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

Artificial Intelligence, Internet of Things

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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Tushaar G. updated status

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Tushaar Gangarapu

Hello everyone, I am Tushaar. I am currently pursuing my bachelors at NITK. I developed passion towards AI and Machine Learning, had mentored the same at a mentorship program and currently was working on "Real Time Sensor based Weather Analysis and Prediction using Cloud Analytics to Improve Agricultural Yield". At the moment all we have are basic regression models which actually work with an 80 percent efficiency approximately. The repositories related to both the mentorship program and the regression models can be found on my GitHub account.

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Rafael S. created project Data Stream Mining

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Data Stream Mining

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.

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Abhirami A. updated status

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Abhirami A

I am working on face recognition. I did a summer project on theft detection and alert system where face recognition is used to recognize the intruder and alerts the authorized people through email and sms (basically an IoT project). I used HOG algorithm for face detection and and openface for face recognition which uses facenet's face embedding. I would like to explore more in deep learning.

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Paulo P. created project TOLLIA - Artificial intelligence to automatically identify and detect problematic behavior on highway tolls

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Paulo P. created project CAMELBE3 - Machine learning and Intel RealSense for drowsiness detection

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CAMELBE3 - Machine learning and Intel RealSense for drowsiness detection

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.

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