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Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device

Flipboard

Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms.

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Faster R-CNNs

PyImageSearch

You’ll typically find IoU and mAP used to evaluate the performance of HOG + Linear SVM detectors ( Dalal and Triggs, 2005 ), Convolutional Neural Network methods, such as Faster R-CNN ( Girshick et al., Object detection is no different. 2015 ), SSD ( Fei-Fei et al., 2004 ), You Only Look Once (YOLO) ( Redmon et al.,

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AI Emotion Recognition and Sentiment Analysis (2025)

Viso.ai

With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks. Generally, the classifiers used for AI emotion recognition are based on Support Vector Machines (SVM) or Convolutional Neural Networks (CNN).

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How R-CNN Works on Object Detection?

Towards AI

Introduction to Region with Convolutional Neural Networks (R-CNNs) Photo by Edward Ma on Unsplash Region with Convolutional Neural Network (R-CNN) is proposed by Girshick et al. Last Updated on July 20, 2023 by Editorial Team Author(s): Edward Ma Originally published on Towards AI.

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The Evolution of Object Detection:

Mlearning.ai

The development of region-based convolutional neural networks (R-CNN) in 2013 marked a crucial milestone. R-CNN introduced the idea of using region proposals to identify potential object locations, which were then processed by a convolutional neural network for classification.

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The Evolution of ImageNet and Its Applications

Viso.ai

2012 – A deep convolutional neural net called AlexNet achieves a 16% error rate. 2013 – Breakthrough improvement in CV (computer vision), top performers are below a 5% error rate. 2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks.

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Computer Vision in Autonomous Vehicle Systems

Viso.ai

Autonomous Driving applying Semantic Segmentation in autonomous vehicles Semantic segmentation is now more accurate and efficient thanks to deep learning techniques that utilize neural network models. Levels of Automation in Vehicles – Source Here we present the development timeline of the autonomous vehicles.