Results 111 to 120 of about 37,990 (211)

Hyperparameter optimization for cardiovascular disease data-driven prognostic system. [PDF]

Vis Comput Ind Biomed Art, 2023
Saputra J, Lawrencya C, Saini JM, Suharjito S.   +3 more
europepmc   +1 more source

Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis

Advanced Science, EarlyView.
Blood‐based amino acid patterns measured by 19F NMR reveal hidden metabolic changes in colorectal cancer. By analyzing how these amino acids interact as a network, machine learning models identify patients at higher risk of recurrence and metastasis.
Ji‐Yeon Lee, Jumi Kim, Taehan Yoon, So Hyun Kwon, Su Chan Park, Dohyun Chun, Dohyeong Kim, Eun Jung Park, Hyunwoo Kim, Ji Min Lee   +9 more
wiley   +1 more source

Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection. [PDF]

Sensors (Basel), 2023
Li X, Guo H, Xu L, Xing Z.
europepmc   +1 more source

Uncertainty‐Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays

Advanced Science, EarlyView.
A reliability‐aware electronic nose is developed using photothermally anchored metal‐catalyst decorated metal oxide nanofiber sensor arrays combined with deep ensemble learning. Diverse catalytic nanofiber channels generate gas‐specific response patterns, enabling selective identification and quantification of sulfur‐containing gases.
Sungwoo Eo, Ji‐Hwan Eum, Suk‐Jeong Kwon, Minji Sagong, Dongchan Kim, Dong‐Ha Kim   +5 more
wiley   +1 more source

Analysis of hyperparameter optimization effects on lightweight deep models for real-time image classification. [PDF]

Sci Rep
Rakesh VK, Mazumdar S, Samanta T, Pandey HK, Das A, Pal S.   +5 more
europepmc   +1 more source

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

Advanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter optimization. [PDF]

Sci Rep
Maleknya K, Alizadehsani R, Pedrammehr S, Shirini K.   +3 more
europepmc   +1 more source

HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network. [PDF]

Sensors (Basel), 2023
Sharma A, Rani S, Sah DK, Khan Z, Boulila W.   +4 more
europepmc   +1 more source

CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning

Advanced Science, EarlyView.
CauFinder combines causal disentanglement modeling and network control to prioritize causal drivers of cell‐state transitions from observational transcriptomic data. The framework separates transition‐relevant signals from spurious associations, nominates intervention targets across biological and disease contexts, and identifies DAAM1 as an actionable
Chengming Zhang, Zexi Chen, Yuanxiang Miao, Zuolin Shen, Deyu Cai, Shijie Tang, Yun Xue, Weifeng Guo, Hongbin Ji, Jian Liu, Kazuyuki Aihara, Luonan Chen   +11 more
wiley   +1 more source

LLM-guided population-based reinforcement learning: A scalable methodology for adaptive hyperparameter optimization. [PDF]

MethodsX
Chowdhury MTA, Ullah F, Hassan MH, Bhushan S, Kamal S, Khan AA.   +5 more
europepmc   +1 more source