Results 121 to 130 of about 667,394 (343)

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma

CPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 540-550, March 2025.
ABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell ...
Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry   +4 more
wiley   +1 more source

Convergence of a semi-Markov process and an accompanying

, 2010
Предлагается подход к доказательству слабой сходимости полумарковского процесса к марковскому в условиях, налагаемых на локальные характеристики полумарковского процесса.We propose an approach to the proof of the weak convergence of a semi-Markov process
Малик, І.В., Самойленко, І.В.   +1 more
core   +1 more source

Proper account of auto-correlations improves decoding performances of state-space (semi) Markov models

Peer Community Journal
State-space models are widely used in ecology to infer hidden behaviors. This study develops an extensive numerical simulation-estimation experiment to evaluate the state decoding accuracy of four simple state-space models.
Bez, Nicolas, Gloaguen, Pierre, Etienne, Marie-Pierre, Lanco, Sophie, Joo, Rocío, Rivot, Etienne, Walker, Emily, Woillez, Mathieu, Mahévas, Stéphanie   +8 more
doaj   +1 more source

Estimation of the stationary distribution of a semi-Markov chain

This article is concerned with the estimation of the stationary distribution of a discretetime semi-Markov process. After briefly presenting the discrete-time semi-Markov setting, wepropose an estimator of the associated stationary distribution. The main
Bulla, Jan, Barbu, Vlad, Maruotti, Antonello   +2 more
core  

Redefining Optimal Coverage Path Planning for FLS‐Equipped AUVs With Deep Reinforcement Learning

Journal of Field Robotics, EarlyView.
ABSTRACT Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage.
Lorenzo Cecchi, Alberto Topini, Alessandro Bucci, Alessandro Ridolfi   +3 more
wiley   +1 more source

Performance Evaluation and Optimal Management of Distance-Based Registration Using a Semi-Markov Process

Modelling and Simulation in Engineering, 2017
We consider the distance-based registration (DBR) which is a kind of dynamic location registration scheme in a mobile communication network. In the DBR, the location of a mobile station (MS) is updated when it enters a base station more than or equal to ...
Jae Joon Suh, Kyeongtaek Kim, Hee-Seon Jang, Jang Hyun Baek   +3 more
doaj   +1 more source

The existence and characterisation of duality of Markov processes in the Euclidean space [PDF]

This thesis examines the existence of dualMarkov processes and presents the full characterization of Markov processes in Euclidean space equipped with the natural order (the Pareto order).
Lee, Rui Xin
core  

Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel, Xavier Boulanger, Vegard Arneson   +2 more
wiley   +1 more source

Non-identifiability of the two state Markovian Arrival process [PDF]

In this paper we consider the problem of identifiability of the two-state Markovian Arrival process (MAP2). In particular, we show that the MAP2 is not identifiable and conditions are given under which two different sets of parameters, induce identical ...
Michael P. Wiper, Rosa E. Lillo, Pepa Ramirez   +2 more
core  

Network Latency Estimation for Telesurgery Using Deep Reinforcement Learning

SmartBot, EarlyView.
Overview of the proposed two‐stage deep reinforcement learning framework for network latency prediction in telesurgery. The pipeline includes data collection from simulated catheter navigation sessions (Philippines–Botswana), feature engineering, DQN‐based direction prediction (85.8% accuracy), direction‐to‐value transformation, and value forecasting ...
Bakang Kgopolo, Rodrigo S. Jamisola Jr., Molaletsa Namoshe   +2 more
wiley   +1 more source