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The paper addresses the problem of designing Brain-Computer Interfaces. It investigates feature selection methods in regression, applied to ECoG-based motion decoding. The problem is to predict hand trajectories from the voltage time series of cortical activity. A special characteristic of this problem is the inherently multi-way structure of feature description. The feature description resides in spatial-spectra-temporal domain and includes the voltage time series and their spectral representation. Since electrocorticographic data is highly correlated in temporal, spectral and spatial domains, redundancy of the feature space as well as its dimensionality become a major obstacle for robust solution of the regression problem both in multi-way and flat cases. Feature selection reduces dimensionality and increases model robustness. It plays the crucial role in obtaining adequate predictions. The main contribution of this paper is the following. We propose a filtering feature selection for multi-way data. The proposed method extends quadratic programming feature selection (QPFS) approach. QPFS selects a subset of features by solving a quadratic problem. It incorporates estimates of similarity between features and their relevance to the regression problem. QPFS offers an effective way to leverage similarities between features and their importance. Our modification allows to apply QPFS to multi-way data. By taking the multi-way structure of features into account, the proposed modification reduces computational costs of optimization problem in QPFS. Experimental outcomes demonstrate that the proposed modification improves prediction quality of resultant models. The proposed method is model-free and provides interpretable results, which makes it relevant for knowledge extraction and domain analysis.

In this paper, an extension and an application to the green procurement model based on Coloured Petri Nets is presented. The large number of services and providers for services that can exist in a company means a problem when it comes to managing purchases and much more if the purchases. If the purchases have a benefit such as the sustainability of the companies processes that provide the service, the problem is even greater. This paper aims to develop an extension for this proposed model using the petri nets tools thanks to its ease of modeling complex systems. Likewise, a new algorithm applied to the place of selection of suppliers and a methodical mathematical formalization was developed. Finally, two examples are implemented. The first example allows understanding and applying the model in a simple way. In the second example or study case, be proposed an application in a services company. This example describes the step by step of the evolution of the model and the results obtained by each stage. The application of the final example shows the qualitative advantages of the developed model and the need to apply some technique of quantitative analysis of easy integration to the model that will be defining in a future research.

There are approaches that successfully recognize activities of daily living by using a trained classifier on feature vectors created from binary sensor data. Although these approaches have been successful, there are still open issues such as the evaluation of multiple temporal windows, ensembles of classifiers or unbalanced classes which need to be addressed in order to improve the performance of the real-time activity recognition process. In this paper, we present a methodology for Real-Time Activity Recognition based on the diverse fields of Machine Learning, including Fuzzy Logic and Recurrent Neural Networks. The methodology uses a long-term and short-term representation of binary-sensor activations based on Fuzzy Temporal Windows. The paper proposes an ensemble of activity-based classifiers for the purposes of balanced training, where each classifier in the ensemble is a Long Short-Term Memory. The approach was evaluated using two binary-sensor datasets of daily living activities and benchmarked against previous approaches based on the combination of sensor activation features.

To assess the current mental state of an individual, several monitoring systems have been developed. In this paper, we explore the possibility to exploit information recorded noninvasively from the human cortex to develop a brain-computer interface (BCI) able to estimate brain workload and the mental efforts during a cognitive task. The EEG-based workload classifier presented in this paper combines a power spectral density (PSD) analysis and a statistical criterion. The proposed classifier is applied in the context of distance education and online course platform through two experimental protocols. The first one proposes solving a set of matrices products using pen and paper, while the second one proposes answering problems of logic mathematics on a computer-based learning environment. Experimental results show that the averaged accuracy of distinguishing changes in the theta [4–7 Hz] (θ) band is 79%. For the alpha band [8–11 Hz] (α) the averaged accuracy reached 78%. Based on this classifier, we demonstrate that θ and α powers in central, and posterior sites decrease with the increase in difficulty level of the cognitive task. The accuracy of correct decisions obtained from our results are significantly enhanced while comparing our investigation to some similar works from literature. In the realm of intelligent expert systems, our work results represent a first step to an implementation of an intelligent system for the evaluation of reeducative therapies to be used in physiotherapy centers for children with cognitive disorders as it is the case for Cerebral Palsy.

Being able to understand the implicit power structures and dynamics among members plays a crucial role for the management of the organization. This paper introduces a novel and comprehensive approach to analyzing organizational discourse data. The proposed approach provides a holistic view of the power structure implied by the communications. The paper contributes to the domain of text mining by integrating various text-mining techniques to demonstrating different aspects of a power structure within an organization. It also contributes to the domain of supply chain management by using the conventional communication discourse method as the guideline for the development of the tool. We applied the proposed approach to a seven-year collection of meeting minutes from a co-op and our findings were largely confirmed by members of the organization. We provide a roadmap of using the multi-aspect approach to analyzing organizational discourse data in supply networks.

In this paper, we study the visualization of complex structures in the context of automatic graph drawing. Constructing geometric representations of combinatorial structures, such as networks or graphs, is a difficult task that requires an expert system. The automatic generation of drawings of graphs finds many applications from software engineering to social media. The objective of graph drawing expert systems is to generate layouts that are easy to read and understand. This main objective is achieved by solving several optimization problems. In this paper we focus on the most important one: reducing the number of arc crossings in the graph. This hard optimization problem has been studied extensively in the last decade, proposing many exact and heuristic methods to minimize the total number of arc crossings. However, despite its practical significance, the min–max variant in which the maximum number of crossings over all edges is minimized, has received very little attention. We propose new heuristic methods based on the strategic oscillation methodology to solve this NP-hard optimization problem. Our experimentation shows that the new method compares favorably with the existing ones, implemented in current graph drawing expert systems. Therefore, a direct application of our findings will improve these functionality (i.e., crossing reduction) of drawing systems.

This paper characterizes a class of repeated moral hazard problem where the agent's replacement action is necessary for assuring the performance of important materials and equipment. However, due to the principal's costly monitoring, the continuously decreasing performance caused by the agent's successive shirking behaviors will significantly harm the principal's profits or welfare. For sake of performance assurance and monitoring efficiency, this paper adopts two reinforcement learning algorithms to find and test the strategies on the final path satisfying the conditions of (Restricted) Experience Based Equilibrium. Experimental results in a case of quality assurance of grain demonstrate the effectiveness of the model and the solution methods. Practically, this framework especially fits a kind of government-enterprise relation, such as in the field of emergency materials management and safety production management. Given the increasing need of developing systems for the government's management, this paper aims to provide applicable strategies and guidelines from a theoretical perspective.

This study evaluates the performance of different data clustering approaches for searching the profitable consumer segments in the UK hospitality industry. The paper focuses on three aspects of datasets including the ordinal nature of data, high dimensionality and outliers. Data collected from 513 sample points are analysed in this paper using four clustering approaches: Hierarchical clustering, K-Medoids, fuzzy clustering, and Self-Organising Maps (SOM). The findings suggest that Fuzzy and SOM based clustering techniques are comparatively more efficient than traditional approaches in revealing the hidden structure in the data set. The segments derived from SOM has more capability to provide interesting insights for data-driven decision making in practice. This study makes a significant contribution to literature by comparing different clustering approaches and addressing misconceptions of using these for market segmentation to support data-driven decision making in business practices.

In recent years, the growing availability of huge amounts of information, generated in every sector at high speed and in a wide variety of forms and formats, is unprecedented. The ability to harness big data is an opportunity to obtain more accurate analyses and to improve decision-making in industry, government and many other organizations. However, handling big data may be challenging and proper data integration is a key dimension in achieving high information quality. In this paper, we propose a novel approach to data integration that calibrates online generated big data with interview based customer survey data. A common issue of customer surveys is that responses are often overly positive, making it difficult to identify areas of weaknesses in organizations. On the other hand, online reviews are often overly negative, hampering an accurate evaluation of areas of excellence. The proposed methodology calibrates the levels of unbalanced responses in different data sources via resampling and performs data integration using Bayesian Networks to propagate the new re-balanced information. In this paper we show, with a case study example, how the novel data integration approach allows businesses and organizations to get a bias corrected appraisal of the level of satisfaction of their customers. The application is based on the integration of online data of review blogs and customer satisfaction surveys from the San Francisco airport. We illustrate how this integration enhances the information quality of the data analytic work in four of InfoQ dimensions, namely, Data Structure, Data Integration, Temporal Relevance and Chronology of Data and Goal.

Existing viral-marketing network models commonly assume a preliminary phase in which a marketer actively infects a subset of social network’s users, represented by nodes, followed by a passive viral process, in which nodes infect other nodes without external intervention. However, in real-world commercial scenarios, substantial efforts are often invested by companies to promote their products, suggesting that the adoption of products is rarely the consequence of a viral spread alone. Under this observation, this paper proposes a new diffusion model, named Active Viral Marketing, which better fits real-world marketing scenarios, where adoption of products relies on continuous active promotion efforts by the marketer. In the proposed model, the success of a marketing attempt to infect a potential customer (uninfected node), depends on the number of adopting friends (infected neighbors) of this user, assuming a user is more likely to adopt a product if more of his/her friends have already adopted it, while taking into account that social influence diminishes over time due to a memory-loss effect. The paper further proposes a set of heuristics to schedule the marketing attempts. The main idea behind these heuristics is to utilize the information on the dynamic adoption-states of neighbor nodes, in addition to the static social network topology, when choosing the next node to seed. An extensive experimentation demonstrates how the proposed seeding heuristics improve the adoption rate of products by 30%–75% in comparison to existing state-of-the-art methods that mainly rely on the network topology.

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-kNN algorithm are compared with two well-known algorithms: Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed PSO-kNN algorithm was employed for recognizing human activities. The experimental results proved that the PSO-kNN 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.

The learning algorithm for multivariable data analysis (LAMDA) is a learning method to group or classify quantitative and qualitative historical data. LAMDA can be applied for self-learning and directed learning. Considering self-learning, LAMDA groups historical data through the global membership degrees of a sample with respect to a class, considering the contribution of all features for each sample. Such contribution is called marginal adequacy degree (MAD). To mix all MADs, several aggregation functions such as linear and geometrical connectives are used. The aggregation function calculates the global adequacy degree (GAD) of a sample with respect to a class. This procedure avoids assigning the class number as initial parameter due to that LAMDA applies an automatic threshold called non-informative class (NIC) which finds the best assignment of a portion of data to one class. One improvement for the LAMDA algorithm is the use of a full reinforcement operator called Yager–Rybalov Triple Pi, which can reduce the number of classes. However, this kind of solution ignores the degree to which one sample belongs to one class relative to all samples. Another limitation is that the classes reduction is not controlled by a parameter or an algorithm, which removes some classes required by a human expert. Such removed classes can be useful for the human expert in the data analysis of complex processes or for intelligent systems when the searching of additional information is required in decision-making. In this paper, the typicality degree and the intuitionistic fuzzy sets (IFSs) are used to propose one improvement of the LAMDA method in order to develop a strategy that avoids the loss of significant classes. This paper shows two functions: global typicality degree (GTD) and intuitionistic global adequacy degree (IGAD). By integrating both functions, a new function called typicality and intuitionistic global adequacy degree (TIGAD) is proposed. The proposal is applied in three study cases. The results show an improvement of clustering which were validated by the human expert and the information of data. Furthermore, the impact of the research is focused on data analysis, where the expert should make the best decision.

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.

Data envelopment analysis (DEA) is a frontier analysis procedure for evaluating the relative performance of decision making units (DMUs) with multiple inputs and multiple outputs. To improve its discrimination power, an important extension is proposed as cross-efficiency, which uses peer DMUs’ optimal relative weights to evaluate the relative performance. However, the existing cross-efficiency methods show an inconsistent and unbalanced evaluation standard, since each DMU might determine a different total (or mean) efficiency value across all DMUs. The different values imply that the DMUs that have assigned larger cross-efficiency scores will have a larger effect in aggregating the ultimate cross-efficiency scores and different DMUs’ effects are unbalanced in cross-efficiency methods. In this paper, we will deal with this unbalanced cross-efficiency evaluation problem. To this end, we first suggest a practical adjustment measure to rectify the traditional cross-efficiency, which will provide a common evaluation standard for all DMUs and make each DMU dispatch an identical total efficiency score across all DMUs. Further, we propose a game-like iterative procedure to obtain the optimal balanced cross-efficiency. Finally, we present both a numerical example and an empirical study derived from the literature and a real-world problem to demonstrate the usefulness and efficacy of the new balanced cross-efficiency evaluation approach. The work presented in this paper can extend the traditional cross-efficiency approaches to situations involving unbalanced evaluation standards, and make the evaluation results more practical significance.

Performance measurement is one of the most essential real life decision making problems faces several criteria and different types of uncertainty and vagueness. Human expertise is mandatory for such situation although it is not enough to overcome such complicated cases. Expert systems which can sense, analyze and incorporate all details of real life decision making problems are required. In this paper an expert system is proposed in form of an uncertain Data Envelopment Analysis (DEA) approach. More formally a mixed fuzzy-robust uncertainty is addressed in the proposed DEA. Two scenario-based robust DEA models under Constant Return to Scale (CRS) and Variable Return to Scale (VRS) conditions have been proposed. Since the observed values for the inputs and outputs of Decision Making Units (DMUs) in each scenario may be ambiguous or vague, fuzzy DEA models corresponding to each of the robust scenarios are developed. So, this paper proposes four fuzzy-robust DEA models, which simultaneously maintain the advantage of each of the fuzzy and robust approaches, and at same time calculate the upper and lower bounds of the efficiency scores of DMUs under CRS and VRS conditions. Finally, to evaluate the validity and applicability of the proposed models, two numerical examples and a real case study in Small and Medium-sized Enterprises (SMEs) are presented and discussed.

Sentiment analysis helps evaluating the performance of products or services from user generated contents. Lexicon based sentiment analysis approaches are preferred over learning based ones when training data is not adequate. Existing lexicons contain only unigrams along with their sentiment scores. It is observed that sentiment n-grams formed by combining unigrams with intensifiers or negations show improved results. Such sentiment n-gram lexicons are not publicly available. This paper presents a methodology to create such a lexicon called Senti-N-Gram. Proposed rule-based approach extracts the n-grams sentiment scores from a random corpus containing product reviews and corresponding numeric rating in five-point scale. The scores from this automated procedure are compared with that of the human annotators using t-test and found to be statistically equivalent. The paper also proposes a sentiment classification methodology by using a ratio based approach based on counts of positive and negative sentences of a document. When used Senti-N-Gram lexicon, proposed method outperforms well-known unigram-lexicon based approach using VADER and an n-gram sentiment analysis approach SO-CAL.

Online social networks (OSNs) are receiving great attention from the research community for different purposes, such as event detection, crisis management, and forecasting, among others. The increasing amount of research conducted with social networks opens the need for a classification methodology regarding trends in the field. This work does not cover all types of social networks; it focuses on the analysis of microblogs as a data source in the context of recommender systems (RSs). The main goal of this work is to provide authors with insights on the trends of academic literature reviews in the proposed context and to provide a comparison of different research approaches. The authors searched for up-to-date research papers related to RS methods using microblogs within a time period of five years, from 2012 to January 2018. Starting from 2012, a significant amount of research related to the subject field of RSs was conducted and identified by the authors of this work. After the filtering process, 39 papers were finally selected from journals and conferences in four different databases related to Internet technologies (i.e., IEEE, ACM, Science Direct, and Springer). A general classification presented in this work is then adopted and used to describe state-of-the-art social network recommendation approaches for microblogging. This work can be extended in the future to include novel methodologies and trends of RSs for microblogs.

A model of the unequal-area facility layout problem is described.A modified multi-objective particle swarm optimization algorithm is proposed.We apply the heuristic strategy to update layout.The gradient method is applied to execute local search.The objective space division method is used to find the Pbest and Gbest. The facility layout problem (FLP) is the problem of placing facilities in a certain shop floor so that facilities do not overlap each other and are satisfied with some given objectives. Considering practical situations, this study focuses on the multi-objective unequal-area facility layout problem (UA-FLP), where the facilities have unequal-areas and fixed shapes and are placed orthogonally in the continuous shop floor. The objectives of the problem aim to optimize the material handling cost, the total adjacency value and the utilization ratio of the shop floor. The chief difficulties of this version of the FLP lie in the satisfaction of non-overlapping constraint between any two different facilities and the optimization of multiple objectives in the huge solution space. In this paper, we put forward a heuristic configuration mutation operation and subsequent local search based on the gradient method to satisfy the non-overlapping constraint, and the multi-objective particle swarm optimization (MOPSO) algorithm, which has recently proven its high effectiveness and robustness in solving multi-objective problems, to obtain a set of Pareto-optimal solutions of the problem. The novelty of the paper lies in the use of an objective space division method in the MOPSO which governs the neighborhood topology and the local best swarm used to assess the global fitness of a solution and choose the global leader particle. The proposed algorithm is tested on three sets of different UA-FLPs from the literature with the size of the problem up to 62 facilities. The numerical results show that the proposed method is effective in solving the multi-objective UA-FLP.

International audience; Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. The proposed method is verified by comparison with several benchmark methods on five public datasets. Then, a real case study concerning fault detection in a braking system of a high-speed train is considered.

In statistics, machine learning, and related fields, feature selection is the process of choosing a smaller subset of features to work with. This is an important topic since selecting a subset of features can help analysts to interpret models and data, and to decrease computational runtimes. While many techniques are purely automatic, the data visualization community has produced a number of interactive approaches where users can make decisions taking into account their domain knowledge. In this paper we propose a new visualization technique based on radial axes that allows analysts to perform feature selection effectively, in contrast to previous radial axes methods. This is achieved by employing alternative scaled axes that provide insight regarding the features that have a smaller contribution to the visualizations. Therefore, analysts can use the technique to carry out interactive backwards feature elimination, by discarding the least relevant features according to the information on the plots and their expertise. Our approach can be coupled with any linear dimensionality reduction method, and can be used when performing analyses of cluster structure, correlations, class separability, etc. Specifically, in this paper we focus on combining the proposed technique with methods designed for classification. Lastly, we illustrate the effectiveness of our proposal through a case study analyzing high-dimensional medical chronic conditions data. In particular, clinicians have used the technique for determining the most important features that discriminate between patients with diabetes and high blood pressure.

Demand Side Management (DSM) has been a subject of much research due to the demand of strategies which enable the development of power generation, transmission and distribution in Smart Grids (SG). Throughout the years, many authors have applied mathematical programming and, or, metaheuristic methods for obtaining a satisfactory solution for load management, mainly considering only one performance function in their respective optimization approaches. However, in addition to maximum load peak minimization (an often used performance function), it may be interesting to deal with some other merit functions, such as energy production cost, costumer’s preferences and constraints, among others. Due to this multi-criteria nature of this problem, in this paper it is proposed an exact multi-objective methodology to the optimal Demand Side Management. Essentially, the DSM problem is addressed by using an ϵ-constraint approach, in which one of the objectives is handled with Quadratic Programming whilst the others are considered as linear constrains. This paper also discusses the behavior of the solutions under the presence of uncertainties on the problem parameters due to possible unpredictable variations of the consumption pattern, energy price and number of appliances. The results, regarding the case studies analyzed, have shown promising solutions with interesting trade-off relations between power consumption and energy production cost. Moreover, the robustness analysis has indicated not sensitive solutions even when some parameters of the problem are varied, such as the energy price and the number of types of appliances, which can be regarded as a useful information for the utility.

Mining incrementally closed item sets is an essential problem in data mining. Once closed item sets and their frequencies are found, the frequentation of a found closed item set is always determined. In recent times, concept lattice has been used as an intermediate structure for the purpose of mining incrementally (frequent) closed sets. However, lattice operations are very time-consuming when data fluctuates as well as the memory for lattice is consumed a lot when data becomes large. This paper proposes an intermediate structure called constructive set to produce closed sets along with their occurrence frequencies. The constructive set is constructed from a set of group patterns – an extended form of bit chains. This paper also proposes algorithms based on the constructive set for mining incrementally closed sets when adding and removing transactions. The proposed algorithms along with storing and calculating on bit data have significant advantages shown through experiments and comparisons.

A bi-objective Fuel efficient Green Vehicle Routing Problem (F-GVRP) with varying speed constraint is discussed in this paper as an extension of Green Vehicle Routing Problem (G-VRP). F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming. The problem is solved using Particle Swarm Optimization with Greedy Mutation Operator and Time varying acceleration coefficient (TVa-PSOGMO). The objective of this paper is to study the behaviour of F-GVRP under varying speed environment and its impact on the route cost and fuel consumption. Experiments are conducted with constant and varying speed constraints and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment.

This paper considers a shared scheduling environment for distributed manufacturing resources. In this paper, we proposed a multi-agent system based approach to promote competition and cooperation among multi-agents and to achieve global optimal scheduling. We first build two multi-agent system (MAS) architectures. One is enterprise multi-agent subsystem (Sub-EMAS) architecture comprises job agents, resource agents and manager agents; the other one is enterprise alliance multi-agent system (EA-MAS) architecture involves the addition of a mediator agent and a scheduling agent. Then we design a Shared Contract Net Protocol (SCNP) to support both the Sub-EMAS and EA-MAS. We propose two heuristic algorithms to solve the scheduling model. The computational experiments show that the EA-MAS, with the integration of shared resource information, is able to provide shared scheduling scheme for distributed manufacturing with a good performance.

Advances in neuroimaging techniques have enabled early diagnosis and better understanding of neuro-psychological disorders using resting state - functional Magnetic Resonance Images (rs-fMRI). However, factors like the large intra-class variability, high homogeneity, limited sample size and varied acquisition methodologies in aggregated datasets have limited their classification performance. These issues especially are highly prevalent in the automatic diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, a Discriminative Spatial Filtering Method (DSFM) is developed to improve the separability between the two classes in the dataset by automatically identifying regions of the brain with abnormal activities. Further, DSFM uses an orthogonal projection to extract highly separable features from raw rs-fMRI data of the identified regions of the brain. Using a projection based learning classifier DSFM achieved a classification accuracy of 73.83 % on the publicly available ADHD200 dataset. The results show that, selective inclusion of data from the regions of the brain with discriminant activity identified using DSFM removes the redundant features and improved the classification performance (improvement of 3.5 % over baseline). Further, the brain activity maps derived by inverse mapping of the DSFM spatial filters describe the nature of the abnormal brain activities and identify the corresponding regions responsible for the cognitive symptoms in neuro-psychological disorders like ADHD. The results in this paper clearly show that DSFM is a reliable diagnostic tool with interpretable results critical for the accurate understanding the etiological factors of neuro-psychological disorders like ADHD using rs-fMRI.

Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.

This paper presents a novel inertia moment estimation algorithm to enable the Active Force Control Scheme for tracking a wheeled mobile robot (WMR) effectively in a specific trajectory within constrained environments such as on roads or in factories. This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving in a pre-planned path with the presence of noisy measurements. The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. A new Proportional-Derivative Active Force Controller (PD-AFC-LS-QC), employing the use of laser simulator logic and quick compensation loop, has been developed in this paper to robustly reject the noise and disturbances. This controller has three feedback control loops, namely, internal, external and quick compensation loops to compensate effectively the disturbances in the constrained environments. A simulation and experimental studies on WMR path control in two kinds of environments; namely, zigzag and highly curved terrains, were conducted to verify the proposed algorithm and controller which was then compared with other existed control schemes. The results of the simulation and experimental works show the capability of the proposed algorithms and the controller to robustly move the WMR in the constrained environments.

Classifying manual and non-manual gestures in sign language recognition is a complex and challenging task. Sign language gestures are the combination of hand, face, and body postures, which often have self-occlusions and inter-object occlusions of both the hands, hands with face, or hands with upper body postures. This paper addresses the characterization of manual and non-manual gestures in recognizing the sign language gestures from continuous video sequences. This paper introduces a novel hyperparameter based optimized Generative Adversarial Networks (H-GANs) to classify the sign gestures, and it works in three phases. In phase-I, it adapts the stacked variational auto-encoders (SVAE) and Principal Component Analysis (PCA) to get the pre-tuned data with reduced feature dimensions. In Phase-II, the H-GANs employed Deep Long Short Term Memory (LSTM) as generator and LSTM with 3D Convolutional Neural Network (3D-CNN) as a discriminator. The generator generates random sequences with noise from the real sequence of frames, and the discriminator detects and classifies the real frames of sign gestures. In Phase-III, the proposed approach employs Deep Reinforcement Learning (DRL) for hyperparameter optimization and regularization. By getting the reward points, Proximal Policy Optimization (PPO) optimizes the hyperparameters, and Bayesian Optimization (BO) regularizes the hyperparameters. The proposed H-GANs used two different large vocabulary sign corpus of continuous sign videos to evaluate the performance and efficiency of the system. The experimental results on different dimensions reveal that the H-GANs improved the accuracy and recognition rate when compared with the state-of-the-art classification methods with reduced complexity.

Convolutional neural network is a prominent innovation in computer vision but is often troubled by problems such as dark light, turbidity, blur and high similarity to the background when applied to underwater object detection. Underwater object detection is one of the basic techniques of underwater grasping automation which plays a very important role in ocean detection and fishery of aquatic products. This paper presented an automatic detection method of underwater sea cucumber based on deep learning, which will provide effective technical support for the automated breeding and harvesting of sea cucumber. The Shortcut Feature Pyramid Network (S-FPN) proposed in this paper improves the existing multi-scale feature fusion strategy through shortcut connection. The ablation experimental results show that the mean average precision (mAP) of S-FPN reaches 91.5% which outperforms the baseline Feature Pyramid Network (88.6%), YOLO v3 (83.7%) and SVM-HOG (61.6%). To resolve the problem of complex environmental background interference of ocean floor, we proposed a Piecewise Focal Loss (PFL) function for balancing the positive and negative samples such that the algorithm can focus on the training difficulty of hard (i.e., positive) samples. And the ablation experimental results show that the mAP of PFL reaches 92.3% which outperforms the baseline Cross Entropy (91.5%) and Focal Loss (91.8%). Also, we chose Exponential Linear Unit as the optimization strategy, and Adaptive Moment Estimation as the activation function by ablation research, finally the mAP reached 94%.

Self-organizing and self-adaptation are known as two necessary means for reducing costs and efforts required for the maintenance of complex software systems. In strong self-organizing systems, decision-making processes are distributed internally among system elements without any centralized control point (internal or external). In these systems, local communications of agents at the micro-level cause the emergent of macro-level behaviors, which can be seen from a global point of view. These global behaviors contain essential information that usually is ignored and is out of reach of agents. Via increasing context-awareness of agents, this information can be used by agents to improve the performance of the system. In this paper, inspired by the concept of consulting, a relation between the macro-level and micro-level is made. In summary, using information hidden in emergent behaviors, it is made an indirect and advice-based (non-compulsory) effect on agents at the micro-level. Therefore, first, the proposed self-advising property (as a property of the self-adaptive hierarchy) is defined. Then using the MAPE-K loop and stigmergic communication, the advising process is described. Besides, SASO-System is provided to describe the self-advising property and the advising process. This system is implemented on a case study of the NASA-ANTS mission. The comparison of results with other paper shows that using self-advising property reduces the time spent for self-protecting of leader agents in the scenario considered. Also, experimental results indicate that when the system is of self-advising property, the number of calls for the self-protecting function is reduced, and the decrease percentage of self-adaptive function calls is at the range of 1.86–13.56. Results also show that in 80% of radio ranges, the context-awareness of the self-advising system is better than or equal to the context-awareness of the non-self-advising system.

In realistic production scheduling, the processing time of operations and the due time of orders always fail to be precisely estimated as deterministic values due to fluctuating manufacturing environments and modest delay tolerance. When fabricating complex products that are assembled by multilevel parts, tree-structure dependencies between parts lead to hierarchical precedence constraints between corresponding jobs. Consequently, this paper studies an uncertain flexible job shop scheduling problem with job precedence constraints (U-FJSP-JPC). Uncertain processing time and due time are represented as interval grey number and trimmed triangular fuzzy number respectively. A tardiness index indicator is devised to assess the delay extent of grey completion time relative to fuzzy due time. To solve U-FJSP-JPC with minimizing three objectives simultaneously involving interval grey makespan, interval grey total machine workload and average tardiness index, this paper elaborately designs a novel multi-objective multiple-micro-swarm leadership hierarchy-based optimization algorithm (MOM2SLHO). This algorithm adopts a two-vector encoding scheme based on job and operation sequencing and a grey active decoding scheme based on heuristic machine assignment. In MOM2SLHO, the entire search agents are divided into multiple micro-swarms in which each one conducts an independent search based on leadership hierarchy and communicates with others by specific strategies. MOM2SLHO embodies an enhanced external grid archive to store and retrieve non-dominated Pareto optimal solutions. Extensive experiments and statistical analyses demonstrate that the proposed MOM2SLHO algorithm outperforms other well-known and state-of-art algorithms significantly for solving the studied U-FJSP-JPC.

This paper presents a novel multi-pooling architecture generated by combining the advantages of wavelet and max-pooling operations in convolutional neural networks (CNNs), focusing on semantic segmentation tasks. CNNs often use pooling to reduce the number of parameters, improve invariance to certain distortions, and enlarge the receptive field. However, pooling can cause information loss and thus is detrimental to further operations such as feature extraction and analysis. This problem is particularly critical for semantic segmentation, where each pixel of an image is assigned to a specific class to divide the image into disjoint regions of interest. To address this problem, pooling strategies based on wavelets-operations have been proposed with the promise to achieve a better trade-off between receptive field size and computational efficiency. Previous works have confirmed the superiority of wavelet pooling over the traditional one in semantic segmentation tasks. However, we have observed in our computational experiments that the expressive gains reported from the use of wavelet pooling in other segmentation tasks were not observed in the scope of aerial imagery due to imprecision in the segmentation of image details. The combination of wavelet pooling and max-pooling, a solution not yet reported in the literature, can address that issue. Such gap observed in the pooling area motivated the two proposals that are the main contributions of this paper: (a) A new multi-pooling strategy combining wavelet and traditional pooling in a new network structure suitable for aerial image segmentation tasks; (b) Two-stream architectures using the traditional max-pooling and wavelet pooling as streams. These proposals were implemented using the Segnet, a known architecture for semantic segmentation. The computational experiments, based on the IRRG images from the Potsdam and Vaihingen data sets, demonstrated that the proposed architectures surpassed the original Segnet architecture’s performance with results comparable to state-of-the-art approaches.

Multi-Criteria Decision Making (MCDM) is a complex process. It aims to support decision makers in making their decisions more effective and consistent. MCDM provides a useful and successful alternative for handling three main types of MCDM problems, namely, choosing, ranking and sorting. The first two are the most common problems studied but the third offers a way to deal with real world MCDM problems that require alternatives to be assigned to ordered categories. The practitioners are currently developing and applying sorting methods to solve problems from different application areas. In spite of its interest and applicability, there is only one previous review on Multiple-Criteria sorting, performed almost 20 years ago. Hence, because of its interest this paper presents a new and systematic review of MCDM sorting methods that includes 30 years of research in the field. This review has systematically analyzed the conventional and non-classical methods of MCDM sorting and then classified the papers published into 16 application areas. The analysis reveals that the methodological development is still in growth phase for MCDM sorting and discovers the applied methods’ trends. It also shows the complete spectrum of the areas of the application addressed, the state of knowledge about methods, the type of contribution to the knowledge, and the application area for the four categories of the MCDM approaches. The systematic review scrutinizes each selected article in order to find out which approach from the multi-criteria sorting presents the most development based on its contribution and application of the methods. We also aim to discover which Multiple-Criteria sorting methods are the most studied in MCDM. The relevant finding is the relation between the most applied methods and the application areas together with future directions for further research.

Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon’s rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms.

Context-aware recommender systems have received considerable attention from industry and academic areas. In this paper, we pay heed to the growing interest in integrating context-awareness and multi-criteria decision making in recommender systems, to deal with the most pressing challenges in music recommender systems, namely the diversity of the recommended playlist, the scalability of the system, and the cold start problem. This paper introduces a new multi-criteria recommendation approach, named MORec, which generates Top-N music recommendations by bootstrapping the system using beforehand collected data. We usher by gauging the relevance of contextual information from the relation between three elements: user, music genre, and the user’s context. Then, we apply an aggregation technique to uncover the relationship between the context and the overall rating. Besides, we apply the K-means algorithm to generate a predictive model that comprises clusters of similar contexts defining the association between contextual dimensions and music genres. Carried out experiments emphasize very promising results of our approach in terms of clustering quality, compared to the Partitioning Around Medoids algorithm in terms of connectivity and stability. The comparison versus pioneering recommendation baselines underscored the effectiveness of MORec in terms of recommendation quality and usefulness.

One of the latest multi-criteria decision-making methods is best-worst method (BWM). In the procedure of BWM, decision maker (DM) identifies the most and the least important criteria namely, best and worst. Thereafter, DM identifies the degrees which he believes the best criterion is better than the other criteria, and identifies the degrees which he believes the other criteria are better than the worst criterion. Because of uncertainties in comparisons due to using linguistic variables for pairwise comparisons and also the lack of complete information, the crisp values of pairwise comparisons cannot appropriately model the problems. This paper develops the BWM for considering fuzzy pairwise comparisons (FBWM) by proposing a new fuzzy mathematical model which yields crisp weights from a fuzzy pairwise comparison matrix. Unlike to some previous papers that obtains fuzzy weights from fuzzy pairwise comparison matrix, the crisp weights of the proposed method of this paper eliminates the supplementary aggregation of fuzzy weights and ranking procedures. Moreover, the proposed method avoids obtaining the different ranking results due to the different ranking procedures of fuzzy numbers. Another outstanding advantage of this paper is that the obtained weights of the proposed method better satisfy the initial judgments compared to previous methods, while as we know, the satisfaction of the initial judgments is essential for pairwise comparison judgments. This paper presents several numerical examples to prove the good performance and the merit of the proposed method. According to the provided numerical examples, the proposed method of this paper absolutely outperforms the two well-known previous methods.

The variety and plethora of research papers available on the Web motivated researchers to propose models that could assist users with personalized citation recommendations. In recent years, citation recommendation models using Network Representation Learning (NRL) methods have shown promising results. Nevertheless, existing NRL-based models are limited in terms of exploiting semantic relations and contextual information between the objects of bibliographic papers’ networks. Additionally, these models cannot adequately explore the structure of heterogeneous information networks, topical relevance, and relevant semantics. Consequently, they suffer from network sparsity and inadequate personalization problems. To overcome these shortcomings, we present a network embedding model termed as Global Citation Recommendation employing Generative Adversarial Network (GCR-GAN). The proposed model exploits the Heterogeneous Bibliographic Network (HBN) to generate personalized citation recommendations. In particular, the proposed model utilizes semantic relations corresponding to the objects of the heterogeneous bibliographic network and captures network structure proximity employing the Scientific Paper Embeddings using Citation-informed Transformers (SPECTER) and Denoising Auto-encoder networks to learn semantic-preserving graph representations. Compared to baseline models, the recommendations generated by our model over the DBLP and ACM datasets prove that it outperforms baseline methods by gaining almost 11% and 12% improvement in terms of Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (nDCG) metrics, respectively. Furthermore, we analyzed the effectiveness of the proposed model considering network sparsity issue, where our model gains almost 7% better recall@100 score against the second-best counterpart.

In the Authorship Attribution (AA) task, the most likely author of textual documents, such as books, papers, news, and text messages and posts are identified using statistical and computational methods. In this paper, a new computational approach is presented for identifying the most likely author of text documents. The proposed solution emphasizes lazy profile-based classification and, by using the Term Frequency-Inverse Document Frequency (TF_IDF) scheme, introduces a new measure for identifying important terms of documents. The importance of the terms is then used to calculate the similarity between an anonymous document and known documents. The proposed solution works with raw text documents and does not require any NLP tools for preprocessing, which makes it language-independent. The efficiency of the proposed solution has been evaluated by conducting several experiments on two English and Persian datasets, each of which contains six corpora with different number of authors. The obtained results demonstrate that the proposed solution outperforms state-of-the-art stylometric features, employed by seven well-known classifiers, by obtaining 0.902 accuracy for the English dataset and 0.931 accuracy for the Persian dataset. In addition, supplementary experiments have been conducted to evaluate the effects of documents’ length on the accuracy of the proposed solution, to examine the computation time of the proposed solution and competitive classifiers, and to identify the most effective stylometric features and classifiers.

Crowdsourcing as an important computing resource for the Crowd-based Cooperative, crowdsourcing workers, due to the complexity of their personnel composition and the ambiguity of group characteristics, make it difficult to accurately recommend suitable worker groups for the task. Recommending crowdsourcing tasks to suitable workers can greatly improve the efficiency and quality of the execution of crowdsourcing tasks. The current crowdsourcing recommendation algorithm is based on the single characteristics of the workers, ignoring the multi-dimensional characteristics of the workers and the community characteristics of the crowd. This paper proposes a worker group recommendation method based on multi-community collaboration (GRMBC) by utilizing the worker characteristics information extracted from crowdsourcing platform. Based on the characteristics of workers'reputation, preference and activity, the method divides the worker group into several characteristics communities with similar behavior to discover the potential multi-community structure in the crowdsourcing worker group, and then selects Top-N worker group for recommendation through the interaction among multi-communities. At the same time, we also proposed two mitigation strategies for the cold start problem of data in crowdsourcing. This paper uses the public data collected by AMT to do the experiments, and compares the aggregation results of the recommendations generated by different algorithms. The results show the recommendations generated by the GRBMC algorithm proposed in this paper performs the best comprehensively. When the GRBMC algorithm that considers both worker attributes and communi-ty characteristics has an accuracy improvement of 0.03 ~ 0.04 compared with not using the recommendation algorithm on each index, the accuracy of 0.01 ~ 0.02 is improved com-pared with the method considering single characteristic. Moreover, compared with the individual recommendation method of the workers, the recommendation method of the workers' group is more in line with the platform requirements, and can well reflect the wisdom of crowd wisdom.

In the field of medicine, decision-making has traditionally been carried out based on the best available scientific information and the experience of specialists using data found in analog formats such as radiographies, medical reports, and handwritten notes, among others. In this sense, the Big Data phenomenon is changing the world of medicine since the technologies that have been developed have made available to researchers and clinicians enormous amounts of data in digital formats that can be used to complement or help in complex tasks such as mentioned decision making. A key element in this process is data analysis techniques, since without them it is not possible to exploit the information. Currently the most used techniques are based on algorithms in the area of artificial intelligence and more specifically machine learning. This paper focuses on a specific domain of medicine, renal replacement therapies for end-stage renal disease, where machine learning is beginning to be used as a complementary tool to predict or make decisions. This paper provides a narrative review of the main machine learning methods that are being used to conduct end-stage renal disease treatment analyses.

In this paper, we propose a Deep Belief Network (DBN) based approach for the classification of audio signals to improve work activity identification and remote surveillance of construction projects. The aim of the work is to obtain an accurate and flexible tool for consistently executing and managing the unmanned monitoring of construction sites by using distributed acoustic sensors. In this paper, ten classes of multiple construction equipment and tools, frequently and broadly used in construction sites, have been collected and examined to conduct and validate the proposed approach. The input provided to the DBN consists in the concatenation of several statistics evaluated by a set of spectral features, like MFCCs and mel-scaled spectrogram. The proposed architecture, along with the preprocessing and the feature extraction steps, has been described in details while the effectiveness of the proposed idea has been demonstrated by some numerical results, evaluated by using real-world recordings. The final overall accuracy on the test set is up to 98% and is a significantly improved performance compared to other state-of-the-are approaches. A practical and real-time application of the presented method has been also proposed in order to apply the classification scheme to sound data recorded in different environmental scenarios.

In this paper, we consider a class of multi-objective optimization (MOP) problems where the objective holders are independent humans or human-based entities. These problems are indeed game problems, which we call non-cooperative multi-objective optimization problems (NC-MOP). We discuss that for such problems, the Pareto-Optimal (PO) solutions are not necessarily valid as they primarily require Nash equilibrium (NE) solutions. Instead, we suggest that a new solution concept of the Pareto-optimal Equilibrium (POE) point could be adopted. Such a solution is, in particular, important in engineering design and articulation of new rules and protocols among independent entities. This paper reviews all relevant works that approach the POE concept and investigates the interplay between game problems and multi-objective optimization problems. We present illustrative examples to deepen our understanding of where a POE solution is achievable, as this is not always the case.

This paper investigates the link prediction in multiplex networks. Multiplex networks that represent multiple types of interaction between the same group of individuals are a special case of complex networks. Each type of interaction is modeled as a layer in a multiplex network. Usually, the topological structures between different layers of a multiplex network have a certain extent of correlation. As a result, the accuracy of link prediction in multiplex networks can be enhanced by combining the information of different layers. In this paper, link prediction in multiplex networks is regarded as a multiple-attribute decision-making problem, in which the potential links in the target layer are considered as alternatives, layers are viewed as attributes, and the similarity score of a potential link in each layer is an attribute value. In implementation, the TOPSIS method is employed to rank alternatives, and interlayer relevance is used to weight the attributes. The experimental results show that the proposed method is not sensitive to the parameter and the interlayer relevance measure, and achieves superior prediction accuracy.

Semantic web technology seems to be in the infant stage as only little efforts have been taken on ontology construction with cross-domain application. This paper intends to take an effort on a new workspace, in which the ontology construction model under cross-domain application is performed. The core concern of this work is on two decision-making process namely data filtering and data annotation. Certain process is followed in this work: (i) Preprocessing (ii) Proposed Jaccard Similarity Evaluation (iii) Data filtering and Outlier Detection (iv) Semantic annotation and clustering. More particularly, data filtering is performed based on the evaluated similarity function. The outliers are identified and grouped separately. The data annotation is performed based on the semantics and thereby the clustering process takes place to form the ontology precisely. This clustering process obviously relies to the optimization crisis as the optimal centroid selection becomes the greatest issue. In order to solve this, this paper extends with the introduction of a hybrid algorithm named Circling Insisted-Rider Optimization Algorithm (CI-ROA), which hybrids the concept of Whale Optimization Algorithm (WOA) and Rider Optimization Algorithm (ROA), respectively. Finally, the performance of proposed work is compared and proved over other state-of-the-art models.

The symbiotic organisms search (SOS) algorithm is investigated and corrected by removing the benefit factors due to their biased search around the original dot. However, the removal of these benefit factors results in performance that is far inferior to the outstanding performance of the basic SOS algorithm. Accordingly, this paper suggests adopting combination schemes for the mutualistic equations in order to prevent premature convergence, and further recommends adopting lower combination rates for parasitic equations. Combination schemes are found to be not applicable to the commensal equation, as this equation is not greedy. Therefore, this paper proposes two types of combination schemes to improve the corrected SOS version in terms of achieving high early convergence speed, attaining convergence precision at a lower cost, arriving at the convergence plateau at either a lower cost or a higher level of precision, handling tests of composition functions well, and achieving competitive performance on CEC2015 test problems.

This paper develops smoothing data association based on integrated probabilistic data association (FLIPDA-S) tracker to identify a multicopter UAV (MUAV) and estimate its trajectory in an uncertain and cluttered environment. Recently, a number of unidentified MUAVs have been flown in prohibited airspace and a radar system has been utilized to identify the MUAV and track the trajectory. However, target tracking methods could have difficulties to track the target MUAV through randomly distributed and moving objects. The vehicle state estimation (VSE) algorithm, which utilize FLIPDA-S in this paper, adopts UAV kinematics and dynamics for tracking a UAV in clutter and false targets with significant UAV identification performance. The performance of the method has been validated with and without an initial position of the target MUAV. Both numerical simulation and experiments are demonstrated to verify the effectiveness and accuracy of FLIPDA-S with VSE algorithm to track UAVs in an uncertain and clutter environment.

Healthcare waste (HCW) management is a complex and challenging problem. It is one of the priorities in health. An increase in the number of the health services provided leads to an increase in the amount of HCW, which has particularly been noticeable in recent years. Since this is the waste that may pose a risk to humans and the environment, it is necessary to ensure an adequate treatment of the same. HCW management is particularly important in developing countries, due to inappropriate disposal methods, underfunding and a lack of the infrastructure. In order to achieve the cost-effectiveness and sustainability of this area, HCW should be minimized through an adequate treatment of the same. The Public Enterprise Zdravstvo Brcko (Brcko Health System) has intensively been addressing the HCW management issue. They have decided to upgrade the HCW system by purchasing a new infectious waste treatment facility. The paper is aimed at creating a new original integrated multicriteria decision-making model based on D numbers for processing fuzzy linguistic information. This model will serve to support management in the procurement of the mentioned facility. The model integrates the benefits of different approaches and theories. An initial model was formed, consisting of the six potential solutions evaluated based on the 18 criteria classified into the following four groups: social, environmental, economic and technological. Four experts in this field evaluated the criteria and potential solutions. Then, a new Best-Worst Method based on D numbers (BWM-D) was applied in order to determine the significance of the criteria. After that, a Multi-Attributive Border Approximation Area Comparison Based on D numbers (MABAC-D) was developed and applied so as to evaluate and select an infectious waste treatment facility. The results have shown that the alternative A1 gives the best results, whereas the alternative A5 shows the worst results. Finally, a sensitivity analysis was performed to validate the obtained results. In this part of the paper, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS-D) and VIsekriterijumska Optimizacija Kompromisnog Resenja (VIKOR-D) were developed in order to validate the results. When procuring a new Contagious Waste Treatment System, the characteristics of the available devices need be perceived and all the criteria need be taken into account in order to provide a device which will solve the HCW problem in the best way. This paper has shown how D numbers can be used when making a selection of an HCW management device, and also how all the characteristics of such a device can be perceived and how the device demonstrating the best characteristics can be selected.

Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.

In recent years, the advancement in technology and the availability of a large number of sensors has led to a huge amount of real-time streaming data. The real-time data measured by Phasor Measurement Units (PMU), are of streaming data type and their analysis can identify the status of the power system failure which leads to system instability. Hence online monitoring of PMU data is extremely important which includes preprocessing, filtering, and analysis before taking any corrective actions in the power system. One of the crucial analyses that can be performed over PMU data is the identification of anomalies that arise due to several factors. Early detection of anomalies in the PMU streaming data is highly useful though in real-time it is difficult to detect anomalies consistently. This paper proposes a novel framework for detecting anomalies in multivariate streaming PMU data based on the Density Estimation technique. The main novelty of this paper is that the proposed model is suitable for any kind of anomaly detection in multivariate streaming PMU data which is not addressed in literatures. The proposed system implements Principal Component Analysis in streaming real-time data to select the relevant features. The K-Means algorithm is performed over the selected feature vectors to find the number of clusters in the streaming PMU data. The Gaussian Mixture Model (GMM) is developed to represent the normal PMU data of the system. The minimum and maximum value of the probability of the training samples being generated by the model is selected as a minimum and maximum threshold. The samples with probability less than the minimum threshold or greater than the maximum threshold are considered as anomalies. The main advantage of this proposed model is that it can perform anomaly detection in multivariate streaming PMU data even online within the specified window. The proposed system can identify real time anomalies of multivariate dataset even by offline training model of GMM. The proposed model is developed in MATLAB 2018b version and is tested in streaming PMU data both for offline and online datasets. The performance of the proposed model is validated using performance metrics and the results show that F1 score is 0.95 for 2 features, 0.91 for 3 features, and 0.99 for 8, 12 and 16 features and 1 for 10 features in offline mode. The performance evaluation of testing data in online mode for 2, 3, 8, 10, 12 and 16 features are obtained which proves that the system is adapting to the streaming PMU data with reduced false positive rate and F1 score of 1 for 8, 10, 12 and 16 features. The experimental results prove that the proposed framework for anomaly detection in streaming data is more effective and accurate in identifying the anomalies in real time PMU streamed dataset.