Results 41 to 50 of about 741,105 (343)
IEEE Access, 2021 This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm.
Leidiane C. M. M. Fontoura +4 more
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Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification [PDF]
, 2017 We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems.
Bacon, David +2 more
core +2 more sources
Generating Buy/Sell Signals for an Equity Share Using Machine Learning [PDF]
Eurasian Journal of Business and Economics, 2018 This study proposes a novel model for predicting 5 days’ ahead share price direction of GARAN (Garanti Bankasi A.Ş.), an equity share that is the top traded stock in BIST100, Istanbul Stock Exchange -Turkey.
Bugra ERKARTA, Linet OZDAMAR
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Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
Complexity, 2020 Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to
Rashid Naseem +6 more
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Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction
Sensors, 2022 One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging.
Richárd Dominik Párizs +3 more
doaj +1 more source
Learning optimal decision trees using constraint programming
Constraints, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hélène Verhaeghe +4 more
openaire +2 more sources
Learning Markov Network Structure with Decision Trees [PDF]
2010 IEEE International Conference on Data Mining, 2010 Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima due to the large space of possible structures. Ravikumar et al. recently proposed the alternative idea of applying L1 logistic regression to learn a set of pair wise features ...
Lowd, Daniel, Davis, Jesse
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Disordered but rhythmic—the role of intrinsic protein disorder in eukaryotic circadian timing
FEBS Letters, EarlyView.Unstructured domains known as intrinsically disordered regions (IDRs) are present in nearly every part of the eukaryotic core circadian oscillator. IDRs enable many diverse inter‐ and intramolecular interactions that support clock function. IDR conformations are highly tunable by post‐translational modifications and environmental conditions, which ...
Emery T. Usher, Jacqueline F. Pelham
wiley +1 more source
An application of decision trees method for fault diagnosis of induction motors [PDF]
, 2006 Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree ...
Oh, Myung-Suck +2 more
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