Results 181 to 190 of about 179,881 (297)

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

LoRa Power Model for Energy Optimization in IoT Applications. [PDF]

Sensors (Basel)
Soler-Fernández JL, Romera O, Diéguez A, Prades JD, Alonso O.   +4 more
europepmc   +1 more source

Lora Gerd

Open Jerusalem, 2014
openaire   +1 more source

Applications of large‐scale artificial intelligence models in bioinformatics

Quantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Large‐scale artificial intelligence (AI) models can mine potential patterns from massive amounts of data and provide more accurate analyses. This capability has enabled its gradual application in various areas of bioinformatics. However, few reviews have comprehensively summarized the applications of different types of large‐scale AI models in
Mingjing Li, Qichen Shang, Ziyang Dong, Zhixuan You, Le Zhang, Ming Xiao   +5 more
wiley   +1 more source

Parameter-efficient fine-tuning for low-resource text classification: a comparative study of LoRA, IA³, and ReFT. [PDF]

Front Big Data
Nwaiwu S.
europepmc   +1 more source

An Overview of Deep Learning Techniques for Big Data IoT Applications

International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.
Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur, Vipin Balyan, Sindhu Hak Gupta   +2 more
wiley   +1 more source

Experimental Evaluation of LR-FHSS: A Comparison with LoRa. [PDF]

Sensors (Basel)
Sanchez-Vital R, Casals L, Jara-Ortínez B, Bodvanski J, Vidal R, Garcia-Villegas E, Gomez C.   +6 more
europepmc   +1 more source

Training robots with natural and lightweight human feedback

AI Magazine, Volume 47, Issue 1, Spring 2026.
Abstract Generalist robot models promise broad applicability across domains but currently require extensive expert demonstrations for task specialization, which is a costly and impractical barrier for real‐world deployment. In this article, which summarizes the author's presentation in the New Faculty Highlights Track of the 39th annual AAAI Conference
Erdem Bıyık
wiley   +1 more source

Transfer learning with pre-trained language models for protein expression level prediction in <i>Escherichia coli</i>. [PDF]

Synth Syst Biotechnol
Yang C, Zhao Y, Wang R, Li H, Liao X, Ma H.   +5 more
europepmc   +1 more source

Quantitative Determination and Stability of Trace Levels of Oxytocin in Human and Rhesus Macaque Urine by LC–MS/MS

Biomedical Chromatography, Volume 40, Issue 3, March 2026.
ABSTRACT Oxytocin (OT) quantification in biological samples is vital for understanding its role in social behavior, emotion, and brain development in humans and animals. Urinary OT analysis has gained attention but is challenging due to variability in matrix effects, background interference, low concentrations, and stability concerns.
Xing Zhang, Lori Fralick, Kelly Ethun
wiley   +1 more source