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Disaster and Emergency Prediction & Impact Model

Large Companies
Small Companies
Entertainment and Media - Metadata Tagging
For:
Digital asset managers.
Scope:
Realizing the value of historical sporting digital media assets by using Artificial Intelligence (AI) to assist in doing the work of 15 interns.
Goal:
Improved Product Development / R&D, Improve Operation Efficiency
AI-Based Solid Waste Classification
For:
Government Municipal Corporations, Solid Waste Management Companies
Education - Automated Content Generators
For:
Online course creators.
Scope:
Intelligently generate, curate, and recommend content educational content.
Goal:
Improved Customer Experience
Crop Diagnosis & Product Recommendations Through AI
For:
Farmers, Biochemical companies, Crop disease treatment manufacturers
Goal:
Improved Product Development / R&D, Improve Operation Efficiency
Other
Computer Vision - Image Segmentation
For:
Farmers
Scope:
Using image processing to implement a livestock monitoring application.
Goal:
Improved Operation
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities
General Public
Healthcare - Improve Investigatory Work
For:
Radiologists
Scope:
Using machine learning to predict lung cancer from CT scans.
Goal:
Anticipate Risks
Prediction of Multidrug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques
For:
Clinicians and medical professionals predicting multidrug-resistant (MDR) patients from drug-sensitive (DS) ones based on CT lung images.
Goal:
Improved Employee Efficiency
General Public
Education - Smart Learning Content
For:
Online course creators and online learners.
Scope:
Empowering course creators to focus on complex decision-making and creativity with Computer Vision and Natural Language Processing.
Goal:
Improved Employee Efficiency
Autonomous Robot Improves Surgical Precision Using AI
For:
Hospitals using Autonomous robotic surgery via the STAR system
Goal:
Improve Operation Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities.
Large Companies
Small Companies
Accounting and Finance - Automate Invoices and Expense Management
For:
Financial Controllers
Scope:
Using Image Processing and Optical Character Recognition to create operational efficiencies through automation of expense approval and reconciliation workflows.
Goal:
Improved Employee Efficiency

Disaster and Emergency Prediction & Impact Model

For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities.
Problem addressed
Whenever a natural disaster like a flood or a heat wave occurs, warnings and other risk-related information might be imprecise or out of date. Most of the risk-related information currently floats on a macro-level, covering hundreds of square meters, and is too complex for at-risk people to comprehend.
It was necessary to localize the risk data to a neighborhood level to support the development of long-term resilience in the communities that are most at risk. Their extensive experience reacting to many emergencies and disasters on the ground must be automated, scaled, and coded using a solution.
Description
A cutting-edge model blending AI and machine learning capabilities is developed to plan and react to disasters more successfully. This model forecasts hyper-local risk information for early warnings and intervention using historical data and satellite photos. 
The basic tenet of the approach is that a house's roofing can serve as a stand-in for its socioeconomic status. Therefore, the adapting and recovering capacities of a family residing in a sizable concrete home and a family living in a temporary metal sheet home would be different. 
The effects of the destruction brought on by a disaster are noticeably different for each of these dwellings when two of them are present in the same region. The backbone of this AI system is the mapping of this roof material data on satellite imagery and other spatial factors.
The solution generates hyper-localized risk data that can be used by various stakeholders in disaster response. These stakeholders include experts in climate change adaptation, government agencies, and communities at risk on a national scale. It provides people with precise instructions on how to protect their homes, pets, livelihood, and possessions.
The solution's scalability is another plus. It can respond to a variety of disasters, including earthquakes, heat waves, and floods.
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Image
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Machine Learning
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Predict / Forecast
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