Scin

Scin

Scin is an app that utilizes a deep feed forward convolutional neural network to detect and diagnose both skin and plant diseases.

Modern Code, Artificial Intelligence

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Description

One out of every twenty people does not have access to medical facilities. Skin diseases and afflictions affect more than 80% of the world’s population and are sometimes a sign of internal problems. Skin diseases are primarily diagnosed visually, and then by more invasive procedures (dermoscopic analysis, biopsy, and histopathological examination). A similar issue arises with plants, with more than 30% of crops in less affluent areas dying due to diseases. Due to this loss of crops, one in nine people are suffering from chronic undernourishment.

Automatically detecting and diagnosing these lesions has been challenging, owing to the variable properties of each disease image. Deep convolutional neural networks (CNNs) are a new method of machine learning, one that is showing to be extremely promising at detecting images with real world variables. (lighting, focus, etc.)

In this project, I classified skin and plant ailments/diseases using a specially developed CNN, trained using only images of the conditions with only pixels and disease labels as inputs. I trained the CNN on a dataset of 200,000 clinical and horticultural images, consisting of 13 human diseases and 17 plant diseases. Outfitted on an IOS device, my application is capable of classifying skin and plant diseases will a level of competence comparable to dermatologists and plant pathologists. All the user must do is aim the camera of the smartphone towards the diseased area, and my application will provide a real-time diagnosis to the user by classifying the image using the CNN. The CNN achieves performance far above any other tested system, and its efficiency and ease of use will prove it to be a helpful tool for people around the world. There are currently 6,000,000,000 mobile subscriptions in place, so, therefore, my application could potentially provide low-cost universal access to vital diagnostics.

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Srinivasan S. updated status

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Srinivasan Saripalli

Design and Development of a Android App for Autistic Children. This App would be simultaneously Developed in both English and Kannada languages and would serve as a great helper App for such physically challenged children. The App subsequently would also have the feature to interact with the child, understand the child behaviour and automagically respond to his/ her requests of any kind.

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Pablo F. created project VR First Person Shooter

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VR First Person Shooter

This project is part my online course in the making, and consists of a first person shooter demo that can run on both mobile and desktop VR platforms.

As part of this project I made a cross-platform "free roaming" teleportation component that can be dropped into any Unity project. The way it works is you have to create invisible planes in the areas where you want to allow movement, then this component can allow you to point towards any point in that area, within a certain distance from the player.

The demo also features collectable ammo and energy. For the gun, I attached a real world space canvas on the side as an energy bar. In VR it is common to place all UI in the game itself, as real element, as "HUD"'s are distracting and break immersion.

The enemy logic is quite simple - they just come towards you when you are close enough. For the blood I used a particle effect.

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Dat N. created project Autonomous Self-Racing Car

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Autonomous Self-Racing Car

This project covers many modern techniques in self-driving car development, yet at lower cost. Deep Transfer Learning, Computer Vision, Sensor Fusion, SLAM, ROS are all implemented from scratch. Fortunately, I could take this car to local MeetUp group around Bay Area, such as DIYRobocars. to learn and compete with others.

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Ralph B. created project VRLA Holographic Easter Egg Hunt

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VRLA Holographic Easter Egg Hunt

One part mixed reality app, one part physical exhibit. We designed and constructed a 30ft x 30ft styrofoam forest set and placed it in the LA Convention Center. We also built a HoloLens app where when worn in the set, you see mixed reality Easter eggs, flowers, and various creatures you can observe and interact with. The experence lasts 3 minutes--after that, your group gathers by the tree to take a mixed reality photo which is emailed to you immediately.

We anchored the experience to the real world using HoloLens' spatial anchor system, and used a LIDAR scan of the set for the placement and occlusion of virtual objects.

Over 1000 people went through this groups of 4-6 over 2 days at VRLA's 2017 expo.

Built under a compressed schedule with Unity3D. Set was designed and previewed in VR using Vive.

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