Search

Citation recommendation can help researchers quickly find supplementary or alternative references in massive academic resources. Current research on citation recommendation mainly focuses on the citing papers, resulting in the enormous cited papers are ignored, including the relations among cited papers and their citation context cited in citing papers. Moreover, cited paper’s content is often denoted with its original title and abstract, which is hard to acquire and rarely considers different citation motivations. Furthermore, the most appropriate method for semantic representation of cited papers’ relations and content is uncertain. Therefore, this paper studies citation recommendation from the perspective of semantic representation of cited papers’ relations and content. Firstly, four forms of citation context are designed and extracted as cited papers’ content considering citation motivations, as well as co-citation relationships are extracted as cited papers’ relations. Secondly, 132 methods are designed for generating semantic vector of cited paper, including four network embedding methods, 16 methods by combining four text representation algorithms with four forms of citation content, and 112 fusion methods. Finally, similarity among cited papers is calculated for citation recommendation and a quantitative evaluation method based on link prediction is designed, to find the most appropriate form of citation content and the optimal method. The result shows that doc2vecC (Document to Vector through Corruption) with the form of CS&SS (Current Sentences and Surrounding Sentences) performs best, in which the AUC (Area Under Curve) and MAP (Macro Average Precision) reach 0.877 and 0.889 and have increased by 0.462 and 0.370 compared with the worst-performing method. This performance is slightly improved by parameters adjustment, and a case study is performed whose results have further proved the effectiveness of this method. In addition, among four forms of cited papers’ content, CS&SS performs best in almost all methods. Furthermore, the fusion methods not always perform better than the single methods, where doc2vecC (CS&SS) performs better than the best fusion method GCN (Graph Convolutional Network). These results not only prove the effectiveness of citation recommendation from the perspective of cited paper, but also provide helpful and useful suggestions for method selection and citation content selection. The data and conclusions can be extended to other text mining-related tasks. Simultaneously, it is a preliminary research which needs to be further studied in other domains using emerging semantic representation methods.

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods.

Every music composition has a composer at the core of its building block, molding it into a style of their own. The creative compositional style of a composer varies dynamically with every composer which is perishable and inimitable but cannot be preserved. This paper proposes a hybrid style transfer model which is an end-to-end approach to transfer style, compose as well as evaluate the style transfer accuracy in the domain of the target music composer. The paper focuses on 3 composer maestros Liszt, Chopin and Schubert taken from the Maestro dataset. The main step of music composer style transfer is accomplished in 3 systematic steps, training composer classifiers according to feature sets, generating a model of a composition in a particular composer’s style from noise, and finally style transfer and its evaluation. The 3 steps are interconnected in a way that each step is the building block for the next step. The GAN architecture of the style transfer step is built out of the GAN architecture of the second step. The compositions generated from each architecture is finally evaluated by the common pre-trained classifier of the first step. The highest accuracy obtained is 80% for composer classification using the maestro dataset, 77.27% for the classification of the generated style transferred version of a composition into the target composer class using the pre-trained classifiers.

This paper presents the Storybox Methodology which combines a novel framework for structuring knowledge and conversations around a story (D-PAF), with a live chatroom-based training approach that builds the conversation knowledge base via live chatroom interactions. Chatbots have achieved success as intelligent interfaces in education, health, sales and support, but their move towards mainstream adoption has been hindered by the large amount of development resources required, in terms of data collection, preparation, user testing and technical knowledge. The complexity of the development task often necessitates both a system author and a domain expert working effectively together, adding further complexity and risk. Overcoming these barriers could increase feasibility of chatbots in a range of expert contexts. In education, there are groups of learners who do not enjoy reading and writing. Storytelling chatbots might be able to introduce these groups to enjoyable new ways to read and write, having a beneficial impact on their education and future prospects. This paper proposes the Storybox Methodology for the rapid development of storytelling chatbots. Storybox is evaluated by creating, training and testing ‘The Ghost’, a chatbot enacting Hamlet's Ghost character from William Shakespeare's dramatic tragedy. The results showed that after a period of live chatbot training of only 25 training conversations, The Ghost was able to conduct convincing conversations with participants.

Lithology identification is an important part of petroleum drilling engineering. Accurate identification of lithology is the foundation to ensure the smooth operation of drilling engineering. Conventional lithology recognition mainly relies on human experience. The recognition accuracy depends on the level of technical personnel and the recognition response time is lagging. It is difficult to meet the demand. How to achieve rapid and intelligent recognition of lithology is one of the core technical problems faced by oil drilling. In order to solve this problem, this paper uses the mechanical specific energy and other ground parameters to establish a rapid and intelligent recognition method of lithology based on the simulated annealing optimization support vector machine model. In order to improve the accuracy of the model recognition, a large number of methods have been developed from two classes and multiple classes, simulation analysis results show that the lithology recognition model based on the principle of mechanical specific energy and the simulated annealing optimization support vector machine algorithm can predict a priori unknown data with a prediction accuracy of over 90%. Compared with the support vector machine model and the K-means model, simulated annealing optimization support vector machine is used for comparative analysis, the algorithm to establish a lithology recognition model has better performance and higher accuracy. The intelligent identification model of lithology based on the principle of mechanical specific energy and simulated annealing optimization support vector machine algorithm established in this paper can quickly and accurately identify lithology, and provide new technical support for oil drilling formation analysis.

With the rapid development of e-commerce, online reviews have played an increasingly important role in consumers' shopping intentions and behaviors. Therefore, how to effectively identify fake reviews has become one of the important issues that need to be resolved. Since the existing methods do not fully consider the time burst characteristics of reviews, this paper proposes a suspicion degree determining method based on the three-dimensional time series. Besides, combining the suspicion degree feature, review text features, and reviewer's behavior features together, this paper proposes a more comprehensive fake review identification framework. The yelp and amazon public data sets are carried out to verify the effectiveness of the proposed method, and the experimental results show that the proposed method outperforms the most advanced methods.

Nowadays the use of visual navigation in orchard is rapidly growing, while current research mainly focuses on orchard row detection or obstacle detection separately. The economic, fast and reliable navigation vision system that completing orchard row following and obstacle avoidance simultaneously and the obstacle avoidance algorithm for vision system without complex multiple navigation systems combining are needed further research. In this paper, a trinocular vision system for orchard vehicle is set up and the obstacle avoidance algorithm based on coupling of geometric constraint and virtual force field is designed for its vision system. First of all, based on analyzing the different vision system characteristics and the detection demand of orchard obstacle avoidance the trinocular vision system for orchard vehicle is set up based on a wide-angle camera and binocular stereo vision system. Then, the image processing algorithm of orchard row and obstacle detection is designed. For orchard row detection, the trunk regions enhancement algorithm is designed based on grayscale morphology filtering the trunk regions prediction algorithm is designed based on optical flow method to improve the trunk regions detection speed and accuracy. For obstacle detection, the background equalization algorithm based on H-channel image characteristics and the interference weaken algorithm with G-channel image characteristics are designed. Based on the row and obstacle detection results the algorithm for obstacle avoidance in orchard based on coupling of geometric constraint and virtual force field is designed. In the obstacle avoidance process, the virtual force field method is used as the mainly control of the vehicle obstacle avoidance process, and the geometric constraint between visual model of trinocular vision system and obstacle position is coupling with it to fulfill the avoidance demand of the obstacle with special shape and other condition that influence the virtual force field calculation. The experimental results show that the trinocular vision system for orchard vehicle built in this paper has strong adaptability to the actual environment which is accurate and stable for orchard row detection and obstacle detection simultaneously. The average deviation is 4.76 cm and 7.05 cm at 0.5 m/s and 1.0 m/s respectively, the average deviation of obstacle distance detection in the Z-axis direction is 3.18 cm, and in the X-axis direction is 0.45 cm. And the obstacle avoidance algorithm designed in this paper is effective in the orchard which can meet the actual production requirements. The research results will lay a foundation for the development of intelligent equipment and unmanned management in orchard.

This paper presents an approach to design better line plans for realistic cases. The planning problem is modelled as a realistic instance of the Transit Network Design and Frequency Setting Problem (TNDFSP). It incorporates additional assumptions taken from practice such as discrete frequencies, a subset of allowed terminal nodes and circular lines. A bi-objective memetic algorithm minimizes the average travel time (ATT) of the passengers and the fleet size. The method to generate the TNDFSP instance from real data is described in detail. Moreover, a new metric is proposed to compare different transit networks. In order to illustrate the approach, it is applied to the area of Utrecht, The Netherlands. The results show that the current network is modelled accurately and that the algorithm successfully generates alternative bus line plans within reasonable CPU time. It returns a subset of non-dominated solutions from which a compromise solution can be selected for practice. There are solutions with the same fleet size as the current solution, but a 6% lower ATT, or solutions with the same ATT, but a fleet size which is 19% smaller. Compared to the current network, the algorithm finds that it is convenient to substantially reduce the number of lines and to leave a small portion of demand unsatisfied. This paper also presents extensive experiments to test the impact of different assumptions about demand, bus capacity and minimum frequency.

Regular Ship hull maintenance is an essential for sustainability. The maintenance work of ship hulls that involve human labor suffers from many shortcomings. Maintenance robots have been introduced for drydocks to eliminate these shortcomings. An energy-efficient Complete Coverage Path Planning (CCPP) is a crucial requirement from a ship hull maintenance robot. This paper proposes a novel energy-efficient CCPP method based on Glasius Bioinspired Neural Network (GBNN) for a ship hull inspection robot. The proposed method accounts for a comprehensive energy model for path planning. This energy model reflects the energy usage of a ship hull maintenance robot due to changes in direction, distance, and vertical position. Furthermore, the proposed method is effective for dynamic workspaces since it performs online path planning. These are the major contributions made to state of the art by the work proposed in this paper. The behavior and the performance of the proposed method have been compared against state of the art through simulations considering Hornbill, a multipurpose ship hull maintenance robot. The validation confirms the ability of the proposed in realizing a complete coverage of a given dynamic workspace. According to the statistical outcomes of the comparison, the performance of the proposed method significantly surpasses that of the state-of-the-art methods in terms of energy usage. Therefore, the proposed method contributes to the development of energy-efficient CCPP methods for a ship hull maintenance robot.

In Data Envelopment Analysis (DEA), a variety of approaches have been used in the context of single-stage and basic serial two-stage systems to attain fairness in the evaluation of decision-making units (DMUs). Little work, however, has been done to address this challenge in a generalised two-stage structure featuring additional inputs in the second stage and a proportion of first-stage outputs as final outputs. In this paper, we argue that in this context, fairness is enhanced by increasing measures related to the discriminatory power and the weighting scheme of the method. We describe a mechanism that gives prominence to a more contemporary concept of fairness, incorporating diversity and inclusion of minority opinions. These aspects have, to our knowledge, not yet received explicit attention in the methodological development of DEA. We propose a novel combination of an additive self-efficiency aggregation model, a minimax secondary goal model, and the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, in order to promote these aspects of fairness, and thus achieve a better degree of cooperation between the stages of a DMU and among DMUs. The additive aggregation model is chosen over the alternative multiplicative approach for a variety of reasons relating to the emphasis on the intermediate products exchanged and the simplification. The minimax model offers peer evaluation in which each DMU aims to evaluate the worst of the others in the best possible light. Application of the CRITIC method to DEA addresses the aggregation problem within the cross-efficiency concept. Practical applications of this approach could include supporting the determination of training needs in job rotation manufacturing, or evaluation of sustainable supply chains. The paper includes a description of a numerical experiment, illustrating the approach.

This paper addresses a bi-objective dynamic multiprocessor open shop scheduling problem in which the simultaneous objectives of minimizing both the mean weighted flow time and the makespan are considered. This problem is commonly encountered in maintenance and healthcare diagnostic systems. Since it is NP-hard for both objectives, efficient heuristics are needed to quickly generate a set of non-dominated solutions that a decision maker would choose from. For this sake, two metaheuristic approaches based on the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) are developed in this paper. Both metaheuristics are hybridized with simulated annealing (SA) local search. Parameter tuning computational experiments are conducted first on a set of 30 small instances from the literature for which Pareto optimal solutions are known. Then, computational experiments on large randomly generated instances are conducted. Computational results for small instances show that the NSGA-II is capable of generating non-dominated solutions that are very close to the optimal Pareto front. Results also reveal that the performance of the NSGA-II is better in most of the cases compared to the MOGWO under different settings of the studied problem for both small and large instances. However, for large instances with large number of workstations and jobs, low loading level and high percentage of busy machines at the beginning of the schedule, the difference in performance between both metaheuristics is minor.

Financial services industries, such as insurance, increasingly use data from their broad cross-section of customers and follow these customers over time. In other areas such as medicine, engineering, and communication systems, it is well known that following subjects over time may result in biased data, for example, the so-called ”dropout effect”. This paper introduces techniques to address dropout commonly encountered in the insurance domain. Specifically, in the insurance context, multivariate claims outcomes may be related to a customer’s dropout or decision to lapse a policy. Insurance claims outcomes are also naturally a hybrid with both discrete and continuous components, which adds complexity to model calibration. Decision makers in the insurance industry will find our work provides helpful guidance in integrating customer loyalty, especially with bundled coverages. This paper introduces a generalized method of moments technique to estimate dependence parameters where associations are represented using copulas. This is especially useful for large data sets. The paper describes how the joint model provides new information that insurers can use to better manage their portfolios of risks. An application to a Spanish insurer data set is presented.

The research and practice of information systems have progressed by leaps and bounds due to the emergence of big data analytics and machine learning. The paper undertakes a bibliometric study to analyze the contributions of major authors, universities/organizations, and countries in terms of productivity, citations, and bibliographic coupling. A sample of 2160 articles from the Scopus for the period 2006–2020 is the basis of the study. The publications are grouped into five clusters, of which Cluster 1 is consistently dominant in the information systems publication landscape. Cluster 2 includes published studies on the Internet of Things, security, and cloud computing, which have also been widely researched. Cluster 3, the third-largest cluster, has attempted to investigate social media analytics. Cluster 4 aims to look into the impact of classification and predictive, which is found to have sustained research interest. Topics with scant coverage in terms of papers are primarily in Cluster 5, indicating saturation in the area and the need for conducting inter-disciplinary studies. The results of our study provide valuable insights for potential contributors and global audiences in terms of emerging topics for research.

Due to speckle noise, changes in kidney shape and size between patients, and similar regions, segmenting kidneys in ultrasound images is challenging. To alleviate this challenge, we proposed a novel CNN model, namely multi-scale fusion network of structural features and detailed features (SDFNet), to segment kidneys accurately and robustly. Specifically, the architecture includes structure feature extraction network (S-Net), detail information extraction network (D-Net) and multi-scale fusion block (MCBlock), which are in charge of extracting structural features, capturing texture details and merging features, respectively. In S-Net, we designed a boundary detection (BD) module to obtain more complete kidney structural features. In addition, this paper also designed a step-by-step training mechanism to improve the generalization ability of the SDFNet. We validated the proposed method and compared the same kidney ultrasound dataset with several state-of-the-art methods using six quantitative indicators. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on kidney ultrasound image segmentation. It is worth noting that this paper quantitatively analyzes the loss function of segment kidney ultrasound images. This work is a good reference for future research.

Group recommender systems have emerged as a solution to recommend interesting, suitable, and useful items that are consumed socially by groups of people, rather than individually. Such systems have pushed for the use of new recommendation methods within such an emerging scenario, in which the use of the collaborative filtering paradigm is the core of the recommender algorithm. However, collaborative filtering presents several drawbacks and limitations in this scenario, such as the need for lots of rating values, as well as their co-occurrence across several items and users (scarcity). In order to overcome these drawbacks, this research explores a taxonomy for content-based group recommendation systems (CB-GRS), and subsequently the paper discusses and analyzes three specific models that can be used to build CB-GRS, which are (1) CB-GRSs supported by recommendation aggregation and individual ranking, (2) CB-GRSs supported by recommendation aggregation and user-item matching, and (3) CB-GRSs supported by the aggregation of user profiles. Furthermore, the paper presents a hybrid CB-GRS that combines the models (2) and (3) and integrates feature weighting and aggregation function switching. An experimental protocol over well-known datasets is then developed in order to evaluate the proposals. The current study aims at providing a basis to develop a research branch concerning content-based group recommender systems.

This short communication provides a discourse emerged after reading “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection, Expert Systems with Applications, 148, 113239, 2020.” The discussed paper proposes a novel application of stacking-based ensemble for seizure detection, where several deep neural networks were used as base classifiers. The ensemble design and experimental results presented by the author show some weaknesses, which is indicated by, one of which, an inability of the proposed model to outperform previous studies. In this note, controversy of the discussed paper is explained and an improved version of stacking-based deep neural network is also further introduced and detailed to prevent it in the future.

During the last decades, sampling-based algorithms have been used to solve the problem of motion planning. RRT*, as an optimal variant of RRT, provides asymptotic optimality. However, the slow convergence rate and costly initial solution make it inefficient. To overcome these limitations, this paper proposes a modified RRT* algorithm, F-RRT*, which generates a better initial solution and converges faster than RRT*. F-RRT* optimizes the cost of paths by creating a parent node for the random point, instead of selecting it among the existing vertices. The creation process can be divided into two steps, the FindReachest, and CreatNode procedures, which require few calculations, and the triangle inequality is used repeatedly throughout the process, thus, resulting in paths with higher performance than those of RRT*. Since the algorithm proposed in this paper is a tree extending algorithm, its performance can be further enhanced when combined with other sampling strategies. The advantages of the proposed algorithm in the initial solution and fast convergence rate are demonstrated by comparing with RRT*, RRT*-Smart, and Q-RRT* through numerical simulations in this paper.

Building facilities condition assessment is considered a fundamental aspect of an effective decision-making maintenance management plan to fulfill service requirements. A noticeable dearth of studies is believed to have delivered condition assessment approaches for existing buildings; however, these approaches are still deemed premature, with some limitations in demand enhancement. This paper presents a novel physical condition assessment framework for existing educational buildings that contribute to the body of knowledge by offering a state-of-the-art approach incorporating an Artificial Neural Network (ANN) predictive model and a Structural Equation Model (SEM). The ANN predictive model aims to forecast the future condition-rating states for each facility component in various building spaces. Simultaneously, the SEM determines the proportionate weights of building facilities components. The primary objectives of this paper are to prioritize building components for maintenance purposes and record the potential effects of several parameters influencing the condition state of building components. These objectives can be achieved via four sequential modules: 1) scan to BIM module; 2) condition assessment prediction module; 3) proportionate weight determination module; and 4) entire space rating value module. Condition-monitoring data on six different buildings' internal components are analyzed to anticipate their future condition. The components carried out are: 1) wooden flooring tiles; 2) gypsum board ceiling tiles; 3) wooden doors; 4) wooden windows, 5) split air conditioner units; and 6) desktop computers. The overall coefficient of determination (R2) of the developed ANN models for the predicted six components conditions are 0.99, 0.99, 0.927, 0.88, 0.97, and 0.972, respectively.

As an important part of the power system, power load forecasting directly affects the national economy. Small improvements in power load forecasts can save millions of dollars for the power industry. Therefore, improving the accuracy of power load forecasting has always been the pursuing goal for a power system. Based on this goal, this paper proposes a novel connection, the dense average connection, in which the outputs of all preceding layers are averaged as the input of the next layer in a feed-forward fashion. Dense average connection can alleviate the problem of gradient explosion without introducing new parameters. Based on dense average connection, we construct the dense average network (DaNet) for power load forecasting. On two public datasets (ISO-NE dataset and NAU dataset), we use MAPE, MAE and RMSE to evaluate the performance of DaNet. The predictions of DaNet are better than those of existing benchmarks. On this basis, this paper uses the ensemble method to reduce the peak value of prediction bias, which helps to alleviate the dispatching problem caused by unexpected loads. To verify the reliability of the model predictions, the robustness is analyzed and verified by adding input disturbances. The experimental results show that the proposed model is effective and robust for power load forecasting.

Social networks content analysis has become more challenging over the years due to the rapidly increasing amount of data. Real social networks are omnipresent in everyday life, which makes the structure of the generated data more complex. A key task in social networks analysis is to reduce the network’ s size and to produce an approximate representation that preserves the original network’ s properties. This task is known as graph’ s reduction and is gaining increasing attention in the scientific community. A review of literature reveals diverse methods to address this task. Some of them are based on graph coarsening and are developed to cope with the problem of communities detection. Others are part of graph sampling and are designed to reduce the graph’ s size while preserving its structure, which is our purpose. In this paper, we put forth a distributed model called DGS ”Distributed Graph Sampling” to generate a sample in a distributed way. The idea behind distributing our model is to cope with large scale social networks. In effect, our model is based on the MapReduce framework that allows to access simultaneously to several data segments for the calculation during the sampling strategy. The main task of our model is to use a new centrality measure based on the degree centrality to sample the graph. We evaluate the performance and the scalability of our DGS model using real world social networks. In this paper, we will compare our proposed model to four well-known sampling strategies in order to demonstrate its efficiency to preserve the original network’ s structure.

In today's digital world, there is an increasing focus on soft skills. On the one hand, they facilitate innovation at companies, but on the other, they are unlikely to be automated soon. Researchers struggle with accurately approaching quantitatively the study of soft skills due to the lack of data-driven methods to retrieve them. This limits the possibility for psychologists and HR managers to understand the relation between humans and digitalisation. This paper presents SkillNER, a novel data-driven method for automatically extracting soft skills from text. It is a named entity recognition (NER) system trained with a support vector machine (SVM) on a corpus of more than 5000 scientific papers. We developed this system by measuring the performance of our approach against different training models and validating the results together with a team of psychologists. Finally, SkillNER was tested in a real-world case study using the job descriptions of ESCO (European Skill/Competence Qualification and Occupation) as textual source. The system enabled the detection of communities of job profiles based on their shared soft skills and communities of soft skills based on their shared job profiles. This case study demonstrates that the tool can automatically retrieve soft skills from a large corpus in an efficient way, proving useful for firms, institutions, and workers. The tool is open and available online to foster quantitative methods for the study of soft skills.

This paper addresses the problem of how to represent education system measurement definitions and the data used to derive them. ISO 37120 is standard for measuring city performance. It defines 100 indicators of which seven focus on Education. As cities adopt the standard and begin publishing their indicator values, citizens, city bureaucrats and politicians will be able to see how well their education systems perform relative to other cities. Hence, education systems will be subject to greater scrutiny and will have to provide evidence-based explanations for their performance. In order to enable the automated analysis of city performance, four problems must be solved: 1) how are indicator definitions represented? 2) how is the data used to derive an indicator value represented? 3) how is educational system specific knowledge represented, and 4) how is city specific educational system knowledge represented? In this paper we present an Education Ontology designed to represent ISO 37120 education indicators and the information delineated above. The ontology is designed to enable an intelligent agent to perform consistency and root cause analysis of city indicators. It can also be used by cities to publish their education indicators and data, using Semantic Web standards, on their Open Data portals.

Bayesian network structure learning is the basis of parameter learning and Bayesian inference. However, it is a NP-hard problem to find the optimal structure of Bayesian networks because the computational complexity increases exponentially with the increasing number of nodes. Hence, numerous algorithms have been proposed to obtain feasible solutions, while almost all of them are of certain limits. In this paper, we adopt a heuristic algorithm to learn the structure of Bayesian networks, and this algorithm can provide a reasonable solution to combine the PC and Particle Swarm Optimization (PSO) algorithms. Moreover, we consider structure priors to improve the performance of our PC-PSO algorithm. Meanwhile, we utilize a new mutation operator called Uniform Mutation by Addition and Deletion (UMAD) and a crossover operator called Uniform Crossover. Experiments on different networks show that the approach proposed in this paper has achieved better Bayesian Information Criterion (BIC) scores than other algorithms.

Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995–2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +- 0,065). The results are robust and efficient, and the model’s predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.

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.