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Evolving the structure of Hidden Markov models for micro aneurysms detection

Jonathan Goh, Lilian Tang, Lutfiah Al turk
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

J. Goh, H. L. Tang, T. Peto, G. Saleh
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

Hongying Lilian Tang, Jonathan Goh, Tunde Peto, Bingo Wing-Kuen Ling, Lutfiah Ismail Al turk, Yin Hu, Su Wang, George Michael Saleh, Joseph Najbauer
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]

Brian C. Lovell, Terry Caelli
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

Valeria De Fonzo, Filippo Aluffi-Pentini, Valerio Parisi
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

E L Sonnhammer, G von Heijne, A Krogh
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

Andreas Sand, Martin Kristiansen, Christian NS Pedersen, Thomas Mailund
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

Joe Wright, Ivan Jordanov
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

J. P. Swanepoel, J. Coetzer
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

Dai-Duong Tran, Majid Vafaeipour, Mohamed El Baghdadi, Ricardo Barrero, Joeri Van Mierlo, Omar Hegazy
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

P. L. Martelli, P. Fariselli, R. Casadio
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

Ji Ni, Bowei Chen, Nigel M. Allinson, Xujiong Ye
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?

R. Casadio
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

Peter S Klosterman, Andrew V Uzilov, Yuri R Bendaña, Robert K Bradley, Sharon Chao, Carolin Kosiol, Nick Goldman, Ian Holmes
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

Michael A. Lones, Andy M. Tyrrell
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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