Results 241 to 250 of about 283,141 (292)

Universal Gripper for Industrial Manipulation With Enhanced Rigid Mechanics and Self‐Adaptable Fingers

Advanced Robotics Research, EarlyView.
An enhanced universal gripper combining rigid mechanics with self‐adaptable fingers is presented for industrial automation. The novel six‐bar linkage with integrated compliant pad eliminates mechanical interference while enabling passive shape adaptation.
Muhammad Usman Khalid, Nirmal Kumar Ravikumar, Mattia Frascio, Sumanth Nagalakunta, Alessandro Seitone, Benoît Lévesque, Alessandro Zanella, Matteo Zoppi   +7 more
wiley   +1 more source

Ensemble Machine Learning Classifiers Combining CT Radiomics and Clinical-Radiological Features for Preoperative Prediction of Pathological Invasiveness in Lung Adenocarcinoma Presenting as Part-Solid Nodules: A Multicenter Retrospective Study. [PDF]

Technol Cancer Res Treat
Li Y, Ding J, Wu K, Qi W, Lin S, Chen G, Zuo Z.   +6 more
europepmc   +1 more source

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions. [PDF]

PLoS One
Chaudhary S, Sharma A, Singh K, Khichar S, Malhotra J.   +4 more
europepmc   +1 more source

Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings? [PDF]

J Nurs Scholarsh
Scroggins JK, Topaz M, Song J, Zolnoori M.   +3 more
europepmc   +1 more source

Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening. [PDF]

Health Inf Sci Syst
Shrivastava T, Singh V, Agrawal A.
europepmc   +1 more source

Reproducible comparison and interpretation of machine learning classifiers to predict autism on the ABIDE multimodal dataset

Dong Y, Batalle D, Deprez M.
europepmc   +1 more source

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Printed Machine Learning Classifiers

2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2020
A large number of application domains have requirements on cost, conformity, and non-toxicity that silicon-based computing systems cannot meet, but that may be met by printed electronics. For several of these domains, a typical computational task to be performed is classification.
Muhammad Husnain Mubarik, Dennis D. Weller, Nathaniel Bleier, Matthew Tomei, Jasmin Aghassi-Hagmann, Mehdi B. Tahoori, Rakesh Kumar 0002   +6 more
openaire   +2 more sources

Machine Learning for Classifying Learning Objects

2006 Canadian Conference on Electrical and Computer Engineering, 2006
Building an ontology for learning objects can be useful for translating such objects between learning contexts. Such translations are important because they afford learners and educators with the opportunity to a survey a wide selection of learning and teaching material.
Girish R. Ranganathan, Yevgen Biletskiy, Dawn MacIsaac   +2 more
openaire   +1 more source

Machine Learning Classifiers in Glaucoma

Optometry and Vision Science, 2008
ABSTRACT Machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn” patterns in data using iterative processes. Such processes can be supervised (guided by a priori group membership information) or unsupervised (guided by patterns
Christopher, Bowd, Michael H, Goldbaum
openaire   +2 more sources

On the kernel Extreme Learning Machine classifier

Pattern Recognition Letters, 2015
Abstract In this paper, we discuss the connection of the kernel versions of the ELM classifier with infinite Single-hidden Layer Feedforward Neural networks and show that the original ELM kernel definition can be adopted for the calculation of the ELM kernel matrix for two of the most common activation functions, i.e., the RBF and the sigmoid ...
Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas   +2 more
openaire   +1 more source