Augmented Reality-Centered Position Navigation for Wearable Devices with Machine Learning Techniques.

People have always relied on some form of instrument to assist them to get to their destination, from hand-drawn maps and compasses to technology-based navigation systems. Many individuals these days have a smartphone with them at all times, making it a common part of their routine. Using GPS technology, these cellphones offer applications such as Google Maps that let people find their way around the outside world. Indoor navigation, on the other hand, does not offer the same level of precision. The development of indoor navigation systems is continuously ongoing. Bluetooth, Wi-Fi, RFID, and computer vision are some of the existing technologies used for interior navigation in current systems. In this article, we discuss the shortcomings of current indoor navigation solutions and offer an alternative approach based on augmented reality and ARCore. Navigating an indoor environment is made easier with ARCore, which brings augmented reality to your smartphone or tablet.

Comparison of CNN Algorithms for Feature Extraction on Fundus Images to Detect Glaucoma.

Glaucoma is a disease where the optic nerve of the eyes is smashed up due to the building up of pressure inside the vision point. This has no symptoms at the initial stages, and hence, patients with this disease cannot identify them at the beginning stage. It is explained as if the pressure in the eye increases, then it will hurt the optic nerve which sends images to the brain. This will lead to permanent vision loss or total blindness. The existing method used for the detection of glaucoma includes k-nearest neighbour and support vector machine algorithms. The k-nearest neighbour algorithm and support vector machine algorithm are the machine learning methods for both categorization and degeneration problems. The drawback in using these algorithms is that we can get accuracy level only up to 80%. The proposed methods in this study focus on the convolution neural network for the recognition of glaucoma. In this study, 2 architectures of VGG, Inception method, AlexNet, GoogLeNet, and ResNet architectures which provide accuracy levels up to 100% are presented.