Environmental Analyzer for Migraines

Environmental Analyzer for Migraines

Rose Day

Rose Day

Hartford, Connecticut

Design of an IoT sensor device and application for reading room data to analyze migraines in terms of the environment using AWS.

Internet of Things

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Description

The environmental analyzer for migraines is designed to take in environmental data from a user’s surroundings, i.e. temperature, humidity, pressure, altitude and light, while also taking in user data about migraines. These two datasets are then combined and utilized to define a user’s common environment and migraine occurrences. Once the data has been collected, an iOS app analyzes room data variables, such as temperature and pressure, against a user’s migraine data to examine migraines in terms of the environment in which the user was living providing users with both a visual and numerical representation of the data. The data collected for migraines included type, severity, date and time of each episode. The project is designed with the user in mind to help those suffering from migraines to better understand what environmental impacts are affecting them and what needs to be done in their environments to live a healthier life style.

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SAI SRI RAM N. updated status

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SAI SRI RAM NIDAMANURI

Hi guys,this is sriram pursuing B.tech 3rd year 2nd semester in vijayawada.I am presently working on a project which requires machine learning,convolutional neural networks,image processing,python,php and other languages (used for building an website).The main motto of my project includes detecting the objects from a given image.This is pretty hard project for me but more interesting one because this project seperates me from others.I have a team with one member.I asked my teammate to learn numpy,scipy and pandas libraries .I have already installed numpy,scipy , pandasand other libraries without installing anaconda.I am getting many confusions in this project.But finally i have got a better overview about it and my team is currently working on it.I surely believe that my team can complete the project.Finally i have completed some courses like machine learning and presently preparing numpy,scipy,pandas,convolutional neural networks.

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Bishaw S. created project Visual data parsing for automated reasoning expert system

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Visual data parsing for automated reasoning expert system

Extract the data available in the input image/visual and process it to find the close search result available for the related terms. when a input visual containing the sentence "where is Mt. Everest ?" is given the output will be "Nepal", when a input visual containing the sentence "how to get to pokhara from kathmandu" the output will be google maps result with route, when the input visual containing "Gautam Buddha" is given the output will be wikipedia result about Buddha. the system will be able to

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muthu s. updated status

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muthu samy

RFP, Proactive pitch in , blockchain,IOT and Big Data sales pitch and strategies, engage with customers and provide solution design, consulting and recommendations. Provide consulting to customers in identifying Blockchain , IOT and Big Data use cases and then guiding them towards implementation of use cases. Big Data strategic planning, technology roadmap, talent acquisitions and mentor team for cutting edge technology competitiveness such as Hadoop, HIVE, HBase, Cassandra, AWS, Spark, R, big data analytics, data visualizations. Establish and manage governance for the implementation process , related to design of the target organization, production performance and managing change,

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muthu s. updated status

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muthu samy

Working on Trident Techno Minds Business & Emerging Markets Pre-Planning and delivery team for block chain , Internet of things ,Big Data services, strategy engagements with our existing and new customers in Big Data space.

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carmine s. created project EdibleMush

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EdibleMush

Currently Ai-based applications are available, but none of these offers a guarantee of truthfulness. Since in nature there are several chameleon mushrooms, the application may not be able to recognize it.

The idea of this project offers a more accurate solution for the mushroom recognition based on 3 different images to uniquely validate the mushroom: Cap, Stem and Gills of Mushroom.

The project involves the application of image recognition techniques based on deepLearning algorithms, for now trained with caffe framework.

Once the prototype environment for the workflow has been developed, specific test and validation datasets will be made for the most common mushrooms.

Furthermore, the use of Movidius in the prototyping phase is envisaged, which uses the training models realized with the help of the colfax cluster to do inference from the edges without the cloud.

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