Results 291 to 300 of about 2,993,514 (349)
Some of the next articles are maybe not open access.
Basic Corrosion Technology for Scientists and Engineers
, 2023Basic corrosion technology for scientists and engineers , Basic corrosion technology for scientists and engineers , مرکز فناوری اطلاعات و اطلاع رسانی ...
E. Mattsson
semanticscholar +1 more source
International Conference on Human Factors in Computing Systems, 2020
Machine learning (ML) models are now routinely deployed in domains ranging from criminal justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and grown into an engineering discipline.
Harmanpreet Kaur +5 more
semanticscholar +1 more source
Machine learning (ML) models are now routinely deployed in domains ranging from criminal justice to healthcare. With this newfound ubiquity, ML has moved beyond academia and grown into an engineering discipline.
Harmanpreet Kaur +5 more
semanticscholar +1 more source
Intelligent Systems for Engineers and Scientists
, 2021INTRODUCTION Intelligent Systems Knowledge-Based Systems The Knowledge Base Deduction, Abduction, and Induction The Inference Engine Declarative and Procedural Programming Expert Systems Knowledge Acquisition Search Computational Intelligence Integration
A. Hopgood
semanticscholar +1 more source
Review of Measuring Metabolic Rates: A Manual for Scientists
Physiological and Biochemical Zoology, 2021Animal calorimetry quantifies the heat resulting from the intricate metabolic combustion process termed “the fire of life” by pioneering nutritionistMax Kleiber (1975). This fire is expressed in joules, watts, or calories, with the latter being preferred
K. Kaiyala
semanticscholar +1 more source
SSRN Electronic Journal, 2012 We introduce archetypal analysis as a tool to describe and categorize scientists. This approach identifies typical characteristics of extreme ('archetypal') values in a multivariate data set. These positive or negative contextual attributes can be allocated to each scientists under investigation.
Christian Seiler, Klaus Wohlrabe
openaire +2 more sources
Probability, Statistics, and Reliability for Engineers and Scientists
Introduction Introduction Knowledge, Information, and Opinions Ignorance and Uncertainty Aleatory and Epistemic Uncertainties in System Abstraction Characterizing and Modeling Uncertainty Simulation for Uncertainty Analysis and Propagation Simulation ...
B. Ayyub, R. McCuen
semanticscholar +1 more source
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models
International Conference on Human Factors in Computing Systems, 2019Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations. This has led to a rallying cry for model interpretability.
Fred Hohman +4 more
semanticscholar +1 more source