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 worldโ€™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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Soubhik D. created project Presentify-let's know where we stand

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Presentify-let's know where we stand

Suppose we're speaking in a hall before a hige audience but are unable to know whether or not the audience is interested so that we can mix up with lightning talks, motivational stories, quotes, etc. At that time, our talk starts becoming boring and we start getting the tag of 'unwanted speaker'. This will never happen if we have the data, specifically data of dynamic and running time users' rating of the speakers. There just needs to be a few Realsense Cameras placed optimally in the hall and a person/bot with AR and or VR headset tracks the user's rating collectively (heatmap). Realsense cameras and the headset are connected. This promises to make every talk in the world interesting after gathering enough data.

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Jayesh A. updated status

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Jayesh Ahire

I am working on a AI with basically a high EQ. It is a kind of chatbot which can help people to get out of a emotional stress and it will make life if a emotionally weak people really easy in my opinion.

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In this project there will be need for data to build a predictive model with a machine learning algorithm. However, I have had a a lot of issues trying to access the data in my home country because of some health policies in place. Any ideas on how to get an open and relevant data from another country to work with

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You find yourself shipwrecked on an island after a very recent storm. When you come to, you are greeted warmly by the island chieftain who happens to point out that your ship is broken and beyond repair. Your pockets are empty and you are stranded. The chieftain tells you to introduce yourself to the local island folks while she prepares a place for you to stay.

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