Page 282 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

• REQ-14: It is required to have resource optimization systems (cost, energy, sustainable
logistics).
• REQ-15: It is required to have data validation and model accuracy verification processes.
• REQ-16: It is recommended to have cybersecurity measures and ethical AI guidelines
(encryption, bias audits).
• REQ-17: It is required to have renewable energy-powered infrastructure (data centers,
sensors).
• REQ-18: It is recommended to have integration with industrial emission sources for
microalgae deployment.
• REQ-19: It is recommended to have interactive dashboards and visualization tools for
policymakers.
• REQ-20: It is required to comply with global environmental and data regulations.

4 Sequence Diagram

1) Data Processing

This stage focuses on collecting, cleaning, and storing data for further analysis and AI model
training.

SRC-Polar (Sensor Data: Polar, Microalgae Carbon)

Sensor data from polar ecosystems (e.g., glacier dynamics, sea ice, permafrost) and microalgae
carbon monitoring is generated.

Collector (Data Aggregation)

Data from SRC-Polar is aggregated into a centralized system for further processing.

Preprocessor (Data Cleaning)
The aggregated data is cleaned and preprocessed to remove errors, inconsistencies, or
irrelevant information, ensuring quality input for downstream tasks.

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