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Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

Related works for applying keystroke dynamics (KD) on free text identification indicated that applying KD can improve the accuracy of personal authentication on free text. As the result, this paper proposes a new biometrics, i.e., the keystroke clusters map (KC-Map), by clustering users’ keystrokes in order to effectively enhance the accuracy of personal authentication in free text. Since KC-Map is conducted via clustering, it is not suitable for traditional classifiers. In order to tackle this problem, the paper further proposes a keystroke clusters map similarity classifier (KCMS classifier). Experimental results positively show that the proposed KC-Map and KCMS classifier can efficiently improve the accuracy of personal authentication on free text with up to 1.27 times. In addition, one of the huge disadvantages on the current approaches in free text identification is that users are generally required to be trained for several months. Longer training time makes free text identification more impractical. Another motivation of this paper is to explore whether it is possible to shorten the training time into an acceptable range. Experimental results show that, to achieve relatively fair identification accuracy, users only need to carry out about 20 min for training.

This paper proposes an adaptive S transform (AST) to extract the feature vectors of voltage sags. With the effective window width matches the Fourier spectrum of sag signals, the standard deviation σ of Gaussian window may be determined as well. The narrowest and the widest window width of AST are obtained without additional parameters and iterative computing. Then, the optimal frequency resolution and time resolution are got respectively. Compared with ST, AST provides better time–frequency resolution to extract more precise feature vectors of eight types of voltage sags. Based on the time–frequencyrepresentation of AST, five disturbance features are extracted to construct the feature vector in this paper. In addition, four machine learning classifiers and two fuzzy clustering classifiers are used to analyze the validity and redundancy of these features. Through analyzing the classification accuracies and time costs of these classifiers with different training sets and different level of noise, it can be concluded that the machine learning classifiers perform better in classification accuracy and stability than fuzzy clustering classifiers.

Demand response (DR) is the response of electricity consumers to time-varying tariffs or incentives awarded by the utility. Home energy management systems are systems whose role is to control the consumption of appliances under DR programs, in a way that electricity bill is minimised. While, most researchers have done optimal scheduling only for non-interruptible appliances, in this paper, the interruptible appliances such as electric water heaters are considered. In optimal scheduling of non-interruptible appliances, the problem is commonly formulated as an optimisation problem with integer decision variables. However, consideration of interruptible appliances leads to a binary optimisation problem which is more difficult than integer optimisation problems. Since, the basic version of binary particle swarm optimisation (PSO) does not perform well in solving binary engineering optimisation problems, in this paper a new binary particle swarm optimisation with quadratic transfer function, named as quadratic binary PSO (QBPSO) is proposed for scheduling shiftable appliances in smart homes. The proposed methodology is applied for optimal scheduling in a smart home with 10 appliances, where the number of decision variables is as high as 264. Optimal scheduling is done for both RTP and TOU tariffs both with and without consideration of consumers’ comfort. The achieved results indicate the drastic effect of optimal scheduling on the reduction of electricity bill, while consumers’ comfort is not much affected. The results testify that the proposed QBPSO outperforms basic binary PSO variant and 9 other binary PSO variants with different transfer functions.

Hesitant fuzzy linguistic preference relations (HFLPRs) as an efficient and common tool to deal with decision-making problems have been widely used in real life. The consistency and consensus are the most two important topics for HFLPRs. In this paper, we develop a new efficient consistency–consensus framework for HFLPRs. Firstly, we transform HFLPRs into probabilistic linguistic preference relations, and develop a programming model to make the HFLPRs achieve the maximum consistency degree. Then, we propose some new rules for addition operation and weighted average operator to fuse individual preference information. After that, a maximum consensus model is developed which maximizes the consensus degree by adjusting the experts’ weights. Then, a numerical case of temporary placement selection after earthquake is presented, and some comparisons with the traditional methods are conducted to verify the rationality and efficiency of our framework. Finally, we end the paper with some conclusions and future research directions.

Constrained optimization problems arise in numerous scientific and engineering applications, and many papers on the online solution of constrained optimization problems using projection neural networks have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on projection neural networks for solving various constrained optimizations as well as their applications. Since convergence and stability are important for projection neural networks, theoretical results of projection neural networks are reviewed in detail. In addition, various applications of projection neural networks, e.g., the motion generation of redundant robot manipulators, coordination control of multiple robots with limited communications, generation of winner-take-all strategy, model predictive control and WSN localizations, are discussed and compared. Concluding remarks and future directions of projection neural networks as well as their applications are provided.

Brain–Computer Interfaces (BCI) are systems that translate brain activity patterns into commands for an interactive application, and some of them recognize patterns generated by motor imagery. Currently, these systems present performances and methodologies that still are not practical enough for realistic applications. Therefore, this paper proposes two methodologies for multiple motor imagery classification. Both methodologies use features extracted by a variant of Discriminative Filter Bank Common Spatial Pattern (DFBCSP) presented in this paper. The frequency bands selection in this variant is carried out by a novel iterative algorithm that selects the frequency band that attains the highest classification accuracy for specific binary classification. For each binary combination of classes, a frequency band is selected. The resulting samples are then set into a matrix which feeds one or many Convolutional Neural Networks previously optimized by using a Bayesian optimization. The first methodology applies a Convolutional Neural Network (CNN) for the classification of all classes and the second is a modular network composed of four expert CNNs. In this modular network, each expert CNN performs a binary classification, and a fully connected network analyzes their results. To validate both approaches two datasets were used, the BCI competition IV dataset 2a and another presented in this paper recorded from eight subjects by using the OpenBCI device. The experimental results demonstrated an improvement in the classification accuracy over many classic intelligent recognition methods, without a high computation time in order that they can be implemented in an online application.

Hybrid optimization algorithms which combine Evolutionary Algorithms (EAs) and Gradient-Based (GB) methods o refine the most promising solutions, are valuable tools for use in engineering optimization. Several hybrid methods can be found in the literature; differences among them are associated with the criteria used to select individuals for refinement through the GB method and the feedback the EA gets from the latter. GB methods require the gradient of the objective functions with respect to the design variables. By employing the adjoint method in problems governed by partial differential equations, the cost of computing the gradient becomes independent of the number of design variables. For multi-objective optimization problems this paper is exclusively dealing with, the availability of the gradients of all objective functions is not enough. Hybrid algorithms require the computation of descent directions in the objective space capable of improving the current front of non-dominated solutions. Using the sum of weighted objectives as the new objective function is ineffective. In this paper, a method which refrains from using arbitrarily defined weights is proposed. The method is driven by data obtained from the Principal Component Analysis (PCA) of the objective function values of the elite individuals at each generation of the EA. The PCA, with computational cost that of the solution of an eigenproblem, identifies the direction in the objective space along which the current front of non-dominated solutions should be improved. This along with the gradients computed by the adjoint method are used by the GB method to refine selected individuals. The efficiency of the proposed hybrid algorithm is further improved by employing online trained surrogate models or metamodels and Kernel PCA within the EA-based search. The proposed method is demonstrated in aerodynamic shape optimization problems, using in-house Computational Fluid Dynamics software and its adjoint.

Credit scoring models have offered benefits to lenders and borrowers for many years. However, in practice these models are normally built on a sample of accepted applicants and fail to consider the remaining rejected applicants. This may cause a sample bias which is an important statistical issue, especially in the online lending situation where a large proportion of requests are rejected. Reject inference is a method for inferring how rejected applicants would have behaved if they had been granted and incorporating this information in rebuilding a more accurate credit scoring system. Due to the good performances of SVM models in this area, this paper proposes a new approach based on the state-of-the-art kernel-free fuzzy quadratic surface SVM model. It is worth pointing out that our method not only performs very well in classification as some latest works, but also handles some big issues in the classical SVM models, such as searching proper kernel functions and solving complex models. Besides, this paper is the first one to eliminate the bad effect of outliers in credit scoring. Moreover, we use two real-world loan data sets to compare our method with some benchmark methods. Particularly, one of the data set is very valuable for the study of reject inference, because the outcomes of rejected applicants are partially known. Finally, the numerical results strongly demonstrate the superiority of the proposed method in applicability, accuracy and efficiency.

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.

This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is proposed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.

The aim of this paper is to develop a novel outranking method using a risk attitudinal assignment model under complex uncertainty based on Pythagorean fuzzy (PF) sets and interval-valued Pythagorean fuzzy (IVPF) sets. Additionally, this paper applies the proposed methodology to address financial decision-making problems to investigate its applicability and effectiveness in the real world. These developed methods and techniques can provide a new viewpoint for capturing the amount of information conveyed by PF and IVPF values and can fully take into account the decision maker’s attitude towards risk-taking in the face of high uncertainty. Considering the diverse attitudes toward risk neutrality, risk seeking, and risk aversion, this paper introduces novel measures of risk attitude-based score functions within the PF/IVPF environments and investigates their desirable and useful properties. These properties provide a means of establishing linear orders and admissible orders in the PF/IVPF contexts. This means that the proposed measures can overcome some drawbacks and ambiguities of the previous techniques through score and accuracy functions and can address incomparable PF/IVPF information more effectively. As an application of the risk attitude-based score functions, this paper develops novel risk attitudinal assignment models to establish a useful outranking approach for solving multiple criteria decision-making problems. Two algorithms for PF and IVPF settings are developed based on the concepts of a precedence frequency matrix and a precedence contribution matrix for conducting multiple criteria evaluation and the ranking of alternatives. A financing decision on aggressive/conservative policies of working capital management is presented to demonstrate the applicability of the proposed outranking approach in real situations. Moreover, a comparison to the technique of risk attitudinal ranking methods is investigated to validate the advantages of the proposed methodology. Furthermore, a comparative analysis with a newly developed outranking method, the IVPF-ELECTRE (for the elimination and choice translating reality), is conducted via an application to the investment problem of software development projects. Compared with these benchmark approaches, the proposed methods can produce a more reasonable and persuasive result for ranking the order relationships of alternatives in the highly uncertain context. The practical studies and comparative discussions demonstrate an excellent performance of the developed methodology that is effective and flexible enough to accommodate more-complex decision-making environments.

This paper presents an improved fingerprint recognition system for overlapping fingerprint of two fingers. The developed system can automatically and precisely separate an overlapping fingerprint into two areas, an overlapping area and a non-overlapping area, by analyzing their orientation fields and complicities. A method being able to remove a misjudging core of the fingerprint from the overlapping area is also proposed in this paper. Moreover, a recursive correction algorithm and a restrictions relaxation labeling algorithm are applied to separate two fingerprints from the determined overlapping fingerprint area, and the Gabor filter is applied to enhance figure quality of the two separated fingerprints. After that, the VeriFinger 6.2 SDK is used to identify the fingerprints. In the experiments, our system uses the fingerprints from the two public databases. Receiver Operating Characteristic (ROC) and Cumulative Match Characteristic (CMC), are used to examine the proposed fingerprint recognition system. The average recognition rate of our system is 85.7%. The experiments results show the effectiveness of the proposed system.

A parking lot (PL) dynamic resource allocation system for recharging electric vehicles (EVs) is introduced in this paper. For scheduling purposes, a day is divided into sequential timeslots. At the beginning of each timeslot, the dynamic system can determine an optimal charging schedule for that timeslot, as well as plan for subsequent timeslots. An EV may arrive at a PL with or without an appointment. Considering the variation in electricity prices during the day, the objective is to minimize the cost of electricity used to charge EVs by scheduling optimal electric quantities at the parking timeslots of each EV. The optimal solution satisfies the EV’s charging rate limit and the PL’s transformer limit. Based on particle swarm optimization (PSO), fuzzy systems and heuristics, this paper describes a heuristic fuzzy particle swarm optimization (PHFPSO) algorithm to solve the optimization problem. From the case studies, the results show the proposed dynamic resource allocation system has a significant improvement in satisfying charging requests and in reducing the electricity cost of the PL when compared with other scheduling mechanisms.

Background: The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients. Aim: The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated countries due to proportionally a smaller number of medical practitioners, testing kits, and other resources, thus becoming essential to identify the infected people. Catering to the above problems, the purpose of this paper is to formulate an accurate, efficient, and time-saving method for detecting positive corona patients. Method: In this paper, an Ensemble Deep Convolution Neural Network model “CoVNet-19” is being proposed that can unveil important diagnostic characteristics to find COVID-19 infected patients using X-ray images chest and help radiologists and medical experts to fight this pandemic. Results: The experimental results clearly show that the overall classification accuracy obtained with the proposed approach for three-class classification among COVID-19, Pneumonia, and Normal is 98.28%, along with an average precision and Recall of 98.33% and 98.33%, respectively. Besides this, for binary classification between Non-COVID and COVID Chest X-ray images, an overall accuracy of 99.71% was obtained. Conclusion: Having a high diagnostic accuracy, our proposed ensemble Deep Learning classification model can be a productive and substantial contribution to detecting COVID-19 infected patients.

COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020 Therefore, it’s the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyses two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from1st Jan 2019 to 23rd March 2020 have been analysed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226668 tweets collected within the time span between December 2019 and May 2020 have been analysed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%., Highlights • This paper aims to bring out the fact that related tweets have been unsuccessful in guiding people around the COVID-19 pandemic. • This study analyses two types of tweets gathered during the pandemic times. • The research demonstrates that no useful words could be a found in WordCloud or computations using word frequency in tweets. • The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. • A Gaussian membership function based fuzzy rule base is also designed to correctly identify sentiments from tweets.

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques. This work has been funded by the Spanish Government TIN2016-76515-R funding for the COMBAHO project, supported with Feder funds. It has also been supported by a Spanish national grant for PhD studies FPU15/04516 (Alberto Garcia-Garcia). In addition, it was also funded by the grant Ayudas para Estudios de Master e Iniciacion a la Investigacion from the University of Alicante.

In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches.

Collaborative filtering (CF) is widely applied in recommender systems to predict user preferences or interests according to a user’s historical information. Traditional CF methods mainly adopt batch-processing methods to train recommender models, which require prior preparation of all training data. However, user preferences or interests always change with time, and it is impossible to prepare all of the training data at once. In actuality, the data are obtained with time with a certain sequence. Therefore, to update the model, the trained model needs to be re-trained according to all of the historical datasets. As a result, the cost of re-training is very high, which slows down the recommender model, and makes new updates of the user data difficult to capture. To solve these problems, the online recommender system emerged. In this paper, we propose a confidence-weighted bias model (CWBM) for online collaborative filtering (OCF). This model adds bias into CF and further introduces confidence weights; thus, it can improve the stability and accuracy of OCF. A comparative experiment on two real datasets, Movielens100K and Mvielens1M, show that the method proposed in this paper is superior to other baseline methods.

This paper presents an efficient non-iterative hybrid empirical mode decomposition (EMD) and kernel ridge regression (KRR) for significantly accurate short-term wind speed and wind power prediction. The original non-linear and non-stationary wind speed and wind power time series data are decomposed using EMD to isolate the mutual effects between different components. The proposed EMD-KRR model is tested for predicting wind speed and wind power time series data over a time horizon spanning intervals of 10 min, 30 min, 1 h and 3 h ahead, respectively. Further the performance of EMD-KRR prediction model is compared with two other widely used non-iterative prediction models like EMD based Random vector functional link network (RVFL), and EMD based Extreme Learning Machine (ELM). Also an iterative Mutated Firefly Algorithm with Global Optima concept (MFAGO) optimized RVFL network is used for comparison to prove the advantages of non-iterative models over the iterative ones. The performance metrics of the proposed EMD-KRR and EMD-RVFL confirm the effectiveness and precision in producing an accurate forecast of both wind speed and wind power in comparison to all other prediction models using the wind power data from three real world wind farms. Further a fast reduced version of the EMD-KRR is presented in the paper to reduce the computational overhead substantially using randomly selected support vectors from the data set while resulting in a reasonably accurate forecast.

Sparse coding is based on the concept of having a large dictionary of candidate basis vectors. Any given vector is expressed as a sparse linear combination of the dictionary vectors. It has been developed in the signal processing field, and has many applications in data compression and image processing. In this paper we propose applying sparse coding to the time series forecasting field. Specifically, the paper investigates different dictionary based local learning techniques for building predictive models for the time series forecasting problem. The proposed methodology is based on a local learning framework whereby the query point is embedded and coded in terms of a sparse combination of the training dictionary atoms (vectors). Then this embedding is used for estimating the target value of the query point, by applying the same embedding to the target vectors of the dictionary training atoms. We present an experimental study of several sparse coding algorithms. Experiments are performed on the large monthly time series benchmark from the M3 competition, and these experiments showed that the sparse methods Lasso and Elastic-Net presented the best results among the sparse coding algorithms. Moreover, they outperformed the K-nearest neighbor (KNN) regression and most of the compared machine learning and statistical forecasting techniques, especially for higher horizons.

In this paper, a new hierarchical method of contour analysis is proposed. The first stage of the analysis is based on the syntactic approach in which elementary segments of the contour are extracted by using geometric features. As a result, each extracted segment belongs to one of the equivalence classes which make the set of primitives. The contour is described as a string of primitives. The applied syntactic approach is a generalization of a classical shape language. Such description enables to classify some cases, but not all. Therefore, the second stage of the classification is applied. It consists in analysing other local geometric features of the contour in the context of the syntactic description obtained at the first stage of the analysis. The values of these features are used to define the arguments of the membership functions of the family of fuzzy sets which is the basis for the contour classification at the second stage of the analysis. The proposed formalism has been applied effectively to the recognition of bone contours in X-ray images. Healthy bones, the bones with erosions and the bones with osteophytes have been classified correctly. The proposal described in this paper turned out to be effective in the classification of contours of bones.

In this paper, an iterated local search algorithm is proposed to find the feasible solution for the University Course Timetabling Problem. Three key phases are involved in the proposed algorithm framework: initialization, intensification and diversification. Once a partial-feasible initial timetable is constructed, a simulated annealing based local search and a diversification procedure that brings moderate perturbation or even improvement to the current solution are performed in an iterative manner until a stop condition is met. The proposed algorithm is evaluated on a widely used dataset containing 60 problem instances. The computational results show the iterated local search algorithm achieves highly competitive results compared with the existing algorithms. It is noteworthy that this algorithm can find feasible solutions for 58 instances in reasonable time, including three large instances whose feasible solutions are missed in previous papers. Furthermore, some key elements and properties of the algorithm are also analyzed.

In this paper we present a novel approach for solving the consequent part of neuro-fuzzy modeling with an emphasis on the forgetting factor in the multi-class learning problem. Our solution is based on recursive least squares (RLS) for online and incremental learning applications, where the data stream is not necessarily uniformly distributed over time. Such a setup can lead to forgetting of specific classes that have not been used for a period of time. In this work we present a reasoned and detailed description of elastic memory learning (EML) and EML with the use of confidence interval (EML+) to avoid unnecessary treatment of the forgetting factor. We present the experimental results and evaluation of our methods in order to show their usefulness not only against forgetting of unused classes, but also for dealing with the lowered recognition rate after all classes have been learned. We note that by using EML, forgetting is significantly eliminated and the recognition rate is slightly affected as well, while EML+ puts more emphasis on keeping the recognition rate higher than the forgetting. Thus, this paper presents two methods that significantly eliminate the forgetting factor for incremental learning with a different focus on its importance, i.e. high recognition rate vs high immunity to forgetting; both of these methods perform significantly better than RLS for these aspects.

Classification for cancer diagnosis is a significant topic in bioinformatics. Microarray data generates a big challenge for the diagnosis prediction due to tens of thousands of genes but dozens of samples. The feature dimension needs to be reduced for better diagnosis prediction. Moreover, the parameters of classifiers required to be optimized have a significant impact on the classifiers’ performance. This paper presents a novel effective approach for diagnosis prediction. First, the normalization procedure for the raw data is carried out. Then, considering both the complexity of dimension of the microarray data and the specific classifier, support vector machine method based on recursive feature elimination (SVMRFE) is applied for selecting optimal gene subset. Finally, a hybrid method based on fruit fly optimization and particle swarm optimization (PSOFOA) is proposed to optimizing the classifier (LSSVM) parameters. An integrated work of disease diagnosis is obtained. By this means, the accuracy of 100% is achieved with only 4 features (genes), which is the best result compared to all published papers. The proposed method is also compared with some specific methods The result illustrates that the proposed method achieves a best prediction performance.

Crowd sourcing techniques are used in social networks to propagate information at a faster pace through campaigns. One of the challenges of crowd sourcing system is to recruit right users to be a part of successful campaigns. Fetching this right group of people, who influence a vast population to adopt information, is termed as influence maximization. Concerns of scalability and effectiveness need an effective and a viable solution. This paper proposes the solution in three stages. At the first stage, the large social network is pruned based on the nodal properties to make the solution scalable. At the second stage, Outdegree Rank (OR), is proposed and at the third stage, Influence Estimation (IE) approach estimates user influence. This work amalgamates aspects of structure, heuristic and user influence, to form STORIE. The proposed approach is compared to standard heuristics, on various experimental setups such as RNNDp, RNUDp and TVM. The spread of information is observed for HEP, PHY, Twitter, Infectious and YouTube data, under Independent Cascade model and STORIE gives optimal results, with an increase up to 50%. Although the paper discusses influence maximization, the proposed approach is also applicable to understand the spread of epidemics, computer virus, and rumor spreading in the real world and can also be extended to detect anomalies in web and social networks.

Feature selection has been a significant task for data mining and pattern recognition. It aims to choose the optimal feature subset with the minimum redundancy and the maximum discriminating ability. This paper analyzes the feature selection method from two aspects of data and algorithm. In order to deal with the redundant features and irrelevant features in high-dimensional & low-sample data and low-dimensional & high-sample data, the feature selection algorithm model based on the granular information is presented in this paper. Thus, our research examines experimentally how granularity level affects both the classification accuracy and the size of feature subset for feature selection. First of all, the improved binary genetic algorithm with feature granulation (IBGAFG) is used to select the significant features. Then, the improved neighborhood rough set with sample granulation (INRSG) is proposed under different granular radius, which further improves the quality of the feature subset. Finally, in order to find out the optimal granular radius, granularity λ optimization based on genetic algorithm (ROGA) is presented. The optimal granularity parameters are found adaptively according to the feedback of classification accuracy. The performance of the proposed algorithms is tested upon eleven publicly available data sets and is compared with other supervisory methods or evolutionary algorithms. Additionally, the ROGA algorithm is applied to the enterprise financial dataset, which can select the features that affect the financial status. Experiment results demonstrate that the approaches are efficient and can provide higher classification accuracy using granular information.

In this paper we consider a problem motivated by a real-world environment: scheduling the charging periods for a large set of electric vehicles, subject to a set of hard constraints, with the objective of minimizing the total tardiness. The set of constraints imposed by the charging station makes it difficult to obtain schedules that are both feasible and efficient. We consider both the static variant of the problem, where arrival time, charging time and due date of vehicles are known in advance, and also the dynamic variant. As the problem is NP-hard, metaheuristics are probably the best option to solve it. In fact, the state-of-the-art methods are a simple genetic algorithm (GA) and a method based on priority rules (EVS). In this paper we propose to design hybrid metaheuristics, motivated by the success of these hybridizations in solving a large number of scheduling problems. In particular we define a GRASP-like method and a memetic algorithm that use the Variable Neighborhood Search framework, both specifically designed for the problem at hand. Experimental results illustrate the potential of the proposed methods, reaching in some test-beds improvements larger than 12% with respect to EVS and GA.

Wireless Sensor Networks(WSN) are utilized in many fields such as environmental monitoring and military applications. The nodes of WSNs are not rechargeable, so energy conservation in these networks is important. One of the important issues in these networks is to optimize energy in message broadcasting. Depending on the ability of nodes and antennas, broadcasting is done in two means: directional and omni-directional antennas. There are centralized algorithms to broadcast message in wireless networks either by directional or omni-directional antennas. The problem of minimizing energy in broadcasting and multicasting is Non-polynomial-hard. In this paper, a centralized algorithm is proposed to improve energy and running time of the algorithm by using directional antennas. As evolutionary algorithms by omni-directional antenna are better than heuristic algorithms in terms of the time and the average result; a new approach based on particle swarm optimization (PSO) as an evolutionary algorithm is proposed in this paper. We have also considered and evaluated most of famous evolutionary algorithms such as Simulated Annealing (SA), genetic algorithm (GA), Teaching-Learning-Based Optimization (TLBO), Harmony Search (HS) and Ant Colony Optimization (ACO). The experiment results indicate that the proposed method is effective especially in term of energy conservation.

Background Understanding of molecular mechanism, lying beneath the carcinogenic expression, is very essential for early and accurate detection of the disease. It predicts various types of irregularities and results in effective drug selection for the treatment. Pathway information plays an important role in mapping of genotype information to phenotype parameters. It helps to find co-regulated gene groups whose collective expression is strongly associated with the cancer development. Method In this paper, we have proposed a bi-stage hierarchical swarm based gene selection technique which combines two methods, proposed in this paper for the first time. First one is a multi-fitness discrete particle swarm optimization (MFDPSO) based feature selection procedure, having multiple fitness functions. This technique uses multi-filtering based gene selection procedure. On top of it, a new blended Laplacian artificial bee colony algorithm (BLABC) is proposed and it is used for automatic clustering of the selected genes obtained from the first procedure. We have performed 10 times 10-fold cross validation and compared our proposed method with various statistical and swarm based gene selection techniques for different popular cancer datasets. Result Experimental results show that the proposed method as a whole performs significantly well. The MFDPSO based system in combination with BLABC generates a good subset of pathway markers which provides more effective insight into the gene-disease association with high accuracy and reliability.

Particle swarm optimization (PSO) has long been attracting wide attention from researchers in the community. How to deal with the weak exploration ability and premature convergence of PSO remains an open question. In this paper, we modify the memory structure of canonical PSO and introduce the multi-leader mechanism to alleviate these problems. The proposed PSO variant in this paper is termed as multi-leader PSO (MLPSO) within which the modified memory structure provided more valuable information for particles to escape from the local optimum and multi-leader mechanism enhances diversity of particles' search pattern. Under the multi-leader mechanism, particles choose their leaders based on the game theory instead of a random selection. Besides, the best leader refers to other leaders' information to improve its quality in every generation based on a self-learning process. To make a comprehensive analysis, we test MLPSO against the benchmark functions in CEC 2013 and further applied MLPSO to a practical case: the reconstruction of gene regulatory networks based on fuzzy cognitive maps. The experimental results confirm that MLPSO enhances the efficiency of the canonical PSO and performs well in the realistic optimization problem.

Identifying the language of a text is an important step for several natural language processing applications. State-of-the-art language identification (LID) systems perform very well when discriminating between unrelated languages on standard datasets. However, the LID task has a bottleneck when discriminating between similar languages or language varieties. Furthermore, LID has also proven to be very challenging when dealing with short texts such as the ones from Twitter. In this paper, we propose the use of smoothed n-gram language models to classify tweets in both Brazilian and European Portuguese variants. Word and character n-gram language models were combined and evaluated through five different classifiers. We have compared the smoothed n-gram language models together with the Term Frequency and Inverse Document Frequency weighting scheme. This paper also proposes an ensemble model, in which the class labels output were combined using majority voting and algebraic combiners. The best configuration reached accuracy of 92.71% using an ensemble model, which combines Lidstone (0.1) character 6-gram, Good–Turing word unigram, and Witten–Bell word bigram models, together with the Log-Likelihood Ratio estimation method.

Presently, the Cuckoo Search algorithm is attracting unprecedented attention from the research community and applications of the algorithm are expected to increase in number rapidly in the future. The purpose of this study is to assist potential developers in selecting the most suitable cuckoo search variant, provide proper guidance in future modifications and ease the selection of the optimal cuckoo search parameters. Several researchers have attempted to apply several modifications to the original cuckoo search algorithm in order to advance its effectiveness. This paper reviews the recent advances of these modifications made to the original cuckoo search by analyzing recent published papers tackling this subject. Additionally, the influences of various parameter settings regarding cuckoo search are taken into account in order to provide their optimal settings for specific problem classes. In order to estimate the qualities of the modifications, the percentage improvements made by the modified cuckoo search over the original cuckoo search for some selected reviews studies are computed. It is found that the population reduction and usage of biased random walk are the most frequently used modifications. This study can be used by both expert and novice researchers for outlining directions for future development, and to find the best modifications, together with the corresponding optimal setting of parameters for specific problems. The review can also serve as a benchmark for further modifications of the original cuckoo search.

Fuzzy decision tree algorithms provide one of the most powerful classifiers applied to any kind of data. In this paper, some new Fuzzy Decision Tree (FDT) approaches based on Hesitant Fuzzy Sets (HFSs) have been introduced to classify highly imbalanced data sets. Our proposed classifiers employ k-means clustering algorithm to divide the majority class samples into several clusters. Then, each cluster sample is labeled by a new synthetic class label. After that, five discretization methods (Fayyad, Fusinter, Fixed Frequency, Proportional, and Uniform Frequency) are considered to generate Membership Functions (MFs) of each attribute. Five FDTs are constructed based on five discretization methods Hesitant Fuzzy Information Gain (HFIG) is proposed as a new attribute selection criterion that can be used instead of Fuzzy Information Gain (FIG). HFIG is calculated by aggregating obtained FIGs from different discretization methods by information energy. For predicting the class label of new samples, three aggregation methods are utilized. The combination of splitting criterion (HFIG or FIG), five different discretization methods (for generating MFs) and three aggregation methods (to predict class label of new samples) generate special classifiers for addressing the imbalanced classification. For illustrating the difference between our proposed methods, taxonomy is proposed in the paper that categorizes them in three general categories. The experimental results show that our proposed methods outperform the other fuzzy rule-based approaches over 20 highly imbalanced data sets of KEEL in terms of AUC.

The no-wait flow shop scheduling problem (NWFSSP) performs an important function in the manufacturing industry. Inspired by the overall process of teaching-learning, an extended framework of meta-heuristic based on the teaching-learning process is proposed, which consists of four parts, i.e. previewing before class, teaching phase, learning phase, reviewing after class. This paper implements a hybrid meta-heuristic based on probabilistic teaching-learning mechanism (mPTLM) to solve the NWFSSP with the makespan criterion. In previewing before class, an initial method that combines a modified Nawaz-Enscore-Ham (NEH) heuristic and the opposition-based learning (OBL) is introduced. In teaching phase, the Gaussian distribution is employed as the teacher to guide learners to search more promising areas. In learning phase, this paper presents a new means of communication with crossover. In reviewing after class, an improved speed-up random insert local search based on simulated annealing (SA) is developed to enhance the local searching ability. The computational results and comparisons based on Reeves, Taillard and VRF’s benchmarks demonstrate the effectiveness of mPTLM for solving the NWFSSP.

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers. In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization. In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs. The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.

This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.

The ED-LS method prioritizes defending those nodes that in the current graph state St have:1.a lower number of edges separating them from fire E(St,v).2.a higher number of edges connecting to untouched nodes D(St,v) among nodes equidistant from fireThus, in the example, the priority of nodes is: a, b, c.Display Omitted The Firefighter Problem (FFP) concerns stopping a threat spreading in a graph.The paper presents a new local search method (ED-LS) for the multiobjective FFP.The ED-LS uses heuristics to reduce the computational cost.ED-LS is tested with the MOEA/D and NSGA-II algorithms as global searches.Experiments show that the proposed approach effectively improves the results. One of the approaches to combinatorial optimization is to use global search methods, such as evolutionary algorithms combined with local search procedures. Local search can be very effective in improving the quality of solutions, however, searching large neighbourhoods can be computationally expensive. In this paper a new local search method is described that is dedicated to the Firefighter Problem (FFP). The Firefighter Problem concerns protecting nodes of a graph in order to stop a threat that is spreading in the graph. Because the resources used for protection are limited, the goal is to find the best order in which the nodes are protected and thus the FFP is a combinatorial optimization problem. One of the key elements in the design of a local search method is the definition of the neighbourhoods of solutions from which the local search starts. The method proposed in this paper (ED-LS) aims at lowering the computational cost of the local search by reducing, based on certain heuristics, the size of these neighbourhoods.In this paper the ED-LS is compared to an optimization without the local search, a local search using non-reduced neighbourhoods and a method that reduces the neighbourhood by checking only some randomly selected neighbours. The results show that the ED-LS improves the performance of the algorithm with respect to no local search at all and to the local search using non-reduced neighbourhoods, supporting the view that reducing the size of the neighbourhood is beneficial. Also, the ED-LS produces better results than when the size of the neighbourhood is reduced randomly, which means that the proposed heuristics are indeed selecting promising neighbours effectively. In the last part of the experiments the ED-LS is further improved to obtain faster improvement of solutions at the beginning of the optimization run.

When ambulances’ turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances’ TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of ± 10 min.

Recently, the solar photovoltaic power, a promising renewable energy, is witnessing a rapid development period. However, it is often difficult to perfectly capture the generation of solar photovoltaic plants because of various factors (like weather condition, solar radiation and human activities), increasing the operational risk and cost of power system. Hybrid energy system proves to be an effective measure to address this problem. Motivated by this practical necessity, this paper develops a novel hybrid gravitational search algorithm to solve the coordinative operation model of multiple hydropower reservoirs and solar photovoltaic power plants. In the proposed method, the gravitational search algorithm is set as the unified framework; the neighborhood search strategy is used to improve the convergence rate by considering the social information and individual experience; the adaptive mutation strategy is used to improve the population diversity by elite conservation and mutation operator; the modified elastic-ball strategy and constraint handling technique are used to enhance the solution feasibility. The simulation results of numerical functions demonstrate the superiority of the developed method in convergence rate and global search ability. The hydro–solar operation results in different cases show that compared with the traditional methods, the proposed method can yield high-quality scheduling schemes to alleviate the peak shaving pressure of power system. Thus, the novelty of this paper is to provide an effective HGSA method for solving the complex engineering optimization problem.

In high dimensional data, the Feature Selection (FS) approach plays an important role in overcoming accuracy, time complexity, and space complexity. This paper proposes a binary version of the hybrid two-phase multi-objective FS approach, based on Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO). The first objective is to minimize the classification error rate, and the second objective is to reduce the number of selected features. In the first phase, the proposed approach performs the global search, and in the second phase, it goes for the local search. In the case of global search, i.e., for the exploration phase, the property of PSO is used. In the case of local search, i.e., for the exploitation phase, the proposed method uses the modified version of PSO and GWO. The particles are learning not only from the positions but also from the mass and acceleration, based upon Newton’s second law of motion. In some cases, while in the iterations for updation of particles, a stall arises. Due to which the particles are not updating their positions. To avoid the stall in the iterations, this paper introduces a new term, i.e., population factor, which can omit the stall in the iterations if it arises. The prominent features which are selected from the proposed approach are tested on five well-known classification algorithms. The performance of the proposed approach is investigated on eight high-dimensional gene expression data. Experimental results comparison shows that the proposed approach for FS performs efficiently and effectively than other metaheuristics, statistical, and multi-objective FS methods.

With the increasing utilization of wind generation in power system, the improvement of wind power forecasting precision is attached vital importance. Owing to the stochastic and intermittent nature of wind power, the conventional methods no longer ensure sufficient accuracy of wind power prediction in majority of scenarios. Motivated by recent advancements of ensemble methods based on decomposition technologies, a novel ensemble method based on ensemble empirical mode decomposition (EEMD) and least absolute shrinkage and selection operator–quantile regression neural network (LASSO–QRNN) model for forecasting wind power is proposed in this paper. The model is an ingenious integration of data preprocessing technology, feature selection method, prediction model and data post-processing technology. Thereinto, EEMD is exploited to convert intricate and irregular wind power time series into a collection of subseries relatively easy to analyze; LASSO regression is combined with QRNN model to realize the filtering of important variables and provide more comprehensive and robust prediction results; the KDE method reprocesses the prediction results, greatly improves the prediction accuracy and effectively quantifies the uncertainty of the forecasting process. The suggested model and several benchmark models have been implemented on six wind power datasets, two are gathered from a wind farm in Spain and four from a competition to demonstrate the superiorities of the model proposed in this paper. The compared results reveal that the proposed method has adequate capacity to enhance the performance of wind power forecasting, measure and reduce the uncertainty of prediction process.

In the evolution of an emergency, Internet public opinions usually catalyze escalation and spread of the emergency, and even affect the evolution of public opinions. Therefore, how to effectively manage Internet public opinions has become urgent. As an essential part of the Internet public opinion management, assessing the heat degree of Internet public opinions is necessary. Being problem-oriented, this paper analyzes the development and evolution of Internet public opinions, and identifies the characteristics of the heat degree assessment of Internet public opinions. Taking into account the continuously changing exterior environment, the dynamic nature of Internet public opinions, and the inadequacy and uncertainty of decision-making information, assessing the heat degree of Internet public opinions is regarded as a dynamic multi-attribute decision making problem under the probabilistic linguistic environment. Thus, this paper aims to develop a dynamic decision-making framework to assess the heat degrees of Internet public opinions under the probabilistic linguistic environment. First, the probabilistic linguistic Bayesian network (PLBN) is constructed, in which the nodes denote attributes and related factors, and the hierarchical network structure shows the relationship among attributes. Then, the probability information obtained by PLBN in the form of PLTS is converted into attribute weight information. Moreover, this paper starts from the nature of PLTS, discusses PLTSs in terms of the probability distribution, and then gives the concept of the dominance degree of PLTS, based on which, a dynamic decision-making model based on the idea of prospect theory that considers DMs’ bounded rationality is developed. Finally, five emergency events happened in China are selected as a case to illustrate the proposed approach and some analyses and discussions have been carried out to verify the validity of our approach.

In statistical inference hypotheses related to different kinds of phenomena are formulated, and then data are collected and analyzed, which either confirm or falsify these hypotheses. Considering traditional statistics, in the underlying models hypotheses and sample data should be well defined. However, these models are often inadequate with regard to real-life problems, as theoretical specifications and observed information are frequently imprecise, vague, incomplete, qualitative, linguistic or noisy. To relax this rigidity, numerous researchers have proposed modifications and extensions of statistical inference approaches with the help of concepts of fuzzy statistics. In the meantime there are many papers on the topic of hypothesis testing in fuzzy environments, especially based on fuzzy hypotheses and/or by using fuzzy data. In order to structure this variety of contributions, proposals and applications, we give a comprehensive systematic review in this paper and offer a bibliography on fuzzy hypothesis testing. The paper seeks to consolidate the topic of fuzzy hypothesis testing with the purpose of supporting new researchers in this field and highlighting potential directions for future research.

Over recent years, the development of online social media has dramatically changed the way people connect and share information. It is undeniable that social platform has promoted the quickest type of spread for fake stories. Almost all the current online fact-checking sources and researches are concentrating on the validating political content and context. The proposed system in this paper provides a complete visual data analytics methods to assist users in achieving a comprehensive understanding of malicious activities at multiple levels such as adversary’s behavior, victim’s behavior, content, and context level. In this paper, we investigate a variety of datasets from different aspects such as role, vulnerabilities, influential level, and distribution pattern. The proposed method in this paper focuses on automatic fake/hostile activity detection by utilizing a variety of machine learning (ML) techniques, deep learning models, natural language processes (NLP), and social network analysis (SNA) techniques. Different auxiliary models, such as bot detection, user credibility, and text readability, are deployed to generate additional influential features. The classification performance of ten different machine learning algorithms using a variety of well-known datasets is evaluated by utilizing 10-fold cross-validation.

Aiming at the poor performance and flexibility of traditional assessment methods of network security in dealing with a large number of network attack data, this paper proposes a network security situation assessment method based on adversarial deep learning. Firstly, establish the Deep Autoencoder-Deep Neural Network (AEDNN) model based on Deep Autoencoder (DAE) and Deep Neural Network (DNN). Conduct feature learning on the DAE network and apply the DNN network as a network attacks classifier. In the training process, for considering the results of feature learning in DAE, this paper builds an adversarial training process by changing the training weights. Besides, to increase the performance of the model on the minority network attacks, the Under–Over Sampling Weighted (UOSW) algorithm is designed; Finally, conduct model testing and calculate the network security situation value. The compared results of other models show the proposed model is more accurate for identifying network attacks and can evaluate the network situation more comprehensively and flexibly.

Due to the complexity of spatial and structural patterns of remote sensing images, land cover classification is still an active, ongoing problem in the field of image classification. The limited spectrum and texture features can be used often lead to low accuracy in remote sensing image classifications. Spatial structure information, which is commonly regarded as very useful information for improving land cover classifications, characterizes a composite of several types of objects or land covers. However, it is rarely used because it is difficult to describe and calculate. Generally, images contain the relationships of spatial adjacency, spatial direction and spatial distance among multiple objects. This study selects spatial adjacency relationships as an example, mines the features of spatial adjacency, and uses to improve classification. Therefore, this paper (1) presents a modeling method of remote sensing images based on complex networks to express the spatial structure information and make it computable firstly; (2) gives network expression and calculation methods of spectrum, texture and object features in images; (3) discusses a discovery method of spatial adjacency feature based on network. Finally, this paper gives a strategy to improve the classification for high-resolution image by using the spatial adjacency features. Two kinds of data in coastal zones and city areas are used to verify the method. The results show that the proposed approach, which considers the features of spatial adjacencies, improves the classification accuracy by at least 4% compared with the approaches that do not consider spatial adjacencies. Comparing with the results of deep learning method, our method has higher overall accuracy when the numbers of samples are small.

Emergency supplies pre-positioning is crucial to improve the speed of response and mitigate the effects of disasters. According to the requirements of emergency preparedness for both civilian and military operations in China, the paper presents a problem of integrated civilian–military emergency supplies pre-positioning, the aim of which is to maximize both the expected military operational efficiency and the expected civilian operational efficiency while minimizing the overall cost of pre-positioning. To solve the problem, the paper proposes a solution method that uses a multiobjective algorithm to evolve a population of main location solutions and uses another single-objective algorithm to search optimal allocation sub-solutions for each main solution. Among different heuristics, we select water wave optimization (WWO) to design a multiobjective algorithm for the main problem and select biogeography-based optimization (BBO) for the subproblem, and demonstrate that they have competitive performance advantage in comparison with a set of well-known algorithms.

Strain sensor network-based structural health monitoring systems have been used to assess the safety of high-rise buildings. In consideration of life cycle of high-rise buildings, long-term measurement by sensors should be required. However, because of unpredictable problems such as the lack of durability of sensors and data loggers, disruption in communication, and loss of data, long-term strain measurement of major structural members is currently infeasible. For sustainable safety assessment of high-rise buildings, this paper presents a sustainable strain-sensing model that employs an artificial neural network (ANN) to estimate the strain responses of columns depending on the wind-induced behavior of high-rise buildings. The ANN model used in the paper is based on evolutionary learning consists of training in radial basis function neural network (RBFN) and evolving in genetic algorithm. In this evolutionary RBFN (ERBFN). Weights between layers are trained and variables of Gaussian function in the RBFN are evolved to estimate strain responses of the column of the high-rise building structure. A wind tunnel test was performed to produce wind data and strains in column members in a high-rise building model. In the wind tunnel test, a specimen consisting of a core, perimeter columns, and outriggers is used to simulate the conditions of typical high-rise buildings with a slenderness ratio of 5.0. The proposed model is trained and verified by using the wind data such as wind speeds and directions and the corresponding strains measured with fiber optic grating sensors. In addition to estimation of the maximum and minimum values of strains in vertical members in a high-rise building, it is found that the proposed model can build a relationship between the wind data and strain of vertical members.

Molodtsov's soft set theory provides a general mathematical tool for dealing with uncertainty in decision-making. In the past few years, a large number of generalized soft set concepts and approaches to decision-making have been studied by many researchers. This paper considers a new generalization of Molodtsov's soft set concept, named linguistic value soft set, by introducing a linguistic variable to the definition of soft set theory. Then, the information in several soft sets can be saved in one linguistic value soft set. We define basic notions such as the equality of two linguistic value soft sets, and the subsets and complement of a linguistic soft set. Next, we present the binary operations for linguistic value soft set theory. Then, we discuss the properties of linguistic value soft set for the basic notions and binary operations. Further, we define the concept of linguistic value soft matrix and choice value matrix of a linguistic value soft set, and we discuss the operation laws. By using these new definitions of linguistic value soft set theory, we establish a new approach to multiple criteria group decision-making problems with linguistic value information. We present a detailed description of the decision-making problem and the decision steps. Finally, the validity of the proposed decision-making method is tested by a numerical example with the background of an evaluation decision problem for automobiles. The main contribution of this paper is twofold. One is to propose the concept of linguistic value soft set and establish the main results of the theoretical aspect for the new generalization form. Another is to present a new approach to multiple attribute group decision-making based on linguistic value soft set.