No abstract

No abstract

No abstract

No abstract

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.

Face recognition technology has widely attractedattention due to its enormous application value and marketpotential. It is being implemented in various fields like securitysystem, digital video processing, and many such technologicaladvances. Additionally, music is the form of art, which isknown to have a greater connection with a person's emotion. Ithas got a unique ability to lift up one's mood. Relatively, thispaper focuses on building an efficient music recommendationsystem which determines the emotion of user using FacialRecognition techniques. The algorithm implemented wouldprove to be more proficient than the existing systems. Moreover, on a larger dimension, this would render salvage oftime and labor invested in performing the process manually. The overall concept of the system is to recognize facial emotionand recommend songs efficiently. The proposed system will beboth time and cost efficient.

No abstract

No abstract