Results 231 to 240 of about 283,141 (292)

An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease. [PDF]

Int J Telemed Appl
Hossen MJ, Ramanathan TT, Al Mamun A.
europepmc   +1 more source

Asymmetry in Skipping Enhances Viability Against Control Input Noise

Advanced Robotics Research, EarlyView.
Quadruped animals use asymmetric galloping gaits at high speeds, yet the functional role of this asymmetry remains unclear. This study shows that left–right asymmetry in touchdown angles enhances robustness to control noise. Using a simple two‐legged locomotion model and viability theory, it demonstrates that asymmetric skipping substantially enlarges ...
Yuichi Ambe, Alvin So, Shinya Aoi
wiley   +1 more source

Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases. [PDF]

JCO Clin Cancer Inform
Fajemisin JA, Bryant JM, Saghand PG, Mills MN, Latifi K, Moros EG, Feygelman V, Frakes JM, Hoffe SE, Mittauer KE, Kutuk T, Kotecha R, El Naqa I, Rosenberg SA.   +13 more
europepmc   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort

Adda RB.
europepmc   +1 more source

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source

MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers. [PDF]

J Alzheimers Dis Rep
Bit S, Dey P, Maji A, Alzheimer’s Disease Neuroimaging Initiative, Khan TK.   +4 more
europepmc   +1 more source

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer. [PDF]

PeerJ
Li X, Li C, Wang H, Jiang L, Chen M.
europepmc   +1 more source

Backpropagation Through Soft Body: Investigating Information Processing in Brain–Body Coupling Systems

Advanced Robotics Research, EarlyView.
This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka, Katsuma Inoue, Yasuo Kuniyoshi, Kohei Nakajima   +3 more
wiley   +1 more source