Journal articles on the topic 'Soft computing. Computer networks Fuzzy systems. Probabilities'

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.

The paper deals with the application of fuzzy systems, artificial neural networks (neural systems), and genetic algorithms to solve modeling and control problems in system engineering. Part 1 the paper covers the design of classical PID and fuzzy PID controllers for nonlinear systems with an (approximately) known dynamic model. Optimal controllers are designed based on genetic algorithms. Part 2 considers neural control of a SCARA robot. Part 3 deals with the fuzzy control of a special class of MIMO nonlinear systems and generalizes the method of Wang for such systems.

In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.

This special issue features five papers devoted to fuzzy systems and their applications. Papers were selected from those accepted and presented at the Sixth International Conference on Fuzzy Systems (AFSS' 2004) held in Hanoi, Vietnam on December 15-17, 2004. AFSS' 2004 and Tutorials held in Hue city on December 18-19, 2004, included a wide spectrum of research topics on "fuzzy set theory", "intelligent technology", "fuzzy logic and approximate reasoning", "neural networks", "genetic algorithms", "hybrid systems" and "soft computing". Over 40 papers were accepted and presented by researchers from countries including Brazil, Canada, Taiwan, India, Korea, Malaysia, Japan and Vietnam. Five papers receiving outstanding recommendations in reviews have been selected for this issue. The topics they address include fuzzy logic for robots, data mining, neural networks in medicine, Fuzzy Constraint Satisfaction Problems, and hybrid systems. As editors of this special issue, we are sincerely grateful to the authors. Special thanks also go to the referees for their excellent work, to Mr. Kazuki Ohmori for his aid in coordinating the issue's publication, and to the JACIII Editorial Board, especially Professor Kaoru Hirota for his invaluable support and encouragement. Finally, we thank Professors Masao Mukaidono and Witold Pedrycz for their contributions to AFSS' 2004. Without their support, AFSS' 2004 and this issue would not have been possible.

The "information revolution" of our time affects our entire generation. While a vision of the "Information Society," with its financial, legal, business, privacy, and other aspects has emerged in the past few years, the "traditional scene" of information technology, that is, industrial automation, maintained its significance as a field of unceasing development. Since the old-fashioned concept of "Hard Automation" applicable only to industrial processes of fixed, repetitive nature and manufacturing large batches of the same product1)was thrust to the background by keen market competition, the key element of this development remained the improvement of "Machine Intelligence". In spite of the fact that L. A. Zadeh already introduced the concept of "Machine Intelligence Quotient" in 1996 to measure machine intelligence2) , this term remained more or less of a mysterious meaning best explicable on the basis of practical needs. The weak point of hard automation is that the system configuration and operations are fixed and cannot be changed without incurring considerable cost and downtime. Mainly it can be used in applications that call for fast and accurate operation in large batch production. Whenever a variety of products must be manufactured in small batches and consequently the work-cells of a production line should be quickly reconfigured to accommodate a change in products, hard automation becomes inefficient and fails due to economic reasons. In these cases, new, more flexible way of automation, so-called "Soft Automation," are expedient and suitable. The most important "ingredient" of soft automation is its adaptive ability for efficiently coping with changing, unexpected or previously unknown conditions, and working with a high degree of uncertainty and imprecision since in practice increasing precision can be very costly. This adaptation must be realized without or within limited human interference: this is one essential component of machine intelligence. Another important factor is that engineering practice often must deal with complex systems of multiple variable and multiple parameter models almost always with strong nonlinear coupling. Conventional analysis-based approaches for describing and predicting the behavior of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of "modeling accuracy," they try to describe all structural details of the real physical system to be modeled. This significantly increases the intricacy of the model and may result in huge computational burden without considerably improving precision. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction is, the more work must be invested for obtaining it. Another important component of machine intelligence is a kind of "structural uniformity" giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to that of the ready-to-wear industry, whose products can later be slightly modified in contrast to the custom-tailors' made-to-measure creations aiming at maximum accuracy from the beginning. Machines carry out these later corrections automatically. This "learning ability" is another key element of machine intelligence. To realize the above philosophy in a mathematically correct way, L. A. Zadeh separated Hard Computing from Soft Computing. This revelation immediately resulted in distinguishing between two essential complementary branches of machine intelligence: Hard Computing based Artificial Intelligence and Soft Computing based Computational Intelligence. In the last decades, it became generally known that fuzzy logic, artificial neural networks, and probabilistic reasoning based Soft Computing is a fruitful orientation in designing intelligent systems. Moreover, it became generally accepted that soft computing rather than hard computing should be viewed as the foundation of real machine intelligence via exploiting the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality. Further research in the past decade confirmed the view that typical components of present soft computing such as fuzzy logic, neurocomputing, evolutionary computation and probabilistic reasoning are complementary and best results can be obtained by their combined application. These complementary branches of Machine Intelligence, Artificial Intelligence and Computational Intelligence, serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in journals and conference proceedings worldwide substantiates this statement. Three years ago, a new series of conferences in this direction was initiated and launched with the support of several organizations including the IEEE Industrial Electronics Society and IEEE Hungary Section in technical cooperation with IEEE Robotics & Automation Society. The first event of the series hosted by Bdnki Dondt Polytechnic, Budapest, Hungary, was called "19997 IEEE International Conference on Intelligent Engineering Systems " (INES'97). The Technical University of Vienna, Austria hosted the next event of the series in 1998, followed by INES'99 held by the Technical University of Kosice, Slovakia. The present special issue consists of the extended and revised version of the most interesting papers selected out of the presentations of this conference. The papers exemplify recent development trends of intelligent engineering systems. The first paper pertains to the wider class of neural network applications. It is an interesting report of applying a special Adaptive Resonance Theory network for identifying objects in multispectral images. It is called "Extended Gaussian ARTMAP". The authors conclude that this network is especially advantageous for classification of large, low dimensional data sets. The second paper's subject belongs to the realm of fuzzy systems. It reports successful application of fundamental similarity relations in diagnostic systems. As an example failure detection of rolling-mill transmission is considered. The next paper represents the AI-branch of machine intelligence. The paper is a report on an EU-funded project focusing on the storage of knowledge in a corporate organizational memory used for storing and retrieving knowledge chunks for it. The flexible structure of the system makes it possible to adopt it to different SMEs via using company-specific conceptual terms rather than traditional keywords. The fourth selected paper's contribution is to the field of knowledge discovery. For this purpose in the first step, cluster analysis is done. The method is found to be helpful whenever little or no information on the characteristics of a given data set is available. The next paper approaches scheduling problems by the application of the multiagent system. It is concluded that due to the great number of interactions between components, MAS seems to be well suited for manufacturing scheduling problems. The sixth selected paper's topic is emerging intelligent technologies in computer-aided engineering. It discusses key issues of CAD/CAM technology of our days. The conclusion is that further development of CAD/CAM methods probably will serve companies on the competitive edge. The seventh paper of the selection is a report on seeking a special tradeoff between classical analytical modeling and traditional soft computing. It nonconventionally integrates uniform structures obtained from Lagrangian Classical Mechanics with other simple elements of machine intelligence such as saturated sigmoid transition functions borrowed from neural nets, and fuzzy rules with classical PID/ST, and a simplified version of regression analysis. It is concluded that these different components can successfully cooperate in adaptive robot control. The last paper focuses on the complexity problem of fuzzy and neural network approaches. A fuzzy rule base, be it generated from expert operators or by some learning or identification schemes, may contain redundant, weakly contributing, or outright inconsistent components. Moreover, in pursuit of good approximation, one may be tempted to overly assign the number of antecedent sets, thereby resulting in large fuzzy rule bases and much problems in computation time and storage space. Engineers using neural networks have to face the same complexity problem with the number of neurons and layers. A fuzzy rule base and neural network design, hence, have two important objectives. One is to achieve a good approximation. The other is to reduce the complexity. The main difficulty is that these two objectives are contradictory. A formal approach to extracting the more pertinent elements of a given rule set or neurons is, hence, highly desirable. The last paper is an attempt in this direction. References 1)C. W. De Silva. Automation Intelligence. Engineering Application of Artificial Intelligence. Vol. 7. No. 5. 471-477 (1994). 2)L. A. Zadeh. Fuzzy Logic, Neural Networks and Soft Computing. NATO Advanced Studies Institute on Soft Computing and Its Application. Antalya, Turkey. (1996). 3)L. A. Zadeh. Berkeley Initiative in Soft Computing. IEEE Industrial Electronics Society Newsletter. 41, (3), 8-10 (1994).

Welcome to this special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). I am pleased to introduce 41 selected papers presented at the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held on September 17-21, 2008, at Nagoya University in Nagoya, Japan. This conference featured 401 original papers in presentations attended by some 500 participants. SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of research ranging from fuzzy systems, neural networks, and evolutionary computation to multiagent systems, artificial intelligence, and robotics. This current issue presents 20 papers covering most of the conference topics including fuzzy theory, self-organizing maps, robotics, computer vision, and optimization algorithms. I would like to thank the authors and reviewers and SCIS & ISIS 2008 for making this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the SCIS & ISIS 2008 conference staff for inviting me to guest-edit this Journal.

Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).

We are very pleased and honored to have an opportunity to publish a new journal the "International Journal of Advanced Computational Intelligence" (JACI). The JACI is a new, bimonthly journal covering the field of computer science. This journal focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and evolutionary computations, in order to assist in fostering the application of intelligent systems to industry. This new field is called computational intelligence or soft computing. It has already been studied by many researchers, but no single, integrated journal exists anywhere in the world. This new journal gives readers the state of art of the theory and application of Advanced Computational Intelligence. The Topics include, but are not limited to: Fuzzy Logic, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Network Systems, Multimedia, the Human Interface, Biologically-Inspired Evolutionary Systems, Artificial Life, Chaos, Fractal, Wavelet Analysis, Scientific Applications and Industrial Applications. The journal, JACI, is supported by many researchers and scientific organizations, e.g., the International Fuzzy Systems Association (IFSA), the Japan Society of Fuzzy Theory and Systems (SOFT), the Brazilian Society of Automatics (SBA) and The Society of Instrument and Control Engineers (SICE), and we are currently negotiating with the John von Neumann Computer Society (in Hungary). Our policy is to have world-wide communication with many societies and researchers in this field. We would appreciate it if those organizations and people who have an interest in co-sponsorship or have proposals for special issues in this journal, as well as paper submissions, could contact us. Finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its president, Mr. K. Hayashi, and to the editor, Mr. Y. Inoue, for their efforts in publishing this new journal. Lotti A. Zadeh The publication of the International Journal of Advanced Computational Intelligence (JACI) is an important milestone in the advancement of our understanding of how intelligent systems can be conceived, designed, built, and deployed. When one first hears of computational intelligence, a question that naturally arises is: What is the difference, if any, between computational intelligence (CI) and artificial intelligence (AI)? As one who has witnessed the births of both AI and CI, I should like to suggest an answer. As a branch of science and technology, artificial intelligence was born about four decades ago. From the outset, AI was based on classical logic and symbol manipulation. Numerical computations were not welcomed and probabilistic techniques were proscribed. Mainstream AI continued to evolve in this spirit, with symbol manipulation still occupying the center of the stage, but not to the degree that it did in the past. Today, probabilistic techniques and neurocomputing are not unwelcome, but the focus is on distributed intelligence, agents, man-machine interfaces, and networking. With the passage of time, it became increasing clear that symbol manipulation is quite limited in its ability to serve as a foundation for the design of intelligent systems, especially in the realms of robotics, computer vision, motion planning, speech recognition, handwriting recognition, fault diagnosis, planning, and related fields. The inability of mainstream AI to live up to expectations in these application areas has led in the mid-eighties to feelings of disenchantment and widespread questioning of the effectiveness of AI's armamentarium. It was at this point that the name computational intelligence was employed by Professor Nick Cercone of Simon Fraser University in British Columbia to start a new journal named Computational Intelligence -a journal that was, and still is, intended to provide a broader conceptual framework for the conception and design of intelligent systems than was provided by mainstream AI. Another important development took place. The concept of soft computing (SC) was introduced in 1990-91 to describe an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic (or evolutionary) computing (GC), and probabilistic computing (PC). In essence, soft computing differs from traditional hard computing in that it is tolerant of imprecision, uncertainty and partial truth. The basic guiding principle of SC is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. More recently, the concept of computational intelligence had reemerged with a meaning that is substantially different from that which it had in the past. More specifically, in its new sense, CI, like AI, is concerned with the conception, design, and deployment of intelligent systems. However, unlike mainstream AI, CI methodology is based not on predicate logic and symbol manipulation but on the methodologies of soft computing and, more particularly, on fuzzy logic, neurocomputing, genetic(evolutionary) computing, and probabilistic computing. In this sense, computational intelligence and soft computing are closely linked but not identical. In basic ways, the importance of computational intelligence derives in large measure from the effectiveness of the techniques of fuzzy logic, neurocomputing, genetic (evolutionary) computing, and probabilistic computing in the conception and design of information/intelligent systems, as defined in the statements of the aims and scope of the new journal of Advanced Computational Intelligence. There is one important aspect of both computational intelligence and soft computing that should be stressed. The methodologies which lie at the center of CI and SC, namely, FL, NC, genetic (evolutionary) computing, and PC are for the most part complementary and synergistic, rather than competitive. Thus, in many applications, the effectiveness of FL, NC, GC, and PC can be enhanced by employing them in combination, rather than in isolation. Intelligent systems in which FL, NC, GC, and PC are used in combination are frequently referred to as hybrid intelligent systems. Such systems are likely to become the norm in the not distant future. The ubiquity of hybrid intelligent systems is likely to have a profound impact on the ways in which information/intelligent systems are conceived, designed, built, and interacted with. At this juncture, the most visible hybrid intelligent systems are so-called neurofuzzy systems, which are for the most part fuzzy-rule-based systems in which neural network techniques are employed for system identification, rule induction, and tuning. The concept of neurofuzzy systems was originated by Japanese scientists and engineers in the late eighties, and in recent years has found a wide variety of applications, especially in the realms of industrial control, consumer products, and financial engineering. Today, we are beginning to see a widening of the range of applications of computational intelligence centered on the use of neurofuzzy, fuzzy-genetic, neurogenetic, neurochaotic and neuro-fuzzy-genetic systems. The editors-in-chief of Advanced Computational Intelligence, Professors Fukuda and Hirota, have played and are continuing to play majors roles both nationally and internationally in the development of fuzzy logic, soft computing, and computational intelligence. They deserve our thanks and congratulations for conceiving the International Journal of Advanced Computational Intelligence and making it a reality. International in both spirit and practice, JACI is certain to make a major contribution in the years ahead to the advancement of the science and technology of man-made information/intelligence systems -- systems that are at the center of the information revolution, which is having a profound impact on the ways in which we live, communicate, and interact with the real world. Lotfi A. Zadeh Berkeley, CA, July 24, 1997

Welcome to the second special issue on selected papers from SCIS & ISIS 2008, a joint conference combining the 4th Soft Computing and Intelligent Systems (SCIS) and the 9th International Symposium on advances Intelligent Systems (ISIS) held at Nagoya University, Japan, in September 2008. smallskip Three earlier conferences were held in: the National Institute of Advanced Industrial Science and Technology (AIST), Japan (2002); Keio University, Japan (2004); and Tokyo Institute of Technology, Japan (2006). smallskip Conference topics include fuzzy logic, clustering, evolutionary computation, machine learning, rough sets, man-machine interaction/interfaces, neural networks, computer vision, image processing, cognitive modeling, computational intelligence, etc. smallskip Papers of interest containing novel algorithms and ideas in these fields have been selected, so I hope you will enjoy this issue.smallskip I would like to thank the many people who have produced these special issues for SCIS & ISIS2008, and all of the authors and reviewers. Without your help, this issue would not have been possible.

From year to year the number of investigations on intelligent systems grows rapidly. For example this year 245 papers from 45 countries were sent for the Second International IEEE Conference on Intelligent Systems (www.ieee-is.org; www.fnts-bg.org/is) and this is an increase of more than 50% by all indicators. The presented papers on intelligent systems were marked by big audiences and they provoked a significant interest that ultimately led to the formation of vivid discussions, exchange of ideas and locally provoked the creation of working groups for different applied projects. All this reflects the worldwide tendencies for the leading role of the research on intelligent systems theoretically and practically. The greater part of the presented research dealt with traditional for the intelligent systems problems like artificial intelligence, knowledge engineering, intelligent agents, neural and fuzzy networks, intelligent data processing, intelligent control and decision making systems, and also new interdisciplinary problems like ontology and semantics in Internet, fuzzy intuitionistic logic. The majority of papers from the European and American researchers are dedicated to the theory and the applications of the intelligent systems with machine learning, fuzzy inference or uncertainty. Another big group of papers focuses on the domain of building and integrating ontologies of applications with heterogeneous multiagent systems. A great number of papers on intelligent systems deals with fuzzy sets. The papers of many other researchers underscore the significance of the contemporary perception-oriented methods and also of different applications in the intelligent systems. On the first place this is valid for the paradigm of L. A. Zadeh 'computing with words'. The Guest Editors in the present specialized journal volume would like to introduce a wealth of research with an applied and theoretical character that possesses a common characteristic and it is the conference best papers complemented and updated by the new elaborations of the authors during the last half a year. A short description of the presented in the volume papers follows. In 'Combining Local and Global Access to Ontologies in a Multiagent System' <B>R. Brena and H. Ceballos (Mexico)</B> proposed an original way for operation with ontologies where a part of the ontology is processed by a client's component and the rest is transmitted to the other agents by an ontology agent. The inter-agent communication is improved in this way. In 'Fuzzy Querying of Evolutive Situations: Application to Driving Situations' <B>S. Ould Yahia and S. Loriette-Rougegrez (France)</B> present an approach to analysis of driving situations using multimedia images and fuzzy estimates that will improve the driver's security. In 'Rememberng What You Forget in an Online Shopping Context' <B>M. Halvey and M. Keane (Ireland)</B> presented their approach to constructing online system that predicts the items for future shopping sessions using a novel idea called Memory Zones. In 'Reinforcement Learning for Online Industrial Process Control' the authors <B>J. Govindhasamy et al. (Ireland)</B> use a synthesis of dynamic programming, reinforcement learning and backpropagation for a goal of modeling and controlling an industrial grinding process. The felicitous combination of methods contributes for a greater effectiveness of the applications compared to the existing controllers. In 'Dynamic Visualization of Information: From Database to Dataspace' the authors <B>C. St-Jacques and L. Paquin (Canada)</B> suggested a friendly online access to large multimedia databases. <B>W. Huang (UK)</B> redefines in 'Towards Context-Aware Knowledge Management in e-Enterprises' the concept of context in intelligent systems and proposes a set of meta-information elements for context description in a business environment. His approach is applicable in the E-business, in the Semantic Web and in the Semantic Grid. In 'Block-Based Change Detection in the Presence of Ambient Illuminaion Variations' <B>T. Alexandropoulos et al. (Greece)</B> use a statistic analysis, clustering and pattern recognition algorithms, etc. for the goal of noise extraction and the global illumination correction. In 'Combining Argumentation and Web Search Technology: Towards a Qualitative Approach for Ranking Results' <B>C. Chesñevar (Spain) and A. Maguitman (USA)</B> proposed a recommender system for improving the WEB search. Defeasible argumentation and decision support methods have been used in the system. In 'Modified Axiomatic Basis of Subjective Probability' <B>K. Tenekedjiev et al. (Bulgaria)</B> make a contribution to the axiomatic approach to subjective uncertainty by introducing a modified set of six axioms to subjective probabilities. In 'Fuzzy Rationality in Quantitative Decision Analysis' <B>N. Nikolova et al. (Bulgaria)</B> present a discussion on fuzzy rationality in the elicitation of subjective probabilities and utilities. The possibility to make this special issue was politely offered to the Guest Editors by Prof. Kaoru Hirota, Prof. Toshio Fukuda and we thank them for that. Due to the help of Kenta Uchino and also due to the new elaborations presented by explorers from Europe and America the appearance of this special issue became possible.

PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approachThe paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.FindingsThis paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/valueUsing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

The special issue has been focussed to overcome the challenges of scalability, which includes size scalability, geographical scalability, administrative scalability, network and synchronous communication limitation, etc.The challenges also emerge with the development of recent applications. Hence this proposal has been planned to handle the scalability issues in recent applications. This special issue invites researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing and artificial intelligence to submit original research papers and timely review articles on the theory, design, evaluation, and use of artificial intelligence and parallel and/or distributed computing systems for emerging applications. The ten papers in this special issue cover a range of aspects of theoretical and practical research development on scalable computing. The proposal provides an effective forum for communication among researchers and practitioners from various scientific areas working in a wide variety of problem areas, sharing a fundamental common interest in improving the ability of parallel and distributed computer systems, intelligent techniques, and deep learning mechanisms and advanced soft computing techniques. The issue covers wide range of applications, but with scalable problems that to be solved by perfect hybridization of distributed computing and artificial intelligence.The first paper is “CPU-Memory Aware VM Consolidation for Cloud Data Centers” introduced a CPU Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The Virtual Machines are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host.In “Bird Swarm Optimization-based stacked autoencoder deep learning for umpire detection and classification”, presented the umpire detection and classification by proposing an optimization algorithm. The overall procedure of the proposed approach involves three steps, like segmentation, feature extraction, and classification. Here, the classification is done using the proposed Bird Swarm Optimization-based stacked autoencoder deep learning classifier (BSO-Stacked Autoencoders), that categories into umpire or others.In “Enhanced DBSCAN with Hierarchical tree for Web Rule Mining”, proposed an enhanced web mining model based on two contributions. At first, the hierarchical tree is framed, which produces different categories of the searching queries (different web pages). Next, to hierarchical tree model, enhanced Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique model is developed by modifying the traditional DBSCAN. This technique results in proper session identification from raw data. Moreover, this technique offers the optimal level of clusters necessitated for hierarchical clustering. After hierarchical clustering, the rule mining is adopted. The traditional rule mining technique is generally based on the frequency; however, this paper intends to enhance the traditional rule mining based on utility factor as the second contribution. Hence the proposed model for web rule mining is termed as Enhanced DBSCAN-based Hierarchical Tree (EDBHT).In “A comprehensive survey of the Routing Schemes for IoT applications”, this review article provides a detailed review of 52 research papers presenting the suggested routing protocols based on the content-based, clustering-based, fuzzy-based, Routing Protocol for Low power (RPL) and Lossy Networks, tree-based and soon. Also, a detailed analysis and discussion are made by concerning the parameters, simulation tool, and year of publication, network size, evaluation metrics, and utilized protocols. In “Chicken-Moth Search Optimization-Based Deep Convolutional Neural Network For Image Steganography”, proposed an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error.In “An Efficient Dynamic Slot Scheduling Algorithm for WSN MAC: A Distributed Approach”, an effective TDMA based slot scheduling algorithm needs to be designed. In this paper, we propose a TDMA based algorithm named DYSS that meets both the timeliness and energy efficiency in handling the collision. This algorithm finds an effective way of preparing the initial schedule by using the average two-hop neighbors count. Finally, the remaining un-allotted nodes are dynamically assigned to slots using a novel approach.In “Artefacts removal from ECG Signal: Dragonfly optimization-based learning algorithm for neural network-enhanced adaptive filtering”, proposed a method utilizes the adaptive filter termed as the (Dragonfly optimization + Levenberg Marqueret learning algorithm) DLM-based Nonlinear Autoregressive with eXogenous input (NARX) neural network for the removal of the artefacts from the ECG signals. Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction procedure.In “A Comprehensive Review on State-of-the-Art Image Inpainting Techniques”, this survey makes a critical analysis of diverse techniques regarding various image inpainting schemes. This paper goes under (i) Analyzing various image inpainting techniques that are contributed in different papers. (ii) Makes the comprehensive study regarding the performance measures and the corresponding maximum achievements in each contribution. (iii) Analytical review concerning the chronological review and various tools exploited in each of the reviewed works.In “An Efficient Way of Finding Polarity of Roman Urdu Reviews by Using Boolean Rules”, proposed a novel approach by using Boolean rules for the identification of the related and non-related comments. Related reviews are those which show the behavior of a customer about a particular product. Lexicons are built for the identification of noise, positive and negative reviews.The final paper is “Forecasting the Impact of Social Media Advertising among College Students using Higher Order Statistical Functions”, this research work plans to develop a statistical review that concerns on social media advertising among college students from diverse universities. The review analysis on social media advertising is given under six sections such as: (i) Personal Profile; (ii) Usage; (iii) Assessment; (iv) Higher Order statistics like Community, Connectedness, Openness, Dependence, and Participation; (v) Trustworthiness such as Trust, Perceived value and Perceived risk; and (vi) Towards advertisement which involves attitude towards advertisement, response towards advertisement and purchase intension.

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.

Today's complex industrial and engineering systems - especially with the appearance of large-scale embedded and/or real-time systems - confront researchers and engineers with completely new challenges. Measurement and signal processing systems are involved in almost all kinds of activities in that field where control problems, system identification problems, industrial technologies, etc., are to be solved, i.e., when signals, parameters, or attributes must be measured, monitored, approximated, or determined somehow. In a large number of cases, traditional information processing tools and equipment fail to handle these problems. Not only is the handling of previously unseen spatial and temporal complexity questionable but such problems have also to be addressed such as the interaction and communication of subsystems based on entirely different modeling and information expression methods, the handling of abrupt changes within the environment and/or the processing system, the possible temporal shortage of computational power and/or loss of some data due to the former. Signal processing should even in these cases provide outputs of acceptable quality to continue the operation of the complete system, producing data for qualitative evaluations and supporting decisions. It means the introduction of new ideas for specifying, designing, implementing, and operating sophisticated signal processing systems. Intelligent - artificial intelligence, soft computing, anytime, etc. - methods are serious candidates for handling many theoretical and practical problems, providing a better description, and, in many cases, are the best if not the only alternatives for emphasizing significant aspects of system behavior. These techniques, however, are relatively new methods and up until now, not widely used in the field of signal processing because some of the critical questions related to design and verification are not answered properly and because uncertainty is maintained quite differently than in classical metrology. After the initiation of the 1999 IEEE International Workshop on Intelligent Signal Processing, WISP'99, which was the first event to start linking scientific communities working in the fields of intelligent systems and signal processing and hoping that it will attract more and more scientists and engineers in these hot topics, this special issue continues this pioneering work by offering a selection of nine papers fitting into the profile of the journal from the numerous high quality ones presented at WISP'99. These excellent papers deal with different aspects of advanced computational intelligence in signal processing, including the application of neural networks, fuzzy techniques, genetic and anytime algorithms in modeling, signal processing, noise cancellation, identification, and pattern recognition, multisensorial information fusion and intelligent classification in image processing, exact and nonexact complexity reduction, and nonclassical and mixed data and uncertainty representation and handling. As an editor of this special issue, I would like to express my thanks to all of the contributors and my belief in that the excellent research results it contains provide the basis for further strengthening and spreading of advanced computational intelligence in signal processing opening wide possibilities for new theoretical and practical achievements.

The main objective of the annual International Conference on Intelligent Technologies (InTech) is to bring together researchers and practitioners who implement intelligent and fuzzy technologies in real-world environment. The Fifth International Conference on Intelligent Technologies InTech'04 was held in Houston, Texas, on December 2-4, 2004. Topics of InTech'04 included mathematical foundations of intelligent technologies, traditional Artificial Intelligent techniques, uncertainty processing and methods of soft computing, learning/adaptive systems/data mining, and applications of intelligent technologies. This special issue contains versions of 15 selected papers originally presented at InTech'04. These papers cover most of the topics of the conference. Several papers describe new applications of the existing intelligent techniques. R. Aló{o} et al. show how traditional <I>statistical</I> hypotheses testing techniques – originally designed for processing measurement results – need to be modified when applied to simulated data – e.g., when we compare the quality of two algorithms. Y. Frayman et al. use <I>mathematical morphology</I> and <I>genetic algorithms</I> in the design of a machine vision system for detecting surface defects in aluminum die casting. Y. Murai et al. propose a new faster <I>entropy</I>-based placement algorithm for VLSI circuit design and similar applications. A. P. Salvatore et al. show how <I>expert system</I>-type techniques can help in scheduling botox treatment for voice disorders. H. Tsuji et al. propose a new method, based on <I>partial differential equations</I>, for automatically identifying and extracting objects from a video. N. Ward uses <I>Ordered Weighted Average</I> (OWA) techniques to design a model that predicts admission of computer science students into different graduate schools. An important aspect of intelligence is ability to <I>learn</I>. In A. Mahaweerawat et al., neural-based machine learning is <I>used</I> to identify and predict software faults. J. Han et al. show that we can drastically <I>improve</I> the quality of machine learning if, in addition to discovering traditional (positive) rules, we also search for negative rules. A serious problem with many neural-based machine learning algorithms is that often, the results of their learning are un-intelligible rules and numbers. M. I. Khan et al. show, on the example of robotic arm applications, that if we allow neurons with different input-output dependencies – including linear neurons – then we can <I>extract</I> meaningful <I>knowledge</I> from the resulting network. Several papers analyze the Equivalent Transformation (ET) model, that allows the user to <I>automatically generate code from specifications</I>. A general description of this model is given by K. Akama et al. P. Chippimolchai et al. describe how, within this model, we can transform a user's query into an equivalent more efficient one. H. Koike et al. apply this approach to <I>natural language processing</I>. Y. Shigeta et al. show how the existing <I>constraint</I> techniques can be translated into equivalent transformation rules and thus, combined with other specifications. I. Takarajima et al. extend the ET approach to situations like <I>parallel computations</I>, where the order in which different computations are performed on different processors depends on other processes and is, thus, non-deterministic. Finally, a paper by J. Chandra – based on his invited talk at InTech'04 – describes a <I>general framework</I> for robust and resilient critical infrastructure systems, with potential applications to transportation systems, power grids, communication networks, water resources, health delivery systems, and financial networks. We want to thank all the authors for their outstanding work, the participants of InTech'04 for their helpful suggestions, the anonymous reviewers for their thorough analysis and constructive help, and – last but not the least – to Professor Kaoru Hirota for his kind suggestion to host this issue and to the entire staff of the journal for their tireless work.

In the early 1970s, a concept of intelligent control was proposed by Fu, and since then the advancement of control technologies as a migrate of control theory, artificial intelligence and operations research has been actively attempted. The breakthrough of this concept was to integrate a human judgment and a concept of value as well as management theory into conventional control theoretic approaches, and synthesize these as artificial intelligence. A number of unconventional control techniques have evolved, offering solutions to many difficult control problems in industry and manufacturing. Saridis proposed a general architecture for intelligent control and proposed a design principle of such a hierarchical system as the principle of Increasing Precision with Decreasing Intelligence. During the first generation of intelligent control, a number of intelligent methodologies besides the purely symbolic and logical processing of human knowledge were introduced. They are broadly called soft computing techniques that include artificial neural networks, fuzzy logic, genetic algorithm, and chaos theory. These techniques have contributed much to the advancement of intelligent control from the viewpoint of its ""intelligence"" part, but no solutions are provided from a control theoretic viewpoint, and the definition of intelligence in terms of control theory is still left questionable. To discuss this issue, we initiated a specialist's meeting on survey of intelligent control in 1997 organized under the Institute of Electrical Engineers of Japan, and discussed the current status as well as future perspectives of intelligent control. Some of the papers contributed to this special issue are results obtained in this series of meetings. During that time, the framework of intelligent control has entered the second generation. In the first stage, this framework was discussed in terms of utilized methodologies such as control theory, artificial intelligence, and operations research seeking optimal combinations of these methodologies wherein a distinction is made between the controller, the plant, and the external environment and representations as well as state concepts utilized were a priorily determined and fixed without flexibility. In contrast, the second generation intelligent control system must emphasize a biologically inspired architecture that can accommodate the flexible and dynamic capabilities of living systems including human beings. That is, it must be able to grow and develop increasing capabilities of self-control, self-awareness of representation and reasoning about self and of constructing a coherent whole out of different representations. Actually, a new branch of research on artificial life and system theory of function emergence has shifted the perspectives of intelligence from conventional reductionism to a new design principle based on the concept of ""emergence"". Thus, their approach is quite new in that they attempt to build models that bring together self-organizing mechanisms with evolutionary computation. Such a trend has forced us to reconsider the biological system and/or natural intelligence. In this special issue, we focus on the aspects of semiosis within a multigranular architecture and of emergent properties and techniques for human-machine and/or multiagent collaborative control systems in the coming new generation. These topics are mutually interrelated; the role of multivariable and multiresolutional quantization and clustering for designing intelligent controllers is essential for realizing the abilities to learn unknown multidimensional functions and/or for letting a joint system, which consists of an external environment, a human, and a machine, self-organize distinctive roles in a bottom-up and emerging fashion. This special issue includes papers on proposals of conceptual architecture, methodologies and reports from practical field studies on the hierarchical architecture of machines for realizing hierarchical collaboration and coordination among machine and human autonomies. We believe that these papers will lead to answers to the above questions. We sincerely thank the contributors and reviewers who made this special issue possible. Thanks also go to the editor-in-chief of the Journal of Robotics and Mechatronics, Prof. Makoto Kaneko (Hiroshima University), who provided the opportunity for editing this special issue.

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science.

In today’s world there is rapid increase in the information which makes addressing of security issues more important. Malware detection is an important area for research in effective and secure functioning of computer networks. Research efforts are required to protect the systems from various security attacks. In this paper, we analyze usefulness of Soft Computing and Machine Learning Techniques for network malware detection. Hamamoto et al. [1] used combination of Genetic Algorithm and Fuzzy logic for implementation of network anomaly detection. The research work proposed in this paper extends the concepts discussed in [1]. The proposed work explores use of various Machine Learning algorithms such as K-Nearest Neighbor, Naïve Bayes and Decision Tree for network anomaly detection. The experimental observations are conducted on CIDDS (Coburg Intrusion Detection Data Set) dataset [14]. It is observed that Decision Tree approach gave better results as compared to KNN and Naïve Bayes techniques. Decision Tree technique gives 99% of accuracy and precision of 1 and recall of 1.

In this paper we present a theoretical model based on soft computing to distribute the time/cost among the industry/machine sensors or effectors based on the type of the application. One of the most unstudied significant work is to recognize which sensor in an industry for example has higher priority than others. This is important to know which sensor to be checked first and within time limits of the system response. The problem of such systems is their variant environmental situations. Based on these varied situations, the priority of the importance of each sensor might change from time to another. Due to this uncertainty and lack of some information, soft computing is considered to be one of the plausible solutions. The presented idea is based on initially training of the system and continuously exploiting the system experience of the degree of importance of the sensors. The proposed system has three main stages, the first stage is concerned with training the system to obtain the necessary system time to respond, the necessary time allocated to recognize which sensors to check (or which has higher priority), and the initial importance value for each sensor, which indicates the initial judgment about the sensor importance. The second stage is to use the system experience about the importance of the sensor using fuzzy logic to decide the final values of each sensor 's importance. Based on the output of the second stage and the output of the first stage, the system distributes the time/cost among the sensors (some sensors with lower priority might be neglected). The main idea of the proposed work is based on neurofuzzy.

It is interesting to notice that from “problem’s formulation” point of view “Industrial Computer Aided Diagnosis” and “Biomedical Computer Aided Diagnosis” could be formulated as a same diagnosis riddle: “How point out a correct diagnosis from a set of symptoms?”. The only difference between the two above-mentioned groups of problems is the nature of the monitored (diagnosed) system: in the first group the monitored system is an artificial machinery (plant, industrial process, etc…), while in the second, the monitored system is a living body (animal or human).One of the most appealing classes of approaches allowing handling the Computer Aided Diagnosis Systems’ design in the frame of the aforementioned dual point of view is Soft-Computing based techniques, especially those dealing with neural networks and fuzzy logic. In this article, we present two soft-computing based approaches dealing with CADS design. One aims designing a biomedical oriented CADS and the other sets sights on conceiving a CADS to overcome a real-world industrial quality control dilemma. The goal of the first system is to diagnose the human’s auditory pathway’s health. The target of the second is to detect and diagnose the high tech optical devices’ defects.

PurposeCommunities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.Design/methodology/approachThe proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.FindingsExtensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.Research limitations/implicationsThe study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.Practical implicationsThis study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.Social implicationsThe proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.Originality/valueThis work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.