Results 131 to 140 of about 14,908,682 (295)
Impact of Packaging and Recycling Systems on Material Recirculation: A Stage‐Decomposition Model
Packaging Technology and Science, EarlyView.A system‐level view emerges from decomposing recycling into four stages (participation, collection, sorting and process yield), diagnosing constraints and targeting interventions. Cumulative equivalent uses (CEUs) quantify long‐term retention, revealing marginal improvements at high baselines generate disproportionately larger gains than low‐baseline ...
Diogo Figueirinhas +3 more
wiley +1 more source
Hidden Markov models applied to a subsequence of the Xylella fastidiosa genome
Genetics and Molecular Biology, 2003 Dependencies in DNA sequences are frequently modeled using Markov models. However, Markov chains cannot account for heterogeneity that may be present in different regions of the same DNA sequence.
Silva Cibele Q. da
doaj
VAE‐Based SPC for Botnet Detection in IoT Systems
Quality and Reliability Engineering International, EarlyView.ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has created new security risks, particularly botnet infections. This paper proposes an unsupervised monitoring framework that combines a β$\beta$‐variational autoencoder (VAE) with multivariate statistical process control (SPC) for botnet detection in IoT networks.
Jorge Proaño +3 more
wiley +1 more source
Remote Sensing in Ecology and Conservation, EarlyView.Can deep learning reliably detect a threatened species in passive acoustic monitoring with few annotations? Across 52 sites and 600 recording days, we compared two deep learning recognizers with the traditional machine learning Kaleidoscope for detecting African manatee vocalizations.
Lucas Dubus +7 more
wiley +1 more source
, 2001 Previously, the application of a continuous time hidden Markov model with discrete states was used to model geriatric inpatient behaviour. This was itself built on research using a discrete deterministic model to represent the flow of geriatric patients ...
Christodoulou, G., Taylor, G.J.
core +1 more source
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 +2 more
wiley +1 more source
Small, EarlyView.This mini review highlights how machine learning can accelerate the design of programmable nanosystems. It summarizes data‐driven approaches for predicting liposome properties, guiding DNA origami design and image analysis, and optimizing emerging liposome‐DNA hybrid platforms.
Daniel Q. Zhang, Matthew A. B. Baker
wiley +1 more source
Impacts of Change‐Type and State‐Type System Qualities on Complex System Architectures
Systems Engineering, EarlyView.ABSTRACT System qualities, such as scalability, expandability, and reconfigurability, are key requirements of complex systems. Requirements associated with system qualities are important factors in design trade space analyses, but systems engineering design methodologies make only limited use of these qualities, hindering a holistic understanding of ...
Christina F. Bridges +1 more
wiley +1 more source
Hidden Markov random field and FRAME modelling for TCA-image analysis [PDF]
Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of human teeth. Age is computed by adding the tooth eruption age to the count of annual incremental lines that are called tooth rings and appear in
Katy Streso, Francesco Lagona
core
Bayesian nonparametric hidden Markov models with application to the analysis of copy-number-variation in mammalian genomes [PDF]
, 2009 We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models.
Yau, C. +3 more
core