Results 61 to 70 of about 1,147,666 (317)

Predicting Scientific Success Based on Coauthorship Networks [PDF]

, 2014
We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between
Garas, Antonios, Pfitzner, Rene, Sarigöl, Emre, Scholtes, Ingo, Schweitzer, Frank   +4 more
core   +1 more source

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Technologies
Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions.
Nikoleta Manakitsa, George S. Maraslidis, L. Moysis, G. Fragulis   +3 more
semanticscholar   +1 more source

Identifying Regional Design Templates of Ancient Near Eastern Ivory Sculptures of Women Using Computer Technology [PDF]

, 2012
Our project, the collaboration of an art historian and a mathematician, offers a solution to the regional classification of ancient Near Eastern ivory sculptures of women through cutting-edge machine-learning data analysis techniques. Machine learning is
Amy Rebecca Gansell, Amy Rebecca Gansell
core   +1 more source

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

FEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan, Lu Qi, Pierre Zalloua   +2 more
wiley   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Convex Analysis and Optimization with Submodular Functions: a Tutorial [PDF]

, 2010
Set-functions appear in many areas of computer science and applied mathematics, such as machine learning, computer vision, operations research or electrical networks.
Bach, Francis
core   +3 more sources

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Science, 2019
Predicting catalyst selectivity Asymmetric catalysis is widely used in chemical research and manufacturing to access just one of two possible mirror-image products.
Andrew F. Zahrt, J. Henle, Brennan T. Rose, Yang Wang, William T. Darrow, S. Denmark   +5 more
semanticscholar   +1 more source

Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning

Iraqi Journal for Computer Science and Mathematics, 2022
In the modern era, many terms related to artificial intelligence, machine learning, and deep learning are widely used in domains such as business, healthcare, industries, and military.
Maad M. Mijwil
semanticscholar   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Pedagogical Possibilities for the N-Puzzle Problem

, 2006
In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning.
Markov, Zdravko, Neller, Todd W., Russell, Ingrid, Zlatareva, Neli   +3 more
core   +1 more source