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UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices
Research article (IEEE Access, 2025) · cited 12× · AI/ML
UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices
Summary
UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices is a scholarly article[1].
Key Facts
UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices. Retrieved May 24, 2026, from https://4ort.xyz/entity/ultralightsqueezenet-a-deep-learning-architecture-for-malaria-classification-with-up-to-54-fewer-trainable-parameters-fo
MLA“UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/ultralightsqueezenet-a-deep-learning-architecture-for-malaria-classification-with-up-to-54-fewer-trainable-parameters-fo.
BibTeX@misc{4ortxyz_ultralightsqueezenet-a-deep-learning-architecture-for-malaria-classification-with-up-to-54-fewer-trainable-parameters-fo_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices}}, year = {2026}, url = {https://4ort.xyz/entity/ultralightsqueezenet-a-deep-learning-architecture-for-malaria-classification-with-up-to-54-fewer-trainable-parameters-fo}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification With Up to 54× Fewer Trainable Parameters for Resource Constrained Devices — https://4ort.xyz/entity/ultralightsqueezenet-a-deep-learning-architecture-for-malaria-classification-with-up-to-54-fewer-trainable-parameters-fo (retrieved 2026-05-24)
