28 research outputs found
Analysis of LZW Differential Evolution for Binary Encoding
Differential Evolution (DE) is a fast and robust realvector optimizer. This paper applies DE to discrete problems byconverting a real chromosome to an integer chromosome andthen decompress to a binary chromosome using LZW algorithm.Experimental result shows that this approach is better than theprevious work and the evolution time is very fast. Analysis resultshows that the fitness landscape of LZW encoding is lesscomplex than the original encoding for each test problem
Training-free Measures Based on Algorithmic Probability Identify High Nucleosome Occupancy in DNA Sequences
We introduce and study a set of training-free methods of
information-theoretic and algorithmic complexity nature applied to DNA
sequences to identify their potential capabilities to determine nucleosomal
binding sites. We test our measures on well-studied genomic sequences of
different sizes drawn from different sources. The measures reveal the known in
vivo versus in vitro predictive discrepancies and uncover their potential to
pinpoint (high) nucleosome occupancy. We explore different possible signals
within and beyond the nucleosome length and find that complexity indices are
informative of nucleosome occupancy. We compare against the gold standard
(Kaplan model) and find similar and complementary results with the main
difference that our sequence complexity approach. For example, for high
occupancy, complexity-based scores outperform the Kaplan model for predicting
binding representing a significant advancement in predicting the highest
nucleosome occupancy following a training-free approach.Comment: 8 pages main text (4 figures), 12 total with Supplementary (1 figure
Topics in genomic image processing
The image processing methodologies that have been actively studied and developed
now play a very significant role in the flourishing biotechnology research. This work
studies, develops and implements several image processing techniques for M-FISH and
cDNA microarray images. In particular, we focus on three important areas: M-FISH
image compression, microarray image processing and expression-based classification.
Two schemes, embedded M-FISH image coding (EMIC) and Microarray BASICA:
Background Adjustment, Segmentation, Image Compression and Analysis, have been
introduced for M-FISH image compression and microarray image processing, respectively.
In the expression-based classification area, we investigate the relationship
between optimal number of features and sample size, either analytically or through
simulation, for various classifiers
Reconstruction of Software Component Architectures and Behaviour Models using Static and Dynamic Analysis
Model-based performance prediction systematically deals with the evaluation of software performance to avoid for example bottlenecks, estimate execution environment sizing, or identify scalability limitations for new usage scenarios. Such performance predictions require up-to-date software performance models. This book describes a new integrated reverse engineering approach for the reconstruction of parameterised software performance models (software component architecture and behaviour)
Distributed Online Machine Learning for Mobile Care Systems
Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more
popular. They have two major benefits: first, they drastically improve
the living standards and even health outcomes for patients. In addition,
they allow significant cost savings for adult care by reducing the needs
for medical staff. A common drawback of current Mobile Care Systems
is that they are rather stationary in most cases and firmly installed in
patients’ houses or flats, which makes them stay very near to or even in
their homes. There is also an upcoming second category of Mobile Care
Systems which are portable without restricting the moving space of the
patients, but with the major drawback that they have either very limited
computational abilities and only a rather low classification quality or,
which is most frequently, they only have a very short runtime on battery
and therefore indirectly restrict the freedom of moving of the patients
once again. These drawbacks are inherently caused by the restricted
computational resources and mainly the limitations of battery based power
supply of mobile computer systems.
This research investigates the application of novel Artificial Intelligence
(AI) and Machine Learning (ML) techniques to improve the operation of
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Mobile Care Systems. As a result, based on the Evolving Connectionist
Systems (ECoS) paradigm, an innovative approach for a highly efficient
and self-optimising distributed online machine learning algorithm called
MECoS - Moving ECoS - is presented. It balances the conflicting needs
of providing a highly responsive complex and distributed online learning
classification algorithm by requiring only limited resources in the form of
computational power and energy. This approach overcomes the drawbacks
of current mobile systems and combines them with the advantages of
powerful stationary approaches. The research concludes that the practical
application of the presented MECoS algorithm offers substantial improvements
to the problems as highlighted within this thesis
Intelligent Sensor Networks
In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts