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

Artificial Intelligence, Modern Code

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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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Ashish A. created project Snowball Sampling

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Snowball Sampling

When do we say that a graph has become large or the amount of data in the graph has become big? Why do we sample a graph? When do we say that a graph is sampled? What should be the size of our sampled graph? What difference does it make to work on the original graph and the sampled graph? These are some questions that are very common when people start working on real world of graphs that often span hundreds of millions or even billions of nodes and interactions between them. By the thumb of rule, we can say that 'large graphs' are those graphs exploration of which requires long computation time and 'big data' is typically the data which takes too much memory space to be stored on a single hard drive. Why do we need to sample the original graph? First and the foremost reason is that the sheer size of many networks makes it computationally infeasible to study the entire network. Moreover, the size of the network may not be as large but the measurements required to observe the underlying network are costly. Thus, network sampling is at the heart and foundation of our study to understand network structure. A good sampled graph must include useful knowledge. Our primary goal is to find the important properties that effectively summarizes the graph.

Graphs are used to represent real life situations where entities of internet are related to each other. In such situations, entities can be represented as nodes, and the relationship between them can be represented as edges. Graph Modelling of real life situations results into into networks. Thus, there are transport networks, road networks, biological networks, technology networks etc. Analysis and importance of these networks has given rise to a recent discipline of network science. Analysis of networks that are large and dense is a challenging task because of the associated computational expense. Focusing on smaller and dense areas of network is often preferred due to two reasons : \newline (i) reduced computation in terms of both time and memory (ii) better insights.

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Dheeraj S. updated status

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Dheeraj Sharma

On survey Literate the Peoples and Students regarding all subjects with Modern technology. With Regards to INTEL (Gordon Moore, Robert Noyce) CHIPNXT TECHNOLOGIES PVT LTD

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Mark S. created project IoEuropa

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Desperate for water Io seeks to trade with Europa, rich with water Europa's fragile atmosphere is in danger when the two orbits bring them close together.

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