Results 61 to 70 of about 251,998 (281)

Deep Learning Markov Random Field for Semantic Segmentation

, 2017
Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF.
Li, Xiaoxiao, Liu, Ziwei, Loy, Chen Change, Luo, Ping, Tang, Xiaoou   +4 more
core   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Posterior Mean Super-Resolution with a Compound Gaussian Markov Random Field Prior

, 2012
This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from observed multiple low-resolution images. A compound
Inoue, Masato, Katsuki, Takayuki
core   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

Model of Random Field with Piece-Constant Values and Sampling-Restoration Algorithm of Its Realizations

Entropy, 2019
We propose a description of the model of a random piecewise constant field formed by the sum of realizations of two Markov processes with an arbitrary number of states and defined along mutually perpendicular axes. The number of field quantization levels
Yuri Goritskiy, Vladimir Kazakov, Olga Shevchenko, Francisco Mendoza   +3 more
doaj   +1 more source

Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck, Samuel Yanes, Manuel Perales, Daniel Gutiérrez, Sergio Toral   +4 more
wiley   +1 more source

Segmentation Algorithm of Spine CT Image Based on Hidden Markov Ｒandom Field

Journal of Harbin University of Science and Technology, 2018
With little consideration about spatial information of pixels， most of the traditional image segmentation algorithms are not ideal. To this end，for the segmentation of spine CT images，an improved algorithm based on Hidden Markov random field framework ...
LIU Xia, KUANG Ｒui-xue, WANG Bo, YUE Yu-ting   +3 more
doaj   +1 more source

Pairwise Markov Random Fields

, 2022
This chapter introduces pairwise Markov random fields (PMRFs), a class of models of which the parameters can be represented as an undirected network. In this undirected network nodes represent variables and edges represent the strength of association between two variables after conditioning on all other variables included in the model.
Epskamp, S., Haslbeck, J.M.B., Isvoranu, A.-M., van Borkulo, C.D.   +3 more
openaire   +3 more sources

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source

ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN MARKOV RANDOM FIELD [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field ...
F. J. Aguilar, M. A. Aguilar, J. L. Blanco, A. Nemmaoui, A. M. García Lorca   +4 more
doaj   +1 more source