Results 91 to 100 of about 141,868 (281)

Clustering and Learning from Imbalanced Data

CoRR, 2018
A learning classifier must outperform a trivial solution, in case of imbalanced data, this condition usually does not hold true. To overcome this problem, we propose a novel data level resampling method - Clustering Based Oversampling for improved learning from class imbalanced datasets.
Naman D. Singh, Abhinav Dhall
openaire   +2 more sources

Nanozymes at the Bio‐Nano Interface: From Synthesis, Defect Engineering, Catalytic Behavior in Biological Microenvironments, and Biosafety Implications

Advanced Materials Interfaces, EarlyView.
Nanozymes (NZs) have emerged as versatile artificial enzymes with tunable catalytic properties driven by atomic coordination, defect engineering, and surface chemistry. This review presents a bio–nano interface framework linking synthesis strategies, structural design, and catalytic behavior within complex biological microenvironments.
Karen Guadalupe Quintero‐Garrido, Aída Jimena Velarde‐Salcedo, Alejandro López‐Amador, Noé Arjona, Citlali Uitz Toalá, Carmen Gonzalez, Beatriz Liliana España‐Sánchez   +6 more
wiley   +1 more source

Handling the Imbalanced Problem in Agri-Food Data Analysis

Foods
Imbalanced data situations exist in most fields of endeavor. The problem has been identified as a major bottleneck in machine learning/data mining and is becoming a serious issue of concern in food processing applications.
Adeyemi O. Adegbenjo, Michael O. Ngadi
doaj   +1 more source

Dynamic Current Routing in Conductive Liquid Crystal Elastomers for Complex Deformation Actuators

Advanced Materials Technologies, EarlyView.
This work introduces a novel actuation control strategy in which electrical stimulation generates localized Joule heating in conductive liquid crystal elastomer (LCE) surfaces, enabling complex 3D deformations from a single homogeneous surface without preprogramming.
Gloria PouWai Lei, Morgan Barnes, Thomas George Thuruthel   +2 more
wiley   +1 more source

A Review on Sensor Technologies, Control Approaches, and Emerging Challenges in Soft Robotics

Advanced Robotics Research, EarlyView.
This review provides an introspective of sensors and controllers in soft robotics. Initially describing the current sensing methods, then moving on to the control methods utilized, and finally ending with challenges and future directions in soft robotics focusing on the material innovations, sensor fusion, and embedded intelligence for sensors and ...
Ean Lovett, Maxwell Hammond, Niloufar Seyfi, Amirreza Fahim Golestaneh, Venanzio Cichella, Caterina Lamuta   +5 more
wiley   +1 more source

TacScope: A Miniaturized Vision‐Based Tactile Sensor for Surgical Applications

Advanced Robotics Research, EarlyView.
TacScope is a compact, vision‐based tactile sensor designed for robot‐assisted surgery. By leveraging a curved elastomer surface with pressure‐sensitive particle redistribution, it captures high‐resolution 3D tactile feedback. TacScope enables accurate tumor detection and shape classification beneath soft tissue phantoms, offering a scalable, low‐cost ...
Md Rakibul Islam Prince, Sheeraz Athar, Pokuang Zhou, Yu She   +3 more
wiley   +1 more source

Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data

Foods
Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in ...
Kunkun Pang, Yisen Liu, Songbin Zhou, Yixiao Liao, Zexuan Yin, Lulu Zhao, Hong Chen   +6 more
doaj   +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

Proposal of self and semi-supervised learning for imbalanced classification of coronary heart disease tabular data

Tecnología en Marcha
Triple Mixup is an augmentation policy in the hidden latent space we introduced in the Contrastive Mixup Self-Semi Supervised learning framework, to address the imbalanced data problem, for Cardiovascular Heart Diseases tabular dataset. Medical tabular
Danny Xie-Li, Manfred González-Hernández   +1 more
doaj   +1 more source

Modular, Textile‐Based Soft Robotic Grippers for Agricultural Produce Handling

Advanced Robotics Research, EarlyView.
This article introduces textile‐based pneumatic grippers that transform simple textiles into robust bending actuators. Detailed experiments uncover how cut geometry and fabric selection shape performance. Successful handling of fragile agricultural items showcases the potential of textile robotics for safe, scalable automation in food processing and ...
Zeyu Hou, Eran Beeri Bamani, Joao Buzzatto, Bryan Busby, Lucas Ferrari Gerez   +4 more
wiley   +1 more source