HazardAhead.ai

Predicting danger, crime, distractions, hazards before it happens. Ability to predict danger, hazards and distractions in advance before they do any damage is no longer a distant fantasy. Subject of surveillance has shown world new ways of predicting hazards ahead.

This book is about using Data Science, Graph Analysis to understand data pattern in crowd gatherings, making use of climate, supply chain, behavior archives of criminological data to understand what causes hazards. HazardAhead.ai demonstrates how to construct predictive algorithms to aid in the search for hazardous, danger, distractions and criminal activity.

One great aspect of hazard is, it works in Mathematical Ways.

This notebook uses different technologies and real life data sources together, and show cases, how a Hazard can be predicted well in advance, so that preventive actions can be taken.

about Author

how to use this book

This book first version is completely free(v1.2) and is published as website under GitHub gh-pages branch.

Most of the source code is MIT License, (except few ML/Deep Learning algorithms, which are proprietary and customer owned content).

Complete source code can be found here. https://github.com/AmitXShukla/HazardAhead.ai

Hazards are everywhere, and often completely unpredictable. But recent advancements in AI, ML Models, Scientific Neural network deep learning methods and advance use of modern mathematics computing like derivative / anti-derivative calculus based deep learning models, enable us to use these technologies learn design and data pattern, which can be used to analyze, visualize, and further train machine learning models and predict certain events.

When I think about Impact using Graph technologies, I'm sure, these technologies will be able to make meaningful predictions which are useful to crowd/event management and is very helpful to the places and people who want to use Graph technologies in different aspects.

I have seen researcher using Graph technologies to predict supply chain in retail industry. Graph technologies are certainly useful, where events occurrences are heavily dependent on entity and their behavior/relationship, like Power Sectors or healthcare industry. However, I am here to showcase of applications using Graph patterns in Law & Order, Hazard, Crowd/Event management areas.

HazardAhead.ai uses extremely complex graph and data algorithms. There is lot of work first needs to happen, to define, gather and transform data sources, then design solution pattern, extracting meaningful data. Cleaning and Wrangling huge amount of variety of data formats from different data sources.

Table of Contents

• • HazardAhead.ai
  • about Author
  • how to use this book
  • Table of Contents
• License Agreement
• Privacy Policy
• Define Problem Statement
• Design Pattern
• • Pattern
  • Create Graph, Vertices and Edges
  • Gathering Visitor, Food Supply and other data
  • IOTs climate data
  • Analyzing patterns
• Internet of Things
• • density / vision
  • visibility
  • temperature
  • moisture
  • sound
  • activities
  • news
• Trouble zone
• • Data Pattern matching queries
  • Food & beverages
  • Climate
  • Crowd Density by CheckIn, CheckOut time
  • Crowd Density by attraction
  • Extended Delays
  • Facilities
• Use Cases
• • Rail Tracks
  • Crowd Density by CheckIn, CheckOut time
  • Extended Delays
  • Facilities
  • Festive Gatherings
  • Food & beverages
  • Climate
  • Crowd Density by attraction
  • Facilities
  • Protests
  • Food & beverages
  • Climate
  • Concerts
  • Games
  • Political Rallies
• AI/ML Predictions
• API

License Agreement

https://raw.githubusercontent.com/AmitXShukla/HazardAhead.ai/main/LICENSE

Privacy Policy

https://raw.githubusercontent.com/AmitXShukla/HazardAhead.ai/main/LICENSE