Identifying Mosquito Species Using Smartphone Cameras

Identifying Mosquito Species Using Smartphone Cameras

The goal of this project is to enable the non-expert person by leveraging their smartphone to detect harmful vs non-harmful mosquitoes.

Artificial Intelligence

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According to WHO(World Health Organization) reports, among all disease transmitting insects mosquito is the most hazardous insect. In 2015 alone, 214 million cases of malaria were registered worldwide. Zika virus is another deadly disease transmitted from mosquitoes. According to CDC report, in 2016 62,500 suspected case of Zika were reported to the Puerto Rico Department of Health (PRDH) out of which 29,345 cases were found positive. There are 3500 different species of mosquitoes present in the world out of which 175 types is found in United States. But only few of them are responsible for these above mentioned fatal disease. Therefore classification between hazardous and regular mosquitoes are very important. For regular person with no expertise in this field would be almost impossible to identify the difference. Even for the mosquito expert, identifying different species is a very tedious and time consuming job. Hence in this paper, we have tried to classify 7 different species of dead mosquitoes with total 60 samples collected from Hillsborough County Mosquito and Aquatic Weed Control Unit,Tampa Florida by capturing image from smart phone cameras. With our approach we want to enable nonexpert population to early identify the risk and act pro-actively. We pre-processed the image for removing noise and applied random forest classification algorithm to distinguish different species. Achieved good precision,recall,F1 measure and aggregate 83:3% accuracy. We are also planning to develop a smart-phone application which will leverage this learning model and help in empowering population to identify mosquito species without any knowledge in this field.

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Siddharth N. created project Gesture Recognition System for soldiers

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Gesture Recognition System for soldiers

It is a gesture classification system for soldiers where cameras cannot be used.The system I made can classify 40 of the standard army gesture.It uses a support vector machine to classify the gestures.I created my own dataset for training. The list of gestures contain static as well as dynamic gestures. Different algorithms in terms of number of features was used for classifying them. The list of gestures can be found here: Further improvement will be made using wireless modules and increasing mobility.

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Burhan K. updated status

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Burhan KAYA

AI , Cyber Security, IOT

Hello I'm an Electrical and Electronics Engineer. I do research on artificial intelligence and IOT. Besides this, I have a project with artificial intelligence algorithms. Therefore, using MODIVUS will contribute to me. I do not want to be deprived of such an impossibility. I'm glad you helped me.

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Chaplin M. updated status

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Chaplin Marchais

Well its now 7am and I have been up for about 30 hours.... but the image recognition is now actually working in azure!! Now time to take a power nap and then do some optimization with Intel's awesome suite of tools!

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Chaplin M. updated status

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Chaplin Marchais

Fifth night in a row I find myself still in front of the computer at 2AM.... I think I got up at-least twice today though!! Progress... Oh well, the future doesn't build itself! ...... yet....

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Moloti N. created project Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

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Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

We propose the use of Drones to help communities enhance their security initiatives, to identify criminals during the day and at night. We use multiple sensors and computer vision algorithms to be able to recognize/detect motion and content in real-time, then automatically send messages to community members cell phones about the criminal activities. Hence, community members may be able to stop house breakings before they even occur.

Machine Intelligence Algorithm Design Methodology


We propose a deep neural network for the prediction of future frames in natural video sequences using CPU. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. The model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. The model we aim to build should be end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human AVA and UCF-101 datasets. We show state-of-the art performance in comparison to recent approaches. This is an end-to-end trainable network architecture running on the CPU with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos.

// We then use this AMCnet pretrained model on the Video feed from the DJI Spark drone, integrated with the Movidius NCS to accelerate real-time object detection neural networks.

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Kaibo Liu

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Corvallis, OR, USA

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Developer by profession, IoT Player by Passion

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