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• The prediction task of CoIL challenge 2000 is addressed in the paper. • Three different methods are proposed for direct mailing problem of CoIL challenge 2000. • The proposed methods outperform the method proposed by the winner of the challenge. • The proposed methods overcome, also, the unbalanced dataset issue of the problem. This paper presents some approaches based on data mining techniques to solve the prediction task of Computational Intelligence and Learning (CoIL) Challenge 2000. The prediction task of the contest is a direct mailing problem and the goal is to improve its response rate. The main issue in this competition is the incompatibility of the dataset in which the distribution of the classes of the target attribute is highly unbalanced. This in turn causes high error rate in identifying the minority class samples. Three different level methods including data-level, algorithm-level, and hybrid method are used to overcome this issue. The specificity, sensitivity, precision-recall, and ROC criteria are employed to compare the performance of the methods. Among the methods proposed in this paper, the best one performs much better than the winner of the competition. [ABSTRACT FROM AUTHOR]

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi–objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi–objective extension of the IWD which returns a Pareto set. Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4 s and the generational distance, spacing, and hyper–area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martínez and Zhang (2011). This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously. [ABSTRACT FROM AUTHOR]

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are a favorable scheme in load forecasting applications mainly due to their endogenous capacity of robust modeling of data sets with highly non-linear relationship between inputs and outputs. Usually, the inputs correspond to historical load values, exogenous variables like temperature, day type identification codes and others. The outputs refer to the load values under examination. The majority of the load forecasting related literature focuses in aggregated load system level. While contemporary research efforts focus in smart grid technologies, there is need to study the characteristics of small scaled loads. Bus load forecasting refers to prediction of the demand patterns in buses of the transmission and distribution systems. Bus load exhibits low correlation with the aggregated system load, since it is characterized by a high level of stochasticity. Hence, a proper selection and formulation of the forecasting model is essential in order to keep the prediction accuracy within acceptable ranges. The treatment of bus load characteristics is held with computational intelligence techniques such as clustering and ANN. Neural network based systems are a favorable scheme in recent years in price and load predictions over traditional time series models. ANN can fully adapt expert knowledge and modify their parameters accordingly to simulate the problem`s attributions through training paradigms. Thus, ANN based systems are an essential choice, justified by the paper`s findings, for highly volatile time series. This work focuses on the short-term load forecasting (STLF) of a number of buses within the Greek interconnected system. Firstly, a modified version of the ANN already proposed for the aggregated load of the interconnected system is employed. To enhance the forecasting accuracy of the ANN, the load profiling methodology is used resulting to the formulation of two novel hybrid forecasting models. These models refer to the combination of the ANN with a clustering algorithm, resulting to superior performance. Simulation results indicate that the combination captures and successfully treats the special characteristics of the bus load patterns. The scope of the present paper is to develop efficient forecasting systems for short-term bus load predictions. This is a current research challenge due to the high interest for smart grids and demand side management applications by utilities, regulators, retailer and energy service companies. Bus load forecasting appears to be a more difficult engineering problem compared to forecasting of the total load of a country. No hybrid models for bus load predictions have been presented so far in the literature. Two novel clustering based tools are developed and successfully tested in a number of loads covering different types of electricity consumers and demand levels. [ABSTRACT FROM AUTHOR]

Urban traffic as one of the most important challenges in modern city life needs practically effective and efficient solutions. Artificial intelligence methods have gained popularity for optimal traffic light control. In this paper, a review of most important works in the field of controlling traffic signal timing, in particular studies focusing on Q -learning, neural network, and fuzzy logic system are presented. As per existing literature, the intelligent methods show a higher performance compared to traditional controlling methods. However, a study that compares the performance of different learning methods is not published yet. In this paper, the aforementioned computational intelligence methods and a fixed-time method are implemented to set signals times and minimize total delays for an isolated intersection. These methods are developed and compared on a same platform. The intersection is treated as an intelligent agent that learns to propose an appropriate green time for each phase. The appropriate green time for all the intelligent controllers are estimated based on the received traffic information. A comprehensive comparison is made between the performance of Q -learning, neural network, and fuzzy logic system controller for two different scenarios. The three intelligent learning controllers present close performances with multiple replication orders in two scenarios. On average Q -learning has 66%, neural network 71%, and fuzzy logic has 74% higher performance compared to the fixed-time controller. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are sustainable solutions for rural zone electrification that use local renewable resources. However, only careful planning at the start of an MG project can ensure its future optimal operation. In this paper, a novel methodology for MG planning by using the uncertainty characterization of renewable resources and demand is presented. Additionally, a model of electricity consumption is proposed and applied in an isolated rural community. In such communities, consumption patterns typically need to be derived as model inputs because consumption measurements are not available for the planning stage. To obtain these inputs, clustering algorithms based on self-organizing maps (SOMs) and fuzzy c-means are used to classify the families of the community given sociodemographic information obtained via surveys. Subsequently, Markov chains (MCs) are employed to generate consumption patterns based on consumption measurements in some dwellings and surveys applied to the community. The nonlinearities and uncertainties associated with renewable resources and consumption are modeled by using prediction interval (PI) models. These PI models provide the required consumption and generation scenarios for deriving the optimal sizing and topological information to address the MG planning problem. The results of the robust planning approach based on scenarios are useful at the feasibility and design phases of an MG project. The proposed methodology is successfully applied to MG planning for a rural Mapuche community, where a conservative criterion was considered to minimize the investment risk. This criterion corresponds to the worst-case scenario in which the demand increases by 19.9% compared to that of the baseline scenario and a lower energy cost is obtained. However, the net present cost and operational costs increase by 14% and 11.75% compared to those of the baseline scenario, respectively. • Clustering algorithms and Markov chains are used to generate consumption patterns. • Prediction interval models contribute to generating scenarios for MG planning. • The proposed methodology is validated in a rural Mapuche community in Chile. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates a number of computational intelligence techniques in the detection of heart disease. Particularly, comparison of six well known classifiers for the well used Cleveland data is performed. Further, this paper highlights the potential of an expert judgment based (i.e., medical knowledge driven) feature selection process (termed as MFS), and compare against the generally employed computational intelligence based feature selection mechanism. Also, this article recognizes that the publicly available Cleveland data becomes imbalanced when considering binary classification. Performance of classifiers, and also the potential of MFS are investigated considering this imbalanced data issue. The experimental results demonstrate that the use of MFS noticeably improved the performance, especially in terms of accuracy, for most of the classifiers considered and for majority of the datasets (generated by converting the Cleveland dataset for binary classification). MFS combined with the computerized feature selection process (CFS) has also been investigated and showed encouraging results particularly for NaiveBayes, IBK and SMO. In summary, the medical knowledge based feature selection method has shown promise for use in heart disease diagnostics. [Copyright &y& Elsevier]

Highlights • A heuristic search strategy was proposed for optimal learning group formation. • NSGA-II, a multi-objective optimization tool, was utilized for optimal grouping. • It was capable of enhancing inter-group homogeneity and Intra-group Heterogeneity. • Efficient and reliable fitness functions were defined to stop iterations. • Various characteristics of any data types and range of variations can be handled. Abstract In modern education systems, plenty of research suggests that clustering the learners into optimal learning groups based on their multiple characteristics is a determining effort in enhancing the effectiveness of collaborative learning. Although there have been several evidences on developing and implementing appropriate computational tools to handle classification processes in expert and intelligent systems, the effectiveness and accuracy of optimal grouping algorithms are still worth improving. For instance, the majority of grouping processes in collaborative learning environments is orchestrated through single-objective optimization algorithms, which need to be revisited due to some intrinsic limitations. In this paper, we propose a novel algorithm capable of properly addressing a variety of optimization problems in optimal learning group formation processes. To this end, a multi-objective version of Genetic Algorithms, i.e. Non-dominated Sorting Genetic Algorithm, NSGA-II, was successfully implemented and applied to improve the performance and accuracy of optimally formed learning groups. In contrast to the previous related works applying single-objective algorithms, the main advantage of our work is simultaneous satisfaction of multiple targets predefined for the formation of optimal learning groups, especially the inter-homogeneity and intra-heterogeneity of each learning group, which significantly enhance both effectiveness and accuracy of optimal grouping processes in the underlying intelligent systems. Challenging the proposed optimization algorithms, both single- and multi-objective optimizers, with a similar grouping problem, clearly proved that the single-objective optimization technique has limited control and sensitivity to the quality of individual groups. Contrary to single-objective optimization techniques, which are mainly governed by adjusting the quality of the groups altogether in average, the proposed multi-objective algorithm not only takes the average desirability of all formed groups into account but also precisely monitors the fitness of each group in a potential solution distinctively. The generality of the proposed algorithm makes it a suitable candidate not only to handle optimal grouping in learning environments but also to be competent enough for grouping problems in other domains as well. [ABSTRACT FROM AUTHOR]

Highlights • This work proposes a data-driven framework for spatio-temporal prediction (FB-STEP). • FB-STEP attempts to address three major challenges in climatological prediction. • FB-STEP is based on combined fuzzy Bayesian and multifractal analysis technique. • Validation has been made by predicting climatic condition for five cities in India. • Study shows improved performance of FB-STEP compared to state-of-the-art methods. Abstract With the recent development of computational intelligence (CI), data-driven models have gained growing interest to be applied in various scientific disciplines. This paper aims at proposing a hybrid CI-based data-driven framework as a complement for the physics-based models used in climatological prediction. The proposed framework, called FB-STEP , is based on a combination of fuzzy Bayesian strategy and multifractal analysis technique. The focus is to address three major research challenges in multivariate climatological prediction: (1) modeling complex spatio-temporal dependency among climatological variables, (2) dealing with non-linear, chaotic dynamics in climatic time series, and (3) reducing epistemic uncertainty in the data-driven prediction process. The present work not only explores Fuzzy-Bayesian modeling of spatio-temporal processes, but also presents an elegant approach of dealing with intrinsic chaos in time series, through a synergism between multifractal analysis and Bayesian inference mechanism. Similar concepts may also be successfully employed in developing expert or intelligent systems for wide range of applications, including reservoir-water dynamics modeling, flood monitoring, traffic flow modeling, chemical-mechanical process monitoring, and so on. Thus, the present research work carries a significant value not merely in the field of climate research, but also in the domains of AI and machine intelligence. The experimentation has been carried out to spatio-temporally extrapolate the climatic conditions of five different locations in India , with the help of historical data on temperature, humidity, precipitation rate , and soil moisture. A comparative study with popular linear and non-linear methods has validated the efficacy of the proposed data-driven approach for climatological prediction. [ABSTRACT FROM AUTHOR]

The increase in mortality rate due to life-threatening diseases has become an issue of concern in today’s world. Early detection and diagnosis of diseases thus becomes necessary to reduce the severity of their side effects. Computational intelligence techniques like rare association rule mining can be extensively used for the analysis of diseases. This paper introduces an efficient technique to identify the symptoms and risk factors for three adverse diseases: cardiovascular disease, hepatitis and breast cancer, in terms of rare association rules. Existing research on rare association rule mining is based on the notion that the entire data to be operated on is available at the onset of the mining process. The medical databases in practice may get modified over time due to the addition of new records or deletion of previous records. Moreover, the user may switch to a new threshold for generating the desired set of rare association rules when the database gets updated. A straightforward yet incompetent solution for generating the current set of rare association rules would be to re-execute the entire mining algorithm from scratch, for each modified bunch of data and updated threshold. The algorithm proposed in this study is capable of generating the new set of rare association rules from updated medical databases in a single database scan without re-executing the entire mining process. It can efficiently handle the cases of transaction insertion and deletion and also provides flexibility to the user to generate the new set of rare association rules when threshold is updated. Experimental analysis illustrates the significance of proposed approach over traditional approach of repeatedly mining the entire updated database. [ABSTRACT FROM AUTHOR]

In this paper, a new strategy for dealing with time series exhibiting a seasonal pattern is proposed. The strategy is applied in the context of time series forecasting using k NN regression. The key idea is to forecast every different season using a different specialized k NN learner. Each learner is specialized because its training set only contains examples whose targets belong to the season that is able to forecast. This way, the forecast of a specialized k NN learner is an aggregation of target values of the same season, reducing the likelihood of misleading forecasts. Although the strategy is applied to k NN, we think that other computational intelligence approaches could take advantage of it. [ABSTRACT FROM AUTHOR]

A good trade-off between exploration and exploitation to find optimal values in search algorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The Gravitational Search Algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy Gravitational Search Algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O ( nd ), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface Gravitational Search Algorithm (PSGSA) and a Modified Gravitational Search Algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O ( nd ), whereas MGSA has complexity O (( nd ) 2 ). [ABSTRACT FROM AUTHOR]

Teaching-Learning-Based Optimisation (TLBO) is one of the more recently developed metaheuristics and has been successfully applied to solve various optimisation problems. However, TLBO has not been academically reported for solving the robust machine layout design (MLD) problems with dynamic demand. Considering internal logistics activities, shortening material flow distance within a manufacturing area can lead to efficient productivity and a decrease in related costs. The robust machine layout is concerned with determining the efficient arrangement of machines/facilities located on a manufacturing shop floor under future demand fluctuation. A robust designed layout is essential for a company to maintain a high productivity rate through multiple time-periods of demand uncertainty with minimum effects related to the re-layout time and cost, manufacturing disruption, and the movement of monument machines. The objectives of this paper were to: i) describe the development of a computer aided layout designing tool for minimising the total material flow distance under dynamic demand scenario, ii) investigate the appropriate setting of TLBO parameters, and iii) propose four TLBO modifications for improving its performance. The modified TLBOs were inspired by multiple teachers with two types of classes and two approaches to teacher selection. The numerical experiments were designed and conducted using eleven MLD benchmarking datasets. Statistical analyses on the experimental results showed a superior performance for the proposed modifications. [ABSTRACT FROM AUTHOR]

From their origins in the sociological field, memes have recently become of interest in the context of `viral' transmission of basic information units (memes) in online social networks. However, much work still needs to be done in terms of metrics and practical data processing issues. In this paper we define a theoretical basis and processing system for extracting and matching memes from free format text. The system facilitates the work of a text analyst in extracting this type of data structures from online text corpuses and n performing empirical experiments in a controlled manner. The general aspects related to the solution are the automatic processing of unstructured text without need for preprocessing (such as labelling and tagging), identification of co-occurences of concepts and corresponding relations, construction of semantic networks and selecting the top memes. The system integrates these processes which are generally separate in other state of the art systems. The proposed system is important because unstructured online text content is growing at a greater rate than other content (e.g. semi-structured, structured) and integrated and automated systems for knowledge extraction from this content will be increasingly important in the future. To illustrate the method and metrics we process several real online discussion forums, extracting the principal concepts and relations, building the memes and then identifying the key memes for each document corpus using a sophisticated matching process. The results show that our method can automatically extract coherent key knowledge from free text, which is corroborated by benchmarking with a set of other text analysis approaches, as well as a user study evaluation. [ABSTRACT FROM AUTHOR]

Identifying students’ learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a student's learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students’ behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions. [ABSTRACT FROM AUTHOR]

Quantifying and validating descriptive heuristic rules that govern someone’s skills and expertise have been a known philosophical quest since the early Greek philosophers. Inherent to sport coaching is the qualitative assessment of complex human motion patterns, relying on subjective and ‘hard-to-quantify’ criteria that can be subject to experts/coaches disagreement. This paper presents an application of Artificial Neural Networks (ANN) for the discovery of predictive power of swing plane heuristic rules influencing golf ball trajectories. The golf data set (531 samples from 14 golfers) utilised in the experiments was captured via a ubiquitous computing device embedded in the handle of a driver club. Out of multiple swing performance factors influencing ball trajectory, the selected subset of features for subspace modelling was linked only to the swing plane concept. Quantitative evidence supporting empirical coaching rules for swing plane assessment were obtained by supervised learning of ANN models. Optimised ANN models Radial Basis Function (RBF) and Support Vector Machine (SVM), were able to draw inference from captured swing data linking ball trajectories with variations of swing plane (with overall classification of 87%). The obtained swing plane computer model inference, data analysis and implemented concept of generic data export utility support kinesiology, golf coaching, inform club fitting, golf manufacturing technology and demonstrate new cross- and multi-disciplinary integration of sport science, augmented coaching, ubiquitous computing, computational intelligence and the applications of expert systems for growing availability of sport, injury prevention/rehabilitation and golf related data sets. [ABSTRACT FROM AUTHOR]

Supplier evaluation and selection constitutes a central issue in supply chain management (SCM). However, the data on which to base the corresponding choices in real life problems are often imprecise or vague, which has led to the introduction of fuzzy approaches. Predictive intelligent-based techniques, such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been recently applied in different research fields to model fuzzy multi-criteria decision processes where the understanding and learning of the relationships between the input and output data are the key to select suitable solutions. In this paper, a hybrid ANFIS-ANN model is proposed to assist managers in their supplier evaluation process. After aggregating the data set through the Analytical Hierarchy Process (AHP), the most influential criteria on the suppliers’ performance are determined by ANFIS. Then, Multi-Layer Perceptron (MLP) is used to predict and rank the suppliers’ performance based on the most effective criteria. A case study is presented to illustrate the main steps of the model and show its accuracy in prediction. A battery of parametric tests and sensitivity analyses has been implemented to evaluate the overall performance of several models based on different effective criteria combinations. [ABSTRACT FROM AUTHOR]

For those railway stations without being automated, railway traffic dispatching still depends on dispatchers, especially under disturbed circumstances. In this study, an agent-based support system, named D-Agent, is developed to assist human dispatchers to make decisions in station operation. To this end, the common knowledge and possible difficulties concerning a station dispatcher in his/her routine work are firstly studied, and the D-Agent is proposed with the purpose of working out practicable solutions to these challenging tasks as a dispatcher does. Then the general model of the D-Agent is established, containing five basic modules: local database, knowledge base, skill base, reasoning mechanism and communication interfaces. The internal skills of the D-Agent are designed to execute various tasks in different scenarios. Besides, a skill extension of the D-Agent with mathematical formulations is particularly discussed in this paper, to find feasible and optimal traffic control solutions in disturbance situations such as train delays and route conflicts. The D-Agent is designed to learn from its own experimental history in applying different skills, and evaluate the skills by preference weights of alternative solutions in a particular task. This procedure allows the agent to have potential for continuous improvement. To verify the applicability of the proposed support system, a D-Agent for a terminal station of subway is simulated. The numerical example of train delays and route conflicts shows that the D-Agent can generally perform as a station dispatcher in fulfilling the specific tasks, estimate the traffic state in different operation strategies and support the decision-making of favored solutions. Significantly, it indicates that the mathematical methods can also been employed by an intelligent agent. [ABSTRACT FROM AUTHOR]

This paper presents a new intelligent optimizer based on the relation between trees and their seeds for continuous optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n -dimensional search space corresponds with the possible solution of an optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function optimization and is an alternative optimization method for solving multilevel thresholding problem. [ABSTRACT FROM AUTHOR]

Recently, associative Petri nets (APNs) have been attracted in knowledge representation and modeling of expert systems. However, it is a challenge in transferring from a complex system to an APNs model. Moreover, the domain knowledge and relationship finding between processes has increased in difficulty and complexity in expert and intelligence systems. This paper addresses this issue by proposing a novel systematic procedure to guide the analyst in creating decision support models. The proposed construction algorithm can not only reduce the effort of construction but also can easily implement for knowledge representation. It can give a summary to describe all states but also simplify the reasoning process. An example of malicious email detection is presented to provide empirical evidence in the utility of the systematic procedure of associative Petri net model construction. Experimental result shows that our constructed APN model outperforms than other methods. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban roads is one of the biggest challenges of 21 century. Despite a myriad of research work in the last two decades, optimization of traffic signals in network level is still an open research problem. This paper for the first time employs advanced cuckoo search optimization algorithm for optimally tuning parameters of intelligent controllers. Neural Network (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are two intelligent controllers implemented in this study. For the sake of comparison, we also implement Q -learning and fixed-time controllers as benchmarks. Comprehensive simulation scenarios are designed and executed for a traffic network composed of nine four-way intersections. Obtained results for a few scenarios demonstrate the optimality of trained intelligent controllers using the cuckoo search method. The average performance of NN, ANFIS, and Q -learning controllers against the fixed-time controller are 44%, 39%, and 35%, respectively. [ABSTRACT FROM AUTHOR]

Fruit fly optimization algorithm (FOA) is one of the recent evolutionary computation approaches. This paper presents an effective and improved FOA (IFOA) for optimizing numerical functions and solving joint replenishment problems (JRPs). In the proposed IFOA, a new method of maintaining the population diversity is developed to enhance the exploration ability. Fruit flies with better fitness values use vision to fly toward a new location, and the others fly randomly in initial search space based on swarm collaboration. In addition, a new parameter to avoid the acquisition of local optimal solution is introduced to implement intelligent searching. Random perturbation is added to the updated initial location to jump out of the local optimum. Comparisons are carried out using 18 benchmark functions to verify the performance of the IFOA. Experimental results show that IFOA has better comprehensive performance than the original FOA, differential evolution algorithm, and particle swarm optimization algorithm. The IFOA is also utilized to solve the typical JRPs that have been proven as non-deterministic polynomial hard problems. Comparative examples reveal that the proposed IFOA can find better solutions than the current best algorithm; thus, it is a potential tool for various complex optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we present neurule-based inference and explanation mechanisms. A neurule is a kind of integrated rule, which integrates a symbolic rule with neurocomputing: each neurule is considered as an adaline neural unit. Thus, a neurule base consists of a number of autonomous adaline units (neurules), expressed in a symbolic oriented syntax. There are two inference processes for neurules: the connectionism-oriented process, which gives pre-eminence to neurocomputing approach, and the symbolism-oriented process, which gives pre-eminence to a symbolic backwards chaining like approach. Symbolism-oriented process is proved to be more efficient than the connectionism-oriented one, in terms of the number of required computations (56.47–63.88% average reduction) and the mean runtime gain (59.95–64.89% in average), although both require almost the same average number of input values. The neurule-based explanation mechanism provides three types of explanations: ‘how’ a conclusion was derived, ‘why’ a value for a specific input variable was asked from the user and ‘why-not’ a variable has acquired a specific value. As shown by experiments, the neurule-based explanation mechanism is superior to that provided by known connectionist expert systems, another neuro-symbolic integration category. It provides less in number (64.38–69.28% average reduction) and more natural explanation rules, thus increasing efficiency (mean runtime gain of 56.65–56.73% in average) and comprehensibility of explanations. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided. [ABSTRACT FROM AUTHOR]

The paper explores the usefulness of hybridizing two distinct nature inspired computational intelligence techniques viz., Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for modeling slump of Ready Mix Concrete (RMC) based on its design mix constituents viz., cement, fly ash, sand, coarse aggregates, admixture and water-binder ratio. The methodology utilizes the universal function approximation ability of ANN for imbibing the subtle relationships between the input and output variables and the stochastic search ability of GA for evolving the initial optimal weights and biases of the ANN to minimize the probability of neural network getting trapped at local minima and slowly converging to global optimum. The performance of hybrid model (ANN-GA) was compared with commonly used back-propagation neural network (BPNN) using six different statistical parameters. The study showed that by hybridizing ANN with GA, the convergence speed of ANN and its accuracy of prediction can be improved. The trained hybrid model can be used for predicting slump of concrete for a given concrete design mix in quick time without performing multiple trials with different design mix proportions. [ABSTRACT FROM AUTHOR]

• Cascading effects of organizational dynamics on business decisions is discussed. • A multi-layer socio-technical change propagation model is developed. • An original cloud-based software tool is presented to compute and track decision consequences. • Performance of the proposed tool is demonstrated on a real world engineering case. • Systems design insight is provided, by detecting and tracking hidden players. Today's organizations are witnessing a growing complexity in making interconnected decisions. Where individuals have a wider range of decisions to influence, the consequence of decisions far more propagate across the system, and the business environment continually influences the status of the system. Predicting the cascading effects of decisions in such situations would be very problematic yet can have several implications for managers and executives to think beyond organizational silos and make local decision with a bigger picture of emergent consequences in mind. A prominent challenge within this realm is the ever-increasing complexity of decision propagations, especially when incorporating the role and influence of people involved in decision-making. This paper tackles this challenge from an engineering change perspective, with the focus on computing the compound risk of decisions when their consequences concurrently propagate across the system. We introduce an interactive tool called Virtual Net Propagator , which incorporates organizational dynamics into decision analysis, with the aim to identify change opportunities and effective set of interventions. Illustrated by a field engineering case study, it is demonstrated that the proposed tool can provide detailed knowledge on how decisions are interconnected and how systemic (cascading) effects of a decision propagate through causal pathways, so highlighting key influencers along with role and influence of interfacing (hidden) players. [ABSTRACT FROM AUTHOR]

Abstract: Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. [Copyright &y& Elsevier]

Abstract: This paper investigates the sick and healthy factors which contribute to heart disease for males and females. Association rule mining, a computational intelligence approach, is used to identify these factors and the UCI Cleveland dataset, a biological database, is considered along with the three rule generation algorithms – Apriori, Predictive Apriori and Tertius. Analyzing the information available on sick and healthy individuals and taking confidence as an indicator, females are seen to have less chance of coronary heart disease then males. Also, the attributes indicating healthy and sick conditions were identified. It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women. However, resting ECG being either normal or hyper and slope being flat are potential high risk factors for women only. For men, on the other hand, only a single rule expressing resting ECG being hyper was shown to be a significant factor. This means, for women, resting ECG status is a key distinct factor for heart disease prediction. Comparing the healthy status of men and women, slope being up, number of coloured vessels being zero, and oldpeak being less than or equal to 0.56 indicate a healthy status for both genders. [Copyright &y& Elsevier]

Abstract: The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research. [Copyright &y& Elsevier]

Abstract: In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANFIS has been illustrated using commonly used nonlinear, non-stationary and chaotic benchmark datasets of Mackey–Glass, Box–Jenkins and biomedical signals of electroencephalogram (EEG). The training and test performances of both hybrid CI techniques have been compared for these datasets. [Copyright &y& Elsevier]

Abstract: The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. [ABSTRACT FROM AUTHOR]

Abstract: A novel hybrid optimization algorithm combining computational intelligence techniques is presented to solve the multifactor highway passenger volume prediction problem. In this paper, we can get and discretize a reduced decision table, which implies that the number of evaluation criteria such as travel quantity, fixed-asset investment, railway mileage, and waterway passenger volume are reduced with no information loss through rough set theory (RST) method. Particle swarm optimization (PSO) algorithm based on the random global optimization is inducted into the network training. The PSO algorithm is used for glancing study in order to confirm the initial values, and then the back propagation neural network (BPNN) is used for given accuracy to found the PSO-BPNN model. And this reduced information is used to form a classification rule set, which is regarded as an appropriate input parameter to training PSO-BPNN model. The RST-PSO-BPNN model is obtained to forecast highway passenger volume. The rules developed by RST analysis show the best prediction accuracy if a case matches any one of the rules. The keystone of this hybrid optimization algorithm is using rules developed by RST for an object that matches any one of the rules and the PSO-BPNN model for one that does not match any of them. The effectiveness of our optimization algorithm was verified by experiments comparing the traditional gray model method. For the experiment, highway passenger volumes of China during the period 1995–2009 were selected, and for the validation, the novel hybrid optimization algorithm is reliable. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a methodology that designs a fault detection Artificial Immune System (AIS) based on immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The key fault detection challenge is determining the difference between normal and potential harmful activities. A promising solution is emerging in the form of AIS. The Self×Nonself theory inspired an immune-based fault detection approach. This article proposes the AIS Multi-Operational Algorithm based on the Negative Selection Algorithm. The proposed algorithm is used to a DC motor fault model benchmark to compare its relative performance to others fault detection algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection. [Copyright &y& Elsevier]

Abstract: This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems. [Copyright &y& Elsevier]

Abstract: Recently, proposed an approach to overcome the limitations of the existing studies and simplify the computational procedures based on the LR deviation degree of fuzzy number. However, there were some problems with the ranking method. In this paper, we want to indicate these problems of Wang’s method and then propose a revised method which can avoid these problems for ranking fuzzy numbers. Since the revised method is based on the Wang’s method, it is easy to rank fuzzy numbers in a way similar to the original method. The method is illustrated by numerical examples and compared with other methods. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel computational intelligence technique, based on the sociological concept of human group formation, with the aim to acquire a better solution to classification problems. The key concept of the human group formation is about the behavior of in-group members that try to unite with their own group as much as possible, and at the same time maintain social distance from the out-group members. This study compares the performance of the proposed model with that of fuzzy ARTMAP, radial basis function network, and learning vector quantization. Experimental results demonstrate the potential of the proposed approach in offering an efficient and effective solution to the problem. [Copyright &y& Elsevier]

• A combined computational intelligence technique for trend classification and trading in Forex markets. • An Ensemble multi-class SVM for efficient trend forecasting into uptrend, sideway, and downtrend. • A fuzzy-based trading system comprising multiple AND-OR Buy/Sell fuzzy rules. • Utilizing NSGA-II to optimize the hyperparameters of the fuzzy trading system. Foreign exchange (Forex) market is the biggest currency exchange market in the world. Existing trading systems in Forex markets based on technical analysis use crisp technical indicators to provide Buy/Sell signals to the trader, only when the indicator value crosses a given threshold level. This strict and noise-sensitive condition can be replaced through uncertainty handling of indicators using fuzzy numbers to generate Buy/Sell signals with fuzzy memberships functions. To achieve this purpose, this paper presents a combined technique based on ensemble multi-class support vector machine (EmcSVM) and fuzzy NSGA-II for efficient trend classification and trading in Forex markets. At first, EmcSVM is used to forecast and classify the future market trend into uptrend, sideway, and downtrend. Then, NSGA-II is applied to optimize the hyperparameters of the proposed fuzzy trading system comprising multiple AND-OR Buy/Sell technical rules for uptrend/downtrend markets. The hyperparameters include indicator selection within each rule, importance weights of the different rules, and final decision thresholds for Buy/Sell models, while the objective is to maximize average return on investment (ROI) and minimize average draw-down of all transactions. The proposed method has been successfully developed and tested on real data from the Forex market for EUR/USD currency pair in a 6-year timeframe from 2014 to 2019. Obtained results show that the proposed method outperforms the existing crisp trading systems, with 80.8% precision, 72.4% recall, 94.1% annual ROI, and 0.58% draw down. [ABSTRACT FROM AUTHOR]

Online banking and e-commerce have been experiencing rapid growth over the past few years and show tremendous promise of growth even in the future. This has made it easier for fraudsters to indulge in new and abstruse ways of committing credit card fraud over the Internet. This paper focuses on real-time fraud detection and presents a new and innovative approach in understanding spending patterns to decipher potential fraud cases. It makes use of self-organization map to decipher, filter and analyze customer behavior for detection of fraud. [Copyright &y& Elsevier]

Abstract: Artificial neural networks and fuzzy systems, have gradually established themselves as a popular tool in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi–Sugeno (TS) fuzzy system are able to provide a more accurate out-of-sample forecast than the traditional auto regressive moving average (ARMA) and ARMA generalized auto regressive conditional heteroskedasticity (ARMA-GARCH) linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15min, 60min and 120min, daily and weekly basis, the one-step-ahead forecast performance is compared. Results indicate that forecast performance is strongly related to the series’ frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy based on forecasts, nonlinear models achieve higher returns when compared to a buy-and-hold strategy and to the linear models. [Copyright &y& Elsevier]

• Definition of a novel AmI system for gym environments, named Gym Intelligence. • The system provides adequate music according to the users' physical effort. • The system is based on a novel multi-agent computational intelligence model. • The system checks for users' physical effort changes and plays the adequate music. • We have provided an exhaustive evaluation of Gym Intelligence. Ambient Intelligence (AmI) is an interdisciplinary research area of ICT which has evolved since the 90s, taking great advantage from the advent of the Internet of Things (IoT). AmI creates, by using Artificial Intelligence (AI), an intelligent ecosystem in which computers, sensors, lighting, music, personal devices, and distributed services, work together to improve the user experience through the support of natural and intuitive user interfaces. Nowadays, AmI is used in various contexts, e.g., for building smart homes and smart cities, providing healthcare, and creating an adequate atmosphere in retail and public environments. In this paper, we propose a novel AmI system for gym environments, named Gym Intelligence, able to provide adequate music atmosphere, according to the users' physical effort during the training. The music is taken from Spotify and is classified according to some music features , as provided by Spotify itself. The system is based on a multi-agent computational intelligence model built on two main components: (i) machine learning methods that forecast appropriate values for the Spotify music features, and (ii) a multi-objective dynamic genetic algorithm that selects a specific Spotify music track, according to such values. Gym Intelligence is built by sensing the ambient with a minimal, low-cost, and non-intrusive set of sensors, and it has been designed considering the outcome of a preliminary analysis in real gyms, involving real users. We have considered well-known regression methods and we have validated them using a collected data (i) about the users' physical effort, through the sensors, and (ii) about the users' music preferences, through an Android app that the users have used during the training. Among the regression methods considered, the one that provided the best results is the Random Forest, which predicted Spotify music features with a mean absolute error of 0.02 and a root mean squared error of 0.05. We have implemented Gym Intelligence and deployed it in five real gyms. We have evaluated it conducting several experiments. The experiments show how, with the help of Gym Intelligence, the users' satisfaction about the provided background music, rose from 3.05 to 4.91 (on a scale from 1 to 5, where 5 is the maximum score). [ABSTRACT FROM AUTHOR]

Over the past few decades, fluid simulation has emerged as an important tool in computer animation. However, traditional physical-based fluid simulation systems are time consuming and requires large computational resources to generate large-scale fluid flows. Intelligent systems provide us a new method of data-driven to accelerate simulations. Previous intelligent system manually crafted feature vectors by using a context-based integral method and resulted in high computational and memory requirements. Unlike the existing techniques, we do not use any manually crafted feature, instead directly operate on Lagrangian fluid simulation data and use machine learned features. This paper presents a novel end-to-end deep learning neural network that can automatically generate a model for fluid animation based-on Lagrangian fluid simulation data. This approach synthesizes velocity fields with irregular Lagrangian data structure using neural network. Every fluid particle is treated independently and identically. We use symmetric functions to capture space structure and interactions among particles and design different network structures to learn various hierarchical features of fluid. We test this method using several data sets and applications in various scenes with different sizes. Our experiments show that the model is able to infer velocity field with realistic details such as splashes. In addition, compared with exiting simulation system, this method shows significant speed-ups, especially on large scene simulations. [ABSTRACT FROM AUTHOR]

• We propose a Semantic-kNN (Sk-NN) algorithm for ML. • Sk-NN deploys a process by leveraging on the semantic itemization. • Sk-NN used to filter the training dataset. • The proposed method is more efficient, intelligent and better accuracy. The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint in filtering the training dataset to yield training data that are most relevant to the intended or the targeted test dataset/file. It has to scan through all the training datasets categories to classify the intended/targeted data. As such, traditional k-NN is considered not intelligent and consequently is suffering poor accuracy performance with high computational complexity. A Semantic-kNN (Sk-NN) algorithm for ML is thus proposed in this paper to address the limitations in the traditional k-NN. The proposed Sk-NN deploys a process by leveraging on the semantic itemization and bigram model to filter the training dataset in accordance with the relevant information engaged in the test dataset. It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. Ultimately, Sk-NN is to elevate the ML performance in pattern extraction and labeling in the big data context. [ABSTRACT FROM AUTHOR]

• Assessing criteria is essential for decision making in decommissioning. • Large oil and gas fields require a large number of criteria evaluations. • We propose feature selection and classification methods for dimensionality reduction. • The dataset is composed by a reduced set of sub-criteria and parameters. • Significant reduction in dimension can have little impact on performance. This paper is motivated by decommissioning studies in the field of oil and gas, which comprise a very large number of installations and are of interest to a large number of stakeholders. Generally, the problem gives rise to complicated multi-criteria decision aid tools that rely upon the costly evaluation of multiple criteria for every piece of equipment. We propose the use of machine learning techniques to reduce the number of criteria by feature selection, thereby reducing the number of required evaluations and producing a simplified decision aid tool with no sacrifice in performance. In addition, we also propose the use of machine learning to explore the patterns of the multi-criteria decision aid tool in a training set. Hence, we predict the outcome of the analysis for the remaining pieces of equipment, effectively replacing the multi-criteria analysis by the computational intelligence acquired from running it in the training set. Computational experiments illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• An overview of the past decade's (from 2009 to 2018) contributions are done. • Quality parameters and their aggregating techniques are thoroughly investigated. • The main research motivations and the active researchers are recognized. • Applied datasets, optimization strategies, and cloud layers are extracted. • Current research challenges and future research directions are identified. Cloud service composition builds new value-added services by combining existing single services. However, because of the exuberant growth of cloud services and the varying quality of service (QoS), discovering required services and creating a service composition with certain quality guarantees becomes a significant technical issue and attracts much concern. Computational intelligence techniques are considered to be effective in solving such problems, and researchers have made substantial efforts in this area. Nevertheless, to the best of our knowledge, there is not any systematic research about this issue with a particular focus on computing intelligence. Thus, the current study aims to create a panoramic view of QoS-aware cloud service composition from the perspective of computational intelligence. The objectives of this paper are to (1) investigate the relevant studies on this field; (2) make a comprehensive examination of the literature from different aspects: active researchers, research motivations, QoS parameters, algorithms, datasets, optimization strategies and could layers; (3) identify the areas which need further research. For this, a search protocol has been well defined, and 105 articles from 2009 to 2018 were selected. This study classified these articles into three groups, including non-heuristic, heuristic and meta-heuristic, and then examined the research works from several aspects. The results indicate that reducing response time is the most important motivation for researchers, and meta-heuristic algorithms, especially genetic algorithms, are the most widely used computational intelligence techniques. Besides, the most widely used QoS attributes and datasets are also revealed. Additionally, the study points out there are still some research challenges in this area, such as QoS evaluation in a dynamic environment and inter-service correlations. In general, this study classified and compared the existing computational intelligence techniques; analyzed the research status and identified future research directions. It can provide a basis for both researchers and practitioners who are interested in this area. More significantly, in the field of expert and intelligent systems, this study can assist in the design and development of expert and intelligent systems in enterprises, it can efficiently assist enterprises in business decisions and risk reduction. [ABSTRACT FROM AUTHOR]