Dear Faculty/Research Scholar

Submissions to special issues in WSEAS Trans. on Systems, a SCOPUS indexed journal, are now open.

Please consider to submit a paper to one of the special issues.

Their Call for Papers are available athttp://www.wseas.org/wseas/cms.action?id=4072

Call for Papers WSEAS TRANSACTIONS ON SYSTEMS

Special Issue on Self Adaptive System and Autonomic Machine Learning

Visit: http://www.wseas.org/main/journals/systems/SpecialIssue9.pdf

I. AIM AND SCOPE

The present special issue is concerned with concepts and techniques which can rely on metaphors of nature and which are inspired from biological and cognitive plausibility. Recent studies are conducted which reveal that the foundations of autonomic machine learning where the term autonomic refers to the emerging non-imperative and highly autonomous machine learning mechanism and self adaptive system, has its root in cognitive informatics theories and automatic computing technologies. Being the basis for many modeling approaches and computational techniques, it offers a very promising foundation for investigating the adaptivity of intelligent systems that evolve in dynamically changing environments. Self adaptive system and autonomic machine learning involves a large spectrum of theories from learning theory to nature inspired optimization metaheuristics. Conventional machine learning systems utilizes the merits of imperative and instructive programming techniques in AI. In recent days learning mechanism in the brain and natural intelligence has greatly enhanced and inspired the investigation into autonomic learning system. The autonomic machine learning systems are a fully goal driven and non-imperative system that possesses powerful machine intelligence for knowledge acquisition, processing, comprehension and memorization based on contemporary denotational mathematics and autonomic learning techniques. Despite the existing literature on adaptivity and machine learning, the notion of “incrementality” as a property of self-adaption, self-organization, self-monitoring and self-growing has not yet been well studied. Rigorous theories, empirical methodologies, and industrial application on adaptive and autonomic machine learning systems are sought for this special issue to advance the cross fertilization between autonomous system cognitive informatics and autonomic computing.

This special issue aims at presenting the latest advances of self-adaptivity and autonomic machine learning with focus on modeling approaches, computational methods, autonomic, autonomous and adaptive machine learning theories, technologies and systems. The special issue is intended for a wide range of audience including neural network scientists, mathematicians, engineers, computer scientists’ biologists, economists and social scientists. This special issue will cover various topics of self-adaptive system and autonomic machine learning concepts. It also aims at presenting coherent view of the issues and a thorough discussion about the future research avenues.

A sample of the targeted topics, which is suggestive rather than exhaustive, includes:

II. TOPICS COVERD

Authors are invited to submit their original and unpublished work in the following areas:

Self growing systems Adaptation in changing environments

Online adaptive and life-long learning Incremental adaptive neuro-fuzzy systems

Incremental and single-pass data mining Incremental classification systems

Incremental clustering Concept drift in evolving systems

Self-monitoring in evolving systems Incremental diagnostics

Novelty detection in evolving learning Incremental feature selection and reduction

Adaptive decision systems Principles of self-organization

Methodologies of self-organization Dynamic optimization

Neural networks Evolutionary computation

Swarm intelligence Fuzzy systems

Mimetic Algorithm Smart systems

Ambient / ubiquitous environments Distributed intelligence

Intelligent agent technology Robotics

Industrial applications E-commerce

Autonomic learning mechanisms AMLS architectures

Denotational mathematics AMLS behaviors

Concept algebra AMLS interactions

Cognitive informatics AMLS communications

Machine tutoring systems AMLS knowledge-base representations

Taxonomy of learning AMLS knowledge acquisitions

Modeling of learning processes AMLS inference engines

Internal knowledge representation Autonomic computing

Problem domains for AMLS’s Formal inferences methods

Web-based learning engines Non-imperative learning methods

AMLS simulations Fuzzy inference methods

Industrial requirements Learning and problem-solving

Case studies on AMLS’s Learning and memorization

Autonomic robots Cognitive agents

Autonomic learning support systems Taxonomy of learning

III. IMPORTANT DATES

May 30, 2015 :

Submission deadline

July 30, 2015 : Notification of the first-round review

September 30, 2015 : Revised submission due and following

publication of the accepted papers

IV. SUBMISSION

Manuscripts should be prepared according to the formatting instructions of available at WSEAS Transactions on Systems athttp://wseas.org/wseas/cms.action?id=4067. Manuscripts submitted to the Special Issue on Self Adaptive System and Autonomic Machine Learning are to be submitted following the standard submission process and notifying the Guest Editors as well. All submitted manuscripts will be reviewed using the standard procedure that is followed for regular submissions.

V. GUEST EDITORS

Asst. Prof. Hari Mohan Pandey

Department of Computer Science & Engineering

Amity School of Engineering & Technology,

Amity University, India

E-mail: [email protected] , [email protected]

EDITOR-IN-CHIEF

Prof. Filippo Neri

Dept. of Computer Science

University of Naples

Naples, Italy

E-mail: [email protected] , [email protected]

The WSEAS Transactions on Systems is indexed by:

American Mathematical Society (AMS)

AMS Digital Mathematics Registry

Cabell Publishing

CiteSeerx

Cobiss

Compendex®

EBSCO

EBSCO | TOC Premier™

EBSCOhost | Academic Search Research and Development

EBSCOhost | Applied Science and Technology Source

EBSCOhost | Engineering Source

Electronic Journals Library

ELSEVIER®

Engineering Index (EI)

Engineering Village

Google Scholar

Index Copernicus

Inspec | The IET

MathSciNet®

Microsoft Academic Search System

RoMEO Database | University of Nottingham, UK

SAO/NASA Astrophysics Data System

SCIRUS

SCOPUS®

SWETS

The Collection of Computer Science Bibliographies

The Informatics Portal io-port.net

TIB|UB | German National Library of Science and Technology

Ulrich's International Periodicals Directory

WorldCat OCLC

Zentralblatt MATH

Page Length: Minimum number of pages should be 10.

Impact Factor: 0.44 (Scimago)

Paper Template: http://www.wseas.org/wseas/cms.action?id=4071

Thank you your kind attention.