Results 91 to 100 of about 5,879,357 (336)

Adversarial Robustness on Image Classification With k-Means

IEEE Access
Attacks and defences in adversarial machine learning literature have primarily focused on supervised learning. However, it remains an open question whether existing methods and strategies can be adapted to unsupervised learning approaches.
Rollin Omari, Junae Kim, Paul Montague
doaj   +1 more source

Hydrogel‐Based Functional Materials: Classifications, Properties, and Applications

Advanced Materials Technologies, EarlyView.
Conductive hydrogels have emerged as promising materials for smart wearable devices due to their outstanding flexibility, multifunctionality, and biocompatibility. This review systematically summarizes recent progress in their design strategies, focusing on monomer systems and conductive components, and highlights key multifunctional properties such as
Zeyu Zhang, Zao Cheng, Patrizio Raffa
wiley   +1 more source

Reveal flocking of birds flying in fog by machine learning

, 2020
We study the first-order flocking transition of birds flying in low-visibility conditions by employing three different representative types of neural network (NN) based machine learning architectures that are trained via either an unsupervised learning ...
Ai, Bao-quan, Guo, Wei-chen, He, Liang
core  

Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization [PDF]

, 2009
Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Hutchison, D., Kim, H., Marnerides, A., Pezaros, D.   +3 more
core  

Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search

Algorithms
Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing ...
Abbas Fadhil Jasim AL-Gburi, Mohd Zakree Ahmad Nazri, Mohd Ridzwan Bin Yaakub, Zaid Abdi Alkareem Alyasseri   +3 more
doaj   +1 more source

A General Approach for Achieving Supervised Subspace Learning in Sparse Representation

IEEE Access, 2019
Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature.
Jianshun Sang, Dezhong Peng, Yongsheng Sang   +2 more
doaj   +1 more source

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Advanced Optical Materials, EarlyView.
Hyperspectral photoluminescence data from graded MoxW1 − xS2 alloys and monolayer MoS2–WS2 lateral heterostructures are analyzed using unsupervised machine learning. The combined use of PCA, t‐SNE, and DBSCAN uncovers distinct excitonic regions that trace how composition, strain, and defects modulate optical responses in these 2D materials.
Maninder Kaur, Nicolas T. Sandino, Jason P. Terry, Mahdi Ghafariasl, Yohannes Abate   +4 more
wiley   +1 more source

Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

Big Data Mining and Analytics
Unsupervised learning algorithms can effectively solve sample imbalance. To address battery consistency anomalies in new energy vehicles, we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three ...
Jiang Chang, Xianglong Gu, Jieyun Wu, Debu Zhang   +3 more
doaj   +1 more source

Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics

Advanced Robotics Research, EarlyView.
This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang, Zhipeng Jiang, Sun Hwa Kwon, Matthew Ibrahim, Alexander Dang, Lin Dong   +5 more
wiley   +1 more source

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source