Meet us at Token2049 Singapore - the world's largest crypto event | Booth CT19 at Level 4 | 18-19 Sept

Join us for an exclusive Tech Mixer at Google UK headquarters by Systango and Google Cloud | 26th September

Meet us at Token2049 Singapore - the world's largest crypto event | Booth CT19 at Level 4 | 18-19 Sept

Join us for an exclusive Tech Mixer at Google UK headquarters by Systango and Google Cloud | 26th September

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Making Sustainability Accessible with AI Technology.

This software fosters a global shift towards sustainability, serving both B2C and B2B users. It empowers individuals and businesses to track their carbon footprint and incentivizes eco-friendly choices through its reward system. Aligning with nine UN Sustainable Development Goals (SDGs), the platform paves the way for a more sustainable future.

Objectives.

This project focused on developing a sustainable mobile app using the Ionic framework. The initial phase prioritised the integration of a “Travel Dashboard” and advanced AI for “Travel Mode Detection.

Promoting Sustainability: Encouraging eco-friendly actions and monitoring carbon emissions.
Ensuring Regulatory Compliance: Facilitating seamless climate-related reporting for businesses.
Enhancing User Engagement: Active user involvement in sustainability efforts with insightful features.
AI Integration: Enhancing accuracy and user experience with advanced AI technology.

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Our Solutions.

To achieve the objectives, Systango developed an AI-based system. This solution seamlessly integrated the “Travel Dashboard” and implemented AI-powered “Travel Mode Detection,” while also incorporating emission calculations into the app’s dashboard for valuable environmental insights. The solution includes:

Data Collection and Preprocessing

Ensured accuracy and reliability of raw mobile device data.

Feature Engineering

Transformed data into meaningful features for precise transport mode prediction.

Model Selection and Training

Utilised rigorous training of machine learning models like Logistic Regression, Random Forest, and KNN.

Accuracy Validation and Integration

Implemented the effectiveness of the AI model through validation processes and integrated geofencing for accurate travel mode detection.

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Key Benefits.

Reduced carbon emissions by 30%
Increased eco-friendly user actions
Streamlined regulatory compliance processes
Enhanced user engagement with insights
Improved accuracy in travel detection
Comprehensive environmental impact tracking

Tech Stack.

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