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CALL FOR PAPERS
<p>Learning and Intelligent OptimizatioN Conference<br>
LION 12, Kalamata, Greece, June 10-15, 2018 <br>
</p>
<p><br>
</p>
<p><a class="moz-txt-link-freetext"
href="http://www.caopt.com/LION12/">http://www.caopt.com/LION12/</a></p>
<p><br>
</p>
<p>Scope of the conference<br>
<br>
The large variety of heuristic algorithms for hard optimization
problems raises numerous interesting and challenging issues.
Practitioners are confronted with the burden of selecting the most
appropriate method, in many cases through an expensive algorithm
configuration and parameter tuning process, and subject to a steep
learning curve. Scientists seek theoretical insights and demand a
sound experimental methodology for evaluating algorithms and
assessing strengths and weaknesses. A necessary prerequisite for
this effort is a clear separation between the algorithm and the
experimenter, who, in too many cases, is "in the loop" as a
crucial intelligent learning component. Both issues are related to
designing and engineering ways of "learning" about the performance
of different techniques, and ways of using past experience about
the algorithm behavior to improve performance in the future.
Intelligent learning schemes for mining the knowledge obtained
from different runs or during a single run can improve the
algorithm development and design process and simplify the
applications of high-performance optimization methods.
Combinations of algorithms can further improve the robustness and
performance of the individual components provided that sufficient
knowledge of the relationship between problem instance
characteristics and algorithm performance is obtained.<br>
<br>
This meeting, which continues the successful series of LION events
(see LION 5 at Rome, and LION 6 at Paris, and LION 7 at Catania),
is aimed at exploring the intersections and uncharted territories
between machine learning, artificial intelligence, mathematical
programming and algorithms for hard optimization problems. The
main purpose of the event is to bring together experts from these
areas to discuss new ideas and methods, challenges and
opportunities in various application areas, general trends and
specific developments. We are excited to be bringing the LION
conference in Greece for the first time. <br>
</p>
<p><br>
</p>
<p>Proceedings will be published by Springer-Verlag<b> </b>in
Lecture Notes in Computer Science</p>
<p><br>
</p>
<p>Revised selected papers of LION 12 will be published in a special
issue of <a href="https://link.springer.com/journal/10472">Annals
of Mathematics and Artificial Intelligence</a></p>
<p><br>
</p>
<p>Important Dates<br>
</p>
<p>January 15, 2018: Paper submission (N.B.: NO other extensions
will be granted!)<br>
February 15, 2018: Author Notification<br>
February 28, 2018: Camera ready for pre-proceedings sent to
cao(at)caopt(dot)com<br>
March 1, 2018: Registration opens<br>
June 10-15, 2018: Conference<br>
</p>
<p><br>
</p>
<p>Conference General Chairs:</p>
<p>Prof. Panos Pardalos, Center for Applied Optimization, University
of Florida (USA) and</p>
<p>Prof. Ilias Kotsireas , CARGO Lab, Wilfrid Laurier University
(Canada) <br>
</p>
<p><br>
</p>
<p>Special Sessions</p>
<p>How machine learning is revolutionizing healthcare<br>
Organizers:<br>
<br>
Dr. Kostas Chrisagis <a class="moz-txt-link-rfc2396E"
href="mailto:kostasnc@gmail.com"><kostasnc@gmail.com></a>,
City University London, United Kingdom<br>
Dr. Serafeim Moustakidis <a class="moz-txt-link-rfc2396E"
href="mailto:smoustakidis@gmail.com"><smoustakidis@gmail.com></a>,
Center for Research and Technology Hellas<br>
<br>
Description:<br>
<br>
The proliferation of massive and heterogeneous health-related data
brings with it a series of special challenges enabling at the same
time opportunities for improving healthcare. Clinicians and health
experts are overwhelmed by the volume, velocity and variety of the
available data including medical imagery, data from wearable
sensors, electronic health records, genomic data, behavioral and
environmental data. The increased availability of data and
computational power has led to a resurgence of machine learning
leading the efforts to transform the vast amount of complex
health-related data into actionable knowledge. Machine learning
and deep learning are now attempting to revolutionize the whole
healthcare sector by improving diagnostics, predicting outcomes,
and changing the way doctors think about providing care.
Reflecting this excitement, this special session aims to identify
opportunities and challenges of the growing intersection of
machine learning and health.<br>
<br>
Topics of interest include but are not limited to:<br>
<br>
Imaging related decision making and computer-aided diagnosis<br>
Multi-modal Clinical Decision Support<br>
Machine learning / deep learning for medical image analysis<br>
Early detection and diagnosis of diseases<br>
Big data analytics in healthcare<br>
Data mining with interpretable models<br>
Enhanced imaging diagnostics<br>
Behavioral analysis with wearables<br>
Variable selection over high dimensional heath related data<br>
Personalized diagnosis and treatment<br>
Drug Discovery using unsupervised learning<br>
Computational Methods in Molecular Biology<br>
</p>
<p>---</p>
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</p>
<p>Computational Intelligence for Smart Cities<br>
Organizers:<br>
<br>
Enrique Alba (Professor), University of Malaga, Spain
(link)<br>
Konstantinos Parsopoulos (Associate Professor),
University of Ioannina, Greece (link)<br>
<br>
Description::)<br>
<br>
Global urbanization is continuously reshaping our world. More than
half of the world's population is currently living in urban areas,
with predictions adding 2.5 billion people to the cities over the
next few decades. This transformation provides great opportunities
for cultural and economic growth. However, it also comes along
with a number of challenging problems such as overpopulation in
metropolitan areas, cost of living, environmental pollution, and
inadequate infrastructures, among others. Smart cities attempt to
provide solutions to the continuously growing needs by integrating
information technologies and interconnected devices in urban
environments. This allows the collection and interpretation of
huge amounts of data that are used for optimizing various aspects
of the cities operation through the design and modeling of ad hoc
solutions and systems. Smart transportation systems, smart
buildings, smart communications and energy networks are some of
the most active research areas in this domain.<br>
<br>
Computational intelligence (C.I.) has played a significant role in
most complex systems existing till now, and they are also expected
to have a prominent position in smart cities. Its constituent
methodologies such as machine learning, data science, artificial
neural networks, evolutionary algorithms, swarm intelligence, and
fuzzy logic offer computationally efficient methodologies for
modeling, analyzing, and optimizing smart cities systems. Indeed,
computational intelligence is one important way to build to the
“smart” part of the city. The interplay of such approaches with
operations research and many other domains (civil engineering,
urban planning, policy makers, companies...) can offer innovative
and sustainable solutions to problems of high complexity. So far,
computational intelligence methodologies reckons a large number of
applications in smart cities, including smart transportation
systems, smart logistics, smart energy grids, smart resources
integration and pollution monitoring.<br>
<br>
The present special session welcomes works on any aspect of
computational intelligence in smart cities environments, both
theoretical and applied, including:<br>
<br>
Computational intelligence in smart transportation and
logistics<br>
Computational intelligence in urban mobility and
planning<br>
Computational intelligence in smart energy systems<br>
Computational intelligence in sustainability
(environmental, social, economic)<br>
Computational intelligence in smart homes and Internet
of Things<br>
Computational intelligence in smart healthcare systems<br>
Computational intelligence in governance<br>
Computational intelligence for people and good living<br>
Computational intelligence to tourism and entertainment
in the city<br>
Computational intelligence in circular economy<br>
Cyberphysical systems and Internet of Things coupled
with C.I.<br>
Computational intelligence for security, big data, open
data, and software for cities<br>
<br>
<br>
Applications involving efficient learning and optimization
methodologies for this type of problems are strongly encouraged.<br>
<br>
Important dates:<br>
<br>
February 15, 2018: Paper submission<br>
February 28, 2018: Author Notification<br>
March 15, 2018: Camera ready<br>
June 10-15, 2018: Conference</p>
<p>---</p>
<p><br>
</p>
<p>On the borderline between Data Analysis and Combinatorial
Optimisation: models, algorithms, and bounds<br>
Organizers:<br>
<br>
Prof. Alexander Kelmanov, Sobolev Institute of Mathematics,
Novosibirsk, Russia<br>
Prof. Michael Khachay, Krasovsky Institute of Mathematics and
Mechanics, Ekaterinburg, Russia<br>
<br>
Description:<br>
<br>
Combinatorial optimization and data analysis appear to be
extremely close fields of the modern computer science. For
instance, various areas in machine learning: PAC-learning,
boosting, cluster analysis, feature and model selection, etc. are
continuously presenting new challenges for designers of
optimization methods due to the steadily increasing demands on
accuracy, efficiency, space and time complexity and so on. In many
cases, learning problem can be successfully reduced to the
appropriate combinatorial optimization problem: max-cut, k-means,
p-median, TSP, and so on. To this end, all the results obtained
for the latter problem (approximation algorithms, polynomial-time
approximation schemes, approximation thresholds) can find their
application in design the high-precision and efficient learning
algorithms for the former one. On the other hand, there are known
examples, where combinatorial optimisation and computational
geometry benefits from using approaches developed in statistical
learning theory. Among them are Chernoff like measure
concentration theorems employed for designing of randomised
algorithms and schemas and Bronnimann-Goodrich epsilon-net
approach to approximation the famous Hitting Set problem. This
session welcomes papers presenting new results on computational
and parametric complexity, design and implementation of efficient
algorithms and schemes for various extremal problems coming from
combinatorial optimisation, classification, clustering,
computational geometry, and so on.<br>
<br>
Topics of interest include but are not limited to:<br>
<br>
computational and parametric complexity<br>
inapproximability issues and approximation thresholds<br>
polynomial time solvable subclasses of intractable problems<br>
polynomial time approximation algorithms and schemes<br>
randomized approximation and asymptotically optimal algorithms<br>
efficient approximation algorithms for geometric settings of
NP-hard problems<br>
efficient techniques of supervised, semi-supervised, and
unsupervised learning<br>
<br>
Important dates:<br>
<br>
February 15, 2018: Paper submission<br>
February 28, 2018: Author Notification<br>
March 15, 2018: Camera ready<br>
June 10-15, 2018: Conference</p>
<p>---</p>
<p><br>
</p>
<p>Graphical model selection and applications<br>
Organizers:<br>
<br>
Dr. Valeriy Kalyagin, Laboratory of Algorithms and
Technologies for Network Analysis, National Research University
Higher School of Economics, Nizhny Novgorod, Russia<br>
Dr. Mario Guarracino, High Performance Computing and
Networking Institute, Italian National Research Council, Naples,
Italy<br>
<br>
Description:<br>
<br>
Graphical models provide a unifying framework for capturing
dependencies in complex systems. Graphical models are recognized
as a useful tool in many applied fields, such as bioinformatics,
communication theory, combinatorial optimization, signal and image
processing, information retrieval, stock market network analysis
and statistical machine learning. Graphical model selection is a
practical problem of identification of the underlying graphical
model from observations. The session will be devoted to
theoretical aspects and practical algorithms for graphical model
selection and its applications. Estimating the graph structure
given a set of observations at the nodes is very common in many
fields and in particular in biology, where the complexity of
processes and functions are widely modeled by networks. From
protein interaction to metabolic pathways, from gene regulatory
circuits to brain connectomes, networks have sizes that range from
few thousands to many trillions vertices. From their analysis, we
can obtain more insights in complex questions, identifying for
example their critical points, robustness and modularity. In this
session, we will address some of the recent advances on graphical
model selection, that can find application in different
disciplines and applications.<br>
<br>
Topics of interest include but are not limited to:<br>
<br>
Graphical model selection in bioinformatics<br>
Graphical model selection in communication<br>
Graphical model selection in combinatorial optimization<br>
Graphical model selection in signal and image processing<br>
Graphical model selection in information retrieval<br>
Graphical model selection in market network analysis<br>
Graphical model selection in statistical machine learning<br>
Graphical model selection in gene expression network<br>
Graphical model selection in gene co expression network<br>
<br>
Important dates:<br>
<br>
February 15, 2018: Paper submission<br>
February 28, 2018: Author Notification<br>
March 15, 2018: Camera ready<br>
June 10-15, 2018: Conference</p>
<p>---</p>
<p><br>
</p>
<p>Optimization and Management in Smart Manufacturing<br>
Organizers:<br>
<br>
Dr. Panos M. Pardalos, <a class="moz-txt-link-rfc2396E"
href="mailto:pardalos@ufl.edu"><pardalos@ufl.edu></a>,
University of Florida, USA<br>
Dr. Xinbao Liu, <a class="moz-txt-link-rfc2396E"
href="mailto:lxb@hfut.edu.cn"><lxb@hfut.edu.cn></a>,
School of Management, Hefei University of Technolog, China<br>
Dr. Jun Pei, <a class="moz-txt-link-rfc2396E"
href="mailto:peijun@hfut.edu.cn"><peijun@hfut.edu.cn></a>,
School of Management, Hefei University of Technolog, China<br>
<br>
Description:<br>
<br>
Current global science and technology innovation shows some new
development trends and characteristics. Emerging information
technology such as internet and big data technology are widely
permeated which drives the group technology revolution
characterized by green, intelligent, and ubiquitous in almost all
the areas. Intelligentization, greenization, servitization, and
interconnection are becoming important for the development and
revolution of manufacturing industry. Interdisciplinary and
networked innovative platform is reforming the innovation system
of traditional manufacturing industry. A new green manufacturing
system based on the value network is forming. This session aims to
apply data-driven resource management and optimization technique
to offer theory support for the management revolution, business
pattern revolution, decision theories and methods innovation, and
intelligent decision system construction.<br>
<br>
Topics of interest include but are not limited to:<br>
<br>
Network Manufacturing<br>
Sustainability Manufacturing Strategy Management<br>
Sustainability Supply Chain Operations Management<br>
R&D project management of high-end equipment<br>
Environmental and Sustainability Assessment<br>
Operations Management of Smart Factory<br>
Behavioral Operations Management<br>
Production engineering management of high-end equipment<br>
Inventory Planning and Control of Green Products<br>
Green Logistics Operation and Management<br>
Service engineering management of high-end equipment<br>
Remanufacturing engineering management of high-end equipment<br>
Quality Management Based on Industrial Big Data<br>
Development Management of Renewable Energy Technologies<br>
Big Data Applications in Smart Manufacturing</p>
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