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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.

This paper takes the four types of resource allocation (randomly oriented, relationship-oriented, cooperation oriented, and knowledge-embedded) as its premise and investigates the complex characteristics of knowledge flow network evolution in strategic alliances, taking into account the mutual variance effects of the evolution mechanism. Existing research has neglected the differences in resource allocation types, by and large employed statistical analysis methods, and identified only the linear relationships among experimental variances of cross-sectional data. The present study differs from existing research in the following ways: First, we thoroughly consider the multi-faceted nature of resource allocation. Second, we use the method of multi-agent imitation according to perspective of dynamic system evolution and the principle of phase theory, allowing the explicitly analysis of nonlinear functional logic, forms and patterns in the variance. Finally, we analyze the appropriateness of different resource allocation models. Our paper features several significant findings: (1) The evolution of the knowledge flow network of a strategic alliance can produce a bifurcation phenomenon composed of saddle-node bifurcation and transcritical bifurcation. (2) The number of nodes exhibits a logarithmic growth distribution, the connection intensity and the network gain exhibit exponential growth distributions, and the connectivity and knowledge flow frequency are mutually influential in the form of a power function. (3) Knowledge-embedded resource allocation is most effective for improving the knowledge flow rate of networks and can further supply ample impetus for evolution. (4) Cooperation-oriented resource allocation is most beneficial for quickly propelling the network into the evolution realm. (5) Relationship-oriented resource allocation can aid the network in capturing more profit. Furthermore, this research is beneficial for understanding the key problems of each resource allocation model and the evolution of strategic alliance in knowledge flow networks. Our proposed methods and framework can be more widely applied to the fields of complex networks, knowledge management, and strategic innovation.

This paper extends the concept of biological co-evolution to explain the performance and survival of one type of service organizations. It proposes that service delivery network design would benefit from complex adaptive systems (CAS) modeling approaches to recreate organizational phenomena driven by the interaction of the organization with its operating environment. This approach, paired with experimental design methods can serve as a virtual laboratory. We take the case of social service delivery (SSD) organizations that are structured as nonprofit organizations providing humanitarian assistance and relief services. These organizations often serve different geographical regions, thus, racial composition, migration patterns, and wealth of the populations served are factors that vary between locations. SSDs operate through service nodes (i.e., field offices, chapters, branches) in a network configuration. Therefore, the managerial decision of where to locate the field offices is an important one. An agent-based model to recreate agents’ interactions as proxies of those exchanges occurring in real SSD settings is used. A series of validation experiments instill confidence that our model can be used as a virtual research laboratory. This paper contributes to the field of organizational design by testing a model able to recreate different policies that combined with different operating conditions impact the network over time and space. In addition, it provides experimental insights on what type of network configuration might provide a higher number of services delivered over time across the service network. The results can inform those defining the service system architecture looking to achieve SSD's goals considering the demographics of the markets served.

Seam-carving is used for adjusting the size of an image as well as removing objects from images. When seam-carving is applied to delete the object in the image, it may lead to the distortion of the information transmitted by the image, misleading the public, and endangering human health. Therefore, detecting the location of objects removed by seam-carving techniques is a scientific challenge to the image forensics. According to the position movement law of the related pixel in the seam-carving process, a method of the detecting and locating the objects deleted based on the block artifact grid (BAG) mispairing characteristic is proposed in this paper. Firstly, the BAG is extracted from the JPEG image; secondly, 10-dimensional features of the BAG chart is constructed; thirdly, clustering technique is applied on the extracted features of the BAG to obtain the classification results; finally, the location of the removed object is computed based on the previous classification results. Experiment result shows that the proposed method can efficiently detect and locate the object removed from an image by seam-carving. The proposed method in this paper opens up a new research direction for the location and detection of tampering, and has important application value for the intelligent forensics system based on seam carving.

Over the last few years, we have been witnessing a dramatic progress of deep learning in many real world applications. Deep learning concepts have been originated in the area of neural network and show a quantum leap in effective feature learning techniques such as auto-encoders, convolutional neural networks, recurrent neural networks etc. In the case of kernel machines, there are several attempts to model learning machines that mimic deep neural networks. In this direction, Multilayer Kernel Machines (MKMs) was an attempt to build a kernel machine architecture with multiple layers of feature extraction. It composed of many layers of kernel PCA based feature extraction units with support vector machine having arc-cosine kernel as the final classifier. The other approaches like Multiple Kernel Learning (MKL) and deep core vector machines solve the fixed kernel computation problem and scalability aspects of MKMs respectively. In addition to this, there are lot of avenues where the use of unsupervised MKL with both single and multilayer kernels in the multilayer feature extraction framework have to be evaluated. In this context, this paper attempts to build a scalable deep kernel machines with multiple layers of feature extraction. Each kernel PCA based feature extraction layer in this framework is modeled by the combination of both single and multilayer kernels in an unsupervised manner. Core vector machine with arc-cosine kernel is used as the final layer classifier which ensure the scalability in this model. The major contribution of this paper is a novel effort to build a deep structured kernel machine architecture similar to deep neural network architecture for classification. It opens up an extendable research avenue for researchers in deep learning based intelligent system leveraging the principles of kernel theory. Experiments show that the proposed method consistently improves the generalization performances of existing deep core vector machine.

Over decades, medical image diagnosis has gained prominence, as it has saved millions of lives from dreadful diseases. Improvising image processing techniques in medical images has provoked substantial increment in the efficiency levels of patient diagnosis. Segregation and visualization of pathologies have been made available by several image processing algorithms, under which tumor recognition and tissue segmentation in Magnetic Resonance (MR) brain images are accomplished in this paper using a novel approach. The novel methodology suggested through this paper ensemble the functioning of two different techniques well known to the research community. The techniques are Bacteria Foraging Optimization (BFO) and Modified Fuzzy C – Means (MFCM) algorithms, where, BFO is familiar for its optimization abilities and MFCM, an advancement of Fuzzy C – Means algorithm initiates the clustering operation. Both these techniques are well employed into a single framework to perform MR brain image segmentation, so that effective tumor segregation and tissue segmentation can be achieved, concurrently. Frequent parameter tuning is not required in the case of the proposed combinational algorithm, which is entirely an automated approach, and development of such algorithm would facilitate the radiologists in patient diagnosing procedures, as they extricate both manual intervention and large time consumption. With the support rendered by an automated algorithm, large volumes of clinical datasets could be assessed with ease. The proposed algorithm is validated by the radiologists and also using the comparison parameters such as sensitivity, Specificity, Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and computational time. By analysing the above said parameters it has been proved that the proposed algorithm is prodigious than the state-of-art segmentation algorithms. The sensitivity and the specificity values offered by the proposed methodology are 0.9048 and 0.9825, respectively. In addition to the clinical datasets, MR brain images from Harvard Brainweb database, Brainweb simulated database and BRATS-2013 challenge database are used to demonstrate the segmentation efficiency of the proposed algorithm. These factors are good enough to prove that the proposed combinational framework can be preferably opted for medical image analysis.

Technological innovation is critically important for high-tech firms in laying the groundwork for corporate growth and competitive advantages. It is hoped that evaluating risk accurately and effectively will contribute to assign appropriate priorities of technological innovation projects and to enhance the success rate for the whole project. In considering the issue of risk evaluation, the essential question that arises concerns strong fuzziness, ambiguity, and inexactness during the process of assessing risks. However, comparatively little research has focused on accommodating higher degrees of uncertainty in evaluating risks associated with technological innovation projects. Because the existing risk evaluation methods (such as models using net present values, internal rates of return, fuzzy logic, real options, and stochastic simulation) have certain limitations, this paper takes the powerfulness of interval-valued Pythagorean fuzzy (IVPF) sets into account to handle imprecise and ambiguous information and to characterize complex uncertainty in practical risk evaluation problems. This paper aims to propose an IVPF inferior ratio (IR)-based assignment model and to establish a novel risk evaluation method of technological innovation for the purpose of appropriately tackling highly uncertain information in intricate and varied circumstances. The proposed method provides an effective way to fuse complicated information about risk assessments and to identify an aggregate ranking of technological innovation projects that has the closest agreement with all indicator-wise precedence ranks. The feasibility and applicability of the IVPF IR-based assignment model are illustrated via a real-world case concerning the risk evaluation of technological innovation in high-tech enterprises. Furthermore, the practicality and effectiveness of the developed methodology are verified through a comparative analysis with other relevant approaches.

DBN- based classification model is proposed in this paper for web spam detection.SMOTE and DAE algorithms is applied in DBN to improve the classification performanceThe content and link feature is combined.The results obtained in this paper is better than the existing systems. With the development of the Internet, the number of web spam increases gradually, which has seriously affected the user experience of search engines. To improve the classification performance of web spam, the deep belief networks (DBN) is used for the first time, and it is effectively combined with the Synthetic Minority Over-Sampling Technique (SMOTE) and De-Noising Auto-Encoder (DAE) algorithm after the multi-aspect research and consideration. After multiple sets of experiments on WEBSPAM-UK2007 dataset, the results show that the classification method proposed in this paper improves the classification performance to a certain extent, which provides a good direction for the future classification of web spam.

Quantum chaotic cuckoo search algorithm is proposed for the data clustering problem.The performance of the proposed approach was assessed on six well known datasets.The Chaos maps and Boundary handling strategy enhance the cuckoo search algorithm.The nonhomogeneous quantum update improves the global search ability.The significant superiority of the proposed algorithm over eight recent algorithms. This paper presents a new quantum chaotic cuckoo search algorithm (QCCS) for data clustering. Recent researches show the superiority of cuckoo search (CS) over traditional meta-heuristic algorithms for clustering problems. Unfortunately, all the cuckoos have identical search behaviours that may lead the algorithm to converge to local optima. Also, the convergence rate is sensitive to initial centroids seeds that are randomly generated.Therefore, the main contribution of this paper is to extend the CS capabilities using nonhomogeneous update inspired by the quantum theory in order to tackle the cuckoo search clustering problem in terms of global search ability. Also, the randomness at the beginning step is replaced by the chaotic map in order to make the search procedure more efficient and improve the convergence speed. In addition, an effective strategy is developed to well manage the boundaries.The experimental results on six famous real-life datasets show the significant superiority of the proposed QCCS over eight recent well known algorithms including, genetic quantum cuckoo search, hybrid cuckoo search and differential evolution, hybrid K-means and improved cuckoo search, standard cuckoo search, quantum particle swarm optimization, differential evolution, hybrid K-means chaotic particle swarm optimization and genetic algorithm for all benchmark datasets in terms of internal and external clustering quality.

Several biological research studies have shown that the number of individuals of certain species of anurans in a specific geographical region, and the evolution of this number over time, can be used as an indicator of climate change. To detect the presence of anurans, Wireless Sensor Networks (WSNs) are usually deployed with the aim of obtaining bio-acoustic information in a set covering numerous locations. However, the identification of the anuran species from a huge number of recordings usually involves an overwhelming task that has to be undertaken by expert and intelligent systems. Previous studies into this issue have proposed several classification techniques with a common approach: they all take into account the sequential characteristic of sounds by considering syllables or other kinds of vocal segments. In noisy sounds, as it is usually the case in recordings made in natural habitats, segmentation of the signal is no straightforward task and may cause low classification accuracy. To override this problem, a new non-sequential approach is proposed in this paper. It is based on considering very small pieces of sounds (frames) each of which is then classified without considering preceding or subsequent information. Up to nine frame-based classifiers are explored in this paper and their performances are compared to the most commonly used sequential classifier: the Hidden Markov Model (HMM). Additionally, for featuring the frames, many choices have been described, although the application of the Mel Frequency Cepstral Coefficients (MFCCs) has probably become the most common method. In this work, an alternative methodology is suggested: the use of a set of MPEG-7 parameters, which offers a normalized solution with a much greater semantic content. The experimental results have shown that the proposed method clearly outperforms the HMM, thereby showing the non-sequential classification of anuran sounds to be feasible. From among the algorithms tested, the decision-tree classifier has shown the best performance with an overall classification success rate of 87.30%, which is an especially striking result considering that the analyzed sounds were affected by a decidedly noisy background.

Proposed a new sentiment analysis method, based on text-based hidden Markov models, that uses word orders without the need of sentiment lexicons.Proposed an ensemble of text-based hidden Markov models using boosting and clusters of words produced by latent semantic analysis.Showed the method has potential to classify implicit opinions by the proposed ensemble method.Showed better performance in comparison to several previous algorithms in several datasets.Applied it to a real-life dataset to classify paper titles. With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews.

This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, a robust catalogue is obtained when incomplete records and those with uncertainty are eliminated from a catalogue. Therefore, a robust catalogue is a collection of association rules with 100% confidence and unitary support. In this paper we demonstrate that robust catalogues contain the same association rules as the datasets from which they were obtained, but can be managed in memory without eliminating any data from the analysis. In fact, the experiments performed show that all robust catalogues contained in a classification dataset are easily obtained, providing millions of associative classification rules with 100% confidence to the expert researcher in data mining.

This paper proposes a methodology to detect candlestick patterns in a stock trading system using fuzzy logic. The fuzzy approach make possible to account for the vagueness and uncertainty of the pattern features. Even more, the use of fuzzy rules allows it to include that uncertainty into a trading decision system that not only advises the investor on when but also on how much capital to invest. In this way, the intelligent system helps the experts to use their knowledge, i.e. the candlestick-based rules, in a more natural and realistic way than the standard one based on crisp rules. In the paper we have illustrated this methodology with the generation of a fuzzy trading system that uses three well-known candlestick patterns that have been fuzzified. The performance of this intelligent stock trading system is tested in two portfolios of different stock markets, Nasdaq-100 and Eurostoxx, and it is compared against its crisp counterpart and the classical Buy-and-Hold trading strategy. Our fuzzy candlestick-based trading system not only improves the pattern recognition with respect to its crisp version, but it also provides promising results since it exhibits a more stable behavior in the markets analyzed, and obtains more profits in a less risky way than the other trading systems considered.

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria. This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R).

Decision-making processes in private banking must comply with standards for risk management and transparency enforced by banking regulations. Therefore, investors must be supported throughout a risk-informed decision process. This paper contributes to the literature by presenting a hybrid integrated framework that considers personal features of the investor and additional characteristics imposed by regulations, for which linguistic evaluations are used with regard to risk exposure. The proposed approach for personal investment portfolios considers legal aspects and investor’s preferences as an input to the novel fuzzy multiple-attribute decision making approach for sorting problems proposed in this paper, called FTOPSIS-Class. Then, the next step of the proposed framework uses the sorting results for a fuzzy multi-objective optimization model that considers the risk and return associated with the investor’s profile over three objectives. The contributions of this paper are illustrated and validated by using a numerical application in line with a new trend for modern portfolio theory which enables a real world investor’s characteristics to be considered throughout the decision-making process.

Digital pre-assembly process can evaluate flaws in design prior to manufacturing.As these reports are written in free text form, they are not fully utilized.This paper proposes a method of applying text mining techniques to extract insights.The proposed method successfully extracts useful information from the text database. After digitally designing components of vehicles, a design team creates a virtual manufacturing environment that resembles actual manufacturing facilities. During this digital pre-assembly process, a review team examines each component, and records its problems and requirements in part verification reports. Once these reports are delivered to specific design team responsible for each part, the design team can make appropriate adjustments to their designs. This digital pre-assembly process can evaluate and prevent flaws in design prior to actual manufacturing, improving production quality and reducing manufacturing cost. As these reports are written in free text form, they, however, are not fully utilized for understanding problems arising from the design process. This paper proposes a method of applying text mining techniques on verification reports to extract insights for quality improvement. In this paper, following three text mining approaches are proposed: (1) Extracting n-grams for text preprocessing and constructing domain ontology; (2) Extracting meaningful insights from text preprocessing; (3) Creating intuitive visual tools to understand the extracted insights. The proposed method is applied on approximately 140,000 reports, and is validated through the quality of the answers obtained for the questions posed by the domain experts. The proposed method successfully extracts useful information from the text database, and provides intuitive graphical interface, thereby satisfying the need of the domain experts. This paper proposes a systematic framework of transforming huge amount of raw text data into intuitive visualization. Through this framework, meaningful knowledge can be extracted, analyzed and shared to improve the quality of the products. Main contribution of our paper is that it proposes a framework for knowledge extraction from pre-assembly process. Not only does it systematically arrange the data, but it also combines various data sources and creates a knowledge system to improve efficiency of the design process.

One new method is proposed based on recurrent neural networks.The method addresses the problem of energy consumption forecasting.The method is evaluated using data form public buildings of University of Granada. Buildings are an essential part of our social life. People spend a substantial fraction of their time and spend a high amount of energy in them. There is a grand variety of systems and services related to buildings, in order to better control and monitoring. The prompt taking of decisions may prevent costs and contamination. This paper proposes a method for energy consumption forecasting in public buildings, and thus, achieve energy savings, in order to improve the energy efficiency, without affecting the comfort and wellness. The prediction of the energy consumption is indispensable for the intelligent systems operations and planning. We propose an Elman neural network for forecasting such consumption and we use a genetic algorithm to optimize the weight of the models. This paper concludes that the proposed method optimizes the energy consumption forecasting and improves results attained in previous studies.

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

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.

In this paper, the new aggregation models for the interrelated multi-criteria decision making (MCDM) problems based on the quantifier guided ordered weighted averaging (QGOWA) and Bonferroni mean (BM) operator with interval type 2 fuzzy sets (IT2FS) are developed. In most MCDM problems, the decision criteria might not be totally independent. For some MCDM methodologies that are not considering the interrelationship of the criteria, the decision results suggested are meaningless. The BM operator can express the interrelationship of the input arguments, which serves as a mean type aggregator. Yager introduced the OWA operator which is associated with the orness level (attitudinal character) by means of the quantifiers. Different quantifier functions are associated with the respective different orness levels. This is referred to as the QGOWA operator. Besides, the real world MCDM problems are mostly under uncertain environments. To address such MCDM problems, the linguistic criteria weights and the alternative rates are better characterized by IT2FS. The major contributions of this paper are to propose the interrelation MCDM aggregation models with various extensions, to construct the mixed integer linear programming models for obtaining the optimal QGOWA BM IT2FS weights, and to formulate a new interrelation MCDM paradigm. The developed aggregation models are: (1). Ordinary MCDM aggregation; (2). BM with OWA weights; (3). BM with OWA weights and personal importance; (4). BM with QGOWA weights; (5). BM with QGOWA weights and personal importance; (6). BM with QGOWA weights and attitudinal characters; (7). BM with QGOWA weights, attitudinal characters and personal importance. A new MCDM aggregation methodology with application based on the developed models is introduced. The application results from the MCDM aggregation methodology demonstrate that the final decision prioritization are actually affected by the various orness levels predetermined by the decision experts.

In order to deploy deep Convolutional Neural Networks (CNNs) on the mobile devices, many mobile CNNs are introduced. Currently, some online applications are usually re-trained because of the constantly-increasing data. However, compared with the regular models, it is not very efficient to train the present mobile models. Therefore, the purpose of this paper is to propose efficient mobile models both in the training and test processes through exploring the main causes of the current mobile CNNs’ inefficiency and the parameters’ properties. Finally, this paper introduces Highly Shared Convolutional Neural Networks (HSC-Nets). The HSC-Nets employ two shared mechanisms to reuse the filters comprehensively. Experimental results showed that, compared with the regular networks and the latest state-of-the-art group-conv mobile networks, the HSC-Nets can achieve promising performances and effectively decrease the model size. Furthermore, it is also more efficient in both the training and test processes.

Electric vehicles are environmental transportation modes that are widely applied in green logistics systems. To guarantee the energy efficiency, the impacts of customer service modes and recharging strategies need to be integrated into the optimization of electric logistics resource. This paper proposes an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies (EVRPMBTW-RS), minimizing the total travel cost with sophisticated constraints on the time-dependent pickup and delivery requests, limited recharging station capacity, and battery remaining capacity of electric vehicles. Mixed service sequences of linehaul and backhaul customers is allocated for the routing planning, with the synchronous optimization of recharging strategies including the selection of recharging stations and determination of recharging time. A time-discretized multi-commodity network flow model is constructed based on an extended space–time-state modeling framework, which is formulated as a quadratic 0–1 programming model by using the augmented Lagrangian relaxation technique. After the dualization and linearized transformation, we decompose the model into a sequence of least-cost path subproblems based on the alternating direction multiplier method (ADMM). The subproblems are alternately minimized and solved using the time-dependent forward dynamic programming algorithm. The solution quality can be guaranteed through calculating the optimality gap between the best lower bound and upper bound for each iteration. The proposed solution approach is examined on examples of a simple 7-node network and real-world Yizhuang road network. This paper provides a theoretical foundation for the route optimization method of electric logistics vehicles, and contributes to improve the operational efficiency of electric logistics systems.