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Highlights: [•] Investigates benchmarked performance of global and emerging knowledge cities. [•] Introduces a knowledge-based urban development performance assessment model. [•] Applies the assessment model into an international comparative study. [•] Reveals insights on scrutinizing the development perspectives of knowledge cities. [ABSTRACT FROM AUTHOR]

• Quantitative benchmark of classifiers in business process predictive monitoring. • Considers 20 classifiers from 5 families, with different sampling and validation. • Tested on real world data sets. • Credal tree emerges as most likely superior performer. Predictive analytics is an essential capability in business process management to forecast future status and performance of business processes. In this paper, we focus on one particular predictive monitoring task that is solved using classification techniques, i.e. predicting the next event in a case. Several different classifiers have been recently employed in the literature in this task. However, a quantitative benchmark of different classifiers is currently lacking. In this paper, we build such a benchmark by taking into account 20 classifiers from five families, i.e. trees, Bayesian, rule-based, neural and meta classifiers. We employ six real-world process event logs and consider two different sampling approaches, i.e. case and event-based sampling, and three different validation methods in order to acquire a comprehensive evaluation about the classifiers' performance. According to our benchmark, the classifier most likely to be the overall superior performer is the credal decision tree (C-DT), followed by the other top-4 performers, i.e. random forest, decision tree, dagging ensemble, and nested dichotomies ensemble. We also provide a qualitative discussion of how features of an event log can affect the choice of best classifier. [ABSTRACT FROM AUTHOR]

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

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency. [ABSTRACT FROM AUTHOR]

In the framework of regression trees, this paper provides a recursive partitioning methodology to deal with a non-standard response variable. Specifically, either multivalued numerical or modal response of the type histogram will be considered. These data are known as symbolic data, which special cases are classical data, imprecise data, conjunctive data as well as fuzzy data. In spite of pre-processing data in order to deal with standard regression tree methodology, this paper provides, as main contribution, a definition of the impurity measure and of the splitting criterion allowing for building the regression tree for multivalued numerical response variable. We analyze and evaluate the performance of our proposal, using simulated data as well as a real-world case studies. [ABSTRACT FROM AUTHOR]

The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area. In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB = [10.900–1.200], ALB [4.00–4.300], SGPT = [34–37], TB = [0.600–1.200] (by boosted C5.0), A/G = [1.180–1.390], as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature. [ABSTRACT FROM AUTHOR]

The strategic supplier-related activity of supplier segmentation focuses on the evaluation of suppliers, identifying different approaches, identifying the most suitable criteria and proper methods to segment the suppliers. The main aim of the evaluation of suppliers is to form different groups from the selected suppliers to create different supplier management strategies for segments involved. Supplier development is another strategic supplier-related activity designed to upgrade the performance level of suppliers in order to create and maintain a network of competent suppliers, which has a major influence on the competitive advantages of a buying company. To allocate scarce resources more efficiently, we should design different supplier development strategies for different supplier segments. This is where we actually use the evaluation for suppliers. This paper proposes an integrative approach that includes capabilities and willingness as two dimensions for evaluating and subsequently segmenting suppliers. The results of that segmentation are then used as the main basis for supplier development. The integrative approach proposed in this paper is of significant importance, as it helps companies apportion their managerial resources more efficiently. We use a new multi-criteria decision-making method called Best Worst Method (BWM) to segment suppliers. A supplier development conceptual model is proposed to develop the suppliers in the different segments. The proposed framework is further applied to a medium-sized high-tech company as input to validate the model. [ABSTRACT FROM AUTHOR]

Deployment of Phasor Measurement Units (PMU) in the United States transmission grid has brought a new data stream to be processed and an opportunity to improve situational awareness on the grid. This new data stream offers opportunity for a faster detection and response algorithm to minimize wide spread outages. High rate of data collection of PMU systems has also brought a challenge on how to extract information from fast moving PMU data stream in real time to improve situational awareness inside a control room. Despite the fact that mathematical and probabilistic methods are the most accurate methods of stability analysis, online decision making algorithms cannot afford the latency brought by those methods. Traditional batch processing Artificial Intelligence (AI) techniques have been extensively studied as potential replacements for these approaches, however conventional AI techniques do not deal with continuous streams of fast moving phasor data. This paper presented a novel application of the stream mining algorithms for synchrophasor data to meet quick decision making requirement of future situational awareness applications in power systems. To prove that the proposed methods are efficient and capable of handling huge amounts of data with reasonable accuracy and within limited resources of memory and computational power, four different experiments with different conditions (changing/unchanging the load conditions of Real Power and Reactive Power, fixing the size of memory, and comparing the performance of non-adaptive Hoeffding tree with traditional decision tree algorithms) were conducted. The algorithms discussed in this paper support decisions inside the control rooms helping stakeholders make informed decisions to improve reliability of the future smart grid. [ABSTRACT FROM AUTHOR]

The continuous development of advanced manufacturing has triggered a strong demand for technological service platforms. To better support the development of the technological service platform, it is necessary to conduct a performance evaluation for the technological service platform. However, the existing evaluation methods fail to fully consider the ambiguity and uncertainty of decision-makers' judgments. Meanwhile, conventional methods require quite a few pairwise comparisons when determining the criteria weights. This paper proposes a novel approach that integrates rough Z-number, the best worst method (BWM), and TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) to address these issues. The proposed method can significantly reduce the number of pairwise comparisons. It also shows a significant advantage in dealing with vague information and handling the reliability of the evaluation. Moreover, the TODIM method helps capture the decision-makers' risk attitude towards uncertainty. Finally, a case study of seven technological service platforms is conducted to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are one of the most stressful and complex work environments, and the surgical staff are highly exposed to ergonomic problems. In addition, the satisfaction of the surgical staff is directly affected by these factors. In this paper, an integrated intelligent algorithm is introduced to evaluate the performance of ORs based on ergonomics indicators, quantitatively and qualitatively and to examine the impact of these indicators on job satisfaction. Initially, the required data was collected by distributing a standard questionnaire covering items associated with ergonomics and job satisfaction among the surgical staff of a mega-hospital. The study benefited from a combination of artificial neural network (ANN) and statistical methods to calculate the efficiency scores for each decision-making unit (DMU). The data envelopment analysis (DEA) method was used to validate the proposed algorithm. Afterward, sensitivity analysis and statistical tests were performed to evaluate the appropriateness of OR performance based on each of the ergonomics indicators. The results reveal that OR performance was appropriate based on knowledge, situation assessment, and situation analysis (KSS), specifications of work and equipment (SW), and mental power (MP). However, it was inappropriate in terms of information and communication system (IC), teamwork and effective communication (TE), physical condition (PC), and OR condition (OC). [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are one of the most stressful and complex work environments, and the surgical staff are highly exposed to ergonomic problems. In addition, the satisfaction of the surgical staff is directly affected by these factors. In this paper, an integrated intelligent algorithm is introduced to evaluate the performance of ORs based on ergonomics indicators, quantitatively and qualitatively and to examine the impact of these indicators on job satisfaction. Initially, the required data was collected by distributing a standard questionnaire covering items associated with ergonomics and job satisfaction among the surgical staff of a mega-hospital. The study benefited from a combination of artificial neural network (ANN) and statistical methods to calculate the efficiency scores for each decision-making unit (DMU). The data envelopment analysis (DEA) method was used to validate the proposed algorithm. Afterward, sensitivity analysis and statistical tests were performed to evaluate the appropriateness of OR performance based on each of the ergonomics indicators. The results reveal that OR performance was appropriate based on knowledge, situation assessment, and situation analysis (KSS), specifications of work and equipment (SW), and mental power (MP). However, it was inappropriate in terms of information and communication system (IC), teamwork and effective communication (TE), physical condition (PC), and OR condition (OC). [ABSTRACT FROM AUTHOR]

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

Abstract: A large number of items are described, and subsequently bought and sold every day in auction marketplaces across the web. The amount of information and the number of available items makes finding what to buy as well as describing an item to sell, a challenge for the participants. In this paper we consider two functions of electronic marketplaces. First, we address the recommendation of related items for users browsing the items offered in a marketplace. Second, in order to support potential sellers we propose the recommendation of relevant items and terms which can be used to describe an item to be sold in the marketplace. The contribution of this paper lies in the proposal of an innovative system that exploits the hidden topics of unstructured information found in the e-marketplace in order to support these functions. We propose a three-step process in which a probabilistic topic modelling approach is used in order to uncover latent topics that provide the basis for item and term similarity calculation for the corresponding recommendations. We present the design of our system and perform evaluations of the quality of the extracted topics as well as of the recommender system using real life scenarios using data extracted from a widely used auction marketplace. The evaluations demonstrate the perceived usefulness of our approach. [Copyright &y& Elsevier]

Abstract: The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA–DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large. [Copyright &y& Elsevier]

Abstract: In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO. [Copyright &y& Elsevier]

Abstract: Ontologies have been largely exploited in many domains and studies. In this paper, we present a new application of a domain ontology for generating personalized user interfaces for transportation interactive systems. The concepts, relationships and axioms of transportation ontology are exploited during the semi-automatic generation of personalized user interfaces. Personalization deals with the capacity of adaptation of a user interface, reflecting what is known about the user and the domain application. It can be performed on the interface container presentation (e.g., layout, colors, sizes) and in the content provided in their input/output (e.g., data, information, document). In this paper, the transportation ontology is used to provide the content personalization. This paper presents the ontology and how it is used for the personalization of user interfaces for developing transportation interactive systems by model-driven engineering. [Copyright &y& Elsevier]

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

Abstract: Effective online monitoring of the performance of water treatment is critical. Industrial wastewater treatment, for example, has to confront important challenges concerning both cost management of treatment plants and fulfillment of regulations. Moreover, when drinking water is produced, water safety is an important consideration, and possible changes in water quality should be detected as soon as possible. On the other hand, the large amount of measurement data available creates its own challenges for water treatment processes, which means that advanced tools for monitoring, analysis, and control are often required. Therefore, it would be useful to have a monitoring system which is able to handle all available measurements and present the available information in a simple, user-friendly and flexible manner. In this paper, we introduce a piece of software which can be utilized in the monitoring of water treatment processes. The structure of the software is designed so that it can be easily modified according to the user’s needs. The system is demonstrated using process measurements from an activated sludge treatment plant, which is part of a pulp and paper plant, and from a pilot treatment plant for producing drinking water. [Copyright &y& Elsevier]

Abstract: Based on the inherent capability of automatic modulation classification (AMC), a new spectrum sensing method is proposed in this paper that can detect all forms of primary users’ signals in a cognitive radio environment. The study presented in this paper focuses on the sensing of some combined analog and digitally primary modulated signals. In achieving this objective, a combined analog and digital automatic modulation classifier was developed using an artificial neural network (ANN). The ANN classifier was combined with a GNU Radio and Universal Software Radio Peripheral version 2 (USRP2) to develop the Cognitive Radio Engine (CRE) for detecting primary users’ signals in a cognitive radio environment. The detailed information on the development and performance of the CRE are presented in this paper. The performance evaluation of the developed CRE shows that the engine can reliably detect all the primary modulated signals considered. Comparative performance evaluation carried out on the detection method presented in this paper shows that the proposed detection method performs favorably against the energy detection method currently acclaimed the best detection method. The study results reveal that a single detection method that can reliably detect all forms of primary radio signals in a cognitive radio environment, can only be developed if a feature common to all radio signals is used in its development rather than using features that are peculiar to certain signal types only. [Copyright &y& Elsevier]

Abstract: In this paper we consider the problem of colour-based sorting hardwood parquet slabs into lots of similar visual appearance. As a basis for the development of an expert system to perform this task, we experimentally investigate and compare the performance of various colour descriptors (i.e.: soft descriptors, percentiles, marginal histograms and 3D histogram), and colour spaces (i.e.: RGB, HSV and CIE Lab). The results show that simple and compact colour descriptors, such as the mean of each colour channel, are as accurate as more complicated features. Likewise, we found no statistically significant difference in the accuracy attainable through the colour spaces considered in the paper. Our experiments also show that most methods are fast enough for real-time processing. The results suggest the use of simple statistical descriptors along with RGB data as the best practice to approach the problem. [Copyright &y& Elsevier]

Abstract: After credit has been granted, lenders use behavioural scoring to assess the likelihood of default occurring during some specific outcome period. This assessment is based on customers’ repayment performance over a given fixed period. Often the outcome period and fixed performance period are arbitrarily selected, causing instability in making predictions. Behavioural scoring has failed to receive the same attention from researchers as application scoring. The bias for application scoring research can be attributed, in part, to the large volume of data required for behavioural scoring studies. Furthermore, the commercial sensitivities associated with such a large pool of customer data often prohibits the publication of work in this area. This paper focuses on behavioural scoring and evaluates the contrasting effects of altering the performance period and outcome period using 7-years worth of data from the Irish market. The results of this work indicate that a 12-month performance period yields an easier prediction task when compared with other historical payment periods of varying lengths. This article also quantifies differences in the classification performance of logistic regression arising from different outcome periods length. Our findings show that the performance of a logistic regression classifier degrades significantly when the outcome window is extended beyond 6-month. Finally we consider different approaches to how the concept of default is defined. Typically whether the customer is identified as a default risk or not is set based on either (i) whether the account is in default at the end of the outcome period or (ii) at any time during the outcome period. This paper studies both approaches and finds that the latter approach resulted in an easier classification problem, that is, it gives the highest assurance that the classification will be correct. [Copyright &y& Elsevier]

Abstract: The main purpose of the paper is to apply a non-parametric methodology for measuring public police performance in Slovenia. The paper considers how previous studies have modeled the role of policing in their specifications of inputs and output. In particular, the multiple-stage Data Envelopment Analysis (DEA) technique is presented and then applied to measure the relative efficiency of police-work-related data for eleven Police Directorates (PDs) in 2010 and also compared with the efficiency scores in 2005. The data obtained from the police databases is analyzed through the Frontier Analyst 4.0 and SPSS 19.0 statistical package software. The results show that the technical efficiency varies significantly across the police directorates and across the selected years, even when we allow for environmental factors and control for these non-discretionary inputs. Most police directorates in Slovenia could attain a higher output if they were fully efficient. Indeed, all of the inefficient police directorates can learn how to ensure a “good” performance from the efficient ones (i.e. the Maribor PD and the Novo mesto PD) by observing how they are processing their inputs and outputs. Finally, the empirical results of the paper are important indicators of the relative efficiency (or inefficiency) of police directorates that can serve as a guide to the General Police Directorate when further investigating how to enhance the performance efficiency of the various units. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new method for reversible data hiding by employing the hierarchical relationships of original images. There are many parameters for accessing the performances of reversible data hiding algorithms, including the output image quality, the hiding capacity, and the overhead for decoding. Considering the ease of implementation and the little overhead needed for decoding, we employ modification of difference values between pixels by using histogram-based scheme with extensions to pyramidal structure by utilizing inherent characteristics of original images. By doing so, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, better performances can be obtained with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and hidden message can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms. [Copyright &y& Elsevier]

Abstract: This paper investigates a number of computational intelligence techniques in the detection of heart disease. Particularly, comparison of six well known classifiers for the well used Cleveland data is performed. Further, this paper highlights the potential of an expert judgment based (i.e., medical knowledge driven) feature selection process (termed as MFS), and compare against the generally employed computational intelligence based feature selection mechanism. Also, this article recognizes that the publicly available Cleveland data becomes imbalanced when considering binary classification. Performance of classifiers, and also the potential of MFS are investigated considering this imbalanced data issue. The experimental results demonstrate that the use of MFS noticeably improved the performance, especially in terms of accuracy, for most of the classifiers considered and for majority of the datasets (generated by converting the Cleveland dataset for binary classification). MFS combined with the computerized feature selection process (CFS) has also been investigated and showed encouraging results particularly for NaiveBayes, IBK and SMO. In summary, the medical knowledge based feature selection method has shown promise for use in heart disease diagnostics. [Copyright &y& Elsevier]

Abstract: In a supply chain, cross docking is one of the most innovative systems for improving the operational performance at distribution centers. By utilizing this cross docking system, products are delivered to the distribution center via inbound trucks and immediately sorted out. Then, products are shipped to customers via outbound trucks and thus, no inventory remains at the distribution center. In this paper, we consider the scheduling problem of inbound and outbound trucks at distribution centers. The aim is to maximize the number of products that are able to ship within a given working horizon at these centers. In this paper, a mathematical model for an optimal solution is derived and intelligent genetic algorithms are proposed. The performances of the genetic algorithms are evaluated using several randomly generated examples. [Copyright &y& Elsevier]

Abstract: In this paper, an automatic system is presented for target recognition using target echo signals of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms by using X-band pulse radar. The past studies in the field of radar target recognition have shown that the learning speed of feedforward neural networks is in general much slower than required and it has been a major disadvantage. There are two key reasons forth is status of feedforward neural networks: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms (). To resolve these disadvantages of feedforward neural networks for automatic target recognition area in this paper suggested a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) () which randomly choose hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. Moreover, the Discrete Wavelet Transform (DWT) and wavelet entropy is used for adaptive feature extraction in the time-frequency domain in feature extraction stage to strengthen the premium features of the ELM in this study. The correct recognition performance of this new system is compared with feedforward neural networks. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithm’s performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology. [Copyright &y& Elsevier]

Abstract: ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors’ use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems’ design and monitoring as well as audit committees’ effectiveness, and implications of using such systems for small and medium audit firms’ operations and survival, audit education, public sector organisations’ audit, auditor independence and audit expectations-performance gap. [Copyright &y& Elsevier]

Abstract: This paper analyzes the use of feedforward multilayer perceptron neural networks in the support of the decision process during the conceptual design phase of engineering systems. A user friendly software tool is proposed in order to increase the quality of the designers’ decisions by offering intuitive insights during the early design phase. The performance gain is evaluated through controlled experiments performed with non-experienced users. This paper proposes using the prediction capabilities of Artificial Neural Networks to recommend design solutions based on earlier successful designs. It implements a feedforward multilayer perceptron architecture with sigmoid activation functions, and uses the Levenberg–Marquardt training algorithm for weight matrix update. The network complexity was abstracted to ease the utilization by non-expert users. Different stopping conditions were developed including one where the training and testing error divergence is monitored to tackle the bias/variance dilemma. The advantages of this approach for decision support were measured through a set of six different case studies such as gaseous automobile emissions prediction based on institutional data, or the choice of a launcher for a specific space mission. The decision support tool generated quality performance gains between 13% and 88% for examples ranging from simple continuous single variable to complex discrete multivariate problems. [Copyright &y& Elsevier]

Abstract: This paper deals with algorithms for text localization and character segmentation in images for process automation in the steel-making industry. Each character which comprises slab identification numbers may be corrupted severely before it is captured by network cameras. Therefore, proper processing is required to localize the target texts successfully. In this paper, we propose (1) a method to evaluate the closeness of an edge patch to the form of a closed contour, (2) an edge inspection method to determine character colors and estimate font thickness, and (3) three reasonable binarization methods to increase the performance of the algorithm for the detection of the left and right boundaries of the text rectangle. The experimental results show that the proposed algorithms are reliable. [Copyright &y& Elsevier]

Abstract: This paper aims to contribute to the debate on the role of knowledge management in supply chain management by reviewing the published literature. A total of 58 selected referred journal articles were systematically analyzed. This review identifies various theoretical and methodological characteristics of the way in which knowledge management applications are proposed in the supply chain context. The review shows that little evidence exists of the positive relation between the use of IT solutions and firms’ performance. Some issues remain unexplored such as the problem of knowledge obsolescence in supply chain management. A deeper understanding of the knowledge accumulation process could give new insights. The paper concludes with some future directions for theory construction and empirical research. [Copyright &y& Elsevier]

Abstract: Product family planning is important for mass customization. All the products in a product family are actually transformed from a common product, which is referred to as baseline-product in this study. In this paper, we propose a heuristic method for the baseline-product design within a product family. The proposed heuristic method is based on the genetic algorithm (GA). In contrast to the existing methods for product family planning that need users to specify baseline-product prior to optimize it, the new GA-based method proposed by this paper focuses on searching a proper balance between the commonality of baseline-product and the performance of product family derived from the baseline-product. The method improves as more commonality of the baseline-product as possible, within the satisfactory of the diverse customer needs, and then determines the variable product attributes and their variation ranges, and the common parameters of the baseline-product and their optimal values. This method of baseline-product design is validated by a case study of the small-size induction motor design. At last we compare the results from our GA-based method with the benchmark products that are individually designed to the optimal performance. [Copyright &y& Elsevier]

Abstract: The international trade of Korea and Taiwan has been heavily dependent upon international seatransportation owing to geo-political aspects. Therefore, the two countries have exerted to develop and promote ocean-going shipping industry in order to support their economies. Recent financial crisis in together with the economic slowdown has reduced seaborne trade cargoes, which resulted in remarkably deteriorated revenues of the container shipping sector. Major container shipping companies of both countries such as Evergreen, Yang Ming, Hyundai, and Hanjin under our study are no exception. This paper intends to achieve two-fold aims. The first applies entropy to find the relative weights of financial ratios of the four companies each year. In so doing, we can find the weights variance for the period of 1999–2009 based on the financial performance of the above companies. The second is to rank the companies in the period by grey relation analysis. On the basis of findings in this paper, we suggest business policy implications to mitigate impacts of the financial tsunami in the context of world shipping area. [Copyright &y& Elsevier]

Abstract: A hybrid ant colony optimization algorithm is proposed by introducing extremal optimization local-search algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper. ACO algorithms have already successfully been applied to combinatorial optimization; however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of optimization problems. Hence in this paper, a hybrid ACO algorithm, named ACO–EO algorithm, is proposed by introducing EO to ACO to improve the local-search ability of the algorithm. The ACO–EO algorithm is applied to multiuser detection in DS-UWB communication system, and via computer simulations it is shown that the proposed hybrid ACO algorithm has much better performance than other ACO algorithms and even equal to the optimal multiuser detector. [Copyright &y& Elsevier]

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

Abstract: Interval-valued fuzzy sets involve more uncertainties than ordinary fuzzy sets and can be used to capture imprecise or uncertain decision information in fields that require multiple-criteria decision analysis (MCDA). This paper takes the simple additive weighting (SAW) method and the technique for order preference by similarity to an ideal solution (TOPSIS) as the main structure to deal with interval-valued fuzzy evaluation information. Using an interval-valued fuzzy framework, this paper presents SAW-based and TOPSIS-based MCDA methods and conducts a comparative study through computational experiments. Comprehensive discussions have been made on the influence of score functions and weight constraints, where the score function represents an aggregated effect of positive and negative evaluations in performance ratings and the weight constraint consists of the unbiased condition, positivity bias, and negativity bias. The correlations and contradiction rates obtained in the experiments suggest that evident similarities exist between the interval-valued fuzzy SAW and TOPSIS rankings. [Copyright &y& Elsevier]

Abstract: This paper presents a fuzzy-Pareto dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization (MOPSO). This multi-objective planning model captures the possibilistic variations of the system loads using a fuzzy triangular number. The MOPSO based on the Pareto-optimality principle is used to obtain a set of non-dominated solutions representing different network structures under uncertainties in load demands and these non-dominated solutions are stored in an elite archive of limited size. Normally, choosing the candidate non-dominated solutions to be retained in the elite archive while maintaining the quality of the Pareto-approximation front as well as maintaining the diversity of solutions on this front is very much computationally demanding. In this paper, the principles of fuzzy Pareto-dominance are used to find out and rank the non-dominated solutions on the Pareto-approximation front. This ranking in turn is used to maintain the elite archive of limited size by discarding the lower ranked solutions. The two planning objectives are: (i) minimization of total installation and operational cost and (ii) minimization of risk factor. The risk factor is defined as a function of an index called contingency-load-loss index (CLLI), which captures the effect of load loss under contingencies, and the degree of network constraint violations. The minimization of the CLLI improves network reliability. The network variables that are optimized are: (i) number of feeders and their routes, and (ii) number and locations of sectionalizing switches. An MOPSO (developed by the authors), based on a novel technique for the selection and assignment of leaders/guides for efficient search of non-dominated solutions, is used as the optimization tool. The proposed planning approach is validated on a typical 100-node distribution system. Performance comparisons between the planning approaches with the possibilistic and deterministic load models are provided highlighting the relative merits and demerits. It is also verified that the proposed solution ranking scheme based on the fuzzy-Pareto dominance is very much better from both quality and computational burden point of view in comparison with the other well-known archive truncation techniques based on clustering and solution density measurement etc. [Copyright &y& Elsevier]

• Test dataset can never be modified. • Optimal Training dataset composition for detection of minority classes. • Feature selection without under-sampling performs better. • Under-sampling Normal class instances is more fruitful compared to oversampling U2R and R2L category of attacks. • Naïve Bayes Classifier has good detection rate for U2R and Probe category of attacks and can be utilized for the same in a multistage classifier. This paper analyses the recently proposed article "A feature reduced intrusion detection system using ANN classifier" by Akashdeep, Ishfaq Manzoor & Neeraj Kumar, (Expert systems with Applications, 2017) which has a limitation in its experimental setup. The work of Akashdeep et.al has crafted the test dataset to attain improved accuracy. They have utilized 5 fractional test datasets for performance evaluation. The reduced list of features obtained in their work does not give the asserted performance for the original test dataset. Table 18 of the above article by Akashdeep et.al gives the performance comparison of their work with existing works which isn't appropriate as these works have different test dataset composition. Another issue with the work of Akashdeep et.al is the utilization of partial training dataset for determining the reduced list of features. Their work reduces the training dataset by random undersampling of the majority class instances and random replication of the minority class instances. The reduced list of features obtained by Akashdeep et.al comprises 25 features. This work applies the feature selection algorithm proposed by Akashdeep et.al on the original training dataset leading to a feature subset having 29 features. It has been observed experimentally that the reduced feature subset (29 features) obtained in later outperforms the former reduced feature set (25 features). This work uses the classification algorithms c4.5, Naive Bayes and Random Forest for performance comparison of these reduced feature sets. Oversampling one class may deteriorate the performance of another class. This work also evaluates random undersampling/oversampling of a specific class to design an optimal training dataset. The results show that the classification models developed using this training dataset have a better detection rate for the minority classes. [ABSTRACT FROM AUTHOR]

Abstract: The influences of the network properties on the transfer of knowledge within the network have been extensively studied. However, the “knowledge” properties of the network largely less-attended in literature. In this paper we investigate whether the performance of knowledge transfer in a network can be influenced by adjusting the “knowledge-connection” structure of that network, as a primitive attempt to study knowledge transfer from the aspect of the “knowledge” properties of the network. By the “knowledge-connection” structure we mean the network structure that describes the knowledge distribution within the network. Therefore, the agent-based modeling approach is adopted in this paper to compare the performance of knowledge transfer in a series of networks which differ from one another in their “knowledge-connection” structures. The results of computational simulations illustrate that the network adjustment to increase the knowledge diversity in the directly-connected agent-pairs is helpful for improving the overall performance of knowledge transfer in the entire network in the short term; but the improvement of the long-term performance is less significant. Especially, if the local knowledge-exchange follows the mutually-advantageous bidirectional-knowledge-diffusion (BKD) model, the proposed network adjustment would instead hamper the long-term effectiveness of knowledge transfer. Further investigations show that the limitations can be overcome by adopting a periodical re-adjustment mechanism, through which the knowledge diversity in the network is maintained and persistent knowledge flow becomes possible. [Copyright &y& Elsevier]

Abstract: This paper adopts data envelopment analysis (DEA), a robust and reliable evaluation method widely applied in various fields to explore the key indicators contributing to the learning performance of English freshmen writing courses in a university of Taiwan from the academic year 2004 to 2006. The results of DEA model applied in learning performance change our original viewpoint and reveal that some decision-making units (DMUs) with higher actual values of inputs and outputs have lower efficiency because the relative efficiency of each DMU is measured by their distance to the efficiency frontier. DMUs may refer to different facet reference sets according to their actual values located in lower or higher ranges. In the managerial strategy of educational field, the paper can encourage inefficient DMUs to always compare themselves with efficient DMUs in their range and make improvement little by little. The results of DEA model can also give clear indicators and the percentage of which input and output items to improve. The paper also demonstrates that the benchmarking characteristics of the DEA model can automatically segment all the DMUs into different levels based on the indicators fed into the performance evaluation mechanism. The efficient DMUs on the frontier curve can be considered as the boundaries of the classification which are systematically defined by the DEA model according to the statistic distribution. [Copyright &y& Elsevier]

Abstract: Supply planning of two-level assembly system is considered in this paper in which lead times are stochastic at both levels. Finding optimal order release dates of components is our aim which optimizes problem’s performance metrics. There is a determined due date that if demands are not prepared for delivery in that day, backlogging cost would happen. The other noticeable cost that should be taken in to consideration is holding cost, which may occur if components arrive before their time of assembling. In contrast to most of studies in this field that consider sum of these cost, this paper attempts to make a tradeoff between these objectives’ affects using multi-objective approach. This approach hybridizes an evolutionary optimizer, genetic algorithm, with an electromagnetism-like mechanism (EM). The algorithm’s effectiveness is compared against an adapted non-dominated sorting genetic algorithm II (NSGA-II) which has been presented for this problem. [Copyright &y& Elsevier]

Abstract: Methods of fuzzy rule extraction based on rough set theory are rarely reported in incomplete interval-valued fuzzy information systems. This paper deals with such systems. Instead of obtaining rules by attribute reduction, which may have a negative effect on inducting good rules, the objective of this paper is to extract rules without computing attribute reducts. The data completeness of missing attribute values is first presented. Two different approximation methods are then defined. Two algorithms based on the two approximation methods, called MRBFA and MRBBA are proposed for rule extraction. The two algorithms are evaluated by a housing database from UCI. The experimental results show that MRBFA and MRBBA achieve better classification performances than the method based on attribute reduction. [Copyright &y& Elsevier]

Abstract: This paper is aimed to present a fuzzy decision making approach to deal with the performance measurement in supply chain systems. In the manufacturing environment, performance measurement is based on different quantitative and qualitative factors. Some of these factors may have a larger effect on the performance measure than others. Units of measure of the quantitative factors are different such as time, money, percentage, ratio, and counts. Thus, this paper presents a performance measurement approach based on fuzzy set theory and the pair-wise comparison of Analytical Hierarchy Process (AHP), which ensures the consistency of the designer’s assignments of importance of one factor over another to find the weight of each of the manufacturing activity in the departmental organization. In the proposed model, various input factors have been selected, and treated as a linear membership function of fuzzy type. It is tested on a numerical example. The approach provides an effective decision tool for the performance measurement of a supply chain in manufacturing environment. [Copyright &y& Elsevier]

Abstract: This paper proposes two quadratic-constrained DEA models for evaluation of mutual funds performance, from a perspective of evaluation based on endogenous benchmarks. In comparison to previous studies, this paper decomposes two vital factors for mutual funds performance, i.e. risk and return, in order to define mutual funds’ endogenous benchmarks and give insights and suggestions for managements. Of the two quadratic-constrained DEA models, one is a partly controllable quadratic-constrained programming. The approach is illustrated by a sample of twenty-five actual mutual funds operating in the Chinese market. It identifies the root reasons of inefficiency and ways for improving performance. The results show that although the market environment in year 2006 was much better than that in 2005, average efficiency score declines in year 2006 due to relaxing of system risk control. The majority of mutual funds do not show persistence in efficiency ranking. The most important conclusion is that the ranking of mutual funds in China depends mostly on system risk control. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this paper is to construct three sets of road safety performance indicators, which are regional road safety performance indicators, urban road safety performance indicators and highway safety performance indicators, respectively. Fuzzy Delphi Method and Grey Delphi Method are applied to quantify experts’ attitudes to regional road safety, urban road safety and highway safety. Comparing the results of two methods, the different results of two methods are analyzed, and then the final safety performance indicators are obtained by taking the intersection of results of two methods. Finally, three sets of performance indicators are constructed, which can be described and evaluated the safety level of region, urban road and highway, respectively. The research findings show that the method used in this paper is feasible and practical and can be provided as a reference for the administrative authority of road safety. [ABSTRACT FROM AUTHOR]

Abstract: Customer relationship is increasingly influencing the performance of inventory management in a supply chain, and customer retention or migration directly affect the number of customer orders and demands as well as the inventory level. Therefore, the aim of this paper is to propose a customer satisfaction inventory (CSI) model that incorporates customer relationship management into an inventory model, where the probabilistic concepts of Markov chains of uncertainties in customer relationships of retention or migration are adopted. This enables us to determine both a CSI level for replenishing the inventory level to best fit future customer demand and a customer CSI value of the net profit or loss of an organization from a customer over its purchasing life against the inventory cost of an organization. The modeling and development of the proposed customer satisfaction inventory CSI model are discussed in this paper. Simulation and analysis are conducted for the proposed model, and satisfactory results are achieved, such that the proposed model can help to determine a CSI level that can fully meet orders and demand as well as minimizing inventory cost and maximizing CSI value. [Copyright &y& Elsevier]

Abstract: Data Envelopment Analysis (DEA) is a managerial powerful tool to evaluate the relative efficiency of each decision making unit (DMU). Nowadays, multi-objective DEA models in static environment are an attractive technique for evaluation quantity and quality aspects of performance analysis because there is some weakness in single objective DEA such as one-dimensional performance analysis and also it is important to consider the decision maker(s) preference over the potential adjustments of various inputs and outputs when DEA is employed. In this paper, a fuzzy dynamic multi-objective DEA model is presented in which data are changing sequentially. This paper assesses the performance of the railways using presented model as a numerical example to evaluate the results of the model. Results indicate that the multiple objective program model improves discriminating power of classical DEA models with just one time calculation of the efficiency achievement for all DUMs. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an integrated music video browsing system for personalized digital television. The system has the functions of automatic music emotion classification, automatic theme-based music classification, salient region detection, and shot classification. From audio (music) tracks, highlight detection and emotion classification are performed on the basis of information on temporal energy, timbre and tempo. For video tracks, shot detection is fulfilled to classify shots into face shots and color-based shots. Lastly automatic grouping of themes is executed on music titles and their lyrics. With a database of international music videos, we evaluate the performance of each function implemented in this paper. The experimental results show that the music browsing system achieves remarkable performances. Thus, our system can be adopted in any digital television for providing personalized services. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the performance of global vs. local techniques applied to the training of neural network classifiers for solving medical diagnosis problems. The presented methodology of the investigation involves systematic and exhaustive evaluation of the classifier performance over a neural network architecture space and with respect to training depth for a particular problem. In this study, the architecture space is defined over feed-forward, fully-connected artificial neural networks (ANNs) which have been widely used in computer-aided decision support systems in medical domain, and for which two popular neural network training methods are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). Both training techniques are compared in terms of classification performance over three medical diagnosis problems (breast cancer, heart disease, and diabetes) from Proben1 benchmark dataset and computational and architectural analysis are performed for an extensive assessment. The results clearly demonstrate that it is not possible to compare and evaluate the performance of the two algorithms over a single network and with a fixed set of training parameters, as most of the earlier work in this field has been carried out, since training and test classification performances vary significantly and depend directly on the network architecture, the training depth and method used and the available dataset. We, therefore, show that an extensive evaluation method such as the one proposed in this paper is basically needed to obtain a reliable and detailed performance assessment, in that, we can conclude that the PSO algorithm has usually a better generalization ability across the architecture space whereas BP can occasionally provide better training and/or test classification performance for some network configurations. Furthermore, we can in general say that the PSO, as a global training algorithm, is capable of achieving minimum test classification errors regardless of the training depth, i.e. shallow or deep, and its average classification performance shows less variations with respect to network architecture. In terms of computational complexity, BP is in general superior to PSO for the entire architecture space used. [Copyright &y& Elsevier]

Abstract: Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool. [ABSTRACT FROM AUTHOR]