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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Description

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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Anurag K. updated status

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Anurag Kushwaha

I'm currently working on a project called DoorBell. This is a IoT virtual door bell that sends respose as an email or sms if someone knocks the door. Also it can send the picture of the person standing behind the door.

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RITVIK D. updated status

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RITVIK DAVE

Hii, I am Ritvik Dave currently doing graduation in Electronics & Communication Engineering at Techno India NJR Institute of Technology. Right now I am working on a Smart City project called "Smart Well/Lake Monitoring System.

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jnc j. updated project Mini Operadora Bluetooth status

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jnc jnc

A Mini Operadora Bluetooth consta em substituir os ramais de empresas no que ocasiona em um baixo consumo de energia. Tendo com base sua distância de 10 metros, no que consegui expandi-la para o dobro ou mais a partir de um pequeno banco de dados que armazena e transfere a voz em tempo real.

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suryaveer s. created project Virtual Multi-controller

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Virtual Multi-controller

Virtual Multi-controller is a virtual game controlling gesture. This can control each and every PC game virtually using a single device. We can calibrate this device according to user’s choices that he /she wants to play. Most of us want to play video games virtually because it looks so fascinating, but can’t play because these type of devices are so expensive and even cannot be afforded. But with the help of this controller we can play virtually. Construction Idea: We are going to use some basic sensors like orientation sensor, heart beat sensor etc. These sensors can give us position and behavior of player which we need during playing the game.

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donel a. updated status

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donel adams

Google or Bing name to view associate networks fostering business development by maximizing any platform potential for growth utilizing AI

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Dhruv R. created project Business Card Scanner

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Business Card Scanner

Here’s how it works:

It uses Google’s Tesseract to built an Optical Character Recognition Engine inside the phone once installed, thus it works completely offline.

Then using Leptonica Image Processing and various other algorithms the image clicked is enhanced so as to best suite for the OCR purpose.

The engine then extracts the text which undergoes entity detection using Open Natural Language Processing(OpenNLP).

The entities are put under appropriate fields and the contact is saved in the phone directory along with the business card.

Using Parse as backend, Android Studio as IDE, stackoverflow as mentor I finally completed the app in 1 month time period.

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