Results 91 to 100 of about 6,098 (213)

Comparison of AutoML frameworks for effective analysis in MAPE-K self adaptive systems

Discover Artificial Intelligence
Typically, the MAPE-K (Monitoring, Analysis, Planning, Executing–Knowledge) loop represents the cornerstone of Self-Adaptive Systems (SASs). These systems require highly intelligent analysis mechanisms to ensure reliable performance under changing ...
Sabah Lecheheb, Soufiane Boulehouache, Said Brahimi   +2 more
doaj   +1 more source

Comparative Analysis of Automated vs. Expert-Designed Machine Learning Models in Age-Related Macular Degeneration Detection and Classification

Türk Oftalmoloji Dergisi
Objectives: To compare the effectiveness of expert-designed machine learning models and code-free automated machine learning (AutoML) models in classifying optical coherence tomography (OCT) images for detecting age-related macular degeneration (AMD) and
Ceren Durmaz Engin, Ufuk Beşenk, Denizcan Özizmirliler, Mustafa Alper Selver   +3 more
doaj   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, Volume 28, Issue 20, 21 October 2026.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, Volume 16, Issue 39, 21 October 2026.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Dynamic Phytohormone Trajectories Reveal Predictive Markers of Ozone Exposure Through Longitudinal Sampling and Machine Learning

Plant, Cell &Environment, EarlyView.
Ellinor Hedberg, Till Aust, Jaime Sebastián‐Azcona, Virginia Hernandez‐Santana, Göran Stemme, Heiko Hamann, Antonio Diaz Espejo, Niclas Roxhed   +7 more
wiley   +1 more source

Hybrid Graph Neural Network–Reinforcement Learning Framework for Intelligent Programmable Network Automation

Applied AI Letters, Volume 7, Issue 3, October 2026.
This paper proposes a hybrid GNN‐RL system coupled with a cloud‐based automated experimentation system to optimise topology‐aware, multi‐objective, and scalable programs in the network. ABSTRACT Software‐Defined Networking (SDN) and Network Function Virtualisation (NFV) offer the benefit of dynamic control and flexible resources that can be managed ...
Muhammad Hasnain, Faisal Naeem, Imran Ghani   +2 more
wiley   +1 more source

Interpreting Non‐Linear Compaction Mechanics of Expansive Soils Using Reliability Calibrated Framework

Applied Research, Volume 5, Issue 5, October 2026.
This study introduces a reliability‐calibrated machine learning framework to predict the maximum dry density and optimum moisture content of heterogeneous soils. By integrating 15 algorithms with conformal prediction, the models deliver accurate compaction estimates alongside rigorous, distribution‐free 90% confidence intervals.
Farjad Aziz, Muhammad Waseem, Zeeshan Asghar, Megersa Kebede Leta   +3 more
wiley   +1 more source

ДОСЛІДЖЕННЯ ІНСТРУМЕНТІВ ШТУЧНОГО ІНТЕЛЕКТУ ДЛЯ ІНТЕЛЕКТУАЛЬНОГО АНАЛІЗУ ДАНИХ

Кібербезпека: освіта, наука, техніка
У статті проведено комплексне дослідження сучасних інструментів штучного інтелекту (ШІ), призначених для інтелектуального аналізу даних (Data Mining), їх обчислювальних можливостей та сфер практичного застосування.
Ірина Лисенко, Роман Минайленко, Сергій Смірнов, Костянтин Буравченко, Наталія Якименко, Олексій Смірнов   +5 more
doaj   +1 more source

Efficient AutoML via combinational sampling [PDF]

, 2021
Automated machine learning (AutoML) aims to automatically produce the best machine learning pipeline, i.e., a sequence of operators and their optimized hyperparameter settings, to maximize the performance of an arbitrary machine learning problem ...
Sendhoff, B., Bäck T.H.W., Nguyen, D.A., Kononova, A.V., Menzel, S.   +4 more
core  

AutoML in Cybersecurity: An Empirical Study

Automated machine learning (AutoML) has emerged as a promising paradigm for automating machine learning (ML) pipeline design, broadening AI adoption. Yet its reliability in complex domains such as cybersecurity remains underexplored. This paper systematically evaluates eight open-source AutoML frameworks across 11 publicly available cybersecurity ...
Sherif Saad, Kevin Shi, Mohammad Mamun 0001, Haytham Elmiligi   +3 more
openaire   +3 more sources