Message from Editors-in-Chief

It is our great pleasure to congratulate the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) on its 20th anniversary. The concept of Computational Intelligence was originally authorized in the first IEEE World Congress on Computational Intelligence (WCCI) held in Orlando, Florida in 1994, where the key issues of CI were characterized by fuzzy, neuro, and EC (Evolutionary Computation). The JACI (Journal of Advanced Computational Intelligence) Vol.1 No.1 was published from Fuji Technology Press in Oct 1997, and the name of the journal has been changed to JACIII since Vol.7 No.1 Feb 2003 to accept more widely developed issues related to intelligent informatics. The JACIII is currently published in bimonthly, six issues a year, in cooperation with International Fuzzy Systems Association (IFSA), Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT), Brazilian Society of Automatics (SBA), The Society of Instrument and Control Engineers (SICE), John von Neumann Computer Society (NJSZT), Vietnamese Fuzzy Systems Society (VFSS), Fuzzy Systems and Intelligent Technologies Research Society of Thailand (FIRST), Korean Institute of Intelligent Systems (KIIS), and Taiwanese Association for Artificial Intelligence (TAAI), and is indexed in ESCI, SCOPUS, and COMPENDEX (Ei-Index). In the past 20 years personally, so many issues happened in the JACIII editing process. Some of them were very tough but all are very good memories to us. We occasionally organize the JACIII editorial meetings in Tokyo and remotely. As Editors-in-Chief, we are most grateful to all who have worked with the JACIII and helped reach its 20th anniversary. We thank the editorial board members for editing this journal with their high discernment and the guest editors for arranging special issues with their high profession. We would also like to thank the peer reviewers for their accurate evaluations in a short time, and finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its founder, Mr. K. Hayashi, and to the editors, Ms. Reiko Ohta and Mr. Kunihiko Uchida for their efforts in publishing this journal.

Intelligent Engineering Systems

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution this permits a rapid and broad dissemination of research results.The field of Artificial Intelligence developed important concepts for simulating human intelligence. Its sister field, Applied Intelligence, has focused on techniques for developing intelligent systems for solving real life problems in all disciplines including science, social science, art, engineering, and finance. The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) is to promote and disseminate research in Applied Intelligence. It seeks quality papers on a wide range of topics in applied intelligence that are employed in developing intelligent systems for solving real life problems in all disciplines. Every year this conference brings together scientists, engineers and practitioners, who work on designing and developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains. The book is comprised of seventeen chapters providing up-to-date and state-of-the-art research on the applications of applied Intelligence techniques.

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.

Intelligent Engineering Systems

SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of researches from fuzzy systems, neural networks, evolutionary computation, multi-agent systems, artificial intelligence or robotics. SCIS & ISIS 2006 falls on the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held at Tokyo Institute of Technology, in Tokyo, Japan, on September 20-24, 2006. In this conference, 464 original papers were accepted for presentation and the number of attendees was 526. After preliminary selection and review made by the session chairs and the International Program Committees of SCIS & ISIS 2006, we have selected more than 50 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. The accepted papers are published as the special issues in Vol.11, No.6, 7, and 8 in 2007. This current issue presents 23 papers and covers most of the topics of the conference including fuzzy theories, self-organizing maps, and the optimization of neural networks. The learning and search methods in computational intelligence and real-world applications to image processing, robotics and manufacturing systems are highlighted in this current issue. I would like to thank all the authors and reviewers for their contribution to make this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University and Prof. Kaoru Hirota, Tokyo Institute of Technology, Editors-in-chief, for inviting me to serve as Guest Editor of this Journal.

The autonomous city implies a global vision that incorporates artificial intelligence, deep learning, big data, decision-making, ICT and the Internet of Things (IoT) to promote sustainable development. The ageing issue is that researchers, companies and the government should devote efforts to developing smart health care, innovative technology and applications. For an extended period, conventional intelligence systems have played a critical role in health care. However, with the increased popularity and widespread use of these hybrid intelligent computer systems, there is a significant shift in the healthcare sector. Diagnosis and detection of various diseases using several techniques can be resolved by using these techniques. Different novel methods are applied to biomedical engineering to diagnose diseases, and new models are being studied and compared with the existing technologies. Hybrid intelligence systems can be implied in decision-making, remote monitoring, healthcare logistics, medical diagnosis, and modern information system. This success's fundamental cause seems to be derived by different intelligent computational mechanisms, such as genetic algorithms, evolutionary computation, convolutional neural network (CNN), long short-term memory (LSTM), autoencoders, deep generative models and deep belief networks. To solve complex problems, we need domain knowledge that comprises the methodologies that provide hybrid systems with complementary reasoning and empirical data. This chapter will focus on the need for a hybrid intelligent system in the healthcare industry and their medical diagnosis applicability.KeywordsIntelligent systemDeep learningGenetic algorithmsNeural networksHybrid intelligence systemComputational intelligence

Soft computing is applied for a multidisciplinary system that includes neural networks (NN), fuzzy logic, and evolutionary computing techniques such as genetic algorithm (GA), genetic programming (GP), simulated annealing (SA), and particle swarm optimization (PSO). Soft computing techniques are the key to obtaining effective simulating of human-like decisions. This chapter comprehends an extensive literature study of soft computing techniques and emerging trends recently surfaced through research. It is observed that genetic algorithms, fuzzy sets theory, neural nets, neuro-fuzzy systems, adaptive neuro-fuzzy inference systems (ANFIS), coactive neuro-fuzzy inference systems (CANFIS), evolutionary computing, probabilistic computing, deep learning, convolutional network and computational intelligence (CI) have been extensively deployed for various engineering problems individually or in hybrid forms. The main intention of this chapter is to introduce engineers and students to the latest trends in soft computing.

Rapid development in computer and sensor technology not only used for highly specialised applications but widespread and pervasive across a wide range of business and industry has facilitated easy capture and storage of immense amounts of data. Examples of such data collection include medical history data in health care, financial data in banking, point of sale data in retail, plant monitoring data based on instant availability of various sensor readings in various industries, or airborne hyperspectral imaging data in natural resources identification to mention only a few. However, with an increasing computer power available at affordable prices and the availability of vast amount of data there is an increasing need for robust methods and systems, which can take advantage of all available information. In essence there is a need for intelligent and smart adaptive methods but do they really exist? Are there any existing intelligent techniques which are more suitable for certain type of problems than others? How do we select those methods and can we be sure that the method of choice is the best for solving our problem? Do we need a combination of methods and if so then how to best combine them for different purposes? Are there any generic frameworks and requirements which would be highly desirable for solving data intensive and unstationary problems? All these questions and many others have been the focus of research vigorously pursued in many disciplines and some of them will be discussed in the talk and have been addressed in greater detail in our recently compiled book with the same title: ”Do Smart Adaptive Systems Exist?”. One of the more promising approaches to constructing smart adaptive systems is based on intelligent technologies including artificial neural networks, fuzzy systems, methods from machine learning, parts of learning theory and evolutionary computing which have been especially successful in applications where input-output data can be collected but the underlying physical model is unknown. The incorporation of intelligent technologies has been used in the conception and design of complex systems in which analytical and expert systems techniques are used in combination. Viewed from a much broader perspective, the above mentioned intelligent technologies are constituents of a very active research area known under the names of soft computing, computational intelligence or hybrid intelligent systems. However, hybrid soft computing frameworks are relatively young, even comparing to the individual constituent technologies, and a lot of research is required to understand their strengths and weaknesses. Nevertheless hybridization and combination of intelligent technologies within a flexible open framework seem to be the most promising direction in achieving the truly smart and adaptive systems today. Despite all the challenges it is unquestionable that smart adaptive intelligent systems and intelligent technology have started to have a huge impact on our everyday life and many applications can already be found in various commercially available products as illustrated in the recent report compiled by one of the world’s leading think tank advanced technology organisations and very suggestively titled: ”Get smart: How intelligent technology will enhance our world”.

The primary focus of this conference was to bring together academicians, researchers and scientists for knowledge sharing in various areas of Computational Intelligence and Intelligent Systems. During the conference, Data science and soft computing, Image analysis and processing, Modern information theory and technology, Robot and intelligent system and some other topics are discussed.

The expansive research field of computational intelligence combines various nature-inspired computational methodologies and draws on rigorous quantitative approaches across computer science, mathematics, physics, and life sciences. Some of its research topics, such as artificial neural networks, fuzzy logic, evolutionary computation, and swarm intelligence, are traditional to computational intelligence. Other areas have established their relevance to the field fairly recently: embodied intelligence (Pfeifer and Bongard, 2006; Der, 2014), information theory of cognitive systems (Lungarella and Sporns, 2006; Polani et al., 2007; Ay et al., 2008), guided self-organization (Prokopenko, 2009; Der and Martius, 2012), and evolutionary game theory (Vincent and Brown, 2005). The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent. In short, innovation and creativity shown within a rich space shaped by diverse, “entropic” forces, appeal to us as cognitive traits (Wissner-Gross and Freer, 2013). Can this intuition be formalized within rigorous and generic computational frameworks? What are the crucial obstacles on such a path? Intuitively, intelligent behavior is expected to be predictable and stable, but sensitive to change. Attempts to formalize this duality date back at least to cybernetics. For example, Ashby’s well-known Law of Requisite Variety states that an active controller requires as much variety (number of states) as that of the controlled system to be stable (Ashby, 1956). In order to explain the generation of behavior and learning in machines and living systems, Ashby also linked the concepts of ultrastability and homeostatic adaptation (Di Paolo, 2000; Fernandez et al., 2014). The balance between robustness and adaptivity is often attained near “the edge of chaos” (Langton, 1990), and the corresponding phase transitions are typically detected via high sensitivities to underlying control parameters (thermodynamic variables) (Prokopenko et al., 2011). Stability in self-organizing systems can be generally related to negentropy, the entropy that the system exports (dissipates) to keep its own entropy low (Schrodinger, 1944). Despite significant advances in this direction, the fundamental question whether stability, within processes developing far from an equilibrium, necessitates specific entropy dynamics is still unanswered. Clarifying the connections between entropy dynamics and stable but adaptive behavior is one of the grand challenges for computational intelligence. Put simply, we need to know whether learning and self-organization necessitate phase transitions in certain spaces, in terms of some order parameters. Is it possible to characterize the richness of self-generated choice, intrinsic to intelligent behavior, with respect to generic thermodynamic principles? The notion of generating and actively exploiting new behaviors, which adequately match the environment highlights that to be intelligent is to be complex in creating innovations. And so a mechanism producing computational novelty needs to exceed some threshold of complexity. To be truly impressive in generating endogenous innovation, it needs to be capable of universal computation, or to approach this capability in finite implementations (Casti, 1994; Markose, 2004). In other words, computational novelty may be fundamentally related to undecidability. Again, serious advances have been made in this foundational area of computer science. For example, Casti (1991) analyzed deeper interconnections between dynamical systems, Turing Machines, and formal logic systems: in particular, the complex, class IV, cellular automata were related to formal systems with undecidable statements (Godel’s incompleteness theorem) and the Halting Problem. Nevertheless, the question whether universal computation is the ultimate innovation-generator is still unresolved, offering another grand challenge: how computational intelligence, including mechanisms producing richness of choice and novelty, is related to

Computational Intelligence (CI) is an emerging field covering a highly interdisciplinary methodological framework that is useful for supporting the design, analysis, and deployment of intelligent systems. According to Bezdek (1994), “. . . a system is computationally intelligent when it: deals only with numerical (low-level) data, has a pattern recognition component, and does not use knowledge in the AI sense; and additionally when it (begins to) exhibit (i) computational adaptivity; (ii) computational fault tolerance; (iii) speed approaching human-like turnaround, and (iv) error rates that approximate human performance . . .”. Indeed, CI involves innovative models with a high level of machine learning quotient that combine elements of learning, adaptation, and evaluation. Examples of CI paradigms include fuzzy computing, neural computing, evolutionary computing, probabilistic computing, rough set theory, knowledge-based systems, adaptive learning algorithms, and hybrids of these paradigms. These techniques can be applied to a wide range of problems including optimization, decision making, information processing, pattern recognition, and intelligent data analysis. In this special issue, a number of papers that address theoretical advances as well as practical applications of various CI techniques are presented. The first two papers cover investigation into fuzzy measures and intelligent data analysis. The next two

Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized to be experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Summary This is the first article of a three-article series on virtual intelligence and its applications in petroleum and natural gas engineering. In addition to discussing artificial neural networks, the series covers evolutionary programming and fuzzy logic. Intelligent hybrid systems that incorporate an integration of two or more of these paradigms and their application in the oil and gas industry are also discussed in these articles. The intended audience is the petroleum professional who is not quite familiar with virtual intelligence but would like to know more about the technology and its potential. Those with a prior understanding of and experience with the technology should also find the articles useful and informative. Background and Definitions This section covers some historical background of the technology, provides definitions of virtual intelligence and artificial neural networks, and offers more general information on the nature and mechanism of the artificial neural network and its relation to biological neural networks. Virtual intelligence has been referred to by different names. Among these are artificial intelligence, computational intelligence, and soft computing. There seems to be no uniformly acceptable name for this collection of analyticools among the researchers and practitioners of the technology. Of these, artificial intelligence is used the least as an umbrella term because artificial intelligence has historically referred to rule-based expert systems and today is used synonymously with expert systems. Expert systems made many promises of delivering intelligent computers and programs but did not fulfill these promises. Many believe that soft computing is the most appropriate term to use and that virtual intelligence is a subset of soft computing. While this argument has merit, we use the term virtual intelligence throughout these articles.

Intelligent systems and soft computing are part of the movement towards developing effective intelligent systems for problem solving and decision making, and systems that can deal with complex and ill-structured situations, i.e. contexts for which discovery and learning can positively impact the outcome of the problem solving process. In the cutting-edge practice of intelligent systems design there are modeling techniques that govern the design of KBS systems, and there are several choices of computational models, that can be used to implement those KBS architectures.In the minitrack, we want to explore intelligent systems designs and computational models and to identify emerging paradigms underlying the design and development of intelligent systems of the future. In recent years, a number of innovative applications have been presented and published; in the minitrack, we want to explore and understand both successes and failures with new systems constructs. The next generation of modeling tools and support systems will include (but is not limited to) the use of intelligent technologies (machine intelligence, neural nets, genetic algorithms), soft computing (fuzzy logic, approximate reasoning, probabilistic modeling) and advanced mathematical modeling.There is an increasing demand for smart systems (standard software tools enhanced with intelligent modules) for interactive planning, problem solving and decision making, by individuals or by groups of users. The resulting systems will be more robust, more adaptive and easier to use than conventional tools. The optimization models (most of the time multiple criteria models) will be more easily incorporated in support systems. The expected end result is a generation of support systems which give the users knowledge-based support which is adapted both to the problems they need to solve and the decision making expected of them and, furthermore, to the internal logic of the context in which they will have to carry out their activities.There is a growing interest in soft computing tools, which are used to handle imprecision and uncertainty, and to build flexibility and context adaptability into intelligent systems. The application of soft computing to decision problems is focused on the ?new economy? decision context, where fast and correct decision making is becoming instrumental as the context is becoming increasingly complex, and will change more and more rapidly. There is no great consensus on what exactly will form the ?new economy? context, but some of the key elements will most probably be, (i) virtual teamwork in different places and in different time zones, (ii) decision support systems on mobile devices, with (iii) access to and the use of multilayer networks (Internet(s), intranets), through which (iv) access to and the use of a multitude of data sources (databases, data warehouses, text files, multimedia sources, etc.), and with support from (v) intelligent technologies for filtering, sifting and summarizing (software agents, machine intelligence, evolutionary computing, neural nets, etc.) and (vi) multiple criteria (crisp, soft) algorithms for problem solving. In the minitrack on Intelligent Systems and Soft Computing we aim to aim to explore the issues raised by the introduction of new technology to handle decision problems. The papers accepted for the minitrack include: Prototype Matching-Finding Meaning in the Books of Bible, A. Visa, J. Toivonen, H. Vanharanta and B. Back Operational Knowledge Representation for Practical Decision Making, L. Pasquier, P. Brezillon and J.C. Pomerol An Approach to Multiple Attribute Decision Making Based on Preference Information on Alternatives, J. Ma, Q. Zhang, Z. P. Fan and J. Liang A Language for the Rapid Prototyping of Mobile Evolving Agents, W. Muller, A. Meyer and H. Zabel Effects of Symbiotic Evolution in Genetic Algorithms for Job-Shop Scheduling, Y. Tsujimura, Y. Mafune and M. Gen Reducing the Bullwhip Effect by Means of Intelligent, Soft Computing Methods, C. Carlsson and R. Fuller A Fingerprint Recognizer Using Fuzzy Evolutionary Programming, T.V. Le, K.Y. Cheung and M.H. Nguyen

SCIS & ISIS is a biennial international joint conference on soft computing and intelligent systems, with research ranging from fuzzy systems, neural networks, and evolutionary computation to multi-agent systems, artificial intelligence, and robotics. SCIS & ISIS 2010 consisted of the 5th International Conference on Soft Computing and Intelligent Systems (SCIS) and the 11th International Symposium on Advanced Intelligent Systems (ISIS), held at Okayama Convention Center on December 8-12, 2010. Original presentations numbered 302 and participants 322. After preliminary selection by SCIS & ISIS 2010 session chairs, we listed over 70 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. After inviting these authors to submit papers for this special issue, we had two referees to review them and accepted 27 for publication in Vol.15, Nos.7 and 8 in 2011. This special issue presents 15 of these papers covering most conference topics, including fuzzy theory, learning methods, neural networks, and evolutionary computation, with a focus on reinforcement learning, multi-agent system, nonlinear estimation, and real-world applications to visual system, robotics and energy. We thank the authors and reviewers for their invaluable contributions toward making this special issue possible. We are also grateful to Editors-in-chief Prof. Toshio Fukuda of Nagoya University and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting us to serve as Guest Editors.