Random fields in physics, biology and data science
E Hernández-Lemus - Frontiers in Physics, 2021 - frontiersin.org
A random field is the representation of the joint probability distribution for a set of random
variables. Markov fields, in particular, have a long standing tradition as the theoretical …
variables. Markov fields, in particular, have a long standing tradition as the theoretical …
Deep bidirectional recurrent neural networks ensemble for remaining useful life prediction of aircraft engine
Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to
improve its reliability and availability, and reduce its maintenance costs. This article …
improve its reliability and availability, and reduce its maintenance costs. This article …
DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape
P Ding, Y Wang, X Zhang, X Gao, G Liu… - Briefings in …, 2023 - academic.oup.com
Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending
transcriptional regulatory processes and investigating cellular function. Although several …
transcriptional regulatory processes and investigating cellular function. Although several …
A survey of feature selection methods for Gaussian mixture models and hidden Markov models
Feature selection is the process of reducing the number of collected features to a relevant
subset of features and is often used to combat the curse of dimensionality. This paper …
subset of features and is often used to combat the curse of dimensionality. This paper …
Feature selection for high dimensional data using weighted k-nearest neighbors and genetic algorithm
S Li, K Zhang, Q Chen, S Wang, S Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
Too many input features in applications may lead to over-fitting and reduce the performance
of the learning algorithm. Moreover, in most cases, each feature containing different …
of the learning algorithm. Moreover, in most cases, each feature containing different …
Mac-layer packet loss models for wi-fi networks: A survey
Technical reports indicate that wireless and mobile devices will account for 71% of all IP
traffic by 2022, an increase of 19% over four years. This increase is related to advances in …
traffic by 2022, an increase of 19% over four years. This increase is related to advances in …
Large-scale feedforward neural network optimization by a self-adaptive strategy and parameter based particle swarm optimization
Feedforward neural network (FNN) is one of the most widely used and fastest-developed
artificial neural networks. Much evolutionary computation (EC) methods have been used to …
artificial neural networks. Much evolutionary computation (EC) methods have been used to …
Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection
With the increase of computational power and memory capacity, it is possible to record and
analyse lots of features of different nature in real time or in a data stream manner …
analyse lots of features of different nature in real time or in a data stream manner …
[PDF][PDF] An optimal machine learning model based on selective reinforced Markov decision to predict web browsing patterns
The abundance of user usage data has gained exponential dimensions as a result of the
ongoing expansion and spread of Web applications and Web-based systems. Web user …
ongoing expansion and spread of Web applications and Web-based systems. Web user …
[HTML][HTML] Feature selection in jump models
Jump models switch infrequently between states to fit a sequence of data while taking the
ordering of the data into account We propose a new framework for joint feature selection …
ordering of the data into account We propose a new framework for joint feature selection …