arbiter
JournalsAccount

Hybrid neuromorphic AI framework for energy-efficient and low-latency supply chain analytics

Mousumi Karmakar et al.

International Journal of Logistics: research and applications2026https://doi.org/10.1080/13675567.2026.2645705article
AJG 1ABDC B
Weight
0.50

Abstract

No abstract available.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1080/13675567.2026.2645705

Or copy a formatted citation

@article{mousumi2026,
  title        = {{Hybrid neuromorphic AI framework for energy-efficient and low-latency supply chain analytics}},
  author       = {Mousumi Karmakar et al.},
  journal      = {International Journal of Logistics: research and applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/13675567.2026.2645705},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Hybrid neuromorphic AI framework for energy-efficient and low-latency supply chain analytics

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.