Results 71 to 80 of about 73,803 (311)

Sparseness Achievement in Hidden Markov Models [PDF]

14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irrelevant parameters are set exactly to zero. Alternatively to standard Maximum Likelihood Estimation (Baum Welch training), in the proposed approach the parameters estimation ...
Manuele Bicego, Marco Cristani, Vittorio Murino   +2 more
openaire   +2 more sources

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

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

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

DifferentiableHMM: Neural Differentiable Hidden Markov Model

ITEGAM-JETIA
Modeling 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
doaj   +1 more source

Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model

, 2011
The chromosome which represents the HMM can be extracted from its corresponding neural network. The general structure of the chromosome is divided into two sections, input layer and hidden layer. Each section contains many slots, and each slot represents
Neuroevolution Mechanism for Hidden Markov Model (5584958), Neuroevolution Mechanism for Hidden Markov Model   +1 more
core   +1 more source

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

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, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

Estimating Components in Finite Mixtures and Hidden Markov Models [PDF]

When the unobservable Markov chain in a hidden Markov model is stationary the marginal distribution of the observations is a finite mixture with the number of terms equal to the number of the states of the Markov chain.
D.S. Poskitt, Jing Zhang
core  

Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)

Advanced Intelligent Systems, EarlyView.
This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley   +1 more source

Option pricing using hidden Markov models

, 2006
Includes bibliographical references (leaves 144-149).This work will present an option pricing model that accommodates parameters that vary over time, whilst still retaining a closed-form expression for option prices: the Hidden Markov Option Pricing ...
Anderson, Michael
core