Search

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.

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.

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.

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.

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.

Emergency medical services (EMS) are essential components for post-disaster rescue activities. Considering injury heterogeneity and deterioration over time of the casualties, the uncertainty in the number of the casualties can effectively improve the medical service performance. This paper develops a robust optimization model for combined facility location and casualty transportation under uncertainty in the number of casualties. We divide casualties into two types: mild casualties who are transported to on-site clinics with rescue vehicles and serious casualties who are transported to general hospitals with helicopters. Meanwhile, we consider the Injury Severity Score (ISS) increment to describe the injury deterioration of the casualties over time. The objective is to minimize the total weighted ISS increment of mild casualties and serious casualties. Then, we employ the robust optimization method to deal with the uncertainty and derive the robust counterpart of the proposed model in this paper. Case studies based on Yushu Earthquake show that the model can get the optimal emergency facility location and casualty transportation scheme to minimize the total weighted ISS increment. The total weighted ISS increment increases as the constraint violation probability decreases, which reflects the trade-off between performance and robustness. Sensitivity analyses show that the greater uncertainty of the casualty number is, the greater the impact on the total weighted ISS increment is, the more conservative the decision scheme is. The capacity of general hospitals has a greater effect on the objective value compared with the capacity of on-site clinics and the decision scheme of the robust optimization model has a greater optimality compared with the deterministic model when the problem size is magnified.

Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.

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

This paper presents a robust system for the document layout analysis.The proposed system is based on multilevel homogeneity structure (MHS).The proposed system is designed to work with many different document languages.Our system is tested on four published datasets with different document languages.The proposed system (MHS) won the RDCL-2015 competition (ICDAR2015). One of the difficulties in the understanding of document images is document layout analysis, which is the first step in document image modeling. In this paper, a robust system for which a multilevel-homogeneity structure is used in accordance with a hybrid methodology is proposed to deal with this problem. Our system consists of the following three main stages: classification, segmentation, and refinement and labeling. Different from other page segmentation methods, the proposed system includes an efficient algorithm to detect table regions in document images. Besides, to create an effective application, the proposed system is designed to work with a variety of document languages. The proposed method was tested with the ICDAR2015 competition (RDCL-2015) and three other published datasets in different languages. The results of these tests show that the accuracy of proposed system is superior to the previous methods.

Automatic linguistic description of the available data about complex phenomena is a challenging task that is receiving the attention of data scientists in recent years. As an evolution of previous research results, there is a need of creating new linguistic computational models that allow us dealing with more complex phenomena and more complex descriptions of a growing amount of heterogeneous and real-time data. This paper contributes to this field by presenting three new ways of describing added-value information automatically extracted from data. Also, we extend previous computational models by including a description of the reliability of the available input data. Namely, we face this challenge by using a new implementation of the concept of Z-number proposed by Zadeh. We demonstrate the possibilities of the proposed extension with a practical application. The application generates automatic linguistic reports about the deforestation evolution in the Amazon region, e.g., “The deforestation last month was high. Because of the cloudiness, the reliability of this information is moderate”. Additionally, we evaluate the quality of the generated linguistic descriptions through fuzzy rating scale-based questionnaires. Moreover, we have also made a comparative study between reports generated with and without the new contributions introduced in this paper. The results show that the new types of computational perceptions introduced in this paper are ready to help data scientists to automatically generate good quality reports.

This paper introduces a three-step framework for classifying multiclass radiography images. The first step utilizes a de-noising technique based on wavelet transform (WT) and the statistical Kolmogorov Smirnov (KS) test to remove noise and insignificant features of the images. An unsupervised deep belief network (DBN) is designed for learning the unlabelled features in the second step. Although small-scale DBNs have demonstrated significant potential, the computational cost of training the restricted Boltzmann machine is a major issue when scaling to large networks. Moreover, noise in radiography images can cause a significant corruption of information that hinders the performance of DBNs. The combination of WT and KS test in the first step helps improve performance of DBNs. Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations. Five frequently used classifiers including naive Bayes, radial basis function network, random forest, sequential minimal optimization, and support vector machine and four different case studies are implemented for experiments using the Image Retrieval in Medical Application data set. The experimental results show that the three-step framework has significantly reduced computational cost and yielded a great performance for multiclass radiography image classification. Along with effective applications in image processing in other fields published in the literature, deep learning network in this paper has again demonstrated its robustness in handling a complex set of medical images. This implies that the proposed approach can be implemented in real practice for analysing noisy radiography images, which have many useful medical applications such as diagnosis of diseases related to lung, breast, musculoskeletal or pediatric studies.

This paper assesses inventory allocation decisions in light of fuzzy inference systems.It builds upon the scant previous studies in this field of knowledge.Priority indexes are developed based on a comprehensive total cost structure.The proposed approach outperforms specially in terms of total costs.Results are robust for larger retail networks. Inventory allocation decisions in a distribution system concern issues such as how much and where stock should be assigned to orders in a supply chain. When the inventory level of an inventory point is lower than the total number of items ordered by lower echelons in the chain, the decision of how many items to allocate to each ``competing'' order must take into consideration the trade-off between cost and service level. This paper proposes a decision-support system that makes use of fuzzy logic to consider inventory carrying, shortage and ordering costs as well as transportation costs. The proposed system is compared through simulation with three other inventory allocation decision support models in terms of cost and service levels achieved. Conclusions are then drawn.

Venue recommendation has attracted a lot of research attention with the rapid development of Location-Based Social Networks. The effectiveness of venue recommendation largely depends on how well it captures users’ contexts or preferences. However, it is quite difficult, if not impossible, to capture the whole information about users’ preferences. In addition, users’ preferences are often heterogeneous (i.e., some preferences are static and common to all users while some preferences are dynamic and diverse). Existing venue recommendation does not well address the aforementioned issues and often recommends the most popular, the cheapest, or the closest venues based on simple contexts. In this paper, we cast the venue recommendation as a ranking problem and propose a recommendation framework named VRer (Context-Based V enue R ecommendation using e mbedded space r anking SVM) employing an embedded space ranking SVM model to separate the venues in terms of different characteristics. Our proposed approach makes use of ‘check-in’ data to capture users’ preferences and utilizes a machine learning model to tune the importance of different factors in ranking. The major contribution of this paper are: (1) VRer combines various contexts (e.g., the temporal influence and the category of locations) with the check-in records to capture individual heterogeneous preferences; (2) we propose an embedded space ranking SVM optimizing the learning function to reduce the time consumption of training the personalized recommendation model for each group or user; (3) we evaluate our proposed approach against a real world LBSN and compare it with other baseline methods. Experimental results demonstrate the benefits of our proposed approach.

We propose estimating the quality of a contrast pattern using unseen objects.We evaluate quality measures by correlating their values and the estimated quality.We describe the influence of database characteristics in correlations.We perform a study to find how to synergistically combine quality measures. Contrast patterns, which lie in the core of most understandable classifiers, are frequently evaluated by quality measures. Since many different quality measures are available, they should be compared to select the most appropriate for each applications. This paper introduces a method to compare quality measures, using a set of mined patterns and a collection of objects not used for mining. The comparison is performed by correlating quality values with a quality estimation of the patterns. Additionally, a meta-learning study is performed to show that combining quality measures could be better than using the best single measures in isolation. The results of this paper can help researchers to create new quality measures or to find new combinations of quality measures to create better understandable classification systems.

A facial beauty analysis toward attractiveness assessment application is presented.A geometric facial beauty score function is proposed for facial aesthetic perceptron.A semi-supervised learning with Hessian graph and random projection is proposed.A novel geometric facial beauty (GFB) database is provided in this paper. Facial beauty analysis has been an emerging subject of multimedia and biometrics. This paper aims at exploring the essence of facial beauty from the viewpoint of geometric characteristic toward an interactive attractiveness assessment (IAA) application. As a result, a geometric facial beauty analysis method is proposed from the perspective of machine learning. Due to the troublesome and subjective beauty labeling, the accurately labeled data scarcity is caused, and result in very few labeled data. Additionally, facial beauty is related to several typical features such as texture, color, etc., which, however, can be easily deformed by make-up. For addressing these issues, a semi-supervised facial beauty analysis framework that is characterized by feeding geometric feature into the intelligent attractiveness assessment system is proposed. For experimental study, we have established a geometric facial beauty (GFB) dataset including Asian male and female faces. Moreover, an existing multi-modal beauty (M2B) database including western and eastern female faces is also tested. Experiments demonstrate the effectiveness of the proposed method. Some new perspectives on the essence of beauty and the topic of facial aesthetic are revealed. The impact of this work lies in that it will attract more researchers in related areas for beauty exploration by using intelligent algorithms. Also, the significance lies in that it should well promote the diversity of expert and intelligent systems in addressing such challenging facial aesthetic perception and rating issue.

Traditionally, the probably approximately correct (PAC) learning refers the single concept class. We discuss the PAC framework of the multiple tasks in the joint inference model. And we extend PAC learning to multi-concept classes.We present an ensemble learning approach to joint inference on the three NLP sub-tasks. We explain how to combine those weak learners to a strong ability and present the dynamic weighted combination method in the ensemble joint inference model.Our Ensemble Markov Logic Networks (EMLNs) address the problem of the Markov Logic Networks intractable dealing with the large scale data. Experiments show that this approach leads to a higher precision and recall than that of pipeline approaches. Joint inference is a fundamental issue in the field of artificial intelligence. The greatest advantage of the joint inference is demonstrated by its capability of avoiding errors from cascading and accumulating on a pipeline of multiple chained sub-tasks. Markov Logic Network(MLN) is the most common joint inference model that provides a flexible representation and handles uncertainty. It has been applied successfully to joint inference on many natural language processing tasks to avoid error propagation. However, due to the great expressiveness of first-order logic, the representation for it in MLN generates rather complicated graph structures, which makes the learning and inference on large scale data intractably. In this paper, we present an ensemble learning approach to deal with the challenges in MLNs. Firstly, we give a proof within the probably approximately correct (PAC) framework. The proof points out what conditions are necessary for successful applying the ensemble learning approach to MLN. Secondly, the paper explains how to combine the learners. Finally, in order to illustrate the working mechanism of the ensemble joint inference model, we present an Ensemble Markov Logic Networks (EMLNs) method and use it to extract knowledge from a large scale corpus published by Google.11code.google.com/p/relation-extraction-corpus/. Experiments suggest that significant speedup can be gained by the EMLNs. Meanwhile, it show that this approach leads to a higher precision and recall than that of those pipeline approaches.

This paper proposes a new method for multicriteria group decision-making processes.The method deals with the subjectivity issues in group decision-making processes.The method employs two concepts such as preference clustering and mediating agent.Sample concrete bridge decks were considered as a case study.The case study demonstrated the effectiveness to minimize the subjectivity issues. The problem-solving decision-making process often requires involvement of a group of individuals who have differing interests and conflicting multiple evaluation criteria. Therefore, the greatest concern in multiobjective group decision-making problems is how to arrive at a best decision that is agreeable to all the members of the group. Many previous studies focused on handling this concern based on decision rules, such as the consensus or ranking selection approaches. Although many contributions to the literature were made by past studies on this issue, disagreement remains on finding an effective way to address the subjectivity issue in group decision-making. This paper introduces a new approach called the preference clustering-based mediating group decision-making (PCM-GDM) method for minimizing the subjectivity issue. The PCM-GDM method basically employs two concepts: (1) clustering the preferences of the group members in a decision and (2) utilizing a mediating agent as a final decision-making tool. The new approach was applied to a case study of sample concrete bridge decks in the state of Indiana. The results of this study confirm that the proposed approach can significantly improve the multicriteria group decision-making results by providing a way to exclude biased judgments by decision-makers that can interfere with the development of one best alternative. The proposed approach advanced the reliability of the conventional decision-making knowledge, which is dependent on a consensus or the ranking of approaches by human experts to reach one solution.

We proposed a new approach to extract finger-vein pattern.The proposed approach is based on the curvature in Radon space.This paper rigorously evaluates the verification performance of our approach. Finger-vein verification has drawn increasing attention because it is highly secured and private biometric in practical applications. However, as the imaging environment is affected by many factors, the captured image contains not only the vein pattern but also the noise and irregular shadowing which can decrease the verification accuracy. To address this problem, in this paper, we proposed a new finger-vein extraction approach which detects the valley-like structures using the curvatures in Radon space. Firstly, given a pixel, we obtain eight patches centered on it by rotating a window along eight different orientations and project the resulting patches into Radon space using the Radon transform. Secondly, the vein patches create prominent valleys in Radon space. The vein patterns are enhanced according to the curvature values of the valleys. Finally, the vein network is extracted from the enhancing image by a binarization scheme and matched for personal verification. The experimental results on both contacted and contactless finger-vein databases illustrate that our approach can significantly improve the accuracy of the finger-vein verification system.

Address the fake corner and pose diversity problem in indoor corner recognition.Propose a hierarchical architecture consisting of three classifiers.Propose to classify diverse poses into only two pose groups.Conduct field experiments to confirm the superiority of the proposed scheme. Recently, fingerprint crowdsourcing from pedestrian movement trajectories has been promoted to alleviate the site survey burden for radio map construction in fingerprinting-based indoor localization. Indoor corners, as one of the most common indoor landmarks, play an important role in movement trajectory analysis. This paper studies the problem of indoor corner recognition in crowdsourced movement trajectories. In a movement trajectory, smartphone internal sensor measurements experience some signal changes when passing by a corner. However, the state-of-the-art solutions based on signal change detection cannot well deal with the fake corner problem and pose diversity problem in most practical movement trajectories. In this paper, we study the corner recognition problem from an expert system viewpoint by applying machine learning techniques. In particular, we extract recognition features from both the time and frequency domain and propose a hierarchical corner recognition scheme consisting of three classifiers. The first pose classifier is to classify various poses into only two groups according to whether or not a smartphone is kept in a fixed position relative to a user upper body when collecting sensor measurements. Feature selection is then applied to train two corner classifiers each for one pose group. Field experiments are conducted to compare our proposed scheme with three state-of-the-art algorithms. In all cases, our scheme outperforms the best of these algorithms in terms of much higher F1-measure and precision for corner recognition. The results also provide insights on the potentials of using more advanced techniques from expert systems in indoor localization.

Systematic Mapping Study about entity reconciliation in Big Data.Eleven databases were consulted. Rigorous process where 2255 papers were analyzed leaving 61 primary studies.Analytic data statistics after the classification of the primary studies.Discussion that presents some interesting intelligent system-based papers for solving entity reconciliation and conclusions obtained from the data retrieved. The entity reconciliation (ER) problem aroused much interest as a research topic in today's Big Data era, full of big and open heterogeneous data sources. This problem poses when relevant information on a topic needs to be obtained using methods based on: (i) identifying records that represent the same real world entity, and (ii) identifying those records that are similar but do not correspond to the same real-world entity. ER is an operational intelligence process, whereby organizations can unify different and heterogeneous data sources in order to relate possible matches of non-obvious entities. Besides, the complexity that the heterogeneity of data sources involves, the large number of records and differences among languages, for instance, must be added. This paper describes a Systematic Mapping Study (SMS) of journal articles, conferences and workshops published from 2010 to 2017 to solve the problem described before, first trying to understand the state-of-the-art, and then identifying any gaps in current research. Eleven digital libraries were analyzed following a systematic, semiautomatic and rigorous process that has resulted in 61 primary studies. They represent a great variety of intelligent proposals that aim to solve ER. The conclusion obtained is that most of the research is based on the operational phase as opposed to the design phase, and most studies have been tested on real-world data sources, where a lot of them are heterogeneous, but just a few apply to industry. There is a clear trend in research techniques based on clustering/blocking and graphs, although the level of automation of the proposals is hardly ever mentioned in the research work.

From unsupervised to supervised learning a fault detection model (for robots).Insights to why and when it becomes more accurate.Theoretical analysis and a prediction tool.Empirical results on 3 real-world domains that back these insights. The use of robots in our daily lives is increasing. As we rely more on robots, thus it becomes more important for us that the robots will continue on with their mission successfully. Unfortunately, these sophisticated, and sometimes very expensive, machines are susceptible to different kinds of faults. It becomes important to apply a Fault Detection (FD) mechanism which is suitable for the domain of robots. Two important requirements of such a mechanism are: high accuracy and low computational-load during operation (online). Supervised learning can potentially produce very accurate FD models, and if the learning takes place offline then the online computational-load can be reduced. Yet, the domain of robots is characterized with the absence of labeled data (e.g., faulty, normal) required by supervised approaches, and consequently, unsupervised approaches are being used. In this paper we propose a hybrid approach - an unsupervised approach can label a data set, with a low degree of inaccuracy, and then the labeled data set is used offline by a supervised approach to produce an online FD model. Now, we are faced with a choice should we use the unsupervised or the hybrid fault detector? Seemingly, there is no way to validate the choice due to the absence of (a priori) labeled data. In this paper we give an insight to why, and a tool to predict when, the hybrid approach is more accurate. In particular, the main impacts of our work are (1) we theoretically analyze the conditions under which the hybrid approach is expected to be more accurate. (2) Our theoretical findings are backed with empirical analysis. We use data sets of three different robotic domains: a high fidelity flight simulator, a laboratory robot, and a commercial Unmanned Arial Vehicle (UAV). (3) We analyze how different unsupervised FD approaches are improved by the hybrid technique and (4) how well this improvement fits our prediction tool. The significance of the hybrid approach and the prediction tool is the potential benefit to expert and intelligent systems in which labeled data is absent or expensive to create.

Efficient character segmentation algorithm for machine typed documents is presented.The efficient algorithm supports general pipeline from grayscale conversion to segmentation.The mathematical background of the novel idea is also presented in detail.The algorithm core part is shown in pseudo-code proved with a large set of empiric results. In this paper an efficient approach for segmentation of the individual characters from scanned documents typed on old typewriters is proposed. The approach proposed in this paper is primarily intended for processing of machine-typed documents, but can be used for machine-printed documents as well. The proposed character segmentation approach uses the modified projection profiles technique which is based on using the sliding window for obtaining the information about the document image structure. This is followed by histogram processing in order to determine the spaces between lines, words and characters in the document image. The decision-making logic used in the process of character segmentation is describes and represents the most an integral aspect of the proposed technique. Beside the character segmentation approach, the ultra-fast architecture for geometrical image transformations, which is used for image rotation in the process of skew correction, is presented, and its fast implementation using pointer arithmetic and a highly optimized low-level machine routine is provided. The proposed character segmentation approach is semi-automatic and uses threshold values to control the segmentation process. Provided results for segmentation accuracy show that the proposed approach outperforms the state-of-the-art approaches in most cases. Also, the results from the aspect of the time complexity show that the new technique performs faster than state-of-the-art approaches and can process even very large document images in less than one second, which makes this approach suitable for real-time tasks. Finally, visual demonstration of the proposed approach performances is achieved using original documents authored by Nikola Tesla.

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

An anomaly detection algorithm to identify AIS on-off switching is proposed.The algorithm exploits the AIS message Received Signal Strength Indicator.Machine Learning algorithms are used to build normality models.AIS reception is characterized by using real word data.The methodology is scalable from one station to a network of receivers. The Automatic Identification System (AIS) is a ship reporting system based on messages broadcast by vessels carrying an AIS transponder. The recent increase of terrestrial networks and satellite constellations of receivers is making AIS one of the main sources of information for Maritime Situational Awareness activities. Nevertheless, AIS is subject to reliability and manipulation issues; indeed, the received reports can be unintentionally incorrect, jammed or deliberately spoofed. Moreover, the system can be switched off to cover illicit operations, causing the interruption of AIS reception. This paper addresses the problem of detecting whether a shortage of AIS messages represents an alerting situation or not, by exploiting the Received Signal Strength Indicator available at the AIS Base Stations (BS). In designing such an anomaly detector, the electromagnetic propagation conditions that characterize the channel between ship AIS transponders and BS have to be taken into consideration. The first part of this work is thus focused on the experimental investigation and characterisation of coverage patterns extracted from the real historical AIS data. In addition, the paper proposes an anomaly detection algorithm to identify intentional AIS on-off switching. The presented methodology is then illustrated and assessed on a real-world dataset.

We propose a new fully automatic method to detect and segment brain lesions.The method is based on a texture-based and a contour-based algorithm.The algorithm is independent of multi-spectral MRI, and local or global registration. Automatic detection of brain tumors in single-spectral magnetic resonance images is a challenging task. Existing techniques suffer from inadequate performance, dependence on initial assumptions, and, sometimes, the need for manual interference. The research reported in this paper seeks to reduce some of these shortcomings, and to remove others, achieving satisfactory performance at reasonable computational costs. The success of the system described here is explained by the synergy of the following aspects: (1) a broad choice of high-level features to characterize the images texture, (2) an efficient mechanism to eliminate less useful features (3) a machine-learning technique to induce a classifier that signals the presence of a tumor-affected tissue, and (4) an improved version of the skippy greedy snake algorithm to outline the tumors contours. The paper describes the system and reports experiments with synthetic as well as real data.

Solving a real-world problem with an uncommon dataset.Highly time-efficient and accurate for classification of high resolution X-Ray images.Emphasizing on keypoints of the image instead of considering all parts of the image.Considering the dependency between the visual words in the bag of visual words.Adopting Tree-Augmented Bayes in the task of image classification. Smuggling has long played an important role in the inefficiency of economies. To secure the borders against this illegal act, X-Ray Inspection Systems are often deployed at the borders and customs. In this paper, we present a new method for classification of shipping containers X-Ray images, produced in the inspection lines. The aim is to improve the matching accuracy of the presented manifest, which lists the claimed contents of the shipping containers, with the real contents of the container. The proposed method is based on utilizing Scale Invariant Feature Transforms (SIFT) feature vectors, Bag of visual words (BOVW) and tree augmented naive Bayes (TAN) approach for classifying containers X-Ray images. The prior research on classification of X-Ray images of shipping containers has focused mostly on working with greedy algorithms such as sliding windows for task of classification. More recent studies introduced filter banks and visual words for extraction of features. The proposed method for the first time considers the salient points and keypoints for the task of feature extraction. In addition, this paper presents a framework using the tree augmented naive Bayes based on the theory of learning Bayesian networks, which is proved to have a significant improvements upon the prior designed systems by considering the correlations among the extracted features. For experimental evaluations, our method is compared with two recently proposed methods on containers X-Ray images categorization. The results show that the proposed method is more accurate and time-efficient in categorization of X-Ray images.

Industrial application using image data collected from actual steelworks.A deep learning based algorithm for localizing slab identification numbers.Deep Convolutional Neural Network (DCNN) for classifying sub-regions.Accumulated confidence for using adjacent outputs of DCNN. This paper proposes a novel algorithm for localizing slab identification numbers (SINs) in factory scenes. Automatic identification of product information is important for the process management, and localization of SINs in complex scenes is a major challenge for the recognition. A previous rule-based localization algorithm for SINs requires lots of prior knowledge and heuristic tuning for parameters. In this paper, a deep convolutional neural network (DCNN) is employed to overcome these limitations, and accumulated confidence is proposed to utilize neighboring outputs of the DCNN in a scene. The localization error is remarkably reduced to 1.44% by the proposed algorithm compared to 4.59% in the previous work. The proposed data-driven method can be applied to construct other automatic identification systems with minimal manual handling.

A taxonomy that classifies ensemble models in the literature is presented.Surface and deep features integration is explored to improve classification.Several ensembles of classifiers and features are proposed and evaluated.Performance of the proposed models is evaluated on several sentiment datasets. Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. These long-established approaches can yield strong baselines, and their predictive capabilities can be used in conjunction with the arising deep learning methods. In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features. The contributions of this paper are sixfold. First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm. This classifier serves as a baseline to compare to subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in Sentiment Analysis. Third, we also propose two models for combining both surface and deep features to merge information from several sources. Fourth, we introduce a taxonomy for classifying the different models found in the literature, as well as the ones we propose. Fifth, we conduct several experiments to compare the performance of these models with the deep learning baseline. For this, we use seven public datasets that were extracted from the microblogging and movie reviews domain. Finally, as a result, a statistical study confirms that the performance of these proposed models surpasses that of our original baseline on F1-Score.

Present and formally define the Temporal Multi-Agent Resource Allocation diagnosis problem.Propose an efficient solution to this problem by a novel encoding to Boolean Satisfiability (SAT).Demonstrate empirically the scalability of the proposed approach. In the not-so-far future, autonomous vehicles will be ubiquitous and, consequently, need to be coordinated to avoid traffic jams and car accidents. A failure in one or more autonomous vehicles may break this coordination, resulting in reduced efficiency (due to traffic load) or even bodily harm (due to accidents). The challenge we address in this paper is to identify the root cause of such failures. Identifying the faulty vehicles in such cases is crucial in order to know which vehicles to repair to avoid future failures as well as for determining accountability (e.g., for legal purposes). More generally, this paper discusses multi-agent systems (MAS) in which the agents use a shared pool of resources and they coordinate to avoid resource contention by agreeing on a temporal resource allocation. The problem we address, called the Temporal Multi-Agent Resource Allocation (TMARA) diagnosis problem (TMARA-Diag), is to find the root cause of failures in such MAS that are caused by malfunctioning agents that use resources not allocated to them. As in the autonomous vehicles example, such failures may cause the MAS to perform suboptimally or even fail, potentially causing a chain reaction of failures, and we aim to identify the root cause of such failures, i.e., which agents did not follow the planned resource allocation. We show how to formalize TMARA-Diag as a model-based diagnosis problem and how to compile it to a set of logical constraints that can be compiled to Boolean satisfiability (SAT) and solved efficiently with modern SAT solvers. Importantly, the proposed solution does not require the agents to share their actual plans, only the agreed upon temporal resource allocation and the resources used at the time of failure. Such solutions are key in the development and success of intelligent, large, and security-aware MAS.

A new initialization technique is proposed to improve the performance of K-means.A data transformation approach is proposed to solve empty cluster problem.An efficient method is proposed to estimate the optimal number of clusters.Proposed clustering method provides more accurate clustering results. Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.

The identification and prediction of situations in smart houses.An approach developed with the principles of reactive and proactive behavior.A case study presented with sensors and appliances.The proactivity and reasoning about uncertainty supported by probabilistic ontology. Considering that the world population is aging, health-support issues are in evidence, and many dangerous situations concerning users in their living environment may arise. This paper presents an approach to allow Ambient Assisted Living (AAL) systems to identify and predict situations that may endanger users in their living environment. Elderly people generally have difficulties to perform daily activities as using some electronic device, cooking, dressing among others. This paper offers an alternative to acquire comprehension of a former person's behavioral providing this knowledge when cognitive impairments will occur. This is a significant improvement to traditional expert systems applied to AAL as the handling of the knowledge base is adapted to the failing users behavior. We consider the user profile dynamics where new and different situations not initially planned in the development of the systems may occur. This approach was developed considering the principles of reactive and proactive behavior and the extensibility capacity of the system. Systems developed following the above-described principles can react to the current situations and anticipate proactively a dangerous situation eliminating it or controlling its impact. The approach is evaluated and verified by a case study where unwanted, dangerous situations were created and subsequently detected. The case study is based on a simulated smart house fulfilled with sensors and appliances. An application was build based on the developed approach, and it was capable of choosing the most appropriate actions to solve the unwanted situations. The main contributions of the present work are the identification and prediction of situations in smart houses, an approach developed with the principles of reactive and proactive behavior validated with a case study and supported by a probabilistic ontology.