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Evolving the structure of Hidden Markov models for micro aneurysms detection
2010
2010 UK Workshop on Computational Intelligence (UKCI)
In this paper, a novel technique based on Genetic Algorithms is used to evolve the structure of the Hidden Markov Models to obtain an optimised model that indicates the presence of micro aneurysms located ...
This technique not only identifies the optimal number of states, but also determines the topology of the Hidden Markov Model, along with the initial model parameters. ...
The authors also thank King Abdul-Aziz University, Kingdom of Saudi Arabia, and the Department of Computing, University of Surrey, UK, for their financial support to the project. ...
doi:10.1109/ukci.2010.5625579
fatcat:uhdlj7zmvzf25otw34q4swu7qi
An evolutionary approach for determining Hidden Markov Model for medical image analysis
2012
2012 IEEE Congress on Evolutionary Computation
This paper addresses these problems by automatically selecting the best feature set while evolving the structure and obtaining the parameters of a Hidden Markov Model. ...
This novel algorithm not only selects the best feature set, but also identifies the topology of the HMM, the optimal number of states, as well as the initial transition probabilities. ...
ACKNOWLEDGEMENT This work is primarily in collaboration with the Reading Centre, Department of Research and Development, Moorfields Eye Hospital NHS Foundation Trust, United Kingdom. ...
doi:10.1109/cec.2012.6252996
dblp:conf/cec/GohTPS12
fatcat:4gjpefdin5eg7bb33qhuaopgoe
The Reading of Components of Diabetic Retinopathy: An Evolutionary Approach for Filtering Normal Digital Fundus Imaging in Screening and Population Based Studies
2013
PLoS ONE
Furthermore, evolutionary algorithms are employed to optimize the Hidden Markov Models, feature selection and heterogeneous ensemble classifiers. ...
In order to evaluate its capability of identifying normal images across diverse populations, a population-oriented study was undertaken comparing the software's output to grading by humans. ...
Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, United Kingdom. ...
doi:10.1371/journal.pone.0066730
pmid:23840865
pmcid:PMC3698085
fatcat:leyaqxitx5h2xnpic5ifwm6tuq
HIDDEN MARKOV MODELS FOR SPATIO-TEMPORAL PATTERN RECOGNITION
[chapter]
2005
Handbook of Pattern Recognition and Computer Vision
In this chapter, we deal with these issues and use simulated data to evaluate the performance of a number of alternatives to the traditional Baum-Welch algorithm for learning HMM parameters. ...
There are also only a few theoretical principles for guiding researchers in selecting topologies or understanding how the model parameters contribute to performance. ...
We denote the HMM model parameter set by λ = (A, B, π). ...
doi:10.1142/9789812775320_0002
fatcat:4vhoqhnsgzfwppnxwo7dnovbwe
Hidden Markov Models in Bioinformatics
2007
Current Bioinformatics
In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks. ...
Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. ...
C) Learning problem: given a sequence of observations, find an optimal model. The most used algorithms start from an initial guessed model and iteratively adjust the model parameters. ...
doi:10.2174/157489307779314348
fatcat:xnx7dyuzdjcz3glldxkqiizwhu
A hidden Markov model for predicting transmembrane helices in protein sequences
1998
Proceedings. International Conference on Intelligent Systems for Molecular Biology
It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. ...
Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. ...
This work was supported by the Danish National Research Foundation and the Swedish Natural Sciences Research Council. ...
pmid:9783223
fatcat:k3666dxp5fb2rilvau7rhsmvpa
zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm
2013
BMC Bioinformatics
The most time consuming part of using hidden Markov models is often parameter fitting, since the likelihood of a model needs to be computed repeatedly when optimising the parameters. ...
Depending on the optimisation strategy, this means that the forward algorithm (or both the forward and the backward algorithm) will be evaluated in potentially hundreds of points in parameter space. ...
The preprocessing of a specific sequence can be saved and later reused in the analysis of a different HMM topology. ...
doi:10.1186/1471-2105-14-339
pmid:24266924
pmcid:PMC4222747
fatcat:ys3rxxx7vrdfxfayn6lq4jymmq
Intelligent Approaches in Locomotion - A Review
2014
Journal of Intelligent and Robotic Systems
A summary of references from this review, grouped by method, target system and type of data presented, is given in Table 2 . ...
Table 1 : Reviewed references organised by control method, each of which are examined in separate sections of this review, and parameterisation technique. ...
Given the cost function and constraints, a sparse sequential quadratic programming optimisation algorithm was used to determine the parameters of the model. ...
doi:10.1007/s10846-014-0149-z
fatcat:wpzs5i4lsbhxfdgkf5i7ij6xa4
Off-line Signature Verification Using Flexible Grid Features and Classifier Fusion
2010
2010 12th International Conference on Frontiers in Handwriting Recognition
B.3.1 Parameter optimisation Apart from the initial state distribution π, which is permanently bound by the leftright topology considered in this study, the set of HMM parameters may theoretically be initialised ...
HMM topology If, apart from having to adhere to the rules of probability, no additional constraints are imposed on the model parameters π and A, the HMM is referred to as fully connected or ergodic. ...
This is achieved by the requirement It is not strictly required that x q and x k share the same dimension, as indicated by Constraint A.5, although this is always the case for base classifiers developed ...
doi:10.1109/icfhr.2010.52
dblp:conf/icfhr/SwanepoelC10
fatcat:747kntwfvjbrlcinghr3gacc3e
Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies
2019
Renewable & Sustainable Energy Reviews
Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies. ...
Accordingly, in the present study, various powertrain systems and topologies of (plug-in) hybrid electric vehicles and full-electric vehicles are assessed. ...
Acknowledgements This research was funded by EMTECHNO project, grant number IWT150513. We also acknowledge Flanders Make and VLAIO for the support of our research group. ...
doi:10.1016/j.rser.2019.109596
fatcat:ybks774km5htvb6f7foyetum7m
An ENSEMBLE machine learning approach for the prediction of all-alpha membrane proteins
2003
Bioinformatics
Results: We implement a cascade-neural network (NN), two different hidden Markov models (HMM), and their ensemble (ENSEMBLE) as a new method. ...
It is therefore possible to train/test predictors only with the set of proteins known with atomic resolution and evaluate more thoroughly the performance of different methods. ...
MaxSubSeq uses the outputs of a given predictive method and by model optimisation locates the TM segments along the protein sequence. ...
doi:10.1093/bioinformatics/btg1027
pmid:12855459
fatcat:6czrq7q4nnd3jcktnhnt4uynjq
A hybrid model for predicting human physical activity status from lifelogging data
2019
European Journal of Operational Research
The model has a two-stage hybrid structure (in short, MOGP-HMM) -- a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov ...
We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models. ...
Acknowledgment This work was conducted with the support of the EPSRC grant MyLifeHub EP/L023679/1 and European FP7 collaborative project MyHealthAvatar (GA No: 600929). ...
doi:10.1016/j.ejor.2019.05.035
fatcat:aunqailq25fdnoz2hamxn6igw4
In silico prediction of the structure of membrane proteins: Is it feasible?
2003
Briefings in Bioinformatics
Unlike globular proteins, a 3D model for membrane proteins can hardly be computed starting from the sequence. Why is this so? What can we really compute and with what reliability? ...
His main fields of interests include bioinformatics and computational biophysics. ...
In the case of HMM, the topological model is derived from the prediction, according to an optimisation algorithm or again after dynamic programming. 21 All methods improve the predictive performance ...
doi:10.1093/bib/4.4.341
pmid:14725347
fatcat:zath6lkavjadzei5bmzli7cyky
XRate: a fast prototyping, training and annotation tool for phylo-grammars
2006
BMC Bioinformatics
maximum-likelihood parameters and phylogenetic trees using a novel "phylo-EM" algorithm that we describe. ...
Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. ...
Also included is an implementation of the neighbor-joining algorithm for fast estimation of tree topologies [77] , and another version of the EM algorithm for rapidly optimising branch lengths of trees ...
doi:10.1186/1471-2105-7-428
pmid:17018148
pmcid:PMC1622757
fatcat:3jr7rgeus5g4vbad3whvbdij24
The evolutionary computation approach to motif discovery in biological sequences
2005
Proceedings of the 2005 workshops on Genetic and evolutionary computation - GECCO '05
This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. ...
and where it might lead us in the future. ...
Molecular model data was provided by the RCSB PDB website at http://www.pdb.org/ [7] . ...
doi:10.1145/1102256.1102258
dblp:conf/gecco/LonesT05
fatcat:lwlwl2cfabeazi7jpxnhzdki2q
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