Results 51 to 60 of about 524,166 (313)
Applied Medical Informatics, 2023 Stroke is an acute neurologic disorder of blood vessels in brain due to blockage of blood flow to the brain resulting in less oxygen. Stroke remains one of the leading causes of death worldwide.
Helmi Saifullah MANSUR +2 more
doaj
Optimal Decision Tree Policies for Markov Decision Processes [PDF]
Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence Main Track, 5457-5465 (2023), 2023 Interpretability of reinforcement learning policies is essential for many real-world tasks but learning such interpretable policies is a hard problem. Particularly rule-based policies such as decision trees and rules lists are difficult to optimize due to their non-differentiability. While existing techniques can learn verifiable decision tree policies
arxiv +1 more source
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
doaj +1 more source
Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique
Cybernetics and Information Technologies, 2018 Researchers all over the world have provided significant and effective solutions to detect malicious URLs. Still due to the ever changing nature of cyberattacks, there are many open issues.
Patil Dharmaraj R., Patil J. B.
doaj +1 more source
Interval Temporal Logic Decision Tree Learning [PDF]
, 2019 Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining. From a logical point of view, a decision tree can be seen as a structured set of logical rules written in ...
Andrea Brunello +2 more
openaire +5 more sources
Advanced Materials Interfaces, EarlyView., 2023 This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi +4 more
wiley +1 more source
Heat demand prediction: A real-life data model vs simulated data model comparison
Energy Reports, 2021 In the recent years machine learning algorithms have developed further and various applications are taking advantage of this advancement. Modern machine learning is now used in district heating for more precise and realistic heat demand prediction ...
Kevin Naik, Anton Ianakiev
doaj
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
Integrating Decision Tree Learning into Inductive Databases [PDF]
, 2007 In inductive databases, there is no conceptual difference between data and the models describing the data: both can be stored and queried using some query language. The approach that adheres most strictly to this philosophy is probably the one proposed by Calders et al.
Fromont, Elisa +2 more
openaire +5 more sources
Decision Tree Learning for Uncertain Clinical Measurements [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2021 Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical contexts such as medical diagnosis. However, learning DTs from uncertain data leads to poor generalization, and generating predictions for uncertain data hinders prediction ...
Cecilia Nunes +5 more
openaire +3 more sources