Environmental AI:Using Machine Learning to Address Mosquito-Borne Diseases
Who is this presentation for?CIO, CEO, CFO, Senior Director, CPO, Senior Manager
Wovenware, a U.S. nearshore provider of AI-based services and solutions, has developed a machine learning solution for the Puerto Rico Science, Technology & Research Trust, which is automating the identification and classification of various species of mosquitoes in order to control the spread of diseases, such as Zika and Dengue across Puerto Rico, and ultimately worldwide. The end goal is also to help researchers develop safe and effective insecticides to combat them.
Leslie De Jesus will share with attendees how her company is applying AI to gain an understanding of why many mosquitoes have become immune to insecticides approved by the FDA.
The challenges of undertaking such a project are great. Researchers have spread out across the island, capturing different mosquito species in traps; monitoring and testing them for viral presence and insecticide resistance; and labelling and classifying them. Manually capturing and classifying thousands of mosquitoes in different locales can take many months before any specific patterns can emerge.
With a deep learning solution, Wovenware has automated this time-consuming task, and through a private crowd of data specialists, it is identifying and labelling thousands of images of mosquitoes and data sets to train an algorithm to automatically identify and classify specific species. The AI-based solution will save the PRVCU months of time that can be used to more quickly analyze the findings and identify the root cause of resistance to insecticides, as well as disease spread and prevention routes.
The counting and classification of specific mosquitoes is being accomplished using convolutional neural networks (CNNs), with a dataset that contains multiple mosquito annotations per image. Its experiments are incrementally adding complexity to the solution. As a baseline approach it is implementing custom, deep CNNs for gender and species classification using Keras, which is being trained end-to-end, independently, from scratch.Following this approach the model is identifying singular features in order to overcome current challenges confronted from specimens mutilated by weather conditions and the retrieval of the mosquitoes from traps. Models are being trained on 4 GPUs Octoputer powered by NVIDIA’s GeForce Titan XP Pascal. The speed of the server allows for more effective decision-making in the training phase.
A key outcome realized to date is an order-of-magnitude improvement in the time required for counting and classifying specific vectors.
This project truly underscores the impact AI-based technology can have when bright minds are augmented by smart technology in helping to solve some of the world’s most pressing challenges.
Prerequisite knowledgeA basic understanding of AI and its growing role in solving key challenges.
What you'll learnHow deep learning can be applied to automate counting and classification of objects. How AI is addressing critical environmental challenges. How custom CNNs can be used to drive improved algorithms The tools and technologies that are enabling faster, more cognitively sound algorithms Key algorithm training techniques.
Leslie De Jesus
With more than 20 years of expertise in software, product development and data science, Leslie drives disruptive strategies and solutions, including AI and enterprise cloud solutions, to clients in a variety of markets — from healthcare and telco, to insurance, education and defense industries. Leslie is responsible for designing advanced deep learning, machine learning and chatbot solutions, including patented ground breaking products. One of her biggest strengths is team building, which is the foundation of repetition in the product creation process. In the course of her career, Leslie has held positions, such as Senior Software Product Architect; CTO; and VP, Product Development for key firms.
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