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The density of population in cities is growing at a faster rate to make the life of people in cities comfortable and save. The city needs to be smart. It can be mainly achieved by intelligent decision making process using computational intelligence based systems. Keeping this in view, many researchers and organizations are working to develop and implement computational intelligence decision support systems. To obtain a comprehensive overview on the current status on SI based smart city community the present investigation has been made. To achieve this objective recently published standard articles on this important sub area have been collected and reviewed. The summary of the review has been presented in systematic manner to facilitate the researchers who are currently working in the area of smart city community. The important findings of the review have been made and presented. The important performance measures in various aspects of smart city obtained by the computational intelligence methods have been listed. It is expected that the findings and the contribution of the paper will benefit the researchers, the related government and private organizations in terms of furthering their research efforts and producing different smart products pertaining to community development and improvement of comfort level of the dwellers of the smart city. [ABSTRACT FROM AUTHOR]

The world recently has faced the COVID-19 pandemic, a disease caused by the severe acute respiratory syndrome. The main features of this disease are the rapid spread and high-level mortality. The illness led to the rapid development of a vaccine that we know can fight against the virus; however, we do not know the actual vaccine's effectiveness. Thus, the early detection of the disease is still necessary to provide a suitable course of action. To help with early detection, intelligent methods such as machine learning and computational intelligence associated with computer vision algorithms can be used in a fast and efficient classification process, especially using ensemble methods that present similar efficiency to traditional machine learning algorithms in the worst-case scenario. In this context, this review aims to answer four questions: (i) the most used ensemble technique, (ii) the accuracy those methods reached, (iii) the classes involved in the classification task, (iv) the main machine learning algorithms and models, and (v) the dataset used in the experiments. [ABSTRACT FROM AUTHOR]

In the new era of computational intelligence, the ever-expanding abundance of information storage and processing power enables researchers and users to tackle applications on various scientific domains of Decision Support Systems (DSS). The general focus of this special issue is to provide insights on how computational intelligence methodologies and algorithms can be employed in real world applications, so as to produce information systems capable of solving important and hard decision problems. This special issue on "Innovative Decision Systems" contains extended versions of seven (7) papers selected from the 12th EANN/7th AIAI 2011 Joint Conference. The manuscripts were accepted for publication, after passing through a peer review process by at least two independent academic referees. AIAI and EANN are two well-established annual events, technically sponsored by the Technical Committee 12, Working Group 12.5 (TC12-WG12.5) of the International Federation for Information Processing (IFIP) and the International Neural Network Society (INNS) respectively. The 2011 event was held on September 15-18 2011 in Corfu, Greece. [ABSTRACT FROM AUTHOR]

A cubic bipolar fuzzy set (CBFS) is a new approach in computational intelligence and decision-making under uncertainty. This model is the generalization of bipolar fuzzy sets to deal with two-sided contrasting features which can describe the information with a bipolar fuzzy number and an interval-valued bipolar fuzzy number simultaneously. In this paper, the Dombi's operations are analyzed for information aggregation of cubic bipolar fuzzy numbers (CBFNs). The Dombi's operations carry the advantage of more pliability and reliability due to the existence of their operational parameters. Owing to the pliable nature of Dombi's operators, this research work introduces new aggregation operators named as cubic bipolar fuzzy Dombi weighted averaging (CBFDWA) operator and cubic bipolar fuzzy Dombi ordered weighted averaging (CBFDOWA) operator with ℙ -order and ℝ -order, respectively. Additionally, this paper presents some significant characteristics of suggested operators including, idempotency, boundedness and monotonicity. Moreover, a robust multi-criteria decision making (MCDM) technique is developed by using ℙ -CBFDWA and ℝ -CBFDWA operators. Based on the suggested operators a practical application is demonstrated towards MCDM under uncertainty. The comparison analysis of suggested Dombi's operators with existing operators is also given to discuss the rationality, efficiency and applicability of these operators. [ABSTRACT FROM AUTHOR]

Multi-Robot Task Allocation (MRTA) is a complex problem domain with the majority of problem representations categorized as NP-hard. Existing solution approaches handling dynamic MRTA scenarios do not consider the problem structure changes as a possible system dynamic. RoSTAM (Robust and Self-adaptive Task Allocation for Multi-robot teams) presents a novel approach to handle a variety of MRTA problem representations without any alterations to the task allocation framework. RoSTAM's capabilities against a range of MRTA problem distributions have already been established. This paper further validates RoSTAM's performance against the more conventional dynamics, such as robot failure and new task arrival, while performing allocations against two of the most frequently faced problem representations. The framework's performance is evaluated against a state-of-the-art online auction scheme. The results validate RoSTAM's capability to allocate tasks across a range of dynamics efficiently. [ABSTRACT FROM AUTHOR]

The idea of a cubic bipolar fuzzy set (CBFS) is a new hybrid extension of the cubic set (CS) and the bipolar fuzzy set (BFS). A CBFS is a strong model to deal with bipolarity and fuzziness in terms of positive membership grades (PMGs) and negative membership grades (NMGs). A positive interval and a positive numbers represent a PMG to express the degree of belongingness of a specific property, and a negative interval and a negative number represent a NMG which defines the degree of non-belongingness of the specific property (or satisfaction level of its counter property). The aim of this paper is to define the cubic bipolar fuzzy topology under P-order (CBFSP topology) as well as the cubic bipolar fuzzy topology under R-order (CBFSR topology). We investigate certain properties and results of CBFSP topology and CBFSR topology. Topological structures on CBFSs are helping in the development of new artificial intelligence (AI) techniques for healthcare domain strategies and investigating various critical diseases. Such techniques allow for the early detection and investigation of diseases, assisting clinicians in minimizing the possible risk factors. An extended linear assignment model (LAM) and superiority and inferiority ranking method (SIR method) are proposed for healthcare diagnosis based on newly developed structures. The proposed LAM and SIR method are successfully applied for investigation of critical diseases. Moreover, we discuss a comparison analysis of investigations made by suggested techniques with some existing approaches. [ABSTRACT FROM AUTHOR]

Improving the network lifetime is a major concern in wireless sensor networks (WSNs). Due to the limited energy capacity of the sensor nodes, wireless sensor network faces several challenges for improving the lifetime. Clustering is the influential technique used to minimize the energy consumption of the sensor nodes. Researchers have developed lot of clustering algorithms with unique features and challenges. First, this paper begins with the discussion of the clustering characteristics of the different approaches in detail and the radio model used. Further, the survey of the clustering algorithms which is classified into two categories: Traditional and computational based is presented and performance comparison is given according to the requirements of the WSN like energy efficiency, scalability, delivery delay and link quality. [ABSTRACT FROM AUTHOR]

Natural Language processing (NLP) derives its roots from artificial intelligence and computational linguistics. The proliferation of large-scale web corpora and social media data as well as advances in machine learning and deep learning have led to practical applications in diverse NLP areas such as machine translation, information extraction, named entity recognition (NER), text summarization and sentiment analysis. Named-entity recognition (NER), is a sub task of information extraction that seeks to discover and categorize specific entities such as nouns or relations in unstructured text. In this paper, we present a review of the foundations three tolerance-based granular computing methods (rough sets, fuzzy-rough sets and near sets) for representing structured (documents) and unstructured (linguistic entities) text. Applications of these methods are presented via semi-supervised and supervised learning algorithms in labelling relational facts from web corpora and sentiment classification (non-topic based text). The performance of the three presented algorithms is discussed in terms of bench marked datasets and algorithms. We make the case that tolerance relations provide an ideal framework for studying the concept of similarity for text-based applications. The aim of our work is to demonstrate that approximation structures viewed through the prism of tolerance have a great deal of fluidity and integrate conceptual structures at different levels of granularity thereby facilitating learning in the presented NLP applications. [ABSTRACT FROM AUTHOR]

How do you curate your data today to ensure you can capitalize on it to build a successful information business of tomorrow? Can artificial intelligence support and enhance human intelligence? What activities might help us build information services that are essentially the foundation of artificial intelligence in this information community? Where is the best source of user behavioral data? This paper will attempt to answer these questions and more regarding the status of artificial intelligence today. [ABSTRACT FROM AUTHOR]

Financial time series prediction and trading decision-making are priorities of computational intelligence for researchers in academia and the finance industry due to their broad application areas and substantial impact. However, these methods remain challenging because they retain various complex statistical properties, and the mechanism behind the processes is unknown to a large extent. A significant number of machine learning-based methods are proposed and demonstrate impressive results, especially deep learning-based models. Nevertheless, due to the high complexity of massive, nonlinear, and nonindependent data and the difficulties and time consumption of complicated training models of deep learning, the performance of online trading decisions is still inadequate for practical application. This paper proposes the Integrated Framework of Forecasting Based Online Trading Strategy (IFF-BOTS) to satisfy better prediction performance and dynamic decisions for real-world online trading systems. Our method adopts a novel isomorphic convolutional neural network (CNN)-based forecaster-classifier-executor architecture to exploit CNN-based price and trend integrated prediction and direct-reinforcement-learning-based trading decision-making. IFF-BOTS can also achieve better real-time performance for online trading. We empirically compare the proposed approach with state-of-the-art prediction and trading methods on real-world S&P and DJI datasets. The results show that the IFF-BOTS outperforms its competitors in predicting metrics, trading profits, and real-time performance. [ABSTRACT FROM AUTHOR]

The design of real-world industrial systems is subject to a natural tendency towards modularization in order to manage complexity. In addition, this article considers that patterns of self-similarity in many problem domains have made many such solutions naturally representable as holarchies. Likewise, the increasing need for autonomous local decision making as well as the demand to produce solutions at scale has increased the relevance of the multi-agent paradigm to the creation of modern software systems. A variety of software development patterns are explored for their compatibility with holonic multi-agency. The current skill sets required by software development workers and concomitant training activities focus on instilling computational thinking abilities, a set of related cognitive competencies useful in the development of such systems. Intelligent systems play an increasingly important role in modern development and often benefit from computational intelligence techniques for the purpose of parameter tuning. This position paper explores the intersections between holonic multi-agency, modern information systems development, the computational intelligence which train them and the computational thinking skills those developers should be trained in. [ABSTRACT FROM AUTHOR]

This paper presents a method to assess the state of convergence of Ant Colony Optimization algorithms (ACO) using network science metrics. ACO is a computational intelligence technique inspired by the behavior of ants in nature, and it is commonly used to solve combinatorial optimization problems. In the ACO, artificial ants seek for solutions to the tackled problem, transversing the graph that represents the combinatorial problem. For each displacement, the ants deposit pheromone on the edges which they passed. The ants use the pheromone for indirect communication, and the amount of pheromone weighs the intensity of interaction between the ants. The graph of pheromones forms a network that evolves along the iterations. Network Science allows studying the structure and the dynamics of networks. This area of study provides metrics used to extract global information from networks in a particular moment. This paper aims to show that two network science metrics, the Clustering coefficient, and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO. We analyze the convergence of the four variations of the ACO in the Traveling Salesman Problem (TSP). Based on the obtained results, we demonstrate that it is possible to evaluate the convergence of the ACO for the TSP based on the proposed metrics, especially the adapted clustering coefficient. [ABSTRACT FROM AUTHOR]

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance. [ABSTRACT FROM AUTHOR]

Each day more and more new technological devices appear in our world. We want to have everything connected so that we can monitor different aspects inside a city such as the traffic, or inside a building such the position of the people. Even nearly everyone now owns a mobile phone that carries with himself all the time and that enables them to receive information of that type in real time. The problem with such infrastructures is how expensive it is to install and maintain an ambient intelligence environment, so a tool to help in this task would be really appreciated. In this paper we propose the creation of such tool as an AmI simulator that integrates both a network and a social simulator. The authors performed a case study where four experts and twenty-one learners were involved in order to analyze the qualitative characteristics of an AmI simulator, studied whether a simulator can draw more conclusions than the execution of a social and network simulator separately; and finally studied the user satisfaction with an AmI simulator. [ABSTRACT FROM AUTHOR]

In this paper, the generalized intelligent water drops (IWD) algorithm is proposed to solve robot path planning problem. Path planning is modeled by IWD algorithm, because there is inherent similarity between this and finding most appropriate route in natural rivers, that occurs by interaction between water drops and the river bed. The proposed algorithm has two levels; first level, finds the best global feasible path. Second level, performs local search at relatively near distances of global path and reduces its length and time to reach optimal solution. The IWD algorithm, like other inspired nature algorithms, has not a mechanism to deal with constrained optimization problem in its original version. So, a mechanism has been proposed based on repair of infeasible solutions. In this mechanism, a fuzzy inference system is proposed for selecting the best possible infeasible solution and then the local search operator repairs any of them in violation locations. Another innovation of this paper provides a mechanism to local soil update, which is based on fuzzy systems and is independent of size and complexity of environment and its soil altering will be defined in fixed range. Simulation result shows ability of IWD algorithm in finding of the optimal path. [ABSTRACT FROM AUTHOR]

The development of computational intelligence based trading strategies for financial markets has been the focus of research over the last few years. To develop efficient and effective automated trading strategies, we need to understand the workings of the market and the patterns emerging as a result of the traders interactions. In this paper, we develop an adaptive Genetic Programming (GP) agent-based trading system under Intraday Seasonality Model (ISM), which is abbreviated as GPISM trading system. ISM is used for creating maps and visualizing the dynamic price evolution of the asset during the day. This new model permits the recognition of periodic patterns and seasonalities in the price time series and hence eliminates any unnecessary data input. We use a high-frequency dataset of historical price data from Saudi Stock Market, which enables us to run multiple market simulation runs and draw comparisons and conclusions for the developed trading strategies. The goal of our work is to develop automated computational intelligence-based strategies for real markets, and this study facilitates a more thorough understanding of a specific market's workings and constitutes the basis for further exploration into such strategies designed for the stock market. We evaluate the intelligence of the GP-ISMtrading system through agent-based simulation market index trading. For comparison, we also include four other types of trading agents in this contest, namely, zero-intelligence agents, Buy-and-Hold agents, fundamental agents and technical analysis agents. As a result, GP-ISM performs the best, which provides a general framework for the further development of automated trading strategies and decision support systems. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to analyze the tourism information and data derived from the Web, particularly seat availability data of bullet trains in Japan, and to discover some useful knowledge for the tourism. For the fast development of information and communication technologies, the relation between the web data and tourism is inseparable. However, the Web data include various types of information such as numerical, linguistic, and graded data. Furthermore, the expert tourism planner's subjectivity is also an important factor to develop new favorable plans. A simplified fuzzy reasoning method, which is one of the useful approaches in Data mining, is introduced in order to deal with these data mathematically. The analysis of the tourism data and the knowledge discovery are performed using actual data of bullet trains in Japan. [ABSTRACT FROM AUTHOR]

Cognitive Informatics is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. This editorial introduces the emerging field of cognitive informatics and its applications in cognitive computing, abstract intelligence, computational mathematics, and computational intelligence. The themes and structure of this special issue on cognitive informatics are described, and then, focuses of the selected papers in Part II of this special issue are highlighted. [ABSTRACT FROM AUTHOR]

Estimation of software cost (ESC) is considered a crucial task in the software management life cycle as well as time and quality. Prior to the development of a software project, precise estimations are required in the form of person month and time. In the last few decades, various parametric and non-algorithmic or non-parametric approaches regarding the estimation of software costs have been developed. Among them, the constrictive cost model (COCOMO-II) is a commonly used method for estimating software cost. To further improve the accuracy of this model, researchers and practitioners have applied numerous computational intelligence algorithms to optimize their parameters. However, accuracy is still a big problem in this model to be addressed. In this paper, we proposed a biogeography-based optimization (BBO) method to optimize the current coefficients of COCOMO-II for better estimation of software project cost or effort. The experiments are conducted on two standard data sets: NASA-93 and Turkish Industry software projects. The performance of the proposed algorithm called BBO-COCOMO-II is evaluated by using performance indicators including the manhattan distance (MD) and the mean magnitude of relative error (MMRE). Simulation results reveal that the proposed algorithm obtained high accuracy and significant error minimization compared to original COCOMO-II, particle swarm optimization, genetic algorithm, flower pollination algorithm, and other various baseline cost estimation models. [ABSTRACT FROM AUTHOR]

According to the large amount of and diversity of current industrial manufacturing data, on the basis of analyzed limitations of current industrial networking sensing system, a framework of industrial networking sensing system based on edge computing and artificial intelligence is put forward, including data-getting sensors group, first-level sensor routers, second-level sensor routers and backend severs. The above framework mainly is studied from three perspectives: structure level, workflow and key technologies, and the technology of artificial intelligence is the key to achieve this system with analysis of involved computational intelligence technology, information fusion technology and decision technology. This framework of industrial networking sensing system has important reference value on improving timeliness and certainty of industrial networking communications, largely reduces the entire power consumption and reduce stress of background sever. [ABSTRACT FROM AUTHOR]

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences. [ABSTRACT FROM AUTHOR]

Execution traces comprehension is an important topic in computer science since it allows software engineers to get a better understanding of the system behavior. However, traces are usually very large and hence they are difficult to interpret. Parallel, execution traces comprehension is a very important topic into the algorithms learning courses since it allows students to get a better understanding of the algorithm behavior. Therefore, there is a need to investigate ways to help students (and teachers) find and understand important information conveyed in a trace despite the trace being massive. In this paper, we propose a new approximation for execution traces comprehension based on fuzzy linguistic descriptions. A new methodology and a data-driven architecture based on linguistic modelling of complex phenomenon are presented and explained. In particular, they are applied to automatically generate linguistic reports from execution traces generated during the execution of algorithm implemented by the students of an introductory course of artificial intelligence. To the best of our knowledge, it is the first time that linguistic modelling of complex phenomenon is applied to execution traces comprehension. Throughout the article, it is shown how this kind of technology can be employed as a useful computer-assisted assessment tool that provides students and teachers with technical, immediate and personalised feedback about the algorithms that are being studied and implemented. At the same time, they provide us with two useful applications: they are an indispensable pedagogical resource for improving comprehension of execution traces, and they play an important role in the process of measuring and evaluating the "believability" of the agents implemented. To show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of the authors, and it has been incorporated into the process of assessment of an introductory artificial intelligence course. Finally, an empirical evaluation to confirm our hypothesis was performed and a survey directed to the students was carried out to measure the quality of the learning-teaching process by using this methodology enriched with fuzzy linguistic descriptions. [ABSTRACT FROM AUTHOR]

Recent advances in electronic media and computer networks have allowed the creation of large and distributed repositories of information. However, the immediate availability of extensive resources for use by broad classes of computer users gives rise to new challenges in everyday life. These challenges arise from the fact that users cannot exploit available resources effectively when the amount of information requires prohibitively long user time spent on acquaintance with and comprehension of the information content. Thus, the risk of information overload of users imposes new requirements on the software systems that handle the information. Such systems are called Recommender Systems and attempt to provide information in a way that will be most appropriate and valuable to its users and prevent them from being overwhelmed by huge amounts of information that, in the absence of recommender systems, they should browse or examine. [ABSTRACT FROM AUTHOR]

Intelligent data analysis methods usually require as input a matrix, in which each row is an object to be analysed and each column is an attribute. In most cases it is assumed that attributes are Boolean, categorical or numerical. With the advent of semantic domain information in the form of ontologies, it is now common to find also semantic attributes, which may take as value a list of concepts. This paper proposes a new ontology-based procedure to compute the similarity between lists of semantic values, which may be used to compare objects. This measure is employed in an enhanced version of the k-means clustering method. The usefulness of the obtained classes has been tested in the context of a Web-based personalised recommender of Tourist destinations. [ABSTRACT FROM AUTHOR]

E-health for both chronic patients and wellness persons has recently attracted the interest of researchers and practitioners. The physical vital signs are one of the most important factors used to evaluate individual wellness. The variations of daily vital signs are even significant in analyzing physical health trends that can be applied in self-caring for individuals at home. In this paper, an Intelligent-Mamdani Inference Scheme (IMIS) based on fuzzy markup language (FML) is proposed to define approximate health conditions of the individuals via the blood pressure and the body mass index in out-of-hospital. The IMIS can fuse these vital signs and infer semantic health summary using the constructed fuzzy rules and the knowledge base. The main contributions of the this paper are: (1) to leverage personal vital signs by fuzzy logic technology for self-health management at home; (2) to design the novel scheme in detail for practicability and reproduction. The experimental results show that the scheme is feasible to infer personal health status. An individual can easily recognize self-health trend through semantic sentence generation, which further advances self-health management in out-of-hospital. [ABSTRACT FROM AUTHOR]

This paper discusses capital budgeting problem in uncertain environment, in which investment outlays and annual net cash flows of available projects are given subject to experts' estimations. Net present value (NPV) is extended to uncertain environment. As a result, three mathematical models are constructed for the problem, i.e., expected value model, chance-constrained programming model and chance-measure programming model. After that, some crisp equivalences are also discussed for different models. In addition, a hybrid intelligent algorithm for solving the proposed models in general cases is presented. To illustrate the modeling idea and the effectiveness of the proposed algorithm, some numerical examples are provided. [ABSTRACT FROM AUTHOR]

A call for papers for the "International Conference on Smart Learning Environment (ICSLE 2014)" to be held in Hong Kong, China on July 24–25, 2014 is presented.

Strength of high performance concrete (HPC) is not depends upon water to cement ration only but it is also persuaded by the several components of the concrete. The HPC is a vastly compound material, which create its behavior property very difficult in modeling to analyze. The main aim of this paper is to provide the utilization possibilities of Gene Expression Programming (GEP) to predict the HPC compressive strength (HPCCS) at highly complex behavior. A set of 1030 samples of HPC was collected from open access repository that was developed in the laboratory and represented suitable experimental results, which includes eight attributes (i.e., age, blast furnaces slag, cement, fly ash, water, superplasticizer, fine aggregate and coarse aggregate). The obtained results are compared with other computational intelligence technique (i.e., RBF neural Network) to validate the performance analysis of the proposed approach. This method is used to predict the HPCCS in high strength level. Moreover, as per available literature, this is the first attempt to implement the GEP in this domain to predict the HPCCS. [ABSTRACT FROM AUTHOR]

With the increasingly growing internal and external attacks on computer systems and online services, cybersecurity has become a vibrant research area. Countering intrusive attacks is a daunting task with no universal magic solution that can successfully handle all scenarios. A variety of machine-learning and computational intelligence techniques have been applied extensively to detect and classify these attacks. However, the effectiveness of these techniques greatly depends on the adopted data preprocessing methods for feature extraction and engineering. This paper presents an extended taxonomy of the work related to intrusion detection and reviews the state-of-the-art techniques for data preprocessing. It offers a critical up-to-date survey which can be an instrumental pedagogy to help junior researchers conceive the vast amount of research work and gain a holistic view and awareness of various contemporary research directions in this domain. [ABSTRACT FROM AUTHOR]

The project and implementation of autonomous computational systems that incrementally learn and use what has been learnt to, continually, refine its learning abilities throughout time is still a goal far from being achieved. Such dynamic systems would conform to the main ideas of the automatic learning model conventionally characterized as never-ending learning (NEL). The never-ending approach to learning exhibits similarities to the semi-supervised (SS) model which has been successfully implemented by bootstrap learning methods. Bootstrap learning has been one of the most successful among the SS-methods proposed to date and, as such, the natural candidate for implementing NEL systems. Bootstrap methods learn from an available labeled set of data, use the induced knowledge to label some unlabeled new data and, recurrently, learn again from both sets of data in a cyclic manner. However the use of SS methods, particularly bootstrapping methods, to implement NEL systems can give rise to a problem known as concept-drift. Errors that may occur when the system automatically labels new unlabeled data can, over time, cause the system to run off track. The development of new strategies to lessen the impact of concept-drift is an important issue that should be addressed if the goal is to increase the plausibility of developing such systems, employing bootstrap methods. Coupling techniques can play an important role in reducing concept-drift effects over machine learning systems, particularly those designed to perform tasks related to machine reading. This paper proposes and formalizes relevant coupling strategies for dealing with the concept-drift problem in a NEL environment implemented as the system RTWP (Read The Web in Portuguese); initial results have shown they are promising strategies for minimizing the problem taking into account a few system settings. [ABSTRACT FROM AUTHOR]

Computational Intelligence (CI) paradigms reveal to be potential tools to face under the Web uncertainty. In particular, CI techniques may be properly exploited to handle Web usage data and develop Web-based applications tailored on users preferences. The main rationale behind this success is the synergy resulting from CI components, such as fuzzy logic, neural networks and genetic algorithms. In fact, rather than being competitive, each of these computing paradigms provides complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. This paper focuses on the major Computational Intelligent combinations applied in the context of Web personalization, by providing different examples of intelligent systems which have been designed to provide Web users with the information they search, without expecting them to ask for it explicitly. In particular, this paper emphasizes the suitability of hybrid schemes deriving from the profitable combination of different CI methodologies for the development of effective Web personalization systems. [ABSTRACT FROM AUTHOR]

This paper reports research concerning issues involved in adopting a computational dialectics approach to develop a human-computer dialogue system for educational debate. In particular, we propose a dialogue model and a set of computationally usable strategic heuristics to enable the computer to engage its users in debate on a controversial issue. The system operationalising the proposed dialogue model and strategy has been fully implemented. It has also been subject to initial evaluation exercises. The results of the evaluation are essentially favourable. It is anticipated that the work reported in this paper will contribute toward the development of human-computer dialogue in general and of computer-based educational debate in particular, and help to illuminate research issues in the field of dialectics itself. [ABSTRACT FROM AUTHOR]

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases. In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well‐known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems. [ABSTRACT FROM AUTHOR]

This paper evaluates the progress in the field of Ambient Intelligence since its early days. It discusses the extent to which earlier visions of Ambient Intelligence have materialized arguing that to a large extent we already live in an era of Ambient Intelligence, despite that ‘true intelligence’ appears to remain elusive. We examine the extent to which this is so, focusing on shifting mind-sets and ambitions for researchers in the last years. We discuss the various reincarnations of the vision from a historical and societal perspective, together with some of the critiques leveled against it. With many research challenges still remaining open, we discuss how Ambient Intelligence research can be aligned to contemporary societal needs. Particularly important is to create mechanisms that will enable people to interact and benefit from the envisioned technological infrastructure avoiding some of the dangers that embedding computational intelligence in our physical environment brings about. Observing the diversification of research to address different application areas, we note how the impetus towards Ambient Intelligence needs to be renewed with a uniting challenge that transcends the development of technological infrastructure. [ABSTRACT FROM AUTHOR]

The main goal of the paper is to show the idea of incorporating paradigmatic and syntagmatic relations into processing data stored in information tables using rough set methods. Input data, in a tabular form, are used in many machine learning and computational intelligence methods and algorithms, among others, those based on rough set theory. Additional knowledge about semantic relations (both paradigmatic and syntagmatic) can be considered as a useful context affecting data classification or clustering. [ABSTRACT FROM AUTHOR]

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of mining concept-drifting data streams. However, most of these approaches can only be applied to uni-dimensional classification problems where each input instance has to be assigned to a single output class variable. The problem of mining multi-dimensional data streams, which includes multiple output class variables, is largely unexplored and only few streaming multi-dimensional approaches have been recently introduced. In this paper, we propose a novel adaptive method, named Locally Adaptive-MB-MBC (LA-MB-MBC), for mining streaming multi-dimensional data. To this end, we make use of multi-dimensional Bayesian network classifiers (MBCs) as models. Basically, LA-MB-MBC monitors the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a concept drift is detected, LA-MB-MBC adapts the current MBC network locally around each changed node. An experimental study carried out using synthetic multi-dimensional data streams shows the merits of the proposed method in terms of concept drift detection as well as classification performance. [ABSTRACT FROM AUTHOR]

In the late 1400's, Leonardo da Vinci (Reynal & Company, 1956) advised his future readers to study the science of machines to reduce the burden (drudgery) on humanity. Today, the question remains--what technologies can best meet the needs of mankind (Asimov & Frenkel, 1985; Beleyania, 1983; Caswell & Gossain, 1987; Hirzinger, 1997; IFREMER (The French Institute Of Research And Exploration Of The Sea), 1996; Jacobsen et al., 1984; Jacobson, 1996; Lowe, 1985; Morecki et al., 1971; Tesar & Butler, 1989; Volpe, 1996; Weisbin, 1997)? This paper will outline the ability of the discipline of mechanical engineering to satisfy a spectrum of these needs and its potential to match the excitement and vitality of its neighboring disciplines (electronics, computer science, etc.). First, a representative selection of needs will be described along with a listing of technologies (present and future) that may be associated with the discipline of mechanical engineering. Second, a matrix of these needs and technologies will be used to establish the relative importance of the discipline's development capacity and to prioritize five possible future areas for development of multi-disciplinary programs. Third, the critical role of the intelligent actuator as a basic building block of the Next Wave of Technology (NWT) will be described in terms of five classes of actuator technology. Then, the NWT will be described briefly in terms of its ten primary technical components and illustrated in terms of a brief description of open architecture vehicles of the future. [ABSTRACT FROM AUTHOR]

We instantiate a social things paradigm in terms of a social network of facts which drives our household appliances in our name. The appliances are connected to the network, directly receiving the sequence of instructions (parameter/value pairs) constituting the recipe to perform a task requested by the user. The recipes are issued by the computational intelligence algorithms in the core of the social network. Users, appliances and social network are part of an entire ecosystem that we realized in the frame of the EU project Social&Smart. In this paper we describe the architecture, the information infrastructure and the first achievements of the Social&Smart ecosystem within the general cadre of an Open Internet of Things (Open IoT). [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple attribute decision making problems in which the attribute weights are usually correlative, attribute values take the form of linguistic variables, some new methods are developed. Then a new aggregation operator called induced generalized 2-tuple linguistic choquet ordered averaging (IG-2TCOA) operator is proposed, and some desirable properties of the IG-2TCOA operator are studied, such as idempotent, commutative, monotonic and bounded. An IG-2TCOA operators-based approach is developed to solve the MADM problems. Finally, an illustrative example for evaluating the performance of the mechanical products is given to verify the developed approach and to demonstrate its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

In European countries, the last decade has been characterized by a deregulation of power production and electricity became a commodity exchanged in proper markets. This resulted in an increasing interest of the scientific community on electricity exchanges for modeling both market activity and price process. This paper analyzes electricity spot-prices of the Italian Power Exchange (IPEX) and proposes three different methods to model prices time series: a discrete-time univariate econometric model (ARMA-GARCH) and two computational-intelligence techniques (Neural Network and Support Vector Machine). Price series exhibit a strong daily seasonality, addressed by analyzing separately a series for each of the 24 hours. One-day ahead forecasts of hourly prices have been considered so to compare the prediction performances of three different methods, with respect to the canonical benchmark model based on the random walk hypothesis. Results point out that Support Vector Machine methodology gives better forecasting accuracy for price time series, closely followed by the econometric technique. [ABSTRACT FROM AUTHOR]

This paper presents a method to capture the computational intensity and computing resource requirements of data analysis in intelligent transportation systems (ITS). These requirements can be transformed into a generic methodological framework for Cyber-ITS, mainly consisting of region-based ITS data divisions and tasks scheduling for processing, to support the efficient use of cyber-infrastructure (CI). To characterize the computational intensity of a particular ITS data analysis, the computational transformation is performed by data-centric and operation-centric transformation functions. The application of this framework is illustrated by two ITS data analysis cases: multi-sensor data fusion for traffic state estimation by integrating rough set theory with Dempster-Shafer (D-S) evidence theory, and geospatial computation on Global Positioning System (GPS) data for advertising value evaluation. To make the design of generic parallel computing solutions feasible for ITS data analysis in these cases, an approach is developed to decouple the region-based division from specific high performance computer (HPC) architecture and implement a prototype of the methodological framework. Experimental results show that the prototype implementation of the framework can be applied to divide the ITS data analysis into a load-balanced set of computing tasks, therefore facilitating the parallelized data fusion and geospatial computation to achieve remarkable speedup in computation time and throughput, without loss in accuracy. [ABSTRACT FROM AUTHOR]

The stagnation phenomena regularly happen in solving complex optimization problems by a class of computational intelligence algorithms which lack theoretical guidance for their parameters setting. The search characteristics of these algorithms are analyzed in this paper, based on which an adaptive computational intelligence optimization algorithm called cloud drops algorithm is proposed by adopting a cloud model to express the randomness and fuzziness of its search process. The cloud drops algorithm is characterized by representing, mining and recreating the uncertain knowledge about its search process for the optimal solution. None of search parameters are predefined in the proposed algorithm and, whatever the initial solution set is, the whole system can adaptively approach to the global optimum. Based on the theory of stochastic processes, the almost sure convergence of the proposed algorithm is proved under certain conditions by introducing a martingale approach into traditional Markov Chain analysis. Two benchmark problems are tested with the proposed algorithm and the other two existing algorithms as a comparison. The results show that the proposed algorithm has faster convergence speed, better self-adaptability, and stronger ability to deal with stagnation phenomena effectively. [ABSTRACT FROM AUTHOR]

Goal of feature selection is to find a suitable feature subset that produces higher accuracy for classifier in the user end. Hybrid methods for feature selection comprised of combination of filter and wrapper approaches have recently been emerged as strong techniques for the problem in this domain. In this paper we have presented a novel approach for feature selection based on feature clustering using well known k-means philosophy for the high dimensional gene expression data. Also we have proposed three simple hybrid approaches for reducing data dimensionality while maintaining classification accuracy which combine our basic feature selection through feature clustering (FSFC) approach to other standard approaches of feature selection in different orientation. We have employed popular Box and Whisker plot and ROC curve analysis to evaluate experimental outcome. Our experimental results clearly show suitability of our methods in hybrid approaches of feature selection in micro-array gene expression domain. [ABSTRACT FROM AUTHOR]

Speculative computation is an effective means for solving problems with incomplete information in multi-agent systems. It allows such a system to compute tentative solutions by using default knowledge about agents even if communications between agents are delayed or fail. Previously we have proposed a logical framework for speculative constraint processing for master-slave multi-agent systems. In this paper, we extend the framework to support more general multi-agent systems that are hierarchically structured. We provide an operational model for the framework and present a prototype implementation of the model. [ABSTRACT FROM AUTHOR]

In this paper we present an intelligent scheme, employing a combination of fuzzy logic, pulse coupled neural networks (PCNNs), wavelets and rough sets, for analysing prostrate ultrasound images in order diagnose prostate cancer. Image noise is a principal factor which hampers the visual quality of ultrasound images and can therefore lead to misdiagnosis. To address this issue we first utilise an algorithm based on type-II fuzzy sets to enhance the contrast of the image. This is followed by performing PCNN-based segmentation in order to identify the region of interest and to detect the boundary of the prostate pattern. Then, a wavelet features are extracted and normalised, followed by application of a rough set analysis to discover the dependency between the attributes, and to generate a set of reducts consisting of a minimal number of attributes. Finally, a rough set classifier is designed for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of our approach, we present tests on different prostate ultrasound images. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach is high compared with other intelligent techniques including decision trees, discriminant analysis, rough neural networks, fuzzy ARTMAP, and neural networks. [ABSTRACT FROM AUTHOR]

Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach. CI develops a coherent set of fundamental theories and denotational mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, knowledge engineering, and computational intelligence. This paper reviews the central doctrine of CI and its applications. The theoretical framework of CI is described on the architecture of CI and its denotational mathematic means. A set of theories and formal models of CI is presented in order to explore the natural and computational intelligence. A wide range of applications of CI are described in the areas of cognitive computers, cognitive properties of knowledge, simulations of human cognitive behaviors, cognitive complexity of software, autonomous agent systems, and computational intelligence. [ABSTRACT FROM AUTHOR]

The abstract, rigorous, and expressive needs in cognitive informatics, intelligence science, software science, and knowledge science lead to new forms of mathematics collectively known as denotational mathematics. Denotational mathematics is a category of expressive mathematical structures that deals with high level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, concepts, knowledge, processes, and systems. Denotational mathematics is usually in the form of abstract algebra that is a branch of mathematics in which a system of abstract notations is adopted to denote relations of abstract mathematical entities and their algebraic operations based on given axioms and laws. Four paradigms of denotational mathematics, known as concept algebra, system algebra, Real-Time Process Algebra (RTPA), and Visual Semantic Algebra (VSA), are introduced in this paper. Applications of denotational mathematics in cognitive informatics and computational intelligence are elaborated. Denotational mathematics is widely applicable to model and manipulate complex architectures and behaviors of both humans and intelligent systems, as well as long chains of inference processes. [ABSTRACT FROM AUTHOR]

Based on comprehensive simulations of bird flocking or fish schooling, an Improved Particle Swarm Optimization algorithm (IPSO) is proposed in this paper. The improvements include mainly the introduction of a new generating mechanism for initial particles to improve the convergence performances, the restarting strategy to avoid a stagnation phenomenon, and the design of novel velocity and position updating formulae to enhance the global search ability of the available PSOs. The numerical results on both a mathematical function and the TEAM Workshop problem obtained by using different optimization algorithms are reported and the performances are compared. Our numerical results suggest that the proposed IPSO algorithm is superior to its precursors in sense of the global search ability. [ABSTRACT FROM AUTHOR]