Results 41 to 50 of about 241,122 (289)

Global Stock Selection with Hidden Markov Model

Risks, 2020
Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market.
Nguyet Nguyen, Dung Nguyen
doaj   +1 more source

Identifying Cytokine Motif‐Containing, Immunomodulatory Bacterial Proteins in Human Gut Microbiome

Advanced Science, EarlyView.
By building and constructing HMM (Upper left, blue), the authors identify CMCPs in bacteria genomes and CRC related metagenomes and enriched CRC‐related CMCPs (Upper right, blue). They analyze sequence and structural similarity of hits (Lower left, green), test function with engineered EcN delivered to tumors in a mouse tumor model (Lower right, pink ...
Ziyu Wang, Siqi Guo, Jing Li, Qianqian Huang, Jing Ning, Binbin Xia, Xun Lv, Xiang Liu, Zeyu Gao, Jine Li, Longchao Liu, Moshi Song, Jun Wang   +12 more
wiley   +1 more source

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long‐Term Active Place Avoidance Memory and “Shadow” Proteins

Advanced Science, EarlyView.
Protein complexes like KIBRA‐PKMζ are crucial for maintaining memories, forming month‐long protein traces in memory‐tagged neurons, but conventional RNA‐seq analysis fails to detect their transcript changes, leaving memory molecules undetected in the shadows of abundantly‐expressed genes.
Jiyeon Han, Alejandro Grau‐Perales, Rayna M. Harris, Edith Lesburguères, Hsin‐Yi Kao, Asit Pal, Juan Marcos Alarcon, Todd C. Sacktor, Stefano Martiniani, Hans A. Hofmann, André A. Fenton   +10 more
wiley   +1 more source

Prediction of annual rainfall pattern using Hidden Markov Model (HMM) in Jos, Plateau State, Nigeria

Journal of Applied Sciences and Environmental Management, 2016
A Hidden Markov Model (HMM) is a double stochastic process in which one of the stochastic processes is an underlying Markov chain, the other stochastic process is an observable stochastic process.
A Lawal, U.Y. Abubakar, H Danladi, A.S. Gana   +3 more
doaj   +1 more source

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

Advanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong, Ji Ma, Yidan Lu, Jiahui Zhang, Wanyue Li, Yeke Chen, Teng Zhang, Xuechao Chen, Zhangguo Yu, Peng Lu   +9 more
wiley   +1 more source

Intrusion detection method based on hierarchical hidden Markov model and variable-length semantic pattern

Tongxin xuebao, 2010
The defects of intrusion detection using fixed-length short system call sequences were analyzed. A method of extracting variable-length short system call sequences, grounded on the function return addresses stored in the process stacks, was proposed ...
DUAN Xue-tao1, JIA Chun-fu 1, LIU Chun-bo1   +2 more
doaj   +2 more sources

Hidden Markov Model [PDF]

, 2006
Hidden Markov Model (HMM) adalah peluasan dari rantai Markov di mana statenya tidak dapat diamati secara langsung (tersembunyi), tetapi hanya dapat diobservasi melalui suatu himpunan pengamatan lain. Pada HMM terdapat tiga
Akmal, Akmal, Firdaniza, Firdaniza, Nurul, Gusriani   +2 more
core  

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source

Propositionalisation of multiple sequence alignments using probabilistic models [PDF]

, 2008
Multiple sequence alignments play a central role in Bioinformatics. Most alignment representations are designed to facilitate knowledge extraction by human experts.
Holmes, Geoffrey, Mutter, Stefan, Pfahringer, Bernhard   +2 more
core   +2 more sources