Blight, Tomato Septoria Leafs

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,Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis,rending is a significant way to help users who want to grow their following by communicating their message to others. In this paper, a recommendation based on pre-trending subjects based on individual user interests, as well as prediction algorithms for identifying tweets that will shortly trend has

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