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Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we approach a method of clustering binary signature of image in order to create a clustering graph structure for building the content‐based image retrieval. First, the paper presents the segmentation method based on low‐level visual features including colour and texture of image. On the basis of segmented image, the paper creates binary signature to describe location, colour, and shape of interest objects. In order to match similar images, the paper presents a similarity measure between the images based on binary signature. From that, the paper proposes the method of clustering binary signature to quickly query similar images. At the same time, the graph data structure is built using the partition cluster technique and the rules of binary signatures' distribution of images. On the basis of data structure, we propose a graph creation algorithm, a cluster splitting/merging algorithm, and a similarity image retrieval algorithm. To illustrate the proposed theory, we build an image retrieval application and assess the experimental results on the image datasets including COREL (1,000 images), CBIR images (1,344 images), WANG (10,800 images), MSRDI (15,720 images), and ImageCLEF (20,000 images). [ABSTRACT FROM AUTHOR]

One of the useful knowledge representation tools, which can describe the joint probability distribution between some random variables with a graphical model and can be trained by a dataset, is the Bayesian network (BN). A BN is composed of a network structure and a conditional probability distribution table for each node. Discovering an optimal BN structure is an NP‐hard optimization problem that various meta‐heuristic algorithms are applied to solve this problem by researchers. The genetic algorithms, ant colony optimization, evolutionary programming, artificial bee colony, and bacterial foraging optimization are some of the meta‐heuristic methods to solve this problem using a dataset. Most of these methods are applying a scoring metric to generate the best network structure from a set of candidates. A Fixed Structure Learning Automata‐Based (FSLA‐B) algorithm is presented in this paper to solve the structure learning problem of BNs. There is a fixed structure learning automaton for each pair of vertices in the BN's graph structure in the proposed algorithm. The action of this automaton determines the presence and direction of an edge between the vertices. The proposed algorithm performs a guided search procedure using the FSLA and escapes from local optimums. Several datasets are utilised in this paper to evaluate the performance of the proposed algorithm. By performing various experiments, multiple meta‐heuristic algorithms are compared with the introduced new one. The obtained results represented that the proposed algorithm could produce competitive results and find the near‐optimal solution for the BN structure learning problem. [ABSTRACT FROM AUTHOR]

The task of a semantic community query is to obtain a subgraph S based on a given query vertex q (or vertex set) and other query parameters in an attributed graph G such that S belongs to G, contains q and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named SCQ in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, SCQ applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs. [ABSTRACT FROM AUTHOR]

Ensemble methods are among the most effective concept‐drift adaptation techniques due to their high learning performance and flexibility. However, they are computationally expensive and pose a challenge in applications involving high‐speed data streams. In this paper, we present a computationally efficient heterogeneous classifier ensemble entitled OMS‐MAB which uses online model selection for concept‐drift adaptation by posing it as a non‐stationary multi‐armed bandit (MAB) problem. We use a MAB to select a single adaptive learner within the ensemble for learning and prediction while systematically exploring promising alternatives. Each ensemble member is made drift resistant using explicit drift detection and is represented as an arm of the MAB. An exploration factor ϵ controls the trade‐off between predictive performance and computational resource requirements, eliminating the need to continuously train and evaluate all the ensemble members. A rigorous evaluation on 20 benchmark datasets and 9 algorithms indicates that the accuracy of OMS‐MAB is statistically at par with state‐of‐the‐art (SOTA) ensembles. Moreover, it offers a significant reduction in execution time and model size in comparison to several SOTA ensemble methods, making it a promising ensemble for resource constrained stream‐mining problems. [ABSTRACT FROM AUTHOR]

Representation‐based classification (RC) is an effective gauge of data similarity between a single instance and the whole dataset, which extends traditional individual‐wise distance metrics using representation coefficients. These coefficients show remarkable discrimination nature via various regularisation terms, but the interference from potentially uncorrelated objects involved in this single‐to‐global relation can degrade the effectiveness of the coefficients. In order to filter out those unproductive, or even counter‐productive, information from the decision making processes, this paper proposes a local representation‐based classification (LRC) algorithm to improve the classification accuracy or the RC approach. LRC uses a single‐to‐local relation induced by the local representation‐based neighbourhood (LRN) of each object, rather than the single‐to‐global relationship used by RC. Thanks to LRN, a compact and relevant dataset can be formed by selecting the most relevant data instances in the original dataset, to render a robust representation of a query. LRC was applied to multiple publicly available datasets, and the experimental results demonstrate the superiority of the proposed LRC algorithm as evidenced by the higher classification accuracy and more noise‐tolerant capability in reference to alternative RC approaches. Moreover, the sampling ability of LRN is also verified via a comparative study. [ABSTRACT FROM AUTHOR]

Neural architecture search can be used to find convolutional neural architectures that are precise and robust while enjoying enough speed for industrial image processing applications. In this paper, our goal is to achieve optimal convolutional neural networks (CNNs) for multiple‐templates matching for applications such as licence plates detection (LPD). We perform an iterative local neural architecture search for the models with minimum validation error as well as low computational cost from our search space of about 32 billion models. We describe the findings of the experience and discuss the specifications of the final optimal architectures. About 20‐times error reduction and 6‐times computational complexity reduction is achieved over our engineered neural architecture after about 500 neural architecture evaluation (in about 10 h). The typical speed of our final model is comparable to classic template matching algorithms while performing more robust and multiple‐template matching with different scales. [ABSTRACT FROM AUTHOR]

Massively complex systems, such as social networks (Camacho, Panizo-LLedot, Bello-Orgaz, Gonzalez-Pardo, & Cambria, 2020; Lara-Cabrera et al., 2017), renewable energy problems (Twidell & Weir, 2015), or Internet-of-Things problems (Lin et al., 2017), generate massive amounts of data. This special issue welcomed submissions of original papers introducing research results on all the aspects covering the application of CI algorithms to massively complex systems, ranging from concepts and theoretical developments to advanced technologies and innovative applications. GRASP algorithm is also used in the paper by Pérez-Peló, Sánchez-Oro, & Duarte (2020), where the authors tackle the I i -separator problem. [Extracted from the article]

Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%. [ABSTRACT FROM AUTHOR]

The ideal gas molecular movement (IGMM) as a metaheuristic optimization method is a prominent option for solving optimization problems. However, in some complex cases, IGMM may possess premature convergence or get trapped in local optima. Therefore, to tackle these issues, this paper indicates a new modified IGMM algorithm named opposition‐based IGMM, which has been incorporated with opposition based learning (OBL), Cauchy mutation (CM), velocity clamping (VC), and mirror operator (MO) to enhance its performance. OBL, VC, and MO improve the convergence of IGMM, whereas CM assists IGMM to escape local optima. The effect of each strategy, OBL, CM, VC, and MO, on IGMM, is confirmed through 30 low and high‐dimensional benchmarks, including 23 well‐known mathematical problems and CEC2017 as complex test functions and three engineering problems. Analysis results represent that integration IGMM with OBL, CM, VC, and MO has the best performance among other IGMM variants and eventually improved IGMM in exploration, exploitation, accelerating convergence, and local optima avoidance. [ABSTRACT FROM AUTHOR]

Images in video may include both Gaussian noise and geometric rotation. Thus, it is challenging to represent an image sequence in its intrinsically low‐dimensional space in a noise‐robust and rotation‐robust manner. In this paper, we propose a novel‐ordered sparse subspace clustering algorithm based on a p‐norm to achieve an effective clustering of sequential data under heavy noise conditions. We also use the wavelet‐histogram of oriented gradient (HOG) transform in the kernel view to extract both the global features (with the wavelet process) and the local features (with the HOG process) from the image. In addition, we assign different weights to different features to obtain a sparse coefficient matrix that helps to emphasize the global and local correlations in each sample. Similarly, the clustering algorithm based on the p‐norm for sequential images emphasizes the within‐class correlations amongst samples. Therefore, in this paper, we select additional denoising main components under a Laplacian constraint to achieve a better block‐diagonal structure and highlight the independence of different clusters. Extensive experiments performed on various public datasets (including the ordered face dataset, handwritten recognition dataset, video scene segmentation dataset, and object recognition dataset) demonstrate that the proposed method is more resilient to noise and rotation than other representative sparse subspace clustering methods. [ABSTRACT FROM AUTHOR]

In the paper, we propose two gene expression programming (GEP)‐based ensemble classifiers with different drift detection mechanisms. In the related work section, we briefly review GEP as a classification tool, incremental classifiers, and concept drift detectors. Next, the structure of our two‐level GEP ensemble with metagenes is described. Further on, two integrated classifiers with drift detection algorithm and Wilcoxon rank sum test drift detector are proposed. The approach is validated in the computational experiment in which several real‐life and artificial datasets with concept drift have been used. Experiment confirmed that the proposed approach can be competitive to existing solutions. In the conclusion section, we briefly outline directions for future research. [ABSTRACT FROM AUTHOR]

Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM. [ABSTRACT FROM AUTHOR]

Multi‐objective optimization problems frequently appear in many diverse research areas and application domains. Metaheuristics, as efficient techniques to solve them, need to be easily accessible to users with different expertise and programming skills. In this context, metaheuristic optimization frameworks are helpful, as they provide popular algorithms, customizable components and additional facilities to conduct experiments. Due to the broad range of available tools, this paper presents a systematic evaluation and experimental comparison of 10 frameworks, covering from multi‐purpose, consolidated tools to recent libraries specifically designed for multi‐objective optimization. The evaluation is organized around seven characteristics: search components and techniques, configuration, execution, utilities, external support and community, software implementation and performance. An analysis of code metrics and a series of experiments serves to assess the last two features. Lesson learned and open issues are also discussed as part of the comparative study. The outcomes of the evaluation process reveal a contrasted support to recent advances in multi‐objective optimization, with a lack of novel algorithms and variety of metaheuristics other than evolutionary algorithms. The experimental comparison also reports significant differences in terms of both execution time and memory usage under demanding configurations. [ABSTRACT FROM AUTHOR]

Chronic kidney disease is a global health issue that affects millions of people worldwide and causes significant social, economic, and medical issues. Several automated detection systems can diagnose chronic kidney disease. This paper proposes the recursive random forest feature selection (RFFS) based ensemble learning algorithm to diagnose chronic kidney diseases (CKD). In the decision point, decision tree‐based classifiers are used. The accuracy and kappa scores are used to determine the classification results. According to the results of the proposed algorithm's performance analyses, the ensemble learning classifiers outperform other classifiers for classifying CKD. The proposed RFFS algorithm achieves 6%–40% improved prediction accuracy using the feature selection algorithm. Further, it attains 15%–39% of reduced mean square error. The performance metrics, precision, sensitivity, specificity, f1 score, and Jaccard scores, have been analysed and show greater results for the RFFS algorithm. Thus, the proposed RFE‐GB algorithm results prove it as a prominent model for CKD estimation and treatment. [ABSTRACT FROM AUTHOR]

As the promotion of technologies and applications of Big Data, the research of business process management (BPM) has gradually deepened to consider the impacts and challenges of big business data on existing BPM technologies. Recently, parallel business process mining (e.g. discovering business models from business visual data, integrating runtime business data with interactive business process monitoring visualisation systems and summarising and visualising historical business data for further analysis, etc.) and multi‐perspective business data analytics (e.g. pattern detecting, decision‐making and process behaviour predicting, etc.) have been intensively studied considering the steep increase in business data size and type. However, comprehensive and in‐depth testing is needed to ensure their quality. Testing based solely on existing business processes and their system logs is far from sufficient. Large‐scale randomly generated models and corresponding complete logs should be used in testing. To test parallel algorithms for discovering process models, different log completeness and generation algorithms were proposed. However, they suffer from either state space explosion or non‐full‐covering task dependencies problem. Besides, most existing generation algorithms rely on random executing strategy, which leads to low and unstable efficiency. In this paper, we propose a novel log completeness type, that is, #TAR completeness, as well as its generation algorithm. The experimental results based on a series of randomly generated process models show that the #TAR complete logs outperform the state‐of‐the‐art ones with lower capacity, fuller dependencies covering and higher generating efficiency. [ABSTRACT FROM AUTHOR]

Traditional cross‐efficiency evaluation models have ignored the problem that large differences may exist among cross‐efficiencies, which may make decision making units (DMUs) unwilling to accept cross‐efficiency evaluation results. Aimed at solving this problem, this paper proposes an altruism cross‐efficiency model based on the conservative point of view. Compared with other cross‐efficiency evaluation models, the proposed model mainly exhibits the following advantages. First, the proposed model no longer guarantees the DMU's self‐evaluation efficiency, allowing the self‐evaluation efficiency to adaptively change. Therefore, the peer‐evaluation process is flexible and adaptable to the actual situation. Second, the proposed model abandons the traditional max‐average secondary objective function and proposes to use a max–min objective function. Thus, the proposed model can maximize the peer‐efficiency of the worst performing peer‐DMU, achieving the effects of reducing the gaps among cross‐efficiencies. Third, the cross‐efficiency evaluation model is based on the conservative point of view, which helps the DMU to distinguish potential competitors among peer‐DMUs. Lastly, to solve the non‐linear model proposed in this paper, the algorithm is designed to describe how to solve the model. The case of a flexible manufacturing system is used to show the appropriateness of the suggested model and algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces the electromagnetic clustering algorithm (ELMC), an enhanced variant of electromagnetic field optimization (EFO), for clustering. The motivation behind ELMC is to overcome the shortcomings of traditional k‐means clustering algorithm. The performance of k‐means primarily depends upon the initial choice of centroids, which can lead the algorithm towards an undesirable local optimum, if chosen incorrectly or inefficiently. The ELMC utilizes the attraction–repulsion concept of the EFO algorithm to maintain the diversity of the population, making it less vulnerable towards the initial choice of centroids. The performance of ELMC is validated on a set of benchmark problems, and the results are compared with other state‐of‐the‐art algorithms. Numerical and graphical results indicate the competence of the proposed ELMC algorithm. [ABSTRACT FROM AUTHOR]

Multiobjective optimization problems (MOPs) are very popular these days and have gained continuous research attention. These problems involve a minimum of two conflicting objectives that need to be optimized simultaneously. Multiobjective evolutionary algorithms (MOEAs) optimize these objectives by targeting solutions on the Pareto front. To judge the performance of different MOEAs, the convergence rate and population diversity are used. In order to create a good algorithm with a high convergence rate and diversity, the k‐means clustering algorithm is combined with a real parameter version of the improved environmental adaptation method (IEAM‐RP) in this paper. This is done as IEAM‐RP converges very fast and the k‐means clustering algorithm is used to provide diversity in decision search space. To check the performance of the proposed algorithm, it has been compared with nine state‐of‐the‐art multiobjective algorithms (MOPSOtridist, MOEA/D‐STM, MOEA/D, MOPSO, NSGA‐II, MOPSOses, MOGOA, MODA, MOALO) using standard multiobjective benchmark functions. Further, the performance of the proposed algorithm is compared against IBEA and SMS‐EMOA using walking fish group (WFG) test suit. The experimental results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Software‐based expert systems typically use an iterative algorithm for rule‐fact network processing. However, other approaches to expert system implementation also exist. This paper proposes simplified quantum principle expert systems and compares the computational efficiency and comparative efficacy of four types of expert systems: software‐based Boolean expert systems, electronic (gate‐based) expert systems, software‐based fractional value (SBFV) expert systems and simplified quantum principal expert systems (SQPES). For two expert system types, software‐based Boolean and SBFV, back chaining and mixed changing techniques, respectively, are also evaluated. The performance of these systems approaches is compared quantitatively, and the efficacy of the different systems types are compared both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is one of the efficient recent meta‐heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA‐VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA‐VNS is able to provide very competitive results and outperforms the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper a multi‐start iterated local search (MS‐ILS) algorithm is presented as a new and effective approach to solve the multi‐mode resource‐constrained project scheduling problem (MRCPSP). The MRCPSP is a well‐known project scheduling NP‐Hard optimization problem, in which there is a trade‐off between the duration of each project activity and the amount of resources they require to be completed. The proposed algorithm generates an initial solution, performs a local search to obtain a local optimum, subsequently, for a certain number of iterations, makes a perturbation to that local optimum and performs a new local search on the perturbed solution. This whole process then restarts with a different initial solution for a certain number of restarts. The algorithm was tested on benchmark instances of projects with 30, 50 and 100 activities from well‐known libraries. The obtained results were compared to recent benchmark results from the literature. The proposed algorithm outperforms other solution methods found in related literature for the largest tested instances (100 activities), while for smaller instances it shows to be quite competitive, in terms of the average deviation against known lower bounds. [ABSTRACT FROM AUTHOR]

The clustering of big data streams has become a challenging task due to time and space constraints of the hardware and decreasing accuracy when the dimensionality of input data grows in time. In this paper, fuzzy growing neural gas is introduced, an online fuzzy approach for clustering data streams based on the growing neural gas algorithm, by adopting more restrictive criteria for selecting the winner nodes in the topological graph constructed at each iteration of the algorithm. The algorithm is tested on public datasets, and the results show improvements over existing clustering methods. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have successfully been applied to classification problems including credit scoring. The tuning of the structural parameters of the NNs has a direct impact on their accuracy. In this paper, a hybrid approach based on the genetic algorithm (GA) is proposed to adjust the structural parameters of a classifier NN to achieve high accuracy. Two well‐known credit scoring datasets—Australian and German datasets—are used to test the proposed approach. The results indicate that the proposed hybrid approach is able to successfully tune the structural parameters, while the accuracy of classification is enhanced and its complexity dramatically diminished in comparison with other existing approaches. The performance of the proposed algorithm has been investigated through statistical analysis The best‐known solutions achieved by the proposed approach have an accuracy equal to 97.78% and 87.1% for Australian and German datasets, respectively. The results indicate 2.68% and 0.1% improvement in comparison with the best results reported in the literature, respectively. This improvement is important for real cases in which millions of loans are allocated using credit scoring approaches. [ABSTRACT FROM AUTHOR]

Multi‐label classification algorithms deal with classification problems where a single datapoint can be classified (or labelled) with more than one class (or label) at the same time. Early multi‐label approaches like binary relevance consider each label individually and train individual binary classifier models for each label. State‐of‐the‐art algorithms like RAkEL, classifier chains, calibrated label ranking, IBLR‐ML+, and BPMLL also consider the associations between labels for improved performance. Like most machine learning algorithms, however, these approaches require careful hyper‐parameter tuning, a computationally expensive optimisation problem. There is a scarcity of multi‐label classification algorithms that require minimal hyper‐parameter tuning. This paper addresses this gap in the literature by proposing CascadeML, a multi‐label classification method based on the existing cascaded neural network architecture, which also takes label associations into consideration. CascadeML grows a neural network architecture incrementally (deep as well as wide) in a two‐phase process as it learns network weights using an adaptive first‐order gradient descent algorithm. This omits the requirement of preselecting the number of hidden layers, nodes, activation functions, and learning rate. The performance of the CascadeML algorithm was evaluated using 13 multi‐label datasets and compared with nine existing multi‐label algorithms. The results show that CascadeML achieved the best average rank over the datasets, performed better than BPMLL (one of the earliest well known multi‐label specific neural network algorithms), and was similar to the state‐of‐the‐art classifier chains and RAkEL algorithms. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this paper is to introduce a novel dynamic multi‐objective optimization algorithm. First, after detecting the environmental changes, Borda count ranking method is applied to population in order to assign the Borda score to each individual, and then the lowest score individuals are removed from population and replaced with new created solutions. Furthermore, fuzzy adaptive multi‐objective cat swarm optimization algorithm is used to estimate the Pareto‐optimal front in which its parameters are tuned to new environment by Mamdani fuzzy rules when a change occurs. Performance of the proposed algorithm is tested on dynamic multi‐objective benchmarks and is compared with recent achievements. The simulations show the quite satisfactory results and higher performance of the proposed method in comparison with traditional approaches. [ABSTRACT FROM AUTHOR]

Many real‐world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class‐imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost‐sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error (GME) and Class‐Weighted Error (CWE), as well as new graphical‐based measures, namely RECTPR, RECTNR, RECG − Mean and RECCWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events. [ABSTRACT FROM AUTHOR]

In this paper, a new solution method is implemented to solve a bi‐objective variant of the vehicle routing problem that appears in industry and environmental enterprises. The solution involves designing a set of routes for each day in a period, in which the service frequency is a decision variable. The proposed algorithm, a muti‐start multi‐objective local search algorithm (MSMLS), minimizes total emissions produced by all vehicles and maximizes the service quality measured as the number of times that a customer is visited by a vehicle in order to be served. The MSMLS is a neighbourhood‐based metaheuristic that obtains high‐quality solutions and that is capable of achieving better performance than other competitive algorithms. Furthermore, the proposed algorithm is able to perform rapid movements thanks to the easy representation of the solutions. [ABSTRACT FROM AUTHOR]

This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark. [ABSTRACT FROM AUTHOR]

Due to the technological developments around the world, the amount of information that researchers work on increases continuously. This information density contains incomplete and uncertain data that cannot be fully expressed with crisp numbers. Fuzzy sets, intuitionistic fuzzy sets, and neutrosophic sets are useful tools to manage such information, but these concepts use a symmetrical and uniform scale to express data, whereas real‐life problems contain nonsymmetrical and non‐uniform information. Intuitionistic multiplicative sets (IMSs) are effective tools for dealing with these real‐life problems. However, IMSs cannot handle real‐life problems completely because indeterminate information depends on membership and non‐membership information of IMSs, which is a restriction for decision makers and also for decision problems. To overcome this limitation, this paper generalizes the IMSs by using simplified neutrosophic set and introduces a novel approach that is called simplified neutrosophic multiplicative sets (SNMSs). Firstly, we define SNMS, show their set‐based operations, and then give a description of simplified neutrosophic multiplicative numbers (SNMNs). Based on SNMNs, we develop two simplified neutrosophic multiplicative aggregation operators on SNMNs that are called simplified neutrosophic multiplicative weighted arithmetic average operator and simplified neutrosophic multiplicative weighted geometric average operator. Furthermore, we define some simplified neutrosophic multiplicative distance measures. Finally, using a model based on water‐filling algorithm for determining criteria weights, we give a numerical example to demonstrate the effectiveness of the introduced concept with the proposed simplified neutrosophic multiplicative simplified neutrosophic multiplicative‐TODIM method. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a field situated at the intersection of natural language processing and information retrieval. Its main objective is to automatically produce a condensed representative form of documents. This paper presents ArA*summarizer, an automatic system for Arabic single document summarization. The system is based on an unsupervised hybrid approach that combines statistical, cluster‐based, and graph‐based techniques. The main idea is to divide text into subtopics then select the most relevant sentences in the most relevant subtopics. The selection process is done by an A* algorithm executed on a graph representing the different lexical–semantic relationships between sentences. Experimentation is conducted on Essex Arabic summaries corpus and using recall‐oriented understudy for gisting evaluation, automatic summarization engineering, merged model graphs, and n‐gram graph powered evaluation via regression evaluation metrics. The evaluation results showed the good performance of our system compared with existing works. [ABSTRACT FROM AUTHOR]

This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems. [ABSTRACT FROM AUTHOR]

The computing-intensive data mining (DM) process calls for the support of a heterogeneous computing system, which consists of multiple computers with different configurations connected by a high-speed large-area network for increased computational power and resources. The DM process can be described as a multi-phase pipeline process, and in each phase there could be many optional methods. This makes the workflow for DM very complex and it can be modeled only by a directed acyclic graph (DAG). A heterogeneous computing system needs an effective and efficient scheduling framework, which orchestrates all the computing hardware to perform multiple competitive DM workflows. Motivated by the need for a practical solution of the scheduling problem for the DM workflow, this paper proposes a dynamic DAG scheduling algorithm according to the characteristics of an execution time estimation model for DM jobs. Based on an approximate estimation of job execution time, this algorithm first maps DM jobs to machines in a decentralized and diligent (defined in this paper) manner. Then the performance of this initial mapping can be improved through job migrations when necessary. The scheduling heuristic used considers the factors of both the minimal completion time criterion and the critical path in a DAG. We implement this system in an established multi-agent system environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. The system evaluation and its usage in oil well logging analysis are also discussed. [ABSTRACT FROM AUTHOR]

The paper presents the pheromone-Q-learning (Phe-Q) algorithm, a variation of Q-learning. The technique was developed to allow agents to communicate and jointly learn to solve a problem. Phe-Q learning combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents. The Phe-Q update equation includes a belief factor that reflects the confidence an agent has in the pheromone (the communication medium) deposited in the environment by other agents. With the Phe-Q update equation, the speed of convergence towards an optimal solution depends on a number of parameters including the number of agents solving a problem, the amount of pheromone deposit, the diffusion into neighbouring cells and the evaporation rate. The main objective of this paper is to describe and evaluate the performance of the Phe-Q algorithm. The paper demonstrates the improved performance of cooperating Phe-Q agents over non-cooperating agents. The paper also shows how Phe-Q learning can be improved by optimizing all the parameters that control the use of the synthetic pheromone. [ABSTRACT FROM AUTHOR]

As a core area in data mining, frequent pattern (or itemset) mining has been studied for a long time. Weighted frequent pattern mining prunes unimportant patterns and maximal frequent pattern mining discovers compact frequent patterns. These approaches contribute to improving mining performance by reducing the search space. However, we need to consider both the downward closure property and patterns' subset checking process when integrating these different methods in order to prevent unintended pattern losses. Moreover, it is also essential to extract valid patterns with faster runtime and less memory consumption. For this reason, in this paper, we propose more efficient maximal weighted frequent pattern (MWFP) mining approaches based on tree and array structures. We describe how to handle these problems more efficiently, maintaining the correctness of our method. We develop two types of maximal weighted frequent mining algorithms based on weight ascending order and support descending order and compare these two algorithms to conclude which is more suitable for MWFP mining. In addition, comprehensive tests in this paper show that our algorithms are more efficient and scalable than state-of-the-art algorithms, and they also have the correctness of the MWFP mining in terms of their pattern generation results. [ABSTRACT FROM AUTHOR]

Two previously proposed heuristic algorithms for solving penalized regression‐based clustering model (PRClust) are (a) an algorithm that combines the difference‐of‐convex programming with a coordinate‐wise descent (DC‐CD) algorithm and (b) an algorithm that combines DC with the alternating direction method of multipliers (DC‐ADMM). In this paper, a faster method is proposed for solving PRClust. DC‐CD uses p × n × (n − 1)/2 slack variables to solve PRClust, where n is the number of data and p is the number of their features. In each iteration of DC‐CD, these slack variable and cluster centres are updated using a second‐order cone programming (SOCP). DC‐ADMM uses p × n × (n − 1) slack variables. In each iteration of DC‐ADMM, these slack variables and cluster centres are updated using ADMM. In this paper, PRClust is reformulated into an equivalent model to be solved using alternating optimization. Our proposed algorithm needs only n × (n − 1)/2 slack variables, which is much less than that of DC‐CD and DC‐ADMM and updates them analytically using a simple equation in each iteration of the algorithm. Our proposed algorithm updates only cluster centres using an SOCP. Therefore, our proposed SOCP is much smaller than that of DC‐CD, which is used to update both cluster centres and slack variables. Experimental results on real datasets confirm that our proposed method is faster and much faster than DC‐ADMM and DC‐CD, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new approach to multicriteria decision analysis. They were named as processing technique of ratings for ranking of alternatives (PROTERRA). The consecutive steps of the process include normalization of the ratings, calculating of the ratings ratios within each pair of the alternatives, creating a component matrix for each of the criteria, and creating a total matrix that is the sum of the weighted component matrices. Aggregated ratings of individual alternatives result from the sums of all the values of the corresponding rows and columns of the total matrix elements. A practical application of the PROTERRA has been illustrated with an example of a decision problem concerning the choice of a shopping centre location. The analysis results of the PROTERRA are compared with other results obtained using the most popular methods, that is, analytic hierarchy process, preference ranking organization method for enrichment of evaluations, and technique for order performance by similarity to ideal solution. In order to assess the stability of the ranking order obtained by the PROTERRA algorithm, the influence of weights variations on the final ranking results were tested. The sensitivity analysis was also carried out for the other methods, that is, preference ranking organization method for enrichment of evaluations, analytic hierarchy process, and technique for order performance by similarity to ideal solution. On this basis, a stability of rankings resulting from all analysed methods were compared. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we review the Wang–Mendel algorithm for the induction of fuzzy IF‐THEN rules in the context of classification problems. A general fuzzy inference architecture for classification is proposed with the aim of studying the influence of alternative configurations of the learning model. Specifically, we analyse the effects of changing the aggregation strategy, and we explore the use of different rule definitions, including or not the possibility to assign weighting factors to the generated rules. We test different rule weighting heuristics at this respect. The notion of rule conflict introduced in earlier versions of the algorithm is also reviewed in the context of the various resulting configurations of the fuzzy inference engine. A generalized version of the algorithm therefore results, bringing more flexibility to the configuration of the fuzzy inference engine, and improving the performance for certain problems. The main objective is to complement the results of previous approaches by offering a comprehensive overview of this popular algorithm for fuzzy rule induction in the context of classification problems. Several well‐known machine learning classification benchmarks are analysed and compared looking for the best possible model configuration. [ABSTRACT FROM AUTHOR]

Abstract: This work addresses the problem of denoising image sequences through an approach that makes use of spatio‐temporal cellular automata‐based filtering. The algorithm is called st‐CAF and one of its key aspects is that the resulting cellular automata contemplate a spatio‐temporal neighbourhood when processing each pixel of the sequence. Additionally, the way the rule sets for the cellular automata are obtained, through evolutionary means, is also relevant, as it allows a good adaptation to any type of image and noise through the appropriate training set. This results in a great advantage over more traditional single frame denoising techniques presented in the literature or even over their adaptation to sequences. A fact that is made relevant in this paper through the application of the algorithm to different types of noisy images and its comparison to other techniques. [ABSTRACT FROM AUTHOR]

To explore the application of neural network algorithm model in lung cancer imaging, and provide reference for the application and development of artificial neural network (ANN) algorithm model in lung cancer medical mirroring, so as to promote the development of ANN in this field. Meanwhile, it is hoped that the application of neural network algorithms in medical imaging can improve the survival rate and cure rate of lung cancer. In this study, an ANN algorithm model was selected to establish a lung cancer recognition model. After determining the lung cancer lesion area, the image segmentation algorithm was used to separately display the lung cancer lesion area, and a comparison experiment was designed to verify the accuracy of the model. ANNs were used to identify lung cancer, which can be concluded that the accuracy is 94.6%, the sensitivity is 95.7%, and the specificity is 93.5%. By combining image retrieval methods with lung cancer image segmentation algorithms, the lesion area of lung cancer can be clearly displayed. Therefore, the lung cancer image segmentation algorithm based on the neural network model has good recognition performance. This research can provide reference for the application of neural network algorithm model in the field of cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets. [ABSTRACT FROM AUTHOR]

The article presents an introduction to articles published within this edition of the journal focusing on medical decision support systems and automated diagnostic systems. These include "Augmentation of a nearest neighbour clustering algorithm with a partial supervision strategy for biomedical data classification," by Salem et al, "Comparison of different classifer algorithms for diagnosing macular and optic nerve diseases," by Polat et al, and "Electromyography signal analysis using wavelet tranform and higher order statistics to determine muscle contraction," by Hussain et al.

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming. [ABSTRACT FROM AUTHOR]

This paper presents a state-of-the-art review of recent control algorithms used primarily for vibration control of smart structures and machines. Control algorithms can be divided into open loop, feedback, feedforward, and combined feedback and feedforward control. Solutions proposed can be subdivided into (a) classical control, (b) optimal control, (c) robust control, (d) intelligent control, and (e) self-sustaining solutions and energy harvesting used in centralized and decentralized configurations. The most recent research on novel autonomous control systems intersects distributed control theory, agent-based modeling, bioinspired computational learning, and optimization algorithms. [ABSTRACT FROM AUTHOR]

Plant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private dataset of 236 images. Features are extracted from the images using histogram of oriented gradients (descriptor). The concepts of artificial plant optimization are then applied to study the features of healthy leaves using IAPO. A machine learning algorithm has been created to make the model adaptive with varied datasets. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. The proposed IAPO is also compared with other classification algorithms including k‐nearest neighbours, support vector machine, random forest and convolution neural network that show accuracies of 78.24%, 83.48%, 87.83%, and 91.26%, respectively, whereas IAPO shows quite accurate results in classification of leaves with an accuracy of 97.45% on training set and 95.0% accuracy on test set. [ABSTRACT FROM AUTHOR]

Because distributed manufacturing technology is the foundation of modernized production and traditional heuristic methods exhibit problems of high complexity and low efficiency, this paper designs a scheduling algorithm based on the singular value decomposition heuristic (SVDH) method. The algorithm uses the device distribution and the transportation relationship between devices in a distributed manufacturing system. The algorithm takes the sequence relationship between tasks and the distance between devices as the implicit relationship between the task and the device. The algorithm makes use of the implicit relationship to amend the processing time matrix of the task and corrects the processing time matrix that contains the transportation relationship. Singular value decomposition principal component analysis is performed on the corrected processing time to find the most suitable processing device for each process, and an initial solution matrix is established. The heuristic solution is used to optimize the initial solution to find the optimal scheduling result based on the initial solution matrix. The establishment of the initial solution can effectively reduce the computational complexity of the heuristic solution, realize a parallelizing solution, and improve the efficiency of the heuristic solutions. In addition, the SVDH scheduling result has a lower transfer time between devices due to the consideration of the topology of tasks and devices, that is, the transit time. In this paper, the experiments are conducted on the heuristic performance, scheduling results, and transportation time. The experimental results show the advantages of SVDH over general heuristic algorithms in terms of efficiency and transit time. [ABSTRACT FROM AUTHOR]

Ant‐based clustering algorithms are inspired by the behaviour of real ants. In most ant‐based clustering algorithms, each ant lacks global visibility and only uses local search to find the best place on a grid to drop its data item. This paper presents a new clustering method called learning automata‐based clustering algorithm using learning automata (LA) and ant swarm intelligence. In this paper, the problem of finding the best location of grid for dropping a data item is solved using LA. We introduced a new drop operation using the global search capability of LA that can increase the quality of clustering. In the proposed method, each ant is equipped with an LA and a two‐dimensional grid is partitioned into a number of clusters. To drop a data item, the LA of each ant will be responsible for finding one of the best clusters on a grid unlike other methods that use the random search of a grid. To evaluate the results, some clustering evaluation criteria, such as the number of obtained clusters, f‐measure, Rand index, intracluster variance, Dunn index, error rate, and time cost were employed. Additionally, the proposed algorithms were compared with k‐means, Lumer and Faieta, ant clustering algorithm, modified version of the ant‐clustering algorithm, Chaotic Ant clustering (CAC), chaotic ant clustering algorithm, adaptive ant‐based clustering algorithm, adaptive artificial ant clustering algorithm, and ant clustering algorithm with information theoretic learning algorithms on both real and synthetic datasets. Experimental results show that the proposed method can increase the efficiency of the real dataset by 11.13% and that of the synthetic dataset by 16.11%. It also reduces the number of neighbouring function calls by 16% for the real dataset and by 28% for the synthetic dataset. [ABSTRACT FROM AUTHOR]

Detecting communities is of great importance in social network analysis. However it is an issue that has not yet been satisfactorily solved, despite the efforts made by interdisciplinary research communities over the past few years, because of the nature of complexity in deciding how community structures should be recognized. In this paper we propose an approach based on cooperative game theory for community detection in social networks. We regard individuals as players, and regard communities as coalitions formed by players, and model community detection problem as the formation and optimization of coalitions. Furthermore, we define coalition profile for players to indicate coalitions that players joined, the order of a coalition profile is defined as the number of coalitions in a coalition profile, and we introduce a utility function to measure preference of coalition profiles. Accordingly, we propose an algorithm to detect a coalition profile with maximal utility function values. We have implemented the algorithms developed in this study and experimental results demonstrate the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

In many data mining tasks, the goal is to classify entities into a set of pre-defined groups (classes). A second and equally important goal is the interpretation, i.e. understanding the nature of the population aggregated in each class. These tasks are rendered even more complex when there is no a-priori information regarding the right classification. The current paper is based on two concepts: (1) Bounded-Rationality theory which implements an S-shaped function that represents human logic as a saliency measure to determine the substantial features that characterize each potential group and (2) Classification by clustering (CBC) that applies Decision Tree-like classification in unsupervised clustering problems, where neither an a-priori classification nor target-attributes are known in advance. In the context of these two concepts, the current research contributes: (1) by expanding the saliency measure to all possible types of variables (nominal as well as numerical), (2) by evaluating, using five datasets, a composite model that combines the CBC method and the saliency concept. The findings show that by using clustering algorithms for classification tasks (CBC method) the results are as accurate as those obtained by conventional Decision Trees, but with a better saliency factor. [ABSTRACT FROM AUTHOR]