This research introduces APAH, an innovative IoT-based autonomous real-time monitoring system designed for industrial wastewater management, particularly in developing countries like India. By integrating multi-parameter sensors and advanced technologies such as machine learning, APAH continuously tracks key water quality metrics and enables timely interventions through automated controls and alerts, demonstrating significant improvements in water quality at industrial treatment plants in Maharashtra.
The authors propose a multi-attribute group decision-making (MAGDM) approach to evaluate and select digital voting tools that facilitate public participation in urban transport decision-making.
Recent scholarly endeavors in the domain of Cyber Intelligence have unveiled its multifaceted implications, intricately interwoven with various Sustainable Development Goals (SDGs), notably encompassing Goal 9 (Industry, Innovation, and Infrastructure), Goal 11 (Sustainable Cities and Communities), Goal 16 (Peace, Justice and Strong Institutions), among others.
Recent advances in Artificial Intelligence (AI) research have opened up new opportunities for leveraging AI research for societal impacts. AI research offers novel ways of tackling societal problems including environmental, health, and education challenges. Despite the potential, there are limited documented use cases and methodologies for translating AI research to societal impact at a large scale. This paper presents AirQo, an AI and advanced technology-driven use case for urban environmental pollution monitoring and modelling and the resulting societal impacts that have been realised.
The authors propose a multi-attribute group decision-making (MAGDM) approach to evaluate and select digital voting tools that facilitate public participation in urban transport decision-making.
