I am working on technology that will enable people with no hands to drive car using just their feet.
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.
Machine Learning, Monte Carlo simulation and data analysis for health.
If the application data is very large and the recent data is the most important, by using data stream mining techniques we can extract knowledge to facilitate and simplify decision making. This project intends to study data streaming mining in internet networks, IoT sensors and social media. The machine learning algorithms need modifications to be adaptive and incremental. Modern code is also required to optimize computational resources.
libldd” is a Linux/Unix Utility to find dependent libraries recursively required at runtime for a binary or library, and plot dependency graph for the same.
Artificially Intelligent Robotic System which Uses Object Detection And Tracking Through An App.i.e Killing Mosquito Machine.
Machine learning and Intel RealSense technology for detecting drowsiness, drunkenness and even for predicting if the driver is going to change lanes.
The algorithm is running into an Upboard/laptop with Intel RealSense for capturing facial expressions and eye movements patterns. We tried to create our own version of the Nystagmus Test (not using fingers or pen, but windshield wipers). So far it's not that accurate, but we're working on it.
Today's Web applications are data intensive and demand environments like tensorflow to execute the workloads. Hence, Containers are best suited to provide the framework and compute resources like CPU and memory for each workload. It decouples the app environment from the running machine/host and encapsulates all dependencies in a single portable unit. Nomad is a state of the art tool for scheduling Docker Containers. Test Model Workload is generated from Model Zoo for each framework.
Hi! I'm developing an AI with consciousness. I'm doing this developing a game and mobile apps.
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