Opening Address

2017 ◽  
Vol 21 (1) ◽  
pp. 8-8
Author(s):  
Editor: Prof. Yasufumi Takama ◽  
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Computational Intelligence ◽  
Smart Grids ◽  
Fuzzy Inference ◽  
Peer Review Process ◽  
Web Intelligence ◽  
The Past ◽  
Research Areas ◽  
Institute Of Technology

The JACIII was first published in 1997, and 2017 marks its 20th anniversary. During the last two decades, the research fields in computational intelligence have rapidly evolved owing to the spread of the Internet, performance improvement of computers, and accumulation of scientific knowledge. To celebrate this 20th anniversary, we have selected 6 important research areas from the JACIII scope, and invited outstanding researchers from each of these areas to contribute papers about the progress and major topics in those areas during the past 20 years. Submitted paper went through a peer-review process by distinguished professors to further improve the quality. The research areas selected were computational intelligence, fuzzy intelligence, intelligent robots, artificial intelligence and web intelligence, data mining, and smart grids. Each of those paper covers broad topics appeared in the research areas, from which readers could grasp what happened during the past 20 years. We also hope readers could find some hints about future directions of their own researches towards the next 20 years. <strong>Invited Paper 1: Computational Intelligence: Retrospection and Future</strong> Author: Witold Pedrycz (University of Alberta, Canada) <strong>Invited Paper 2: Fuzzy Inference: Its Past and Prospects</strong> Authors: Kiyohiko Uehara (Ibaraki University, Japan) and Kaoru Hirota (Beijing Institute of Technology, China) <strong>Invited Paper 3: Relationship Between Human and Robot in Nonverbal Communication</strong> Authors: Yukiko Nakagawa and Noriaki Nakagawa (RT Corporation, Japan) <strong>Invited Paper 4: Web Intelligence and Artificial Intelligence</strong> Author: Yasufumi Takama (Tokyo Metropolitan University, Japan) <strong>Invited Paper 5: A Review of Data Mining Techniques and Applications</strong> Authors: Ratchakoon Pruengkarn, Kok Wai Wong, and Chun Che Fung (Murdoch University, Australia) <strong>Invited Paper 6: Development and Current State of Smart Grids: A Review</strong> Author: Ken Nagasaka (Tokyo University of Agriculture and Technology, Japan)

Selected Papers from ISIS 2007

2008 ◽  
Vol 12 (6) ◽  
pp. 487-487
Author(s):  
G. K. Park ◽  
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
National Park ◽  
Special Issue ◽  
International Program ◽  
Tokyo Institute ◽  
Institute Of Technology

The 8th International Symposium on Advanced Intelligent Systems (ISIS 2007), a biennial joint conference between Korea and Japan, focuses on artificial intelligence, intelligent systems, information technology, and their applications. ISIS 2007, held on September 5-8 at Soraksan National Park under the auspices of the Korea Fuzzy Logic and Intelligent Systems Society (KFIS), was attended by 265 researchers, engineers, and other professionals and featured 206 presentations. Of more than 20 papers preliminarily selected and reviewed by the ISIS 2007 International Program Committees, 7 were chosen for the special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics, centering on advanced intelligent systems including robotics, pattern recognition, data mining, and decision-making systems. The content and conclusions presented in these fine papers should prove both interesting and informative to specialists and generalists alike. I thank the authors and reviewers for their painstaking contributions to this special issue and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting me to guest-edit this work.

scholarly journals Combined artificial intelligence modeling for production forecast in a petroleum production field

2019 ◽  
Vol 9 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Marco Antonio Ruiz- Serna ◽  
Guillermo Arturo Alzate- Espinosa ◽  
Andrés Felipe Obando- Montoya ◽  
Hernán Dario Álvarez- Zapata
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Fuzzy Inference ◽  
Fluid Phase ◽  
Gas Production ◽  
Production Model ◽  
Data Imputation ◽  
Production Forecast ◽  
Well Production ◽  
Inference Systems

This paper presents the results about using a methodology that combines two artificial intelligence (AI) models to predict the oil, water and gas production in a Colombian petroleum field. By combining fuzzy logic (FL) and artificial neural networks (ANN) a novelty data mining procedure is implemented, including a data imputation strategy. The FL tool determines the most useful variables or parameters to include into each well production model. ANN and FIS (fuzzy inference systems) predictive models identification is developed after the data mining process. The FIS models are capable to predict specific behaviors, while ANN models are able to forecast an average behavior. The combined use of both tools under few iterative steps, allows to improve forecasting of well behavior until reach a specified accuracy level. The proposed data imputation procedure is the key element to correct false or to complete void positions into operation data used to identify models for a typical oil production field. At the end, two models are obtained for each well product, conforming an interesting tool given the best accurate prediction of fluid phase production.

Strategic Utilization of Data Mining

2005 ◽  
pp. 2638-2642
Author(s):  
Chandra S. Amaravadi
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Relational Databases ◽  
Online Analytical Processing ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Analytical Processing

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Strategic Utilization of Data Mining

2008 ◽  
pp. 1689-1695
Author(s):  
Chandra S. Amaravadi
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Support ◽  
Information Systems ◽  
Relational Databases ◽  
Online Analytical Processing ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Analytical Processing

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Computational intelligence with applications to general insurance: a review

2012 ◽  
Vol 6 (2) ◽  
pp. 307-343 ◽  
Author(s):  
Pietro Parodi
Keyword(s):  
Data Mining ◽  
Life Insurance ◽  
Computational Intelligence ◽  
Feature Construction ◽  
The Past ◽  
General Insurance ◽  
Rating Factor ◽  
Clustering Data ◽  
Computational Intelligence Methods

AbstractThis paper argues that most of the problems that actuaries have to deal with in the context of non-life insurance can be usefully cast in the framework of computational intelligence (a.k.a. artificial intelligence), the discipline that studies the design of agents which exhibit intelligent behaviour. Finding an adequate framework for actuarial problems has more than a simply theoretical interest: it also allows a knowledge transfer from the computational intelligence discipline to general insurance, wherever techniques have been developed for problems which are common to both contexts. This has already happened in the past (neural networks, clustering, data mining have all found applications to general insurance) but not systematically, with the result that many useful computational intelligence techniques such as sparsity-based regularisation schemes (a technique for feature selection) are virtually unknown to actuaries.In this first of two papers, we will explore the role of statistical learning in actuarial modelling. We will show that risk costing, which is at the core of pricing, reserving and capital modelling, can be described as a supervised learning problem. Many activities involved in exploratory analysis, such as data mining or feature construction, can be described as unsupervised learning. A comparison of different computational intelligence methods will be carried out, and practical insurance applications (rating factor selection, IBNER analysis) will also be presented.

scholarly journals Literature Review on Artificial Intelligence in Journalism: A Bibliometric Analysis of Publications Indexed in the Web of Science and Scopus

2021 ◽  
Vol 10 (4) ◽  
pp. 734-744
Author(s):  
Violetta Zorina ◽  
Elizaveta Osipovskaya
Keyword(s):  
Artificial Intelligence ◽  
Quantitative Research ◽  
Web Of Science ◽  
Empirical Studies ◽  
Academic Literature ◽  
The Past ◽  
Research Areas ◽  
Current State ◽  
Number Of Publications ◽  
The Web

This article reviews the past-to-present academic literature on artificial intelligence (AI) in journalism. Over the past years, these technologies have attracted the sufficient attention of researchers from various fields of scientific study producing a large number of publications. We have reviewed academic articles published between 2015 and 2021 to provide understanding of the current state of the research on AI in various research areas including journalism. The corpus was gathered by searching publications in two international databases, Scopus and the Web of Science (WoS). 70 empirical studies were selected on the basis of applying AI to journalism. Each article was categorized according to the type of database, period of time, the country of publication, the field of study and the frequency of citations. The applied method of quantitative research allows tracking the development of research within six years in the field of automated journalism. Finally, we put forward several proposals for further research in this field.

scholarly journals SURVEY OF DATA MINING ALGORITHM’S FOR INTELLIGENT COMPUTING SYSTEM

2019 ◽  
Vol 01 (01) ◽  
pp. 14-23 ◽  
Author(s):  
Iwin Thanakumar Joseph S
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Computational Intelligence ◽  
Computing System ◽  
Mutual Understanding ◽  
Intelligent Computing ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Intelligent Computing System

The Intelligent computing system, described to be a collection of the connected device working in mutual understanding to attain a particular purpose, is an incorporation of artificial intelligence and the computational intelligence, and are employed in variety of applications. The paper presents the survey on the data mining algorithms and the techniques that could be employed with the intelligent computing system, presenting a basic conception of the data mining along with the prominent algorithms of the data mining and the classification of its techniques, further the survey concludes with the challenges included in the overview of the survey done along with the future enhancement in the research that analyses the data mining techniques in the intelligent computing applications.

scholarly journals Reinforced Social Ant with Discrete Swarm Optimizer for Sensitive Item and Rule Hiding

2019 ◽  
Vol 8 (9) ◽  
pp. 902-908
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Distributed Databases ◽  
Data Publishing ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Swarm Optimization ◽  
Privacy Preserving Data Mining ◽  
The Past ◽  
Research Areas

In data mining Privacy Preserving Data mining (PPDM) of the important research areas concentrated in recent years which ensures ensuring sensitive information and rule not being revealed. Several methods and techniques were proposed to hide sensitive information and rule in databases. In the past, perturbation-based PPDM was developed to preserve privacy before use and secure mining of association rules were performed in horizontally distributed databases. This paper presents an integrated model for solving the multi-objective factors, data and rule hiding through reinforcement and discrete optimization for data publishing. This is denoted as an integrated Reinforced Social Ant and Discrete Swarm Optimization (RSADSO) model. In RSA-DSO model, both Reinforced Social Ant and Discrete Swarm Optimization perform with the same particles. To start with, sensitive data item hiding is performed through Reinforced Social Ant model. Followed by this performance, sensitive rules are identified and further hidden for data publishing using Discrete Swarm Optimization model. In order to evaluate the RSA-DSO model, it was tested on benchmark dataset. The results show that RSA-DSO model is more efficient in improving the privacy preservation accuracy with minimal time for optimal hiding and also optimizing the generation of sensitive rules.

scholarly journals A Review Of Trends In Research On Web Mining

2011 ◽  
pp. 37-41
Author(s):  
Manoj Pandia ◽  
Subhendu Kumar Pani ◽  
Sanjay Kumar Padhi ◽  
Lingaraj Panigrahy ◽  
R. Ramakrishna
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
World Wide Web ◽  
Web Mining ◽  
World Wide ◽  
Research Topic ◽  
Research Areas ◽  
The World ◽  
Fertile Area ◽  
The Web

In recent years the growth of the World Wide Web exceeded all expectations. Today there are several billions of HTML documents, pictures and other multimedia files available via internet and the number is still rising. But considering the impressive variety of the web, retrieving interesting content has become a very difficult task.So, the World Wide Web is a fertile area for data mining research.Web mining is a research topic which combines two of the activated research areas: Data Mining and World Wide Web. Web mining research relates to several research communities such as Database, information Retrieval and Artificial intelligence, visualization.This paper reviews the research and application issues in web mining besides proving an overall view of Web mining.

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