Results 51 to 60 of about 241,122 (289)
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 +4 more
wiley +1 more source
HMM-guided frame querying for bandwidth-constrained video search [PDF]
, 2019 We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints. Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our ...
Chidambaram, Bhairav +2 more
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 +4 more
wiley +1 more source
Using Hidden Markov Chains in Recognition of Vowel Letters in English Language [PDF]
المجلة العراقية للعلوم الاحصائية, 2006 This study deals with hidden Markov models . These models consist of sets of finite states , each one of them is associated with a probability distribution .
doaj +1 more source
Sequential Bayesian inference for implicit hidden Markov models and current limitations
, 2015 Hidden Markov models can describe time series arising in various fields of science, by treating the data as noisy measurements of an arbitrarily complex Markov process.
Jacob, Pierre E.
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Advanced Intelligent Systems, EarlyView.Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee +7 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
, 2017 Systems with interacting degrees of freedom play a prominent role in stochastic thermodynamics. Our aim is to use the concept of detached path probabilities and detached entropy production for bipartite Markov processes and elaborate on a series of ...
Ehrich, Jannik, Engel, Andreas
core +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 +2 more
wiley +1 more source
DifferentiableHMM: Neural Differentiable Hidden Markov Model
ITEGAM-JETIAModeling clinical time series demands interpretable patient states alongside the ability to capture long-range dependencies. Classical HMMs provide discrete, clinically meaningful states but ignore distant history; RNNs capture rich temporal patterns ...
Arbia Boudaoud, Salheddine Kabou
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