Publication2024
Advanced Deep Learning Techniques for Workload Forecasting in Fog Computing, Elevating Performance Efficiency
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- Intelligent Computing and Emerging Communication Technologies ICEC 2024
- Published
- 1 January 2024
- DOI
- 10.1109/icec59683.2024.10837478
- Scopus ID
- 85218052133
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep learning workload prediction for fog computing optimizes resource allocation and edge computing efficiency
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
11.b Substantially increase the number of settlements adopting integrated policies for inclusion, resilience and disaster risk reduction
IoT optimization in fog networks supports smart city and urban computing infrastructure development
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
AI-driven optimization of distributed computing systems advances digital technology and infrastructure
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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Provenance
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Advanced Deep Learning Techniques for Workload Forecasting in Fog Computing, Elevating Performance Efficiency”. Kortex.