Results 211 to 220 of about 36,835 (256)

Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning. [PDF]

RSC Adv
Sun J, Song J, Kim J, Kang S, Park E, Seo SW, Min K.   +6 more
europepmc   +1 more source

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

Advanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 more
wiley   +1 more source

Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance

Advanced Intelligent Discovery, EarlyView.
Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen, Tianlong Liu, Chao Lu, Rimsha Malik, Jin Zhang   +4 more
wiley   +1 more source

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
OxSpred, an eXtreme‐Gradient‐Boosting‐‐based supervised learning model, accurately annotates oxidative stress in innate immune cells at the single‐cell level, providing interpretable embeddings with significant biological relevance. This innovative tool revolutionizes the understanding of innate immune cell functions during inflammation and enhances ...
Po‐Yuan Chen, Tai‐Ming Ko
wiley   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction. [PDF]

IEEE Trans Med Imaging
Yu H, Fessler JA, Jiang Y.
europepmc   +1 more source

Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water quality prediction. [PDF]

Sci Rep
Mattar EA, El-Kenawy EM, Alhammad SM, Eid MM.   +3 more
europepmc   +1 more source

Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma. [PDF]

Sci Rep
Abdelhay EH, Elgamily KM, Badr WOE.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Hyperparameter Tuning of ConvLSTM Network Models

2021 44th International Conference on Telecommunications and Signal Processing (TSP), 2021
Deep learning algorithms have achieved amazing performance in computer vision area. However, a biggest problem deep learning has, is the high dependency on hyper-parameters. The algorithm results may be different, depending on hyper-parameters. This paper presents an effective method for hyper-parameter tuning using deep learning.
Roberta Vrskova, Peter Sykora, Patrik Kamencay, Róbert Hudec, Roman Radil   +4 more
openaire   +1 more source

Beyond Manual Tuning of Hyperparameters

KI - Künstliche Intelligenz, 2015
The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection ...
Frank Hutter, Jörg Lücke, Lars Schmidt-Thieme   +2 more
openaire   +2 more sources