Results 71 to 80 of about 86,722 (264)

Heuristic optimization methods for linear ordering of automata [PDF]

Известия Саратовского университета. Новая серия: Математика. Механика. Информатика
The rapid development of society is associated with two key areas of science and technology: methods of working with Big Data and Artificial Intelligence.
Farakhutdinov, Renat Abuhanovich
doaj   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Monitoring land cover dynamics in the Bahr Al-Najaf wetland: A transferable remote sensing framework for inland and coastal wetland management (2002–2025) [PDF]

Estuarine Management and Technologies
The Bahr Al-Najaf depression is a unique geological and ecological feature in Iraq that has undergone significant environmental transformations over the past two decades. This study quantifies the spatiotemporal dynamics of Land Use and Land Cover (LULC)
Emad A. Al Helaly, Ali H. Al Rammahi, Israa J. Muhsin   +2 more
doaj   +3 more sources

Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis

Advanced Intelligent Systems, EarlyView.
A machine learning framework is applied to photoluminescence spectra to extract linewidths and uncover how NV, SiV, GeV, and SnV centers evolve with growth and processing conditions. Unified normalization and k‐fold validation reveal cross‐method trends and enable rapid prediction of defect size and fabrication parameters, offering a data‐driven route ...
Zhi Jiang, Marco Peres, Carlo Bradac, Gil Gonçalves   +3 more
wiley   +1 more source

INCLUDING RISK IN ECONOMIC FEASIBILITY ANALYSIS:A STOCHASTIC SIMULATION MODEL FOR BLUEBERRY INVESTMENT DECISIONS IN CHILE

Revista Brasileira de Fruticultura, 2015
The traditional method of net present value (NPV) to analyze the economic profitability of an investment (based on a deterministic approach) does not adequately represent the implicit risk associated with different but correlated input variables. Using a
GERMÁN LOBOS, MARCOS MORA, RODRIGO SAENS, TRISTÁN MUÑOZ, BERTA SCHNETTLER   +4 more
doaj   +1 more source

Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs

Advanced Intelligent Systems, EarlyView.
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza, Bruno José Olivieri de Souza, Markus Endler   +2 more
wiley   +1 more source

Early warning system for coffee rust disease based on error correcting output codes: a proposal

Revista Ingenierías Universidad de Medellín, 2014
Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision
David Camilo Corrales, Andrés J Peña Q, Carlos León, Apolinar Figueroa, Juan Carlos Corrales   +4 more
doaj  

Cognitive biases and rational decision making for volcanic hazards and risks

Frontiers in Earth Science
Volcanic systems are governed by complex, non-linear dynamics that make deterministic forecasting impossible and require decisions to be made under substantial uncertainty.
Paolo Papale
doaj   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source