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Highlights • Sampling-based algorithms are commonly used in motion planning problems. • The RRT* algorithm incrementally builds a tree of motion to find a solution. • Taking a shortcut to the ancestry increases the convergence rate to the optimal. • Combination with sampling strategies further improves the performance. Abstract The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which provides asymptotic optimality. This paper proposes Quick-RRT* (Q-RRT*), a modified RRT* algorithm that generates a better initial solution and converges to the optimal faster than RRT*. Q-RRT* enlarges the set of possible parent vertices by considering not only a set of vertices contained in a hypersphere, as in RRT*, but also their ancestry up to a user-defined parameter, thus, resulting in paths with less cost than those of RRT*. It also applies a similar technique to the rewiring procedure resulting in acceleration of the tendency that near vertices share common parents. Since the algorithm proposed in this paper is a tree extending algorithm, it can be combined with other sampling strategies and graph-pruning algorithms. The effectiveness of Q-RRT* is demonstrated by comparing the algorithm with existing algorithms through numerical simulations. It is also verified that the performance can be further enhanced when combined with other sampling strategies. [ABSTRACT FROM AUTHOR]

Highlights • Differentiating between benign and malignant tissues based on image intensities. • Solve the topological problems, the white and the gray areas in patient's image. • Formulate the theoretical data optimization problem by applying curve evolution. • The proposed algorithm saves the operation time in addition to improving accuracy. Abstract Identification of tumor in medical images is an important task which leads to increased survival chances. In many cases, tumor diagnosis in medical images is very complicated since the pixel intensities between benign and malignant tissues may be very close. The identification problem is defined as the intensification of the mutual data between the image pixel intensities and the desired areas (tumors in this paper), depending on a limitation of the overall size of the desired area borders. This paper introduces a new approach for image segmentation in differentiating between benign and malignant tissues through a nonparametric approach. The proposed approach assumes that the probability densities correlated with pixel intensities in the image for each region are not already known. The image intensity is used instead of using the probability. A nonparametric density estimation is proposed and formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm is applied to 100 sets of different real data in patient images with benign or/and malignant tissues. The experimental results show that the proposed algorithm proved effective in tumor identification with an acceptable accuracy of 93.1%, especially in-patient images diagnosed with carcinoma at an early stage. Graphical abstract Image, graphical abstract In this technique, m level-set functions are used to segment up to 2m areas, and the equation of the resulting evolution curve is proving to be an actual generalization of non-parametric area competition as shown in Fig. 4. [ABSTRACT FROM AUTHOR]

Highlights • VNS-triggered memory extraction improves method performance up to 5.2%. • Incorporating real life aspects could improve daily total routing cost up to 8%. • Vehicle capacity and working time utilization could be improved by up to 12.5%. • Real life aspects could improve fleet composition at no extra cost. • Interesting managerial insights regarding real life routing trade-offs. Abstract In this paper we consider a real life Vehicle Routing Problem inspired by the gas delivery industry in the United Kingdom. The problem is characterized by heterogeneous vehicle fleet, demand-dependent service times, maximum allowable overtime and a special light load requirement. A mathematical formulation of the problem is developed and optimal solutions for small sized instances are found. A new learning-based Population Variable Neighbourhood Search algorithm is designed to address this real life logistic problem. To the best of our knowledge Adaptive Memory has not been hybridized with a classical iterative memoryless method. In this paper we devise and analyse empirically a new and effective hybridization search that considers both memory extraction and exploitation. In terms of practical implications, we show that on a daily basis up to 8% cost savings on average can be achieved when overtime and light load requirements are considered in the decision making process. Moreover, accommodating for allowable overtime has shown to yield 12% better average utilization of the driver's working hours and 12.5% better average utilization of the vehicle load, without a significant increase in running costs. We also further discuss some managerial insights and trade-offs. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a recent model in which a group of mobile users uses their smart devices such as smartphones or wearable devices to cooperatively perform a large-scale sensing task. In this paper, a novel model will be introduced for recognizing/classifying human activities that were collected from sensor units on the chest, legs, and arms. The proposed model employed the k -Nearest Neighbor ( k -NN) classifier which is one of the most common classifiers. k -NN has only one parameter, k , to determine the number of selected nearest neighbors to the test or unknown samples for predicting the class labels of the unknown samples. Searching for the value of k which has a great impact on the classification performance is difficult especially with high dimensional data. This paper employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal value of the k parameter in the k -NN Classifier. This paper shows first experimentally how the PSO in the proposed algorithm searches for the optimal value of k parameter to reduce the misclassification rate of the k -NN classifier. Then, in the second experiment, ten standard datasets are utilized to benchmark the performance of the proposed algorithm. For verification, the results of the PSO- k NN algorithm are compared with two well-known algorithms: Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed PSO- k NN algorithm was employed for recognizing human activities. The experimental results proved that the PSO- k NN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k -NN classifier. The results also demonstrated lower error rates compared when GA and ABCO algorithms. [ABSTRACT FROM AUTHOR]

Order picking is the most labour and capital intensive warehousing operation whose primary development field is routing optimisation due to its time consuming nature. The Order Picking Routing Problem is a special case of the vehicle routing problem with loading constraints, when the operator visits picking positions and collects items to build transport unit. Where the stacking and stability challenges are relevant during the picking of ordered items and exact routing algorithms are not available, the order picking operators have huge challenges to sequence the order picking list. They should take into consideration several factors by themselves, such as product properties, order picking list characteristics, and order picking system properties. The goal of the proposed research is to support the order picking operators in order to make more objective decisions in decreasing the order picking lead time, building stable transport units, and avoiding product damages, when industrially relevant, but rarely discussed, order picking sequencing based on stacking property is necessary. The paper defines the Order Picking Routing Problem based on Pallet Loading Feature (OPRP-PLF) and presents Bacterial Memetic Algorithm (BMA) based solutions for it, which is compared to Simulated Annealing (SA) algorithms. BMA has already been applied for Travelling Salesman Problem (TSP) but never used for the defined OPRP-PLF. The paper describes several BMA operators, most of them have an alternative which can be completed with SA based decisions. Using the BMA operators with SA methodology is a novelty of the proposed algorithms, which might support a quicker approximation to the global optimum. The possible combination of BMA operators will be evaluated with shorter and longer order picking lists and compared to SA algorithms on the same basis. The simulation results highlight, that allowing unit load reconstruction could decrease the order picking lead time and the developed BMA algorithms are more effective for OPRP-PLF than the SA algorithms. The paper concludes that the SA combined BMA operators are more effective than the SA-less operators in the case of shorter (less than about 20 records) order picking lists. While the shorter lists are the most commonly occurring order picking lists of warehouses, the SA combined BMA operators can increase the effectiveness of the OPRP-PLF optimisation. [ABSTRACT FROM AUTHOR]

This paper presents original solutions from the field of intelligent expert systems for use in behavioral biometrics. They combine possibilities offered by biometric methods with the theory of fuzzy sets and the theory of population-based algorithms. Behavioral biometrics is concerned with learned behaviors such as a way of signing, movement, speaking, etc. However, these attributes trend to change over time. This is particularly important in a variety of fields including identity authentication. In this paper we present a new approach to the analysis of changes in behavioral characteristics. The purpose of the proposed approach is to predict values of features describing the so-called dynamic signature. It is the signature represented by waveforms describing a number of features including pen pressure and velocity. The proposed approach can be particularly useful in cases in which the interval between signature acquisition sessions is long. Our approach was tested with the use of the dynamic signature database called ATVS-SLT DB. It seems that the solutions proposed in this paper offer a new and interesting look at the solution of the problem of variability (aging) of biometric features. [ABSTRACT FROM AUTHOR]

This paper presents an evolving cloud-based algorithm for the recognition of drivers’ actions. The general idea is to detect different manoeuvres by processing the standard signals that are usually measured in a car, such as the speed, the revolutions, the angle of the steering wheel, the position of the pedals, and others, without additional intelligent sensors. The primary goal of this investigation is to propose a concept that can be used to recognise various driver actions. All experiments are performed on a realistic car simulator. The data acquired from the simulator are pre-processed and then used in the evolving cloud-based algorithm to detect the basic elementary actions, which are then combined in a prescribed sequence to create tasks. Finally, the sequences of different tasks form the most complex action, which is called a manoeuvre. As shown in this paper, the evolving cloud-based algorithm can be very efficiently used to recognise the complex driver’s action from raw signals obtained by typical car sensors. [ABSTRACT FROM AUTHOR]

Recently much research has been conducted towards finding fast and accurate pattern classifiers applied to Structural Health Monitoring (SHM) systems. In this way, researchers have proposed new methods based on Fuzzy ARTMAP Network (FAN) in order to enhance the success rate for structural damage classification applied to SHM applications. Conversely, the performance of methods based on FAN is very dependent of its setup parameters. In several SHM approaches in the literature, authors have proposed selecting those parameters by using several attempts (empirical and manual selection) and keeping them fixed for all cases in the resulting analysis, hampering the success rate of the neural network. To overcoming that, this paper introduces a new strategy for enhancement of structural damage identification focusing on supervised learning of FAN by using Particle Swarm Optimization (PSO) for selecting optimal setup parameters automatically for the FAN algorithm. Also, the Kappa coefficient is used as an objective function to be maximized through the PSO algorithm. As a result, the optimum setup parameters improved the success rate while the damage identification is being carried out. Indeed this proposed method is certainly very promising and constitutes a novelty. The proposed method achieves more than 75% hit rate that is significantly higher than the state-of-the-art approaches as presented in this paper. Furthermore, this approach yields a 20% improvement when considering the worst case scenario. Hence, this approach shows a practical application of expert and intelligent systems applied to damage identification in SHM systems. To conclude, the proposed approach successfully identifies structural damage with accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Process monitoring has been widely recognized as an important and critical tool in system monitoring for detection of abnormal behavior and quality improvement. In manufacturing processes or industrial systems, several sources of out-of-control variation, such as tool wear, gradual equipment deterioration, vibration, inconsistent material etc., often result in dynamic changes in the process parameter, or result in special residual signals of the systems. Detecting such weak or decaying signals is a challenging task. Although the window-limited generalized likelihood ratio (wl-GLR) scheme is widely used in changepoint detection and has some advantages, it may perform poorly when it is used to detect weak or decaying signals. This paper proposes a new change-detection scheme, the window-limited weighted likelihood ratio (wl-WLR) scheme, to improve the wl-GLR scheme. To do this, a new statistical distance measure called the GLR divergence is first defined and then its properties are analyzed. The wl-WLR scheme is designed to monitor the weighted average of the GLR divergences in a moving window, and the wl-GLR scheme can be viewed as a special case of the wl-WLR scheme. Two types of weight functionals are introduced and investigated for the wl-WLR scheme. Numerical algorithms to select the optimal weight parameters are provided based on a calibration sample. Extensive simulation study favors the proposed wl-WLR scheme for detecting weak or decaying signals, and its performance is robust even if the weight parameters are not accurately estimated. This paper has online supplementary materials. [ABSTRACT FROM AUTHOR]

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria. [ABSTRACT FROM AUTHOR]

Given the higher demands in network bandwidth and speed that the Internet will have to meet in the near future, it is crucial to research and design intelligent and proactive congestion control and avoidance mechanisms able to anticipate decisions before the congestion problems appear. Nowadays, congestion control mechanisms in the Internet are based on TCP, a transport protocol that is totally reactive and cannot adapt to network variability because its convergence speed to the optimal values is slow. In this context, we propose to investigate new congestion control mechanisms that (a) explicitly compute the optimal sessions’ sending rates independently of congestion signals (i.e., proactive mechanisms) and (b) take anticipatory decisions (e.g., using forecasting or prediction techniques) in order to avoid the appearance of congestion problems. In this paper we present B-Neck, a distributed optimization algorithm that can be used as the basic building block for the new generation of proactive and anticipatory congestion control protocols. B-Neck computes proactively the optimal sessions’ sending rates independently of congestion signals. B-Neck applies max-min fairness as optimization criterion, since it is very often used in traffic engineering as a way of fairly distributing a network capacity among a set of sessions. B-Neck iterates rapidly until converging to the optimal solution and is also quiescent. The fact that B-Neck is quiescent means that it stops creating traffic when it has converged to the max-min rates, as long as there are no changes in the sessions. B-Neck reacts to variations in the environment, and so, changes in the sessions (e.g., arrivals and departures) reactivate the algorithm, and eventually the new sending rates are found and notified. To the best of our knowledge, B-Neck is the first distributed algorithm that maintains the computed max-min fair rates without the need of continuous traffic injection, which can be advantageous, e.g., in energy efficiency scenarios. This paper proposes as novelty two theoretical contributions jointly with an in-depth experimental evaluation of the B-Neck optimization algorithm. First, it is formally proven that B-Neck is correct, and second, an upper bound for its convergence time is obtained. In addition, extensive simulations were conducted to validate the theoretical results and compare B-Neck with the most representative competitors. These experiments show that B-Neck behaves nicely in the presence of sessions arriving and departing, and its convergence time is in the same range as that of the fastest (non-quiescent) distributed max-min fair algorithms. These properties encourage to utilize B-Neck as the basic building block of proactive and anticipatory congestion control protocols. [ABSTRACT FROM AUTHOR]

Leaks and spills of hazardous fluids like petroleum endanger the environment, while remediation costs and penalties imposed when petroleum contaminates the ecosystem affect economics heavily. Therefore, it is crucial to detect any possible symptoms of a leak as soon as possible. Most of existing leak detection techniques require specialized equipment to be used, while purely software-based methods rely solely on data analysis and are very desirable since they can be deployed on petrol stations without any changes to the existing infrastructure. Moreover, such techniques can be considered as complementary to the hardware leak detection systems, as they provide additional security level. In this paper we present the TUBE algorithm, which detects fuel leaks from underground storage tanks, using only standard measurements that are normally registered on petrol stations, i.e. the amount of stored, sold, and delivered fuel. The TUBE algorithm is an autonomous solution capable of making decisions independently as well as supporting human-made decisions and thus can be considered as an expert leak detection system. The TUBE algorithm introduces a new data mining technique for trend detection and cleaning data over time series, which can be easily adapted to any other problem domain. A trend detection technique, called tubes , created for the TUBE algorithm is a novel data analysis method that allows to envelop uncertainties and oscillations in data and produce stable trends. Trend interpretation technique described in this paper has been designed especially for fuel leak detection purposes using our industrial experience. This paper includes a step-by-step usage example of the TUBE algorithm and its evaluation according to the United States Environmental Protection Agency requirements for leakage detection systems (the EPA SIR standard). Such an evaluation involves calculating the probability of detection and the probability of false alarm. The TUBE algorithm has obtained 98.84% probability of detection and 0.07% probability of false alarm while rejecting 42.22% of analyzed datasets due to their uncertainty. Rejecting datasets from analysis is compliant with the EPA SIR standard; however, rejection rate higher than 20% is not acceptable. Therefore we have evaluated the two-phase filtering stage of the algorithm in order to find the best combination of filters as means of data cleaning. Moreover, we have discussed the results pointing at the overall data quality problem, since it is the main cause of rejecting some datasets from the analysis. Finally, the TUBE algorithm has obtained 93.11% probability of detection and 0.73% probability of false alarm for the best combination of all parameters with 15.56% rejection rate, which is acceptable by the EPA SIR standard. The value of probability of detection is not fully compliant with the EPA SIR standard where 95% probability of detection with probability of false alarm lower than 5% is required. We have found that the requirements for the aforementioned probabilities have been completely fulfilled for datasets representing manifolded tank systems but not for single tank datasets. Such a situation was unexpected since manifolded tank systems are generally claimed to be more complex for analysis as they are in fact systems of multiple single tanks directly connected. In this paper we have also measured the time and memory complexity of the TUBE algorithm as well as discussed the issues connected to the TUBE algorithm deployment on petrol stations using our industrial experience in the topic. [ABSTRACT FROM AUTHOR]

In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L , which retrieves natural subgroups of given categories. In contrast to other semi-supervised models, C3L is parametrized by user-defined leakage level, which controls maximal inconsistency between initial categorization and resulting clustering. Our method can be implemented as a module in practical expert systems to detect clusters, which combine expert knowledge with true distribution of data. Moreover, it can be used for improving the results of less flexible clustering techniques, such as projection pursuit clustering. The paper presents extensive theoretical analysis of the model and fast algorithm for its efficient optimization. Experimental results show that C3L finds high quality clustering model, which can be applied in discovering meaningful groups in partially classified data. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations. [ABSTRACT FROM AUTHOR]

Fingerprint indexing plays a key role in the automatic fingerprint identification systems (AFISs) which allows us to speed up the search in large databases without missing accuracy. In this paper, we propose a fingerprint indexing algorithm based on novel features of minutiae triplets to improve the performance of fingerprint indexing. The minutiae triplet based feature vectors, which are generated by ellipse properties and their relation with the triangles formed by the proposed expanded Delaunay triangulation, are used to generate indices and a recovery method based on k-means clustering algorithm is employed for fast and accurate retrieval. The proposed expanded Delaunay triangulation algorithm is based on the quality of fingerprint images and combines two robust Delaunay triangulation algorithms. This paper also employs an improved k-means clustering algorithm which can be applied over large databases, without reducing the accuracy. Finally, a candidate list reduction criteria is employed to reduce the candidate list and to generate the final candidate list for matching stage. Experimental results over some of the fingerprint verification competition (FVC) and national institute of standards and technology (NIST) databases show superiority of the proposed approach in comparison with state-of-the-art indexing algorithms. Our indexing proposal is very promising for the improvement of real-time AFISs efficiency and accuracy in the near future. [ABSTRACT FROM AUTHOR]

Color-based visual object tracking is one of the most commonly used tracking methods. Among many tracking methods, the mean shift tracker is used most often because it is simple to implement and consumes less computational time. However, mean shift trackers exhibit several limitations when used for long-term tracking. In challenging conditions that include occlusions, pose variations, scale changes, and illumination changes, the mean shift tracker does not work well. In this paper, an improved tracking algorithm based on a mean shift tracker is proposed to overcome the weaknesses of existing methods based on mean shift tracker. The main contributions of this paper are to integrate mean shift tracker with an online learning-based detector and to newly define the Kalman filter-based validation region for reducing computational burden of the detector. We combine the mean shift tracker with the online learning-based detector, and integrate the Kalman filter to develop a novel tracking algorithm. The proposed algorithm can reinitialize the target when it converges to a local minima and it can cope with scale changes, occlusions and appearance changes by using the online learning-based detector. It updates the target model for the tracker in order to ensure long-term tracking. Moreover, the validation region obtained by using the Kalman filter and the Mahalanobis distance is used in order to operate detector in real-time. Through a comparison against various mean shift tracker-based methods and other state-of-the-art methods on eight challenging video sequences, we demonstrate that the proposed algorithm is efficient and superior in terms of accuracy and speed. Hence, it is expected that the proposed method can be applied to various applications which need to detect and track an object in real-time. [ABSTRACT FROM AUTHOR]

The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules. [ABSTRACT FROM AUTHOR]

In this paper an immuno-inspired algorithm is proposed to generate sequences of data close to the ideal white noise. The motivation to propose the algorithm is that in many cases there is a necessity to generate white noises with a small number of samples, and the pseudo-random generators may fail to perform this task. The proposed algorithm is based on the maximization of the whiteness criterion, clearly defined in this paper, and is different from other immuno-inspired algorithms because it presents an automatic regulation of the suppression threshold, that is an important control variable of the algorithm. This feature allows the proposed algorithm to reach good results, even for different sizes of candidate solutions. To test the proposed algorithm, the results obtained from it are compared to the results obtained from a known pseudo-random generator and it is shown that the solutions obtained with the proposed algorithm are better, for time and frequency domain, if the number of samples required is limited. It is also shown that the proposed algorithm consumes a time comparable to the pseudo-random generator to reach solutions that are better than the ones obtained with that kind of algorithm. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment. [ABSTRACT FROM AUTHOR]

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

Fault detection in industrial processes is a field of application that has gaining considerable attention in the past few years, resulting in a large variety of techniques and methodologies designed to solve that problem. However, many of the approaches presented in literature require relevant amounts of prior knowledge about the process, such as mathematical models, data distribution and pre-defined parameters. In this paper, we propose the application of TEDA – Typicality and Eccentricity Data Analytics – , a fully autonomous algorithm, to the problem of fault detection in industrial processes. In order to perform fault detection, TEDA analyzes the density of each read data sample, which is calculated based on the distance between that sample and all the others read so far. TEDA is an online algorithm that learns autonomously and does not require any previous knowledge about the process nor any user-defined parameters. Moreover, it requires minimum computational effort, enabling its use for real-time applications. The efficiency of the proposed approach is demonstrated with two different real world industrial plant data streams that provide “normal” and “faulty” data. The results shown in this paper are very encouraging when compared with traditional fault detection approaches. [ABSTRACT FROM AUTHOR]

Monitoring and analyzing floor vibrations to determine human activity has major applications in fields such as health care and security. For example, structural vibrations could be used to determine if an elderly person living independently falls, or if a room is occupied or empty. Monitoring human activity using floor vibration promises to have advantages over other methods. For example, it does not have the privacy concerns of other methods such as vision-based techniques, or the compliance challenges of wearable sensors. The analysis of the signals becomes a classification problem determining the type of human activity. Unfortunately only a few research groups are performing research of this subject even though there is a significant number of techniques that could be applied to this field. To date, no systematic study about the challenges and advantages of using different types of algorithms for this problem has been performed. This paper proposes a benchmark problem to: (i) encourage researchers to design new algorithms for monitoring human activity using floor vibrations, (ii) provide a dataset to test new algorithms, and (iii) allow the comparison of proposed methods based on a set of standard metrics. The benchmark consists of seven different cases of increasing difficulty. Each case has a specific number of sensors, calibration signals, and type of floor excitation forces to be considered. The paper also proposes specific metrics that enable the direct comparison of different techniques. Research groups interested in monitoring human activity using floor vibrations are encouraged to use the experimental data and evaluation metrics published in this paper to develop their own methodologies. This will enable the community of researchers to easily compare and contrasts techniques and better understand what type of methods will be appropriate in different applications. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for the inclusion of analogy into Explanation-Based Learning (EBL). Analogy can be used when an impasse is reached to extend the deductive closure of EBL’s domain theory. This enables the generation of control laws, via EBL, for hardware which is not catered for in the domain theory. This advantage addresses a problem which represents a dearth in the current literature. Integrated Modular Avionics (IMA) literature has thus far been concerned with the architectural considerations. This paper seeks to address the impact of hardware changes on the controllers within an IMA architecture. An algorithm is proposed and applied to control an aviation platform with an incomplete domain theory. Control rules are generated when no deductive explanations are possible, which still reflect the intent of the domain theory. [ABSTRACT FROM AUTHOR]

Surgical cases assignment problem (SCAP) is among the most investigated interests in operating room planning. The studies related to the SCAP are mainly focused on the single objective optimization. To concern multiple criteria in the management, this paper reformulates a multi-objective SCAP with minimizing total operating cost and maximizing scheduled surgeries number, simultaneously. Although dozens of multi-objective methods have been introduced in recent years, these cannot be applied into the multi-objective SCAP directly considering the characteristics of the problem. To tackle the multi-objective SCAP, a problem-specified multi-objective squirrel search algorithm (MOSSA) is presented. First, a targeted single-list scheme is devised to encode individuals and an efficient decoding scheme integrating a repair strategy is proposed to construct feasible scheduling plan. Second, two simple heuristics are incorporated into the algorithm to imitate the behaviors of squirrels and improve the search ability. Third, an addition archive is employed to store the non-dominated solutions. Moreover, a DOE method is performed to investigate the influence of parameter settings. The performance of the MOSSA is assessed by a representative benchmark dataset with three different evaluating indicators. Extensive experiments and analysis results demonstrate the effectiveness of the proposed algorithm and the superiority over compared approaches in addressing the multi-objective SCAP. [ABSTRACT FROM AUTHOR]

Fuzzy models are known for their ability of precise data approximation. They can be used in automatic control as controllers. One of the techniques is based on inversion of fuzzy models. Fuzzy models can be inverted exactly or approximately. The exact inversion requires special features of fuzzy models. The approximate methods can be applied to a wider class of fuzzy models. The paper presents an approximate inversion of a fuzzy model with parametrized consequences. This fuzzy model cannot be inverted exactly. The presented approach inverts an existing fuzzy model with respect to any input. The inversion procedure uses the Ridders algorithm. The inversion is fast and does not require a separate training for each inverse input. The convergence of this approach is faster than linear. The paper formulates a theorem that shortens a search interval and may help to accelerate an inversion. The paper is accompanied by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper we describe a branch-and-cut algorithm for the vehicle routing problem with unloading constraints. The problem is to determine a set of routes with minimum total cost, each route leaving a depot, such that all clients are visited exactly once. Each client has a demand, given by a set of items, that are initially stored in a depot. We consider the versions of the problem with two and tri dimensional parallelepiped items. For each route in a solution, we also need to construct a feasible packing for all the items of the clients in this route. As it would be too expensive to rearrange the vehicle cargo when removing the items of a client, it is important to perform this task without moving the other client items. Such packings are said to satisfy unloading constraints. In this paper we describe a branch-and-cut algorithm that uses several techniques to prune the branch-and-cut enumeration tree. The presented algorithm uses several packing routines with different algorithmic approaches, such as branch-and-bound, constraint programming and metaheuristics. The careful combination of these routines showed that the presented algorithm is competitive, and could obtain optimum solutions within significantly smaller computational times for most of the instances presented in the literature. [ABSTRACT FROM AUTHOR]

In order to improve the performance of image segmentation, this paper presented a gray level jump segmentation algorithm, which defined the direction of the texture, simultaneously, calculated the width of ridge line, gave the distance characteristics between textures, and established the mathematical model of the texture border, accordingly presented a new texture segmentation algorithm and compared with other texture segmentation algorithms. The simulation results show that the segmentation algorithm has some advantages to texture segmentation, such as has higher segmentation precision, faster segmentation speed, stronger anti-noise capability, less lost information of target, and so on. The segmented regions hardly contain other texture regions and background region. Moreover, this paper extracted the characteristic points and characteristic parameters in various segmented regions for texture image to obtain the characteristic vector, compared the characteristic vector with the standard template vectors, and identified the type of target in a range of threshold value. Experimental results show that the proposed target recognition approach has higher recognition rate and faster recognition speed than the existing target recognition approaches. Advancements in image processing through the study of texture segmentation are not only applicable to image fields, but also are of important theoretical value to target recognition. These researches in this paper will play an important role in a theoretical reference and practical significance to the development of all target recognition departments based on image system such as the aerospace, public security, road traffic, and so on. [ABSTRACT FROM AUTHOR]

Sketching is a natural and easy way for humans to express visual information in everyday life. Despite a number of approaches to understand online sketch maps, the automatic understanding of offline, hand-drawn sketch maps still poses a problem. This paper presents a new approach for novel sketch map understanding. To our knowledge, this is the first comprehensive work dealing with this task in an offline way. This paper presents a system for automatic understanding of sketch maps and the underlying algorithms for all steps. Major parts are a region-growing segmentation for sketch map objects, a classification for isolated objects, and a context-aware classification. The context-aware classification uses probabilistic relaxation labeling to integrate dependencies between objects into the recognition. We show how these algorithms can deal with the major problems of sketch map understanding, such as vagueness in interpretation. Our experiments demonstrate the importance of context-aware classification for sketch map understanding. In addition, a new database of annotated sketch maps was developed and is made publicly available. This can be used for training and evaluation of sketch map understanding algorithms. [ABSTRACT FROM AUTHOR]

Supplier selection has significant impact on life cycle cost of complex product system (CoPS). In this paper, a new variant of supplier selection problem named life cycle supplier selection of CoPS (LSS&CoPS) problem is addressed. There are three kinds of choices for a manufacturer to complete a CoPS: self-made, purchasing the finished component and outsourcing. Different selection not only results in difference of procurement cost of CoPS, but also results in reliability changing after it delivered to customer which greatly influences the operating cost in CoPS’s lifecycle. However, the minimizing of two objectives is mutually conflicted. This paper presents a bi-objective LSS&CoPS model which considering operating stage of CoPS to balance the procurement cost and operating cost. Moreover, a hybridization of Pareto genetic algorithm (PGA) with multi-intersection and similarity crossover (MSC) strategy is proposed to solve the bi-objective problem. Also, a dual-chromosome is used to represent the variable-length chromosome. Finally, a cement equipment supplier optimal in a cement equipment enterprise is provided. Example indicates that the procurement cost and operating cost have been optimized, yields a Pareto optimal solution of supplier schema for project managers to make-decision and decrease the life cycle cost of CoPS. Additionally, the results show that the proposed approach is more preferably in Pareto optimal solution searching. [ABSTRACT FROM AUTHOR]

Nowadays we can witness the proliferation of Android devices, mostly smartphones and tablets. We are particularly interested in one of these types of devices (Android-based multimedia devices) that are also finding their place in the world of consumer electronics. Part of our previous work is related to the design and development of recommendation systems always under the same conditions (shared by the overwhelming majority of these systems): the process of recommendation is executed by powerful servers that try to recommend to the users different types of items (movies, tv series, music, etc.) that will suit them according to different parameters, like their profile, their ratings of previous consumed items, the ratings of people with similar taste, etc. In this paper, we will face a new challenge: we will use our previous background to design and implement a recommendation system that will run in an Android-based multimedia device. In particular, this system will recommend movies to the users that fit in well with their profile and previous ratings. The main challenges that will face the design of this system are the restrictions of this kind of devices, like limited processing capabilities, sharing the CPU with more important processes, restricted connection to Internet, etc. The final system must be able to recommend contents to the user, both available locally or online. The significance of this paper lies in the fact that, to the best of our knowledge, our development is one of the first recommender systems running completely in a multimedia device without the support of a dedicated server for the execution of the recommendation algorithms. Our development is expected to be included soon in an Android-based multimedia device sold by the Spanish company Blu:Sens. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution. [Copyright &y& Elsevier]

Abstract: Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on univariate time-series forecasting. Multivariate time-series forecasting is known to have better performance in load forecasting. In this paper we propose a meta-learning system for multivariate time-series forecasting as a general framework for load forecasting model selection. We show that a meta-learning system built on 65 load forecasting tasks returns lower forecasting error than 10 well-known forecasting algorithms on 4 load forecasting tasks for a recurrent real-life simulation. We introduce new metafeatures of fickleness, traversity, granularity and highest ACF. The meta-learning framework is parallelized, component-based and easily extendable. [Copyright &y& Elsevier]

Abstract: This paper presents the development of a Decision Support System (DSS) for the management of ship locks that relies on fuzzy logic. It contains a brief overview of the history and the construction of locks and basic information related to fuzzy logic, fuzzy linguistic variables and methods used in approximate reasoning. In reality, ship lock control is mostly based on the subjective estimations and the experience of a lock master (ship lock operator). The fuzzy set theory is the most favourable mathematical approach for consideration of indefiniteness and subjective estimates. This paper analyses the control process of a ship lock on a two-way waterway, with one chamber designed for one vessel. A control algorithm is constructed according to a set of linguistic rules that describes the operator’s control strategy. The subjective estimations are therefore implemented in the algorithm as fuzzy sets. Fuzzy rules aggregate the final fuzzy set and defuzzification produces a decision. A set of ship traffic data is generated for analysis and simulation purposes based on the annual distribution of ship arrivals at the lock. Two criteria are presented and used in parallel with the Fuzzy DSS (FDSS). These two extreme criteria reflect the interests of shippers on one side and workers and owners of the lock on the other side. These interests occur in actual systems and are used here to evaluate the results obtained using the FDSS. This paper additionally describes the design of the SCADA (Supervisory Control And Data Acquisition) software. This software relies on a PLC (Programmable Logic Controller) and provides a platform on which to implement the desired fuzzy algorithm. The software was developed with the suggestions of operators who have extensive experience in ship lock control. The presented control system can be used for support in decision making in control processes and in the training of new operators of ship locks. [Copyright &y& Elsevier]

Abstract: Sentiment polarity detection is one of the most popular tasks related to Opinion Mining. Many papers have been presented describing one of the two main approaches used to solve this problem. On the one hand, a supervised methodology uses machine learning algorithms when training data exist. On the other hand, an unsupervised method based on a semantic orientation is applied when linguistic resources are available. However, few studies combine the two approaches. In this paper we propose the use of meta-classifiers that combine supervised and unsupervised learning in order to develop a polarity classification system. We have used a Spanish corpus of film reviews along with its parallel corpus translated into English. Firstly, we generate two individual models using these two corpora and applying machine learning algorithms. Secondly, we integrate SentiWordNet into the English corpus, generating a new unsupervised model. Finally, the three systems are combined using a meta-classifier that allows us to apply several combination algorithms such as voting system or stacking. The results obtained outperform those obtained using the systems individually and show that this approach could be considered a good strategy for polarity classification when we work with parallel corpora. [Copyright &y& Elsevier]

Abstract: Collaborative filtering algorithms predict a rating for an item based on the user’s previous ratings for other items as well as ratings of other users. This approach has gained notable popularity both in academic research and in commercial applications. One aspect of collaborative filtering systems that received interest, but little systematic analysis, is confidence of the rating predictions by collaborative filtering algorithms. In this paper, I address this issue. Specifically: (1) I offer a discussion on the definition of confidence, (2) I propose a method for evaluating performance of confidence estimation algorithms, and (3) I evaluate six different confidence estimation algorithms. Three of those algorithms are introduced in this paper and three have been previously suggested for this purpose. The comparative experimental evaluation demonstrates that two of the algorithms proposed in this study: one using resampling of available ratings and one using noise injection to the available ratings provide the best performance in terms of separation between predictions of high and low confidence. The algorithms that use only the number of ratings available for the user of interest or for the item of interest turned out to be of limited use for confidence estimation. [Copyright &y& Elsevier]

Abstract: The Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the feature-detection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems. [Copyright &y& Elsevier]

Abstract: The ability to find and access geospatial data over the Internet through a single tool will be a great benefit for researchers who try to collect relevant data for a particular study. This requires a unified system to bring together diverse data sources so that they can be searched simultaneously in a single environment. Dealing with diversity of data sources and coping with heterogeneities that exist between them are the major challenges in this endeavor. This paper presents the efforts of combining Open Geospatial Consortium (OGC) specifications, Universal Description, Discovery and Integration (UDDI) standards and ontologies to address these challenges. The primary goal is to enhance discovery of web services compliant with OGC specifications and promote access to geospatial information via the OGC services. To this end, this paper introduces an approach for incorporating ontology into UDDI registry and proposes an advertisement algorithm for representing service metadata contained in the OGC capabilities document via UDDI data structures, providing therefore a way to record semantic, descriptive and technical information of OGC services inside UDDI registry. Then the paper illustrates how this information could be used to enhance service discovery with the aid of semantic reasoning. Based on the proposed approaches and service-oriented architectures (SOA) strategies, a web-based prototype system has been developed in order to help users query, access and visualize geospatial data of different types in a unified interface. Several running examples are given to demonstrate the feasibility and effectiveness of the prototype. [Copyright &y& Elsevier]

Abstract: In this paper we present the application of a hybrid harmony search (HS) algorithm to the Spread-Spectrum Radar Polyphase (SSRP) codes design. Such a design can be formulated as a non-linear max–min optimization problem, hard to be solved using classical numerical techniques. Soft-computing approaches have then been successfully applied to solve the SSRP in the past, such as evolutionary computation techniques, variable neighborhood approaches or tabu search algorithms. In this paper we elaborate on the proposed hybrid HS approach, which consists of a naive implementation of the HS algorithm along with an adaptive-step gradient-guided local search procedure. Intensive computer simulations show that the proposed hybrid HS algorithm is able to outperform existing algorithms for the SSRP design problem (including the best reported so far), with significant differences in large-size SSRP instances. [Copyright &y& Elsevier]

Abstract: This paper proposes an MDA approach to knowledge engineering, centered on the CommonKADS knowledge model. The latter corresponds to the CIM level of MDA whereas PRR, which represents production rules and rulesets, corresponds to the PIM level. The paper explores the mapping between CommonKADS knowledge models and production rules and rulesets based on PRR. Mapping CommonKADS knowledge models into PRR is very useful, due to the fact that the CIM level remains relatively unexplored, despite its key role in MDA. This motivates our choice to focus on the CIM and PIM levels. Furthermore, the mapping between PIM and PSM (i.e. the implementation of production rules in specific rule-based systems) constitutes less of an issue. To map CommonKADS knowledge models into PRR production rules and rulesets, we propose and illustrate a set of transformations. To ease these transformations, we start by grouping elements of the CommonKADS knowledge models into so-called “inference groups”. We propose and illustrate an algorithm that defines these inference groups automatically. The definition of transformations between models (CIM to PIM levels) requires a specific metamodel for CommonKADS as well as a dedicated metamodel for PRR. Unlike PRR, there is no published CommonKADS metamodel. This paper proposes a comprehensive CommonKADS knowledge metamodel. We describe and discuss an example, applying the whole approach. [Copyright &y& Elsevier]

Abstract: This paper develops an evaluation approach based on the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). When the input for a decision process is linguistic, it can be understood that the output should also be linguistic. For that reason, in this paper we propose a modification of the TOPSIS algorithm which develops the above idea and which can also be used as a linguistic classifier. In this new development, modifications to the classic algorithm have been considered which enable linguistic outputs and which can be checked through the inclusion of an applied example to demonstrate the goodness of the new model proposed. [Copyright &y& Elsevier]

Abstract: There is research which reveals negative effects of IT applications in the healthcare sector on both patients and staff. Therefore, methods are necessary to predict the risk of new healthcare information technology in order to reduce the unintended results of new applications. A new predictive risk assessment model for a hospital information system (HIS) has been developed in this paper to estimate risk before the implementation of new HIS. The methodology consists of analytic network process (ANP), reality-design gap evaluation and fuzzy inference system. An application of the proposed algorithm has been applied for a research and education hospital in Istanbul, Turkey. Risk magnitude of a new HIS implementation for the hospital is found as major with a belief of 100%. The relative importances of risk factors for HIS implementation success are obtained. The most effective factors on the HIS implementation are found as technological factors; usefulness, compatibility, user involvement and ease of use. These factors are followed by organizational factors; training and organizational commitment. The most important individual factor is also found as user’s previous HIS experience. A risk assessment model has been proposed in this paper. The model processes experts’ evaluations defined in linguistic forms when there is no sufficient data and it integrates possible risk factors into the decision-making process of risk assessment. In the model, a reality-design gap analysis is used to determine risk likelihood instead of directly risk evaluation. [Copyright &y& Elsevier]

Highlights • Learning automata-variable actions is used to solve the resource selection problem. • The proposed algorithm based on job predicted time. • This algorithm based on the list of resources that discovered in discovery stage. • Two different implementations of the algorithm are performed. • Proposed algorithm outperforms in terms of average response time and throughput. Abstract Grid computing, in simplest terms, is distributed computations that have reached higher evolution level. Grid scheduler is part of Grid. Scheduler generates an assignment of jobs to resources using the resource information provided by the Grid information service. Since the problems raised in the resource management system are NP-hard, classical methods, such as dynamic programming, are useful only for small-size problems. Being capable of producing efficient schedules at an acceptable time, even for large samples of the problem, heuristic algorithms are promising methods for solving the scheduling problem. The resource scheduling process in the Grid consists of three main phases: resource discovery, resource selection and job execution. In this paper, we propose an algorithm based on learning automata to resource selection in computational Grid. In this algorithm, decisions are made based on the list of resources that discovered at the resource discovery phase, and after being selected based on predicted time to execution or completion job, they would sent to the next phase namely the execution phase. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with several existing methods in terms of the average turn-around time, average response time and throughput. [ABSTRACT FROM AUTHOR]

Highlights • Propose a new hybrid feature selection algorithm based on BACO and clustering. • Modify linear binary ant system to reduce the search space complexity. • Inject mutation to increase randomness of search space. • Feature clustering to decrease the challenges of processing high-dimensional dataset. • Experiment the method in several real-world social datasets and obtain more efficiency. Abstract The emergence of ''curse of dimensionality" issue as a result of high reduces datasets deteriorates the capability of learning algorithms, and also requires high memory and computational costs. Selection of features by discarding redundant and irrelevant features functions as a crucial machine learning technique aimed at reducing the dimensionality of these datasets, which improves the performance of the learning algorithm. Feature selection has been extensively applied in many application areas relevant to expert and intelligent systems, such as data mining and machine learning. Although many algorithms have been developed so far, they are still unsatisfying confronting high-dimensional data. This paper presented a new hybrid filter-based feature selection algorithm based on acombination of clustering and the modified Binary Ant System (BAS), called FSCBAS, to overcome the search space and high-dimensional data processing challenges efficiently. This model provided both global and local search capabilities between and within clusters. In the proposed method, inspired by genetic algorithm and simulated annealing, a damped mutation strategy was introduced that avoided falling into local optima, and a new redundancy reduction policy adopted to estimate the correlation between the selected features further improved the algorithm. The proposed method can be applied in many expert system applications such as microarray data processing, text classification and image processing in high-dimensional data to handle the high dimensionality of the feature space and improve classification performance simultaneously. The performance of the proposed algorithm was compared to that of state-of-the-art feature selection algorithms using different classifiers on real-world datasets. The experimental results confirmed that the proposed method reduced computational complexity significantly, and achieved better performance than the other feature selection methods. [ABSTRACT FROM AUTHOR]

Highlights • Automatic tools for touristic applications are highly valuable for the user • Single-scale clustering methods are insufficient to solve real clustering problems • Multi-scale (hierarchical) density-based clustering improves landmark detection • The separation of inhabited population cores facilitates the clustering approach • Increasing the dimensionality improves the results within crowded sample spaces. Abstract The process of determining relevant landmarks within a certain region is a challenging task, mainly due to its subjective nature. Many of the current lines of work include the use of density-based clustering algorithms as the base tool for such a task, as they permit the generation of clusters of different shapes and sizes. However, there are still important challenges, such as the variability in scale and density. In this paper, we present two novel density-based clustering algorithms that can be applied to solve this: K-DBSCAN, a clustering algorithm based on Gaussian Kernels used to detect individual inhabited cores within regions; and V-DBSCAN, a hierarchical algorithm suitable for sample spaces with variable density, which is used to attempt the discovery of relevant landmarks in cities or regions. The obtained results are outstanding, since the system properly identifies most of the main touristic attractions within a certain region under analysis. A comparison with respect to the state-of-the-art show that the presented method clearly outperforms the current methods devoted to solve this problem. [ABSTRACT FROM AUTHOR]

Highlights • A new dataset for the hidden services in the Tor network with 10K samples. • Statistical analysis for the dataset with useful insights for Law Enforcement Agencies. • A new algorithm for ranking the onion domains and detect the influential ones. • The algorithm is tested on three real-world datasets. Abstract The Tor network hosts a significant amount of hidden services related to suspicious activities. Law Enforcement Agencies need to monitor and to investigate crimes hidden behind the anonymity provided by Tor. In this paper, we propose a new algorithm, named ToRank, that ranks hidden services in Tor better than the known algorithms used for the Surface Web. We also thoroughly analyze the content present in Tor, creating a dataset, DUTA-10K, that extends the previous Darknet Usage Text Address (DUTA) dataset. We quantitatively compared ToRank with some of the most popular ranking algorithms, like PageRank, HITS, and Katz. Results showed that our proposal obtains a higher harm to the Tor network robustness than all of them, what indicates its superiority for this problem. The analysis of DUTA-10K reveals that only 20% of the hidden services that can be accessed are related to suspicious activities, and 48% are associated with normal ones. We also discovered that domains related to suspicious activities usually present multiple clones under different addresses, what could be used as an additional feature for identifying them. We consider that our new algorithm, the extended dataset, and the findings obtained from the analysis carried out are helpful for LEAs to fight against crimes that take place in the Tor hidden services. [ABSTRACT FROM AUTHOR]

Highlights • A novel SVM is proposed for high noise and unbalanced distribution data. • Feature selection is improved by a novel method using the hierarchical relations in feature sets. • Roulette algorithm is introduced into cooperative coevolution. Abstract In real applications, noises are often present in the obtained data, which would considerably affect the performance of machine learning models. Although support vector machine (SVM) is a classic and efficient learning model, however, it is sensitive to noises in the training data. In this paper, a novel support vector machine named as reconstructed training-set SVM (RTS-SVM) is proposed to implement classification for high-noise data, where the roulette cooperative coevolution algorithm (R-CC) is used to optimize the parameters of RTS-SVM. The proposed SVM model is applicable to make the classification of high-noise data by tackling with the sensitive effect of the ''soft margin'' of SVM on the original training set. By means of the hierarchical relations existing in feature sets, hierarchical grouping (HG) algorithm is applied to construct feature subsets, based on which R-CC coordinates the parameters of RTS-SVM to achieve the optimization of the whole model. The application of the proposed scheme in the classification of financial time series is mainly discussed. Besides, the proposed model is also verified by using synthetic data with high noises and daily life data sets. Examples are provided to illustrate the effectiveness and practicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Highlights • Sine cosine algorithm is modified to solve high-dimensional optimization problem. • A modified position-updating equation is presented to accelerate convergence. • A novel nonlinear decreasing conversion parameter strategy is designed to balance exploration and exploitation. • The performance of ISCA is tested on 24 high-dimensional functions and three engineering design problem. • Comparisons demonstrate that ISCA is a competitive solver for high-dimensional optimization problems. Abstract The sine cosine algorithm (SCA) is a relatively novel population-based optimization technique that has been proven competitive with other algorithms and it has received significant interest from researchers in different fields. However, similar to other population-based algorithms, SCA tends to be trapped in local optima and unbalanced exploitation. Additionally, to our limited knowledge, the present SCA and its variants have not been applied to the high-dimensional global optimization problems. This paper presents an improved version of the SCA (ISCA) for solving high-dimensional problems. A modified position-updating equation by introducing inertia weight is proposed to accelerate convergence and avoid falling into the local optima. In addition, to balance the exploration and exploitation of the SCA, we present a new nonlinear conversion parameter decreasing strategy based on the Gaussian function. The effectiveness of the proposed ISCA is evaluated using 24 benchmark high-dimensional (D = 30, 100, 500, 1000, and 5000) functions, large-scale global optimization problems from the IEEE CEC2010 competition, and several real-world engineering applications. The comparisons show that the proposed ISCA can better escape from local optima with faster convergence than both the traditional SCA and other population-based algorithms. [ABSTRACT FROM AUTHOR]

Highlights • We propose a real time view of an attributed graph for a peer recommendation system. • We used dynamic community search on an attributed graph for peer recommendation. • We assign weight to the attributes of a node in dynamic graphs. • Skip-gram model is used to expand the attribute sets. Abstract In this paper, we propose a novel model to recommend possible research peers to a user efficiently. We model all the authors along with their attributes as an attributed graph and then perform community search on this attributed graph to find the most appropriate peer for a user. We propose algorithms to recommend a dynamic attributed graph efficiently and create a real-time community search model by deploying an incremental training algorithm. We also propose dynamic weighted attributes for each node (peer). Given a node and set of attributes (query), the proposed model is capable of self-expansion of the attribute set leading to a more significant match between two nodes. Our experimental results show that our proposed model achieved substantial performance gains over the existing models. In a nutshell, we present an intelligent system that incorporates the changes going on in the research world and suggest up-to-date recommendations. The proposed model is also robust enough to ensure that the recommendations do not suffer due to poor queries. [ABSTRACT FROM AUTHOR]

Highlights • An enhanced algorithm for solving multi-objective optimization problems is proposed. • The proposed algorithm improves the original Multi-Objective Grey Wolf Optimizer. • This improvement is related to the decomposition of multi-objective problems. • The proposed approach is evaluated on benchmark functions and real-world applications. • Statistical analysis corroborates the good performance of the proposed algorithm. Abstract An optimization technique aims to find the best solution to an optimization problem. If the problem considers only one objective function, the best solution will provide the optimal value for such objective. However, if the problem considers two or more objectives, the selection of solutions will not be that straightforward since such objective functions are usually in conflict. For this kind of optimization problems, the use of analytical or exact methods becomes impractical. Thus, heuristic or metaheuristic approaches have to be applied for finding the optimal solutions or, at least, approximate solutions to the optimum. As a consequence, a wide variety of metaheuristics inspired by nature has been proposed for solving optimization problems. Among them, the Grey Wolf Optimizer is a metaheuristic of recent creation that in the last few years has attracted the attention of many researchers. Furthermore, a multi-objective extension of this technique was recently introduced proving its high performance comparable to other multi-objective optimization methods. In this paper, a multi-objective grey wolf optimizer based on the decomposition is introduced. Our proposed algorithm approximates Pareto solutions cooperatively by defining a neighborhood relation among the scalarizing subproblems in which the multi-objective problem is decomposed. The performance of our proposed method is compared against those achieved by six state-of-the-art bio-inspired techniques showing its high performance in both, well-known benchmark problems and two real-life engineering problems. [ABSTRACT FROM AUTHOR]

Highlights • A collaborative mechanism is proposed to improve particle swarm optimization. • A worst-replacement scheme is proposed to update particles' positions. • The trajectory of the best particle during optimizing is stored to an external archive. • The stored solutions estimate a promising optimal region in a new environment. • The proposed algorithm shows a competitive power in dynamic optimization problems. Abstract Optimization problems widely exist in many expert and intelligent systems, e.g., greenhouse intelligent control systems in agriculture, energy management systems for hybrid electric vehicle, and job shop scheduling systems in manufacture. For the optimization problems in these systems, the objective functions may change over time. This kind of problem is usually called as dynamic optimization problems (DOPs) or optimizing in dynamic environments. The optimization algorithm plays an important role in designing an expert and intelligent system. In this paper, we present a novel particle swarm optimizer for optimization in dynamic environments. We introduce two schemes to improve performance of particle swarm optimization in dynamic environments. Firstly, the classical particle swarm optimization is enhanced by a collaborative mechanism, in which a target particle learns from another randomly selected particle and the global best one in the swarm. Instead of moving to the new position directly, a worst replacement operator is used to update the swarm, whereby the worst particle in the swarm moves to the better newly generated position. During optimizing, the best solution in each generation is stored. When an environmental change is detected, the historical solutions are retrieved to collaborate with some newly generated solutions to adapt to the new environment. The performance of the proposed algorithm is compared with several reported algorithms over the benchmark problems. Experimental results indicate that the proposed algorithm offers superior performance compared with the competitors. [ABSTRACT FROM AUTHOR]