AI & Environment Resource HubDr Loke KoK Foong; Dr Wan Amizah Wan Jusoh; Dr Vellapandian Ponnusamy · 2025-03-07
This study evaluates four optimization algorithms for enhancing MLP-based occupancy detection in smart office environments, finding MVO-MLP delivers the highest accuracy and generalization, making it ideal for smart building energy systems.
Dr Loke KoK Foong; Dr Wan Amizah Wan Jusoh; Dr Vellapandian Ponnusamy. 2025-03-07. Employing Four Nature-Inspired Algorithms Hybridized with MLP for Accurate Occupancy Detection of an Office Room. https://aisesjournal.com/article-1-21-en.pdf (AI & Environment Resource Hub; record paper-271; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
AI and ML optimize optoelectronic systems for sustainability by enhancing energy efficiency, renewable energy harvesting, environmental monitoring, and smart cities while also advancing ocean optics and photonics for marine ecosystem monitoring and climate change mitigation.
Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
To meet the growing demand for power of AI, we have to enable the entire ecosystem, from chip to grid. This lightning talk brings together how NVIDIA is working with its partners from power generation and transmission to distribution to build flexible and reliable infrastructure. The session focuses on AI for meeting energy demand, using digital technologies inside data centers to manage peak loads, and applying accelerated computing to simulate the grid and build physical infrastructure.
Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
Nature Forward invites you to learn about the major impacts of data center development and how you can advocate for more responsible and sustainable data center development in your community. The course includes seven virtual sessions led by leading researchers, scientists, and advocates involved in the movement for more sustainable data center development.
Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
Matthew Riemland examines how translation professionals can confront the environmental costs of AI, including carbon emissions, water consumption, and rare mineral extraction, through 'eco-translation practice.' He argues these harms stem from structural power imbalances rather than individual choices, and proposes vocational and structural strategies of resistance, from supporting data center activism to demanding transparency from AI developers.