Abstract: With a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. This task now arises in a range of applications such as healthcare monitoring, fitness tracking or user-adaptive systems, where a general model capable of instantaneous activity recognition of an arbitrary user is needed. In this paper, we present a user-independent deep learning-based approach for online human activity classification. We propose using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series. Furthermore, we investigate the impact of time series length on the recognition accuracy and limit it up to 1 s that makes possible continuous real-time activity classification. The accuracy of the proposed approach is evaluated on two commonly used WISDM and UCI datasets that contain labeled accelerometer data from 36 and 30 users respectively, and in cross-dataset experiment. The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.

Abstract: The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.

Abstract: When solving many machine learning problems such as classification, there exists a large number of input features. However, not all features are relevant for solving the problem, and sometimes, including irrelevant features may deteriorate the learning performance.Please check the edit made in the article title Therefore, it is essential to select the most relevant features, which is known as feature selection. Many feature selection algorithms have been developed, including evolutionary algorithms or particle swarm optimization (PSO) algorithms, to find a subset of the most important features for accomplishing a particular machine learning task. However, the traditional PSO does not perform well for large-scale optimization problems, which degrades the effectiveness of PSO for feature selection when the number of features dramatically increases. In this paper, we propose to use a very recent PSO variant, known as competitive swarm optimizer (CSO) that was dedicated to large-scale optimization, for solving high-dimensional feature selection problems. In addition, the CSO, which was originally developed for continuous optimization, is adapted to perform feature selection that can be considered as a combinatorial optimization problem. An archive technique is also introduced to reduce computational cost. Experiments on six benchmark datasets demonstrate that compared to the canonical PSO-based and a state-of-the-art PSO variant for feature selection, the proposed CSO-based feature selection algorithm not only selects a much smaller number of features, but result in better classification performance as well.

Abstract: A third party reverse logistic provider (3PRLP) selection and evaluation process is developed.A multiple attribute decision making (MADM) model to evaluate and select 3PRLPs in the presence of risk factors is proposed.A fuzzy step-wise weight assessment ratio analysis (SWARA) approach to weight the evaluation criteria is applied.A fuzzy (COPRAS) is proposed to rank and select the sustainable third-party reverse logistics providers in the presence of risk factors.A real world case study is used from automotive industry to validate the quality of our model. Reverse logistics is the backward process of collecting and redistributing products at the end-of-life from customers to producers and manufacturers for reuse, remanufacturing and disposal purposes. While reverse logistics brings several economic benefits, it seems to become a necessity for businesses to remain competitive in a world that environmental and social aspects of business activities are key to sustainable development. The operations and management of reverse logistics systems is a complex task that requires substantial level of infrastructure, technology, expertise and experience. Therefore, increasingly many business organizations tend to outsource their reverse logistics activities to third-party reverse logistics providers (3PRLPs). In this paper, we propose a multiple attribute decision making (MADM) model to rank and select 3PRLPs, using fuzzy step-wise weight assessment ratio analysis (SWARA) to weight the evaluation criteria. Accordingly, a developed fuzzy complex proportional assessment of alternatives (COPRAS) was proposed to rank and select the sustainable 3PRLPs in the presence risk factors. The suggested model was applied to a case study from automotive industry. Eventually, COPRAS and COPRAS-G methods were considered for the purpose of comparison and validation. As a result, the most sustainable 3PRLP was selected. While incorporating risk factors into our analysis, our study shows that environmental and social drivers are increasingly becoming dominant when selecting 3PRLPs.

Abstract: The majority of the studies on credit risk assessment models for financial institutions during recent years focus on the improvement of imbalanced data or on the enhancement of classification accuracy with multistage modeling. Whilst multistage modeling and data pre-processing can boost accuracy somewhat, the heterogeneous nature of data may affects the classification accuracy of classifiers. This paper intends to use the classifier, eXtreme gradient boosting tree (XGBoost), to construct a credit risk assessment model for financial institutions. Cluster-based under-sampling is deployed to process imbalanced data. Finally, the area under the receiver operative curve and the accuracy of classifications are the assessment indicators, in the comparison with other frequently used single-stage classifiers such as logistic regression, self-organizing algorithms and support vector machine. The results indicate that the XGBoost classifier used by this paper achieve better results than the other three and can serve as a superior tool for the development of credit risk models for financial institutions.

Abstract: Set pair analysis (SPA) is an updated theory for dealing with the uncertainty, which overlaps with the other existing theories such as vague, fuzzy, intuitionistic fuzzy set (IFS). Keeping the advantages of it, in this paper, we propose some novel similarity measures to measure the relative strength of the different intuitionistic fuzzy sets (IFSs) after pointing out the weakness of the existing measures. For it, a connection number, the main component of SPA theory is formulated in the form of the degrees of identity, discrepancy, and contrary. Then, based on it some new similarity and weighted similarity measures between the connection number sets are defined. A comparative analysis of the proposed and existing measures are formulated in terms of the counter-intuitive cases for showing the validity of it. Finally, an illustrative example is provided to demonstrate it.

Abstract: This paper proposes a new blind watermarking algorithm, which embedding the binary watermark into the blue component of a RGB image in the spatial domain, to resolve the problem of protecting copyright. For embedding watermark, the generation principle and distribution features of direct current (DC) coefficient are used to directly modify the pixel values in the spatial domain, and then four different sub-watermarks are embedded into the different areas of the host image for four times, respectively. When watermark extraction, the sub-watermark is extracted with blind manner according to DC coefficients of watermarked image and the key-based quantization step, and then the statistical rule and the method of “first to select, second to combine” are proposed to form the final watermark. Hence, the proposed algorithm is executed in the spatial domain rather than in discrete cosine transform (DCT) domain, which not only has simple and quick performance of the spatial domain but also has high robustness feature of DCT domain. The experimental results show that the proposed watermarking algorithm can obtain better invisibility of watermark and stronger robustness for common attacks, e.g., JPEG compression, cropping, and adding noise. Comparison results also show the advantages of the proposed method.

Abstract: Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners.

Abstract: In this paper, we motivate and introduce the concept of N-soft set as an extended soft set model. Some useful algebraic definitions and properties are given. We cite real examples that prove that N-soft sets are a cogent model for binary and non-binary evaluations in numerous kinds of decision making problems. Finally, we propose decision making procedures for N-soft sets.

Abstract: This paper presents enhanced fitness-adaptive differential evolution algorithm with novel mutation (EFADE) for solving global numerical optimization problems over continuous space. A new triangular mutation operator is introduced. It is based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better and the worst individuals among the three randomly selected vectors. Triangular mutation operator helps the search for better balance between the global exploration ability and the local exploitation tendency as well as enhancing the convergence rate of the algorithm through the optimization process. Besides, two novel, effective adaptation schemes are used to update the control parameters to appropriate values without either extra parameters or prior knowledge of the characteristics of the optimization problem. In order to verify and analyze the performance of EFADE, numerical experiments on a set of 28 test problems from the CEC2013 benchmark for 10, 30 and 50 dimensions, including a comparison with 12 recent DE-based algorithms and six recent evolutionary algorithms, are executed. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, EFADE is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance.

Abstract: The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

Abstract: Neutrosophic Logic (Smarandache in Neutrosophy neutrosophic probability: set, and logic, American Research Press, Rehoboth, 1998) has been applied to many multicriteria decision-making methods such as Technique for Order Preference by Similarity to an Ideal Solution, Visekriterijumsko kompromisno rangiranje Resenje, and Evaluation based on Distance from Average Solution. Interval-valued neutrosophic sets are subclass of neutrosophic sets. Interval numbers can be used for their truth-membership, indeterminacy-membership, and falsity-membership degrees. The angle between the vector representations of two neutrosophic sets is defined cosine similarity measure. In this paper, we introduce a new Analytic Hierarchy Process (AHP) method with interval-valued neutrosophic sets. We also propose an interval-valued neutrosophic AHP (IVN-AHP) based on cosine similarity measures. The proposed method with cosine similarity provides an objective scoring procedure for pairwise comparison matrices under neutrosophic uncertainty. Finally, an application is given in energy alternative selection to illustrate the developed approaches.

Abstract: The process of criteria prioritization and weighting is an important part of multiple attributes decision making. The most frequently applied multi-attribute decision-making weighting tools include analytical hierarchy process, stepwise weight assessment ratio analysis, factor relationship, and best---worst method. When policies are at the core of decision making, stepwise weight assessment ratio analysis method is the most efficient method for criteria evaluation. It involves two important steps: the first is to prioritize the criteria by consulting experts, while the second is the weighting process. This research seeks to extend stepwise weight assessment ratio analysis to improve the quality of the decision-making process by incorporating the reliability evaluation of experts' idea into the first step. Such a component is absent from the first step in all other similar models. Thus, an extended version of stepwise weight assessment ratio analysis can be applied for such evaluation. To test the applicability and performance of the proposed method, a numerical example from an earlier study was used. The proposed version can replace the classic version in future studies as an improved method in decision-making area.

Abstract: This article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master---slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.

Abstract: Nowadays, operation managers usually need efficient supply chain networks including important design factors such as economic and social considerations The recent decade has seen a rapid development of controlling the uncertainty in supply chain configurations along with proposing novel solution approaches By investigating the related studies, this paper shows that most of the current studies consider the economic aspects and just a few works present the two-stage stochastic programming as well as social considerations to design a closed-loop supply chain network This motivated our attempts to consider economic and social aspects simultaneously by using the mentioned suppositions among the first studies Another main contribution of this paper is the hybridization and tuning of a number of recent algorithms to address the problem The results show that the proposed hybrid metaheuristic algorithms outperform the best existing techniques on the majority of case studies

Abstract: This paper proposes a multi-swarm particle swarm optimization (MSPSO) that consists of three novel strategies to balance the exploration and exploitation abilities. The new proposed MSPSO in this work is based on multiple swarms framework cooperating with the dynamic sub-swarm number strategy (DNS), sub-swarm regrouping strategy (SRS), and purposeful detecting strategy (PDS). Firstly, the DNS divides the entire population into many sub-swarms in the early stage and periodically reduces the number of sub-swarms (i.e., increase the size of each sub-swarm) along with the evolutionary process. This is helpful for balancing the exploration ability early and the exploitation ability late, respectively. Secondly, in each DNS period with special number of sub-swarms, the SRS is to regroup these sub-swarms based on the stagnancy information of the global best position. This is helpful for diffusing and sharing the search information among different sub-swarms to enhance the exploitation ability. Thirdly, the PDS is relying on some historical information of the search process to detect whether the population has been trapped into a potential local optimum, so as to help the population jump out of the current local optimum for better exploration ability. The comparisons among MSPSO and other 13 peer algorithms on the CEC2013 test suite and 4 real applications suggest that MSPSO is a very reliable and highly competitive optimization algorithm for solving different types of functions. Furthermore, the extensive experimental results illustrate the effectiveness and efficiency of the three proposed strategies used in MSPSO.

Abstract: Intuitionistic fuzzy sets (IFSs) are successful to handle the uncertain situations of data. Distance measures of IFSs are important in the evaluation of IFSs relationships. In this paper, we analyzed the disadvantages of existing distance measures of IFSs and proposed a new distance measure called H-max of IFSs. We continued to point out some new results on intuitionistic t-norms and intuitionistic t-conorms and evaluated distance measure between two IFSs which are basically structured from these operations. Further, we combined the classification of t-representable intuitionistic fuzzy t-norms and t-conorms with the proposed distance measure to study some interesting properties. Moreover, we studied De Morgan triplets of IFSs based on the proposed distance measure. Finally, we applied the proposed distance measure to medical diagnosis problem examples and experimental validation on real-world datasets to check the applicability and effectiveness.

Abstract: For realizing the flexible, scalable and fuzzy fine-grained access control, ciphertext policy attribute-based encryption (CP-ABE) scheme has been widely used in the cloud storage system. However, the access structure of CP-ABE scheme is outsourced to the cloud storage server, resulting in the disclosure of access policy privacy. In addition, there are multiple authorities that coexist and each authority is able to issue attributes independently in the cloud storage system. However, existing CP-ABE schemes cannot be directly applied to data access control for multi-authority cloud storage system, due to the inefficiency for user revocation. In this paper, to cope with these challenges, we propose a decentralized multi-authority CP-ABE access control scheme, which is more practical for supporting the user revocation. In addition, this scheme can protect the data privacy and the access policy privacy with policy hidden in the cloud storage system. Here, the access policy that is realized by employing the linear secret sharing scheme. Finally, the security and performance analyses demonstrate that our scheme has high security in terms of access policy privacy and efficiency in terms of computational cost of user revocation.

Abstract: Localization technology has been a core component for Internet of Things (IoT), especially for Wireless Sensor Network (WSN). Among all localization technologies, Distance Vector-Hop (DV-Hop) algorithm is a very frequently used algorithm for WSN. DV-Hop estimates the distance through the hop-count between nodes in which the value of hop-count is discrete, and thus there is a serious consequence that some nodes have the same estimated distance when their hop-count with respect to identical node is equal. In this paper, we ameliorate the value of hop-count by the number of common one-hop nodes between adjacent nodes. The discrete values of hop-count will be converted to more accurate continuous values by our proposed method. Therefore, the error caused by the estimated distance can be effectively reduced. Furthermore, we formulate the location estimation process to be a minimizing optimization problem based on the weighted squared errors of estimated distance. We apply Differential Evolution (DE) algorithm to acquire the global optimum solution which corresponds to the estimated location of unknown nodes. The proposed localization algorithm based on improved DV-Hop and DE is called DECHDV-Hop. We conduct substantial experiments to evaluate the effectiveness of DECHDV-Hop including the comparison with DV-Hop, GADV-Hop and PSODV-Hop in four different network simulation situations. Experimental results demonstrate that DECHDV-Hop can achieve much higher localization accuracy than other algorithms in these network situations.

Abstract: Due to the frequent occurrence of accidental and destructive disasters, it is essential to improve the performance of emergency systems. Facing the fact that the performance of emergency system depends on various factors and it is not feasible to optimize all these factors simultaneously due to the limitation of resources. A feasible solution is to select and improve some important factors. In this paper, a novel enhanced fuzzy evidential decision-making trial and evaluation laboratory (DEMATEL) method to identifying critical success factors (CSFs) is proposed. In the proposed method, we combine Dempster---Shafer evidence theory and DEMATEL method. Firstly, direct relations between factors are evaluated by multiple domain experts with intuitionistic fuzzy numbers (IFNs). Then, IFNs are transformed to basic probability assignments (BPAs) and can be combined by Dempster combination rule. In addition, the uncertainty and fuzziness of BPAs due to the lack of knowledge are taken into consideration to make final decision. Finally, implementing DEMATEL method, we can figure out cause---effect categories of factors with the DEMATEL method. The cause factors are identified as CSFs. The proposed method can well tackle subjectivity and fuzziness of experts evaluations. Based on the proposed method, the optimization of emergency management can be significantly simplified into optimizing CSFs. Through optimizing these CSFs, the performance of the whole systems can be significantly improved.

Abstract: Regression analysis is a method to estimate the relationships among the response variable and the explanatory variables. Assuming the observations of the response variable are imprecise and modeling the observed data via uncertain variables, this paper explores an approach of uncertain regression analysis to estimating the relationships among the variables with imprecisely observed samples. On the principle of least squares, an optimization problem is derived to calculate the unknown parameters in the regression model. In particular, this paper investigates uncertain linear regression model and gives an analytic representation of the unknown parameters.

Abstract: Evaluation based on distance from average solution (EDAS) method is based on the distances of each alternative from the average solution with respect to each criterion. This method is similar to distance-based multi-criteria decision-making methods such as TOPSIS and VIKOR. It simplifies the calculation of distances to the deal solution and determines the final decision rapidly. EDAS method has been already extended to its ordinary fuzzy, intuitionistic fuzzy and type-2 fuzzy versions. In this paper, we extend EDAS method to its interval-valued neutrosophic version with the advantage of considering a expert's truthiness, falsity, and indeterminacy simultaneously. The proposed method has been applied to the prioritization of United Nations national sustainable development goals, and one-at-a-time sensitivity analysis is conducted to check the robustness of the given decisions. The proposed method is also compared with the intuitionistic fuzzy TOPSIS method for its validity.

Abstract: Display Omitted Traditional comparative approach subjectively estimates correction coefficients.Quantitative Comparative Approach can objectively estimate correction coefficients.Quantitative Comparative Approach is more accurate than the two classical Hedonic price approaches, regression analysis and neural networks. The purpose of this study is to propose an innovative real estate valuation approach called Quantitative Comparative Approach, which can estimate correction coefficients to overcome the shortcomings of subjective decisions of correction coefficients of traditional comparative approach. The principle is to assume that the price per unit area of real estate is the average price per unit area of the particular circle of housing supply and demand multiplied by the product of several dimensionless adjustment coefficients of factors. The single regression models of these adjustment coefficients can be built with the stepwise decomposition regression analysis. Then the adjustment coefficients of comparative cases and target case can be estimated with these single regression models, and finally the correction coefficients can be estimated by dividing the adjustment coefficients of target case by those of comparative case. The empirical samples are collected from four circles of supply and demand, and are divided into four data sets. The empirical results show that the Quantitative Comparative Approach is more accurate than the two classical Hedonic price approaches, multivariate regression analysis and neural networks.

Abstract: A novel meta-heuristic optimization algorithm known as QOMJaya to solve different MOOPF problems is proposed.Essential modifications to the basic Jaya algorithm are done (i.e. MJaya).The proposed algorithm is scrutinized and validated using the IEEE 30-bus test system.Simulation results reveal the proposed algorithms supremacy over many previous algorithms in terms of solution optimality and feasibility.The obtained results disclose the proposed algorithms ability to produce real and well-distributed Pareto optimum fronts. This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. An intelligence strategy called quasi-oppositional based learning is incorporated into the proposed algorithm to enhance its convergence property, exploration capability, and solution optimality. Significant modifications to the basic Jaya algorithm are done to create a modified Jaya (MJaya) algorithm that can handle the MOOPF problem. A fuzzy decision-making strategy is proposed and incorporated into the Jaya algorithm as selection criteria for best and worst solutions. A new criterion for comparing updated and current candidate solutions is proposed. The concept of Pareto optimality is used to extract a set of non-dominated solutions. A crowding distance measure approach is utilized to maintain the diversity of Pareto optimality. In addition, a novel external elitist repository is developed to conserve discovered non-dominated solutions and to produce true and well-distributed Pareto optimal fronts. The proposed algorithm is scrutinized and validated using the modified IEEE 30-bus test system. Simulation results reveal the proposed algorithms ability to produce real and well-distributed Pareto optimum fronts for all considered multi-objective optimization cases. Furthermore, the obtained results disclose the superiority of the proposed QOMJaya algorithm over both the proposed MJaya algorithm and several previous algorithms in terms of solution optimality and feasibility.

Abstract: Display Omitted A consensus method via penalty functions for decision making in ensembles of fuzzy rule-based classification systems is introduced.Overlap indices are built using overlap functions.A method for constructing confidence and support measures from overlap indices is presented.A new fuzzy rule mechanism is proposed, considering different overlap indices, which generalizes the classical methods.An example of a generation of fuzzy rule-based ensembles and the decision making by consensus via penalty functions is presented. The aim of this paper is to propose a consensus method via penalty functions for decision making in ensembles of fuzzy rule-based classification systems (FRBCSs). For that, we first introduce a method based on overlap indices for building confidence and support measures, which are usually used to evaluate the degree of certainty or interest of a certain association rule. Those overlap indices (a generalizations of the Zadeh's consistency index between two fuzzy sets) are built using overlap functions, which are a special kind of non necessarily associative aggregation functions proposed for applications related to the overlap problem and/or when the associativity property is not demanded. Then, we introduce a new FRM for the FRBCS, considering different overlap indices, which generalizes the classical methods. By considering several overlap indices and aggregation functions, we generate fuzzy rule-based ensembles, providing different results. For the decision making related to the selection of the best class, we introduce a consensus method for classification, based on penalty functions. We also present theoretical results related to the developed methods. A detailed example of a generation of fuzzy rule-based ensembles based on the proposed approach, and the decision making by consensus via penalty functions, is presented.

Abstract: Mining and analyzing the valuable knowledge hidden behind the amount of data available in social media is becoming a fundamental prerequisite for any effective and successful strategic marketing campaign. Anyway, to the best of our knowledge, a systematic analysis and review of the very recent literature according to a marketing framework is still missing. In this work, we intend to provide, first and foremost, a clear understanding of the main concepts and issues regarding social big data, as well as their features and technologies. Secondly, we focus on marketing, describing an operative methodology to get useful insights from social big data. Then, we carry out a brief but accurate classification of recent use cases from the literature, according to the decision support and the competitive advantages obtained by enterprises whenever they exploit the analytics available from social big data sources. Finally, we outline some open issues and suggestions in order to encourage further research in the field.

Abstract: Combinatorial optimization problems are typically NP-hard, due to their intrinsic complexity. In this paper, we propose a novel chaotic particle swarm optimization algorithm (CS-PSO), which combines the chaos search method with the particle swarm optimization algorithm (PSO) for solving combinatorial optimization problems. In particular, in the initialization phase, the priori knowledge of the combination optimization problem is used to optimize the initial particles. According to the properties of the combination optimization problem, suitable classification algorithms are implemented to group similar items into categories, thus reducing the number of combinations. This enables a more efficient enumeration of all combination schemes and optimize the overall approach. On the other hand, in the chaos perturbing phase, a brand-new set of rules is presented to perturb the velocities and positions of particles to satisfy the ideal global search capability and adaptability, effectively avoiding the premature convergence problem found frequently in traditional PSO algorithm. In the above two stages, we control the number of selected items in each category to ensure the diversity of the final combination scheme. The fitness function of CS-PSO introduces the concept of the personalized constraints and general constrains to get a personalized interface, which is used to solve a personalized combination optimization problem. As part of our evaluation, we define a personalized dietary recommendation system, called Friend, where CS-PSO is applied to address a healthy diet combination optimization problem. Based on Friend, we implemented a series of experiments to test the performance of CS-PSO. The experimental results show that, compared with the typical HLR-PSO, CS-PSO can recommend dietary schemes more efficiently, while obtaining the global optimum with fewer iterations, and have the better global ergodicity.

Abstract: Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative health, environment and economical implications. In recent times, computational intelligence (CI), a set of nature-inspired computational approaches and algorithms, has been used in vehicle routing and congestion mitigation research (also referred to as CI-based vehicle traffic routing systems—VTRSs). In this paper, we conduct a critique of existing literature on CI-based VTRSs and discuss identified limitations, evaluation process of existing approaches and research trends. We also identify potential research opportunities.

Abstract: Text mining has become an increasingly significant role in processing medical information. The research of text mining enhanced medical has attracted much attention in view from the substantial expansion of literature. This study aims to systematically review the existing academic research outputs of the field from Web of Science and PubMed by using techniques such as geographic visualization, collaboration degree, social network analysis, and topic modeling analysis. Specifically, publication statistical characteristics, geographical distribution, collaboration relations, and research topic are quantitatively analyzed. This study contributes to the text mining enhanced medical research field in a number of ways. First, it provides the latest research status for researchers who are interested in the field through literature analysis. Second, it helps scholars become more aware of the research subfields through hot topic identification. Third, it provides insights to researchers engaging in the field and motivates attention on the relevant research.