[AISWorld] Contents of IJIIT 14(4) - International Journal of Intelligent Information Technologies (IJIIT)
Vijayan Sugumaran
sugumara at oakland.edu
Thu Aug 9 10:11:12 EDT 2018
The contents of the latest issue of:
International Journal of Intelligent Information Technologies (IJIIT)
Volume 14, Issue 4, October - December 2018
Indexed by: Compendex (Elsevier Engineering Index), INSPEC, SCOPUS, Web of
Science Emerging Sources Citation Index (ESCI)
For a complete list of indexing and abstracting services that include this
journal, please reference the bottom of this announcement.
Published: Quarterly in Print and Electronically
ISSN: 1548-3657; EISSN: 1548-3665;
Published by IGI Global Publishing, Hershey, USA
www.igi-global.com/ijiit
<https://www.igi-global.com/journal/international-journal-intelligent-inform
ation-technologies/1089>
Editor-in-Chief: Vijayan Sugumaran (Oakland University, USA)
Note: The International Journal of Intelligent Information Technologies
(IJIIT) has an Open Access option, which allows individuals and institutions
unrestricted access to its published content. Unlike traditional
subscription-based publishing models, open access content is available
without having to purchase or subscribe to the journal in which the content
is published. All IGI Global manuscripts are accepted based on a
double-blind peer review editorial process.
ARTICLE 1
Society of Agents: A Framework for Multi-Agent Collaborative Problem Solving
Steven Walczak (University of South Florida, Tampa, USA)
The development of multiple agent systems faces many challenges, including
agent coordination and collaboration on tasks. Minsky's The Society of Mind
provides a conceptual view for addressing these multi-agent system problems.
A new classification ontology is introduced for comparing multi-agent
systems. Next, a new framework called the Society of Agents is developed
from Minsky's conceptual foundation. A Society of Agents framework-based
problem-solving and a Game Society is developed and applied to the domain of
single player logic puzzles and two player games. The Game Society solved
100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe
game. The advantage of the Society of Agents approach is the efficient
re-utilization of agents across multiple independent game domain problems
and a centralized problem-solving architecture with efficient cross-agent
information sharing.
To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/society-of-agents/211189
<https://www.igi-global.com/article/society-of-agents/211189>
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=211189
<https://www.igi-global.com/viewtitlesample.aspx?id=211189>
ARTICLE 2
Towards a Service-Oriented Architecture for Knowledge Management in Big Data
Era
Thang Le Dinh (UQTR Business School, Université du Québec à Trois-Rivières,
Trois-Rivières, Canada), Thuong-Cang Phan (Can Tho University, Can Tho, Viet
Nam), Trung Bui (Adobe Research, San Jose, USA), Manh Chien Vu (Université
du Québec à Trois-Rivières, Trois-Rivières, Canada)
Nowadays, big data is a revolution that transforms conventional enterprises
into data-driven organizations in which knowledge discovered from big data
will be integrated into traditional knowledge to improve decision-making and
to facilitate organizational learning. Consequently, a major concern is how
to evolve current knowledge management systems, which are confronted with a
various and unprecedented amount of data, resulting from different data
sources. Therefore, a new generation of knowledge management systems is
required for exploring and exploiting big data as well as for facilitating
the knowledge co-creation between the society and its business environment
to foster innovation. This article proposes a service-oriented architecture
for elaborating a new generation of big data-driven knowledge management
systems to help enterprises to promote knowledge co-creation and to obtain
more business value from big data. The proposed architecture is presented
based on the principles of design science research and its evaluation uses
the analytical evaluation method.
To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/towards-a-service-oriented-architecture-for-knowl
edge-management-in-big-data-era/211190
<https://www.igi-global.com/article/towards-a-service-oriented-architecture-
for-knowledge-management-in-big-data-era/211190>
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=211190
<https://www.igi-global.com/viewtitlesample.aspx?id=211190>
ARTICLE 3
Improving Polarity Classification for Financial News Using Semantic
Similarity Techniques
Tan Li Im (Universiti Malaysia Sabah, Kota Kinabalu, Malaysia), Phang Wai
San (Universiti Malaysia Sabah, Kota Kinabalu, Malaysia), Patricia Anthony
(Lincoln University, Christchurch, New Zealand), Chin Kim On (Universiti
Malaysia Sabah, Kota Kinabalu, Malaysia)
This article discusses polarity classification for financial news articles.
The proposed Semantic Sentiment Analyser makes use of semantic similarity
techniques, sentiment composition rules, and the Positivity/Negativity (P/N)
ratio in performing polarity classification. An experiment was conducted to
compare the performance of three semantic similarity metrics namely HSO,
LESK, and LIN to find the semantically similar pair of word as the input
word. The best similarity technique (HSO) is incorporated into the sentiment
analyser to find the possible polarity carrier from the analysed text before
performing polarity classification. The performance of the proposed Semantic
Sentiment Analyser was evaluated using a set of manually annotated financial
news articles. The results obtained from the experiment showed that the
proposed SSA was able to achieve an F-Score of 90.89% for all cases
classification.
To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/improving-polarity-classification-for-financial-n
ews-using-semantic-similarity-techniques/211191
<https://www.igi-global.com/article/improving-polarity-classification-for-fi
nancial-news-using-semantic-similarity-techniques/211191>
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=211191
<https://www.igi-global.com/viewtitlesample.aspx?id=211191>
ARTICLE 4
Named Entity System for Tweets in Hindi Language
Arti Jain (Jaypee Institute of Information Technology, Noida, India), Anuja
Arora (Jaypee Institute of Information Technology, Noida, India)
Due to the growing need of smart-health applications in Hindi language,
there is a rapid demand for health-related Named Entity Recognition (NER)
system for Hindi. For the purpose of the same, this research considers
Twitter social network to extract tweets dated 1st October 2016 to 15th
October 2017 from Patanjali, Dabur and other Hindi language-oriented Twitter
based health sites; while considering four NE types- Person, Disease,
Consumable and Organization. To the best of its knowledge, the considered
Twitter dataset and NE types for Hindi language is one of the first
resources that is being taken care. This article introduces three stage NER
system for Tweets in Hindi language (HinTwtNER system)- pre-processing
stage; machine Learning stage (Hyperspace Analogue to Language (HAL) and
Conditional Random Field (CRF)); and post-processing stage. HinTwtNER looks
into binary features and achieves an overall F-score of 49.87% which is
comparable to the Twitter based NER systems for English and other languages.
To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/named-entity-system-for-tweets-in-hindi-language/
211192
<https://www.igi-global.com/article/named-entity-system-for-tweets-in-hindi-
language/211192>
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=211192
<https://www.igi-global.com/viewtitlesample.aspx?id=211192>
ARTICLE 5
Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition
C. Sweetlin Hemalatha (VIT University, Vellore, India), Vignesh Sankaran
(Madras Institute of Technology, Anna University, Chennai, India), Vaidehi V
(VIT University, Vellore, India), Shree Nandhini S (Madras Institute of
Technology, Anna University, Chennai, India), Sharmi P (Madras Institute of
Technology, Anna University, Chennai, India), Lavanya B (Madras Institute of
Technology, Anna University, Chennai, India), Vasuhi S (Madras Institute of
Technology, Anna University, Chennai, India), Ranajit Kumar (NCPW,
Department of Atomic Energy, Mumbai, India)
Face recognition from a large video database involves more search time. This
article proposes a symmetric uncertainty based search space reduction
(SUSSR) methodology that facilitates faster face recognition in video,
making it viable for real time surveillance and authentication applications.
The proposed methodology employs symmetric uncertainty based feature subset
selection to obtain significant features. Further, Fuzzy C-Means clustering
is applied to restrict the search to nearest possible cluster, thus speeding
up the recognition process. Kullback Leibler's divergence based similarity
measure is employed to recognize the query face in video by matching the
query frame with that of stored features in the database. The proposed
search space reduction methodology is tested upon benchmark video face
datasets namely FJU, YouTube celebrities and synthetic datasets namely
MIT-Dataset-I and MIT-Dataset-II. Experimental results demonstrate the
effectiveness of the proposed methodology with a 10 increase in recognition
accuracy and 35 reduction in recognition time.
To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/symmetric-uncertainty-based-search-space-reductio
n-for-fast-face-recognition/211193
<https://www.igi-global.com/article/symmetric-uncertainty-based-search-space
-reduction-for-fast-face-recognition/211193>
To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=211193
_____
For full copies of the above articles, check for this issue of the
International Journal of Intelligent Information Technologies (IJIIT) in
your institution's library. This journal is also included in the IGI Global
aggregated "InfoSci-Journals" database: www.igi-global.com/isj
<https://www.igi-global.com/e-resources/infosci-databases/infosci-journals/>
.
_____
CALL FOR PAPERS
Mission of IJIIT:
The advent of the World Wide Web has sparked renewed interest in the area of
intelligent information technologies. There is a growing interest in
developing intelligent technologies that enable users to accomplish complex
tasks in web-centric environments with relative ease, utilizing such
technologies as intelligent agents, distributed computing in heterogeneous
environments, and computer supported collaborative work. The mission of the
International Journal of Intelligent Information Technologies (IJIIT) is to
bring together researchers in related fields such as information systems,
distributed AI, intelligent agents, and collaborative work, to explore and
discuss various aspects of design and development of intelligent
technologies. This journal provides a forum for academics and practitioners
to explore research issues related to not only the design, implementation
and deployment of intelligent systems and technologies, but also economic
issues and organizational impact. Papers related to all aspects of
intelligent systems including theoretical work on agent and multi-agent
systems as well as case studies offering insights into agent-based problem
solving with empirical or simulation based evidence are welcome.
Indices of IJIIT:
* ACM Digital Library
* Australian Business Deans Council (ABDC)
* Bacon's Media Directory
* Burrelle's Media Directory
* Cabell's Directories
* Compendex (Elsevier Engineering Index)
* CSA Illumina
* DBLP
* DEST Register of Refereed Journals
* Gale Directory of Publications & Broadcast Media
* GetCited
* Google Scholar
* INSPEC
* JournalTOCs
* Library & Information Science Abstracts (LISA)
* MediaFinder
* Norwegian Social Science Data Services (NSD)
* SCOPUS
* The Index of Information Systems Journals
* The Standard Periodical Directory
* Ulrich's Periodicals Directory
* Web of Science
* Web of Science Emerging Sources Citation Index (ESCI)
Coverage of IJIIT:
The International Journal of Intelligent Information Technologies (IJIIT)
encourages quality research dealing with (but not limited to) the following
topics:
* Agent-based auction, contracting, negotiation, and ecommerce
* Agent-based control and supply chain
* Agent-based simulation and application integration
* Cooperative and collaborative systems
* Distributed intelligent systems and technologies
* Human-agent interaction and experimental evaluation
* Implementation, deployment, diffusion, and organizational impact
* Integrating business intelligence from internal and external sources
* Intelligent agent and multi-agent systems in various domains
* Intelligent decision support systems
* Intelligent information retrieval and business intelligence
* Intelligent information systems development using design science
principles
* Intelligent Web mining and knowledge discovery systems
* Manufacturing information systems
* Models, architectures and behavior models for agent-oriented information
systems
* Multimedia information processing
* Privacy, security, and trust issues
* Reasoning, learning and adaptive systems
* Semantic Web, Web services, and ontologies
Interested authors should consult the journal's manuscript submission
guidelines
www.igi-global.com/calls-for-papers/international-journal-intelligent-inform
ation-technologies/1089
<http://www.igi-global.com/calls-for-papers/international-journal-intelligen
t-information-technologies/1089>
=============================================
Vijayan Sugumaran, Ph.D.
Professor of Management Information Systems
Chair, Department of Decision and Information Sciences
Co-Director, Center for Data Science and Big Data Analytics
School of Business Administration
Oakland University
Rochester, MI 48309
Phone: 248-370-4649
Fax: 248-370-4275
Email: <mailto:sugumara at oakland.edu> sugumara at oakland.edu
=============================================
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