Predictive analytics for level of fill data from IoT device

Predictive analytics for level of fill data from IoT device

alfred ongere

alfred ongere

Nairobi, Nairobi County

Design an IoT device for collective level of fill data, and then use machine learning for predictions from collected data

Internet of Things, Artificial Intelligence

Description

The goal is to design an MLMD(Mountable Level Monitoring Device) that can be used to monitor the level of fill of solids and liquids in regularly shaped containers. This data will then be passed through a machine learning algorithm for predictions based on data collected.

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Ravi K. updated status

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Ravi K

Hi, I am very eager to learn AI technologies, I am master student at Governors State University,Chicago.

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Timothy P. created project MR. Configurator Tool

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MR. Configurator Tool

This tool was designed to speed up calibration of your MR setup. This article explains how to use the tool and help you get the most out of your MR experiences. For VR users and streamers, making MR videos is a great way to show a different perspective to people who aren’t wearing a head-mounted display (HMD), while for VR developers, MR videos are a great way to create trailers and show a more comprehensive view of the VR experience.

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Basavaraj H. created project Apparent/Real Image Estimation using Deep Learning

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Apparent/Real Image Estimation using Deep Learning

IMDB-WIKI age dataset processing and data loading into PyTorch model Fine tune VGG16 pre-trained on ImageNet using IMDB-WIKI database to estimate real age Train the fine-tuned model using LAP database to estimate apparent age Real age estimation is evaluated using MAE Used Ensemble 8 VGG16 nets to estimate the apparent age

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Aashiq J. updated status

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Bob D. added a comment on project AI Skin Cancer Detection

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AI Skin Cancer Detection

Between 40 and 50 percent of Americans who live to age 65 will have either basal cell carcinoma or squamous cell carcinoma at least once.

The annual cost of treating skin cancers in the U.S. is estimated at $8.1 billion: about $4.8 billion for nonmelanoma skin cancers and $3.3 billion for melanoma.

An estimated 9,730 people will die of melanoma in 2017.

The estimated 5-year survival rate for patients whose melanoma is detected early is about 98 percent in the U.S. The survival rate falls to 62 percent when the disease reaches the lymph nodes, and 18 percent when the disease metastasizes to distant organs

Our project plans to train and classify skin cancer types so that user can try to detect cancer in the real time. When detected with high confidence score, user will be given suggestion to see dermatologist for effective treatments.

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