Abstract: Graphical abstractThe objective of this paper is to develop ensemble based classifiers that will improve the accuracy of Intrusion Detection. For this purpose, we trained and tested 12 experts and then combined them into an ensemble. We used the PSO algorithm to weight the opinion of each expert. Because the quality of the behavioral parameters inserted by the user into PSO strongly affects its effectiveness, we have used the LUS method as a meta-optimizer for finding high-quality parameters. We then used the improved PSO to create new weights for each expert. For comparison, we also developed an ensemble classifier with weights generated using WMA 12. Fig. 1 depicts the entire process. For simplicity, the system framework was divided into the following seven stages:1.Kdd99 data pre-processing.2.Data classification with six different SVM experts.3.Data classification with six different k-NN experts.4.Data classification with ensemble classifier based on PSO.5.Data classification with ensemble classifier based on LUS improvement of PSO.6.Data classification with ensemble classifier based on WMA.7.Comparison of results for each approach.Display Omitted HighlightsIDS implemented using ensemble of a six SVM and a six k-NN classifier.Ensembles are created with weight generated by PSO and meta-PSO algorithms.These two ensembles outperform third ensemble system that is created with WMA. In machine learning, a combination of classifiers, known as an ensemble classifier, often outperforms individual ones. While many ensemble approaches exist, it remains, however, a difficult task to find a suitable ensemble configuration for a particular dataset. This paper proposes a novel ensemble construction method that uses PSO generated weights to create ensemble of classifiers with better accuracy for intrusion detection. Local unimodal sampling (LUS) method is used as a meta-optimizer to find better behavioral parameters for PSO. For our empirical study, we took five random subsets from the well-known KDD99 dataset. Ensemble classifiers are created using the new approaches as well as the weighted majority algorithm (WMA) approach. Our experimental results suggest that the new approach can generate ensembles that outperform WMA in terms of classification accuracy.

Abstract: Mobile devices have become a significant part of people's lives, leading to an increasing number of users involved with such technology. The rising number of users invites hackers to generate malicious applications. Besides, the security of sensitive data available on mobile devices is taken lightly. Relying on currently developed approaches is not sufficient, given that intelligent malware keeps modifying rapidly and as a result becomes more difficult to detect. In this paper, we propose an alternative solution to evaluating malware detection using the anomaly-based approach with machine learning classifiers. Among the various network traffic features, the four categories selected are basic information, content based, time based and connection based. The evaluation utilizes two datasets: public (i.e. MalGenome) and private (i.e. self-collected). Based on the evaluation results, both the Bayes network and random forest classifiers produced more accurate readings, with a 99.97 % true-positive rate (TPR) as opposed to the multi-layer perceptron with only 93.03 % on the MalGenome dataset. However, this experiment revealed that the k-nearest neighbor classifier efficiently detected the latest Android malware with an 84.57 % true-positive rate higher than other classifiers.

Abstract: Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer GWO, which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO mGWO is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

Abstract: The proposed method uses a local search technique which is embedded in particle swarm optimization (PSO) to select the reduced sized and salient feature subset. The goal of the local search technique is to guide the PSO search process to select distinct features by using their correlation information. Therefore, the proposed method selects the subset of features with reduced redundancy. A hybrid feature selection method based on particle swarm optimization is proposed.Our method uses a novel local search to enhance the search process near global optima.The method efficiently finds the discriminative features with reduced correlations.The size of final feature set is determined using a subset size detection scheme.Our method is compared with well-known and state-of-the-art feature selection methods. Feature selection has been widely used in data mining and machine learning tasks to make a model with a small number of features which improves the classifier's accuracy. In this paper, a novel hybrid feature selection algorithm based on particle swarm optimization is proposed. The proposed method called HPSO-LS uses a local search strategy which is embedded in the particle swarm optimization to select the less correlated and salient feature subset. The goal of the local search technique is to guide the search process of the particle swarm optimization to select distinct features by considering their correlation information. Moreover, the proposed method utilizes a subset size determination scheme to select a subset of features with reduced size. The performance of the proposed method has been evaluated on 13 benchmark classification problems and compared with five state-of-the-art feature selection methods. Moreover, HPSO-LS has been compared with four well-known filter-based methods including information gain, term variance, fisher score and mRMR and five well-known wrapper-based methods including genetic algorithm, particle swarm optimization, simulated annealing and ant colony optimization. The results demonstrated that the proposed method improves the classification accuracy compared with those of the filter based and wrapper-based feature selection methods. Furthermore, several performed statistical tests show that the proposed method's superiority over the other methods is statistically significant.

Abstract: Modeling of uncertainty differential equations is very important issue in applied sciences and engineering, while the natural way to model such dynamical systems is to use fuzzy differential equations. In this paper, we present a new method for solving fuzzy differential equations based on the reproducing kernel theory under strongly generalized differentiability. The analytic and approximate solutions are given with series form in terms of their parametric form in the space $$W_2^2 [a,b]\oplus W_2^2 [a,b].$$W22[a,b]źW22[a,b]. The method used in this paper has several advantages; first, it is of global nature in terms of the solutions obtained as well as its ability to solve other mathematical, physical, and engineering problems; second, it is accurate, needs less effort to achieve the results, and is developed especially for the nonlinear cases; third, in the proposed method, it is possible to pick any point in the interval of integration and as well the approximate solutions and their derivatives will be applicable; fourth, the method does not require discretization of the variables, and it is not effected by computation round off errors and one is not faced with necessity of large computer memory and time. Results presented in this paper show potentiality, generality, and superiority of our method as compared with other well-known methods.

Abstract: This paper presents "A novel adaptive inertia weight with stability condition for particle swarm optimization (SAIW)". This approach determines the inertia weight in different dimensions for each particle on: (1) its performance and (2) distance from its best position, and by considering the stability condition, the acceleration parameters of PSO are adaptively determined. Presents an adaptive method for finding inertia weight in different dimensions for each particle.The success of the particle and displacement in particle's best position are used as the feedback.Stability analysis of proposed model indicates that its performance is usually optimal.The results clearly show the superiority of the proposed model over the existing methods. Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the PSO algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of PSO are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the PSO performance in terms of the solution quality as well as convergence speed in static and dynamic environments.

Abstract: Display Omitted Propose the probabilistic linguistic preference relation (PLPR).Discuss the consistency of PLPR from the perspective of digraph.Present the consistency and acceptable consistency measures of PLPR.Establish an optimization model to improve the consistency of PLPR.Apply the proposed method to risk assessment. In recent years, the Belt and Road has aroused great attention of international society. It not only produces opportunities for China but also brings challenges: when Chinese investors invest to other countries, they will analyze the present situation of alternative countries and then assess the investment risk of these countries. Hence, how to assess the risk level of alternative countries correctly is pivotal. Moreover, affected by many factors such as decision makers (DMs) lacking of knowledge and the complexity of decision making problems, the DMs usually cannot use precise numbers to describe their preference information. Therefore, the use of linguistic variables is practical. As a type of linguistic term set, the probabilistic linguistic term set (PLTS) can reflect different importance degrees or weights of all possible evaluation values of a specific object. Whats more, when the DMs use linguistic variables to express their judgements, they sometimes cannot give their evaluation values for attributes directly. In such a case, the DMs usually provide their judgements by pairwise comparison of alternatives. In this paper, we introduce the concept of probabilistic linguistic preference relation (PLPR) to present the DMs preferences. The additive consistency of the PLPR is discussed from the perspective of the preference relation graph. Then, the consistency index of the PLPR is defined to measure the consistency. We also introduce the acceptable consistency of the PLPR. Moreover, as for the unacceptable consistent PLPR, an automatic optimization method is proposed to improve its consistency until acceptable. Once all the PLPRs are of acceptable consistency, we directly use the aggregation operators to obtain the comprehensive preference values of alternatives and then rank the alternatives according to the derived preference values. Finally, an application example involving the Belt and Road is given and the discussion about the results is conducted.

Abstract: Graphical abstract A generalized improved score function is defined as for IFN A={a, b, c, d}. Here k1, k1?0 and k1+k2=1 such that GIS(A)?0, 1.Display Omitted HighlightsGeneralized improved score function has been present here.Interval valued intuitionistic fuzzy numbers has used for assessing preference of DM.Shortcoming of the existing score functions is overcome.Attributes weights corresponding to attributes are completely unknown.Sensitivity analysis of decision maker preferences has been assessed. The objective of this paper is divided into two folds. Firstly, a new generalized improved score function has been presented in the interval-valued intuitionistic fuzzy sets (IVIFSs) environment by incorporating the idea of weighted average of the degree of hesitation between their membership functions. Secondly, an IVIFSs based method for solving the multi-criteria decision making (MCDM) problem has been presented with completely unknown attribute weights. A ranking of the different attributes is based on the proposed generalized improved score functions and the sensitivity analysis on the ranking of the system has been done based on the decision-making parameters. An illustrative examples have been studied to show that the proposed function is more reasonable in the decision-making process than other existing functions.

Abstract: Graphical abstractDisplay Omitted HighlightsThree methods for detecting a class of cache-based side-channel attacks are proposed.A new tool (quickhpc) for probing hardware performance counters at a higher temporal resolution than the existing tools is presented.The first method is based on correlation, the other two use machine learning techniques and reach a minimum F-score of 0.93.A smarter attack is devised that is capable of circumventing the first method. In this paper we analyze three methods to detect cache-based side-channel attacks in real time, preventing or limiting the amount of leaked information. Two of the three methods are based on machine learning techniques and all the three of them can successfully detect an attack in about one fifth of the time required to complete it. We could not experience the presence of false positives in our test environment and the overhead caused by the detection systems is negligible. We also analyze how the detection systems behave with a modified version of one of the spy processes. With some optimization we are confident these systems can be used in real world scenarios.

Abstract: We introduce a literature review considering articles on artificial neural networks in business published in last two decades.412 suitable articles are identified and classified according to defined methodology.We focus on date, area of interest, type of neural network, benchmark method, journal and citations.The most applied are multilayer feedforward networks with backpropagation learning performed by gradient descent algorithm.Majority (25.73%) of the examined articles were published in Expert Systems with Applications. In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews were introduced more than 15 years ago. The aim of this work is to expand the range of earlier surveys and provide a systematic overview of neural network applications in business between 1994 and 2015. We have covered a total of 412 articles and classified them according to the year of publication, application area, type of neural network, learning algorithm, benchmark method, citations and journal. Our investigation revealed that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks. Besides conventional multilayer feedforward network with gradient descent backpropagation, various hybrid networks have been developed in order to improve the performance of standard models. Even though neural networks have been established as well-known method in business, there is enormous space for additional research in order to improve their functioning and increase our understanding of this influential area.

Abstract: Graphical abstractDisplay Omitted HighlightsAutomatic clinical decision support system for breast cancer malignancy grading.Different methodologies for segmentation and feature extraction from FNA slides.An efficient classifier ensemble for imbalanced problems with difficult data.Ensemble combines boosting with evolutionary undersampling.Extensive computational experiments on a large database collected by authors. In this paper, we propose a complete, fully automatic and efficient clinical decision support system for breast cancer malignancy grading. The estimation of the level of a cancer malignancy is important to assess the degree of its progress and to elaborate a personalized therapy. Our system makes use of both Image Processing and Machine Learning techniques to perform the analysis of biopsy slides. Three different image segmentation methods (fuzzy c-means color segmentation, level set active contours technique and grey-level quantization method) are considered to extract the features used by the proposed classification system. In this classification problem, the highest malignancy grade is the most important to be detected early even though it occurs in the lowest number of cases, and hence the malignancy grading is an imbalanced classification problem. In order to overcome this difficulty, we propose the usage of an efficient ensemble classifier named EUSBoost, which combines a boosting scheme with evolutionary undersampling for producing balanced training sets for each one of the base classifiers in the final ensemble. The usage of the evolutionary approach allows us to select the most significant samples for the classifier learning step (in terms of accuracy and a new diversity term included in the fitness function), thus alleviating the problems produced by the imbalanced scenario in a guided and effective way. Experiments, carried on a large dataset collected by the authors, confirm the high efficiency of the proposed system, shows that level set active contours technique leads to an extraction of features with the highest discriminative power, and prove that EUSBoost is able to outperform state-of-the-art ensemble classifiers in a real-life imbalanced medical problem.

Abstract: Enhanced PSO with levy flight.Random walk of the particles.High convergence rate.Provides solution accuracy and robust. Huseyin Hakli and Harun Uguz (2014) proposed a novel approach for global function optimization using particle swarm optimization with levy flight (LFPSO) Huseyin Hakli, Harun U guz, A novel particle swarm optimization algorithm with levy flight. Appl. Soft Comput. 23, 333-345 (2014). In our study, we enhance the LFPSO algorithm so that modified LFPSO algorithm (PSOLF) outperforms LFPSO algorithm and other PSO variants. The enhancement involves introducing a levy flight method for updating particle velocity. After this update, the particle velocity becomes the new position of the particle. The proposed work is examined on well-known benchmark functions and the results show that the PSOLF is better than the standard PSO (SPSO), LFPSO and other PSO variants. Also the experimental results are tested using Wilcoxon's rank sum test to assess the statistical significant difference between the methods and the test proves that the proposed PSOLF method is much better than SPSO and LFPSO. By combining levy flight with PSO results in global search competence and high convergence rate.

Abstract: Display Omitted We focused on the clustering problem in picture fuzzy sets.A generalized picture distance measure was proposed.A novel hierarchical picture clustering method based on the measure was presented.Its clustering quality is better than those of relevant clustering algorithms.A clustering system for picture fuzzy sets was designed. Picture fuzzy set (PFS), which is a generalization of traditional fuzzy set and intuitionistic fuzzy set, shows great promises of better adaptation to many practical problems in pattern recognition, artificial life, robotic, expert and knowledge-based systems than existing types of fuzzy sets. An emerging research trend in PFS is development of clustering algorithms which can exploit and investigate hidden knowledge from a mass of datasets. Distance measure is one of the most important tools in clustering that determine the degree of relationship between two objects. In this paper, we propose a generalized picture distance measure and integrate it to a novel hierarchical picture fuzzy clustering method called Hierarchical Picture Clustering (HPC). Experimental results show that the clustering quality of the proposed algorithm is better than those of the relevant ones.

Abstract: Flowchart of the proposed OPF solution using ICBO, CBO and ECBO. We developed an Improved Colliding Bodies Optimization (ICBO) algorithm.We solved the optimal power flow for several cases using different constraints, formulations and complexities.The performances of the ICBO algorithm have been evaluated using a comparative study.The ICBO algorithm outperforms many other algorithms for solving optimal power flow problems. This paper proposes Improved Colliding Bodies Optimization (ICBO) algorithm to solve efficiently the optimal power flow (OPF) problem. Several objectives, constraints and formulations at normal and preventive operating conditions are used to model the OPF problem. Applications are carried out on three IEEE standard test systems through 16 case studies to assess the efficiency and the robustness of the developed ICBO algorithm. A proposed performance evaluation procedure is proposed to measure the strength and robustness of the proposed ICBO against numerous optimization algorithms. Moreover, a new comparison approach is developed to compare the ICBO with the standard CBO and other well-known algorithms. The obtained results demonstrate the potential of the developed algorithm to solve efficiently different OPF problems compared to the reported optimization algorithms in the literature.

Abstract: Display Omitted A review of VIKOR and its fuzzy extensions on applications is presented.The systematic classification covers 13 different application areas.It provides an insight for researchers and practitioners on VIKOR applications. Multi criteria decision making (MCDM) is one of the research areas of operations research and management science which has widely studied by researchers and practitioners. It finds a compromise solution for evaluating and ranking alternatives from the best to the worst under conflicting criteria with respect to decision maker(s) preferences. In a compromise approach, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR; that means multi-criteria optimization and compromise solution) continues to be applied satisfactorily across different application areas. This paper conducts a state-of-the-art literature review to categorize, analyze and interpret the current research on VIKOR applications. It also discusses the extensions of VIKOR applied in fuzzy environments. A total of 343 papers are classified into 13 different application areas and a number of sub-application areas. Furthermore, all papers are also categorized with respect to publication year, published journal, country of origin, application type (real case study vs empirical study), and version of fuzzy sets used. This comprehensive literature review provides an insight for researchers and practitioners on VIKOR applications in terms of showing current state and potential areas for future attempts to be focused in the future.

Abstract: Graphical abstractDisplay Omitted HighlightsWe propose two new, simple, and efficient Hybrid Feature Selection techniques.We use a feature-based ranking to initialize the Binary Differential Evolution.We also propose a new fitness function influenced by the features in the population.Several statistical tests show the robustness and effectiveness of the proposals.The reducing of the size of the original set of features is larger than 99%. Microarray experiments generally deal with complex and high-dimensional samples, and in addition, the number of samples is much smaller than their dimensions. Both issues can be alleviated by using a feature selection (FS) method. In this paper two new, simple, and efficient hybrid FS algorithms, called respectively BDE- X Rank and BDE- X Rank f , are presented. Both algorithms combine a wrapper FS method based on a Binary Differential Evolution (BDE) algorithm with a rank-based filter FS method. Besides, they generate the initial population with solutions involving only a small number of features. Some initial solutions are built considering only the most relevant features regarding the filter method, and the remaining ones include only random features (to promote diversity). In the BDE- X Rank f , a new fitness function, in which the score value of a solution is influenced by the frequency of the features in the current population, is incorporated in the algorithm. The robustness of BDE- X Rank and BDE- X Rank f is shown by using four Machine Learning (ML) algorithms (NB, SVM, C4.5, and kNN). Six high-dimensional well-known data sets of microarray experiments are used to carry out an extensive experimental study based on statistical tests. This experimental analysis shows the robustness as well as the ability of both proposals to obtain highly accurate solutions at the earlier stages of BDE evolutionary process. Finally, BDE- X Rank and BDE- X Rank f are also compared against the results of nine state-of-the-art algorithms to highlight its competitiveness and the ability to successfully reduce the original feature set size by more than 99%.

Abstract: Graphical abstractDisplay Omitted HighlightsA hybrid/fusion prognostics framework to predict remaining useful life by combining the data-driven methods and model-based methods.Introduce a data-driven method to estimate the measurement model in a model-based particle filter framework.Introduce a data-driven method to predicted future measurement in long term prediction in a model-based particle filter framework.Shown improved prediction accuracy using battery as a case study. Remaining useful life prediction is one of the key requirements in prognostics and health management. While a system or component exhibits degradation during its life cycle, there are various methods to predict its future performance and assess the time frame until it does no longer perform its desired functionality. The proposed data-driven and model-based hybrid/fusion prognostics framework interfaces a classical Bayesian model-based prognostics approach, namely particle filter, with two data-driven methods in purpose of improving the prediction accuracy. The first data-driven method establishes the measurement model (inferring the measurements from the internal system state) to account for situations where the internal system state is not accessible through direct measurements. The second data-driven method extrapolates the measurements beyond the range of actually available measurements to feed them back to the model-based method which further updates the particles and their weights during the long-term prediction phase. By leveraging the strengths of the data-driven and model-based methods, the proposed fusion prognostics framework can bridge the gap between data-driven prognostics and model-based prognostics when both abundant historical data and knowledge of the physical degradation process are available. The proposed framework was successfully applied on lithium-ion battery remaining useful life prediction and achieved a significantly better accuracy compared to the classical particle filter approach.

Abstract: Graphical abstractDisplay Omitted HighlightsSixty-five clustering methods based on nature-inspired metaheuristics are reviewed.Codification and validity index are basic items in automatic clustering algorithms.Evolutionary computation is the most popular paradigm used in automatic clustering.A strong tendency in using multiobjective and hybrid algorithms is found.Research directions and challenges for automatic clustering problem are formulated. In cluster analysis, a fundamental problem is to determine the best estimate of the number of clusters; this is known as the automatic clustering problem. Because of lack of prior domain knowledge, it is difficult to choose an appropriate number of clusters, especially when the data have many dimensions, when clusters differ widely in shape, size, and density, and when overlapping exists among groups. In the late 1990s, the automatic clustering problem gave rise to a new era in cluster analysis with the application of nature-inspired metaheuristics. Since then, researchers have developed several new algorithms in this field. This paper presents an up-to-date review of all major nature-inspired metaheuristic algorithms used thus far for automatic clustering. Also, the main components involved during the formulation of metaheuristics for automatic clustering are presented, such as encoding schemes, validity indices, and proximity measures. A total of 65 automatic clustering approaches are reviewed, which are based on single-solution, single-objective, and multiobjective metaheuristics, whose usage percentages are 3%, 69%, and 28%, respectively. Single-objective clustering algorithms are adequate to efficiently group linearly separable clusters. However, a strong tendency in using multiobjective algorithms is found nowadays to address non-linearly separable problems. Finally, a discussion and some emerging research directions are presented.

Abstract: We develop a solution approach to solve the green vehicle routing problem.We propose a simulated annealing heuristic to improve the quality of solutions.We present a new formulation having fewer variable and constraints.We evaluate the algorithm in terms of the several performance criterions.Our algorithm is able to optimally solve 22 of 40 benchmark instances. This paper develops a simulated annealing heuristic based exact solution approach to solve the green vehicle routing problem (G-VRP) which extends the classical vehicle routing problem by considering a limited driving range of vehicles in conjunction with limited refueling infrastructure. The problem particularly arises for companies and agencies that employ a fleet of alternative energy powered vehicles on transportation systems for urban areas or for goods distribution. Exact algorithm is based on the branch-and-cut algorithm which combines several valid inequalities derived from the literature to improve lower bounds and introduces a heuristic algorithm based on simulated annealing to obtain upper bounds. Solution approach is evaluated in terms of the number of test instances solved to optimality, bound quality and computation time to reach the best solution of the various test problems. Computational results show that 22 of 40 instances with 20 customers can be solved optimally within reasonable computation time.

Abstract: Fuzzy logic approach is preferable to fix the drawbacks for reprioritization of the Risk Priority Number (RPN).Fuzzy logic could reduce the drawback of occurred Traditional FMEA in evaluation and prioritization of failures.The application of using Fuzzy FMEA in the emergency department can be adopted suitably. All of members were able to assess dependently without any bias from the team members.Fuzzy FMEA can be applied for the first time to improve decision making process in an emergency department of a public hospital. Hospitals are one of the important service industries of health care for patients. The emergency department is the heart of every hospital, because the errors or failures occurring in it will significantly affect the safety of patients and the goodwill of the hospital. Therefore, emergency departments should be monitored carefully. This study proposed the application of Fuzzy failure mode and effects analysis (FMEA) for prioritization and assessment of failures that likely occur in the working process of an emergency department. All individuals were assessed independently without the interference of team members. In addition, this method could reduce the limitations of traditional FMEA. The prioritization of risks could also help the emergency department to choose corrective actions wisely. In conclusion, the Fuzzy FMEA method was found to be suitably adopted in the emergency department. Finally, this method helped to increase the level of confidence on hospitals.

Abstract: This paper proposes a hybrid time-series ANFIS model based on EMD to forecast stock price.In order to evaluate the forecasting performances, the proposed model is compared with other models.The experimental results show that proposed model is superior to the listing models. Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.

Abstract: We study a criteria evaluation system for outsourcing reverse logistics (ORL).A hybrid SWOT and intuitionistic fuzzy AHP model evaluates strategic factors in ORL.Triangular intuitionistic fuzzy numbers are used to model ambiguity and uncertainty.The proposed model is validated through a case study.Focusing on core business is shown to be the organization's strategic priority. We consider the problem faced by a company that must outsource reverse logistics (RL) activities to third-party providers. Addressing RL outsourcing problems has become increasingly relevant issue in the management science and decision making literatures. The correct evaluation and ranking of the decision criteria/priorities determining the selection of the best third-party RL providers (3PRLPs) is essential for the competitive performance of the outsourcing company. The method proposed in this study allows to identify and classify these decision criteria. First, the relevant criteria and sub-criteria are identified using a SWOT analysis. Then, Intuitionistic Fuzzy AHP is used to evaluate the relative importance weights among the criteria and the corresponding sub-criteria. These relative weights are implemented in a novel extension of Mikhailov's fuzzy preference programming method to produce local weights for all criteria and sub-criteria. Finally, these local weights are used to assign a global weight to each sub-criterion and create a ranking. We discuss the results obtained by applying the proposed model to a case study of a real company. In particular, these results show that the most important priority for the company when delegating RL activities to 3PRLPs is to focus on the core business, while reducing costs constitutes one of its least important priorities.

Abstract: Graphical abstractDisplay Omitted HighlightsWe propose a new approach for feature selection in hyperspectral image classification.The problem of band selection is reformulated as a combinatorial problem.We design a new objective function which takes into account two term, the classification error rate and the class separability distance.To optimize the objective function, we propose to use a new meta-heuristic called Gray Wolf Optimizer.The experiments have been conducted in three widely used hyperspectral images and compared with other approaches. In this paper, we propose a new optimization-based framework to reduce the dimensionality of hyperspectral images. One of the most problems in hyperspectral image classification is the Hughes phenomenon caused by the irrelevant spectral bands and the high correlation between the adjacent bands. The problematic is how to find the relevant bands to classify the pixels of hyperspectral image without reducing the classification accuracy rate. We propose to reformulate the problem of band selection as a combinatorial problem by modeling an objective function based on class separability measures and the accuracy rate. We use the Gray Wolf Optimizer, which is a new meta-heuristic algorithm more efficient than Practical Swarm Optimization, Gravitational Search Algorithm, Differential Evolution, Evolutionary Programming and Evolution Strategy. The experimentations are performed on three widely used benchmark hyperspectral datasets. Comparisons with the state-of-the-art approaches are also conducted. The analysis of the results proves that the proposed approach can effectively investigate the spectral band selection problem and provides a high classification accuracy rate by using a few samples for training.

Abstract: Numerical solution of ordinary differential equations using Legendre polynomial based Functional Link Artificial Neural Network (FLANN).It is a single layer neural network, so number of parameters is less than MLP and the hidden layer is eliminated by expanding the input pattern by Legendre polynomials.Unsupervised back propagation algorithm is used here.Obtained results are compared with the existing methods, plots and tables to show the powerfulness of the methodology. In this paper, a new method based on single layer Legendre Neural Network (LeNN) model has been developed to solve initial and boundary value problems. In the proposed approach a Legendre polynomial based Functional Link Artificial Neural Network (FLANN) is developed. Nonlinear singular initial value problem (IVP), boundary value problem (BVP) and system of coupled ordinary differential equations are solved by the proposed approach to show the reliability of the method. The hidden layer is eliminated by expanding the input pattern using Legendre polynomials. Error back propagation algorithm is used for updating the network parameters (weights). Results obtained are compared with the existing methods and are found to be in good agreement.

Abstract: Graphical abstractDisplay Omitted HighlightsA new global optimization meta-heuristic inspired by galactic motion is proposed.The proposed algorithm employs alternating phases of exploration and exploitation.Performance on rotated and shifted versions of benchmark problems is also considered.The proposed GSO algorithm outperforms 8 state-of-the-art PSO algorithms. This paper proposes a new global optimization metaheuristic called Galactic Swarm Optimization (GSO) inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. GSO employs multiple cycles of exploration and exploitation phases to strike an optimal trade-off between exploration of new solutions and exploitation of existing solutions. In the explorative phase different subpopulations independently explore the search space and in the exploitative phase the best solutions of different subpopulations are considered as a superswarm and moved towards the best solutions found by the superswarm. In this paper subpopulations as well as the superswarm are updated using the PSO algorithm. However, the GSO approach is quite general and any population based optimization algorithm can be used instead of the PSO algorithm. Statistical test results indicate that the GSO algorithm proposed in this paper significantly outperforms 4 state-of-the-art PSO algorithms and 4 multiswarm PSO algorithms on an overwhelming majority of 15 benchmark optimization problems over 50 independent trials and up to 50 dimensions. Extensive simulation results show that the GSO algorithm proposed in this paper converges faster to a significantly more accurate solution on a wide variety of high dimensional and multimodal benchmark optimization problems.

Abstract: Original and segmented simulated brain image by different algorithms: (a) axial view of original simulated T1-weighted brain image with INU=0 and 1% noise, (b) skull stripping simulated brain image, (c) manual segmented CSF, GM and WM images, (d) IIFCM algorithm, (e) IFCM algorithm, (f) FLICM algorithm, (g) EnFCM algorithm, (h) FGFCM algorithm, (i) FCM_S1 algorithm, (j) FCM_S2 algorithm, (k) ImFCM algorithm. The segmentation of brain magnetic resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research. However, due to presence of noise and uncertainty on the boundary between different tissues in the brain image, the segmentation of brain image is a challenging task. Many variants of standard fuzzy c-means (FCM) algorithm have been proposed to handle the noise. Intuitionistic fuzzy c-means (IFCM) algorithm, one of the variants of FCM, is found suitable for image segmentation. It incorporates the advantage of intuitionistic fuzzy sets theory. The IFCM successfully handles the uncertainty but it is sensitive to noise as it does not incorporate any local spatial information. In this paper, we have presented a novel approach, named an improved intuitionistic fuzzy c-means (IIFCM), which considers the local spatial information in an intuitionistic fuzzy way. The IIFCM preserves the image details, is insensitive to noise, and is free of requirement of any parameter tuning. The obtained segmentation results on synthetic square image, real and simulated MRI brain image demonstrate the efficacy of the IIFCM algorithm and superior performance in comparison to existing segmentation methods. A nonparametric statistical analysis is also carried out to show the significant performance of the IIFCM algorithm in comparison to other existing segmentation algorithms.

Abstract: Graphical abstractDisplay Omitted HighlightsWe propose a novel binary coded ant colony algorithm by blending of GA and BACO.The proposed algorithm is adopted to handle with the problem of feature selection.Results show the method outperforms GA, BPSO, BACO, ABACO, BDE in feature selection. Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

Abstract: Graphical abstractDisplay Omitted In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.

Abstract: This paper presents KH algorithm to solve optimal placement of distributed generator (ODG) problem.ODG problem is studied with an objective of reducing power loss and energy cost.Three illustrative examples of radial distribution network are presented.Proposed method shows better results when compared with other techniques in terms of the quality of solution. Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.