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This paper presents an algorithmic method that hybridizes solution procedures with an optimization model for solving the problem of scheduling and allocating changeover tasks. This problem involves a limited number of changeover workers with different skills and a set of sequence-dependent changeover tasks on a multi-machines production line, where executing each task requires a particular type and number of workers. For solving this allocation and scheduling problem, the paper identifies and investigates three priority rules expressed as objective functions to minimize the total changeover time and maximize the workers' utilization while satisfying changeover task-sequence-dependency and worker-limit constraints. For illustrating the applicability and suitability of the proposed approach, a real-world case problem of a 10-machine production line is considered for scheduling and allocating 34 sequence-dependent changeover tasks to 4 workers. The computational effort and time for optimizing each of the three identified objective functions using the proposed method were significantly undersized. The key results are the effective changeover time and workers' utilization provided by the proposed hybrid approach.

Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study. info:eu-repo/semantics/publishedVersion

This paper deals with the role of alternative optimal solutions existing in the data envelopment analysis (DEA) models for cross-efficiency evaluation in portfolio selection. The paper shows that incorporating alternative optimal solutions for constructing cross-efficiency matrix improves the result of the mean-variance portfolio selection method. This improvement means that building portfolios with lower risk and higher expected return is possible when alternative optimal solutions are considered. The proposed method in this paper is applied to stock portfolio selection in the Tehran stock market.

This paper presents a systematic procedure to fairly compare experimental performance scores for machine learning methods for fault diagnosis based on vibration signals. In the vast majority of related scientific publications, the estimated accuracy and similar performance criteria are the sole quality parameter presented. However, the experimental design giving rise to these results is mostly biased, based on unacceptably simple validation methods and on recycling identical patterns in test data sets, previously used for training. Moreover, the methods in general overfit their hyperparameters, introducing additional overoptimistic results. In order to remedy this defect, we critically analyse the usual training-validation-test division and propose an algorithmic guideline in the form of a validation framework. This allows a well defined comparison of experimental results. In order to illustrate the ideas of the paper, the Case Western Reserve University Bearing Data benchmark is used as a case study. Four distinct classifiers are experimentally compared, under gradually more difficult generalization tasks using the proposed evaluation framework: K-Nearest-Neighbor, Support Vector Machine, Random Forest and One-Dimensional Convolutional Neural Network. An extensive literature review suggests that most vibration based research papers, particularly for the Case Western Reserve University Bearing Data, use similar patterns for training and testing, making their classification an easy task.

This paper investigates the problem scheduling a set of jobs on parallel batch processing machines with different capacities and non-identical processing powers for minimizing the makespan and the total energy consumption, where the jobs have non-identical sizes, dynamical arrival time and different processing time. To address the bi-objective optimization problem, a three-populations co-evolutionary algorithm is proposed, which is based on exploration and coordination searches among three colonies. For guaranteeing the diversity of solutions, an adaptive search strategy based on the largest angle among adjacent solutions is designed, and a new method is proposed to select ants to update pheromone trails for improving the convergence of solutions. Finally, the proposed algorithm is compared with the existing multi-objective algorithms through extensive simulated experiments, and the simulated results are statistically analyzed. And the experimental results show that the proposed algorithm outperforms all the compared algorithms, which verify the validity of the algorithm proposed in this paper.

Integrating return prediction of traditional time series models in portfolio formation can improve the performance of original portfolio optimization model. Since machine learning and deep learning models have shown overwhelming superiority than time series models, this paper combines return prediction in portfolio formation with two machine learning models, i.e., random forest (RF) and support vector regression (SVR), and three deep learning models, i.e., LSTM neural network, deep multilayer perceptron (DMLP) and convolutional neural network. To be specific, this paper first applies these prediction models for stock preselection before portfolio formation. Then, this paper incorporates their predictive results in advancing mean–variance (MV) and omega portfolio optimization models. In order to present the superiority of these models, portfolio models with autoregressive integrated moving average’s return prediction are used as benchmarks. Evaluation is based on historical data of 9 years from 2007 to 2015 of component stocks of China securities 100 index. Experimental results show that MV and omega models with RF return prediction, i.e., RF+MVF and RF+OF, outperform the other models. Further, RF+MVF is superior to RF+OF. Due to the high turnover of these two models, this paper discusses their performance after deducting the transaction fee cased by turnover. Experiments present that RF+MVF still performs the best among MVF models and omega model with SVR prediction (SVR+OF) performs the best among OF models. Moreover, RF+MVF performs better than SVR+OF and high turnover erodes nearly half of their total returns especially for RF+OF and RF+MVF. Therefore, this paper recommends investors to build MVF with RF return prediction for daily trading investment.

Educational timetabling is an ongoing challenging administrative task that is required in most academic institutions. This is mainly due to a large number of constraints and requirements that have to be satisfied. Educational timetabling problems have been classified as NP-hard problems and can be divided into three types: exam timetabling, course timetabling and high school timetabling. The domain of high school timetabling is not well developed when compared to other fields of educational timetabling such as university exam timetabling and course timetabling. As the evolution of the educational systems are continuous, new challenges often arise, requiring new models and solution methodologies. Over the years, a number of methodologies have been developed to address high school timetabling problems. However, there are no comparative studies or rigorous analysis of these methodologies. This survey paper aims to provide a scientific review of high school timetabling. The paper presents a categorisation of the methodologies conducted in recent years based on chronology, category and application (dataset). We first present comparative studies on the success of proposed methodologies. The components and mechanisms of different methodologies are analysed and compared. We also discuss their performance, advantages, disadvantages and potential for improvement. Methodology wise, a shift of popularity from meta-heuristic to mathematical optimisation is observed in recent years. Another observation is that more researchers are opting for XHSTT formatted datasets as a testbed for their algorithms. Finally, we outline the industrial perspective, trends and future direction in high school timetabling optimisation problems.

Conflicting objectives such as minimizing weight and minimizing the maximum nodal displacement, with constraints on normal stresses in the bars, is a common multi-objective structural optimization problem widely found in the literature. This paper proposes multi-objective structural optimization problems with the combination of new conflicting objectives functions and constraints, such as the natural frequencies of vibration and the load factors concerning the global stability of the structure. The solution for these problems may be of great interest in the field of structural engineering, not yet discussed in the literature. The problems analyzed in this paper deal with both discrete and continuous sizing, shape, and layout design variables. The search algorithm adopted here is a modified version of the Differential Evolution called the Third Evolution Step Differential Evolution (GDE3). Several experiments are analyzed with their Pareto-fronts showing the non-dominated solutions. The solutions are defined after obtaining the Pareto curve, which is one of the most important steps and a task that may not be trivial for the Decision Maker. This paper involves a strategy that establishes criteria defining weights (importance) for each objective function and, through these values, enables comparison scenarios. The numerical experiments include plane and spatial benchmark trusses.

Smartphone sensing capabilities have enabled human activity recognition (HAR) solutions to better understand human behavior through computational techniques. However, such solutions have suffered from scalability problems due to the high consumption of computational resources (e.g. memory and processing) and the difficulty of acting in real time due to not observing data evolution over time. These problems occur because the HAR solutions for smartphones have been solved through offline learning with models limited by a data history. The disadvantage of this approach is that human activities constantly change over time and are strongly influenced by the physical environment and user profile. To overcome such problem, this paper proposes a novel low-cost learning algorithm called NOHAR (NOvelty discrete data stream for Human Activity Recognition), focused on continuous flow of data analysis. NOHAR is an online classification algorithm based on symbolic data generated by a discretization process using algorithms as SAX and SFA. The advantages of these algorithms are their abilities to compress and reduce the dimensionality of data. In addition, this paper proposes a new framework called DISTAR (DIscrete STream learning for Activity Recognition) focused on the data streaming analysis. Its goals include standardizing the development of online algorithms for symbolic data. Experimental results using three databases show that NOHAR is on average 33 times faster than the state of the art and can reduce memory consumption by an average of 99.97%.

Differential Evolution Algorithm (DE) is a commonly used stochastic search method for solving real-valued numerical optimization problems. Unfortunately, DE's problem solving success is very sensitive to the internal parameters of the artificial numerical genetic operators (i.e., mutation and crossover operators) used. Although several mutation and crossover methods have been developed for DE, there is not still an analytical method that can be used to select the most efficient mutation and crossover method while solving a problem with DE. Therefore, selection and parameter tuning processes of artificial numerical genetic operators used by DE are based on a trial-and-error process which is time consuming. The development of modern DE versions has been focused on developing fast, structurally simple and efficient genetic operators that are not sensitive to the initial values of their internal parameters. Problem solving successes of the Universal Differential Algorithms (uDE) are not sensitive to the structure and internal parameters of the related artificial numerical genetic operators used, unlike DE. In this paper a new uDE, Bezier Search Differential Evolution Algorithm, BeSD, has been proposed. BeSD’s mutation and crossover operators are structurally simple, fast, unique and produce highly efficient trial patterns. BeSD utilizes a partially elitist unique mutation operator and a unique crossover operator. In this paper, the experiments were performed by using the 30 benchmark problems of CEC2014 with Dim=30, and one 3D viewshed problem as a real world application. The problem solving success of BeSD was statistically compared with five top-methods of CEC2014, i.e., CRMLSP, MVO, WA, SHADE and LSHADE by using Wilcoxon Signed Rank test. Statistical results exposed that BeSD’s problem solving success is better than those of the comparison methods in general.

Feature selection is crucial to develop a brain computer interface (BCI) system which has high classification accuracy and less computational complexity in especially a large feature space. Feature selection (FS) problem has been solved by many various methods. Among these methods, especially evolutionary computation (EC) techniques have gained a lot of attention in recent years. However, there are very few studies in the literature that consider FS problem as a multi-objective problem to find the optimal trade-off between classification accuracy and the number of selected features. Therefore, in this paper, a non-dominated sorting multi-objective symbiotic organism search (NSMOSOS) algorithm is proposed to generate the optimal feature subset in BCI. The efficiency and robustness of the proposed algorithm as a feature selection method is investigated in two datasets based on motor imagery. The highest classification accuracies of NSMOSOS for dataset 1 and dataset 2 are obtained 97.86% with 11 features and 96.57% with average 19 features, respectively. The obtained results demonstrate that the proposed method achieves satisfying results with regard to both the classification accuracy improvement and feature reduction rates for both datasets. The superiority of the proposed method is verified compared with the existing methods for both datasets. Besides, three different versions of symbiotic search organism (SOS) algorithm are improved, and pros and cons of these algorithms are evaluated compared with each other. In conclusion, the paper indicates that the proposed NSMOSOS algorithm is an efficient and practicable technique for FS problem and could be helpful in developing the BCI applications.

Finding feasible solutions to real-world problems is a crucial task. Metaheuristic algorithms are widely used in many fields due to the variety of solutions they can produce. The grey wolf optimizer (GWO) is a relatively novel population-based metaheuristic algorithm that has been shown to have good optimization performance. However, due to the insufficient diversity of wolves in some cases, this approach can lead to locally optimal situations. Therefore, this paper proposes a grey wolf optimization method based on a beetle antenna strategy (BGWO) that gives the leader wolf a sense of hearing to improve the global search ability and reduce unnecessary searches. In addition, to balance exploration and exploitation, a nonlinear dynamic control parameter update strategy based on the cosine function is proposed. To evaluate the performance of the proposed BGWO, this paper uses 23 standard benchmark functions to test the method in different dimensions. Moreover, four well-known engineering problems are used to evaluate the ability of the proposed algorithm to obtain real-world problem solutions. The experimental results show that BGWO has superior performance and is competitive with many state-of-the-art algorithms in terms of solution accuracy, convergence rate, and stability.

Proteomics is nowadays one of the most important and relevant fields from computational biology, raising a lot of challenging and provocative questions. Gaining an understanding of protein dynamic and function as well as obtaining additional insights into the protein folding process is still of great interest in bioinformatics and medicine. This paper introduces a new approach A n o m a l P for detecting anomalous protein conformational transitions using deep autoencoders for encoding information about the structural similarity between proteins belonging to the same superfamily. Experiments are conducted on real protein data and the obtained results emphasize the potential of autoencoders to learn biological relevant patterns, such as proteins’ structural characteristics and that they are useful for detecting conformations or proteins which are likely to be anomalous with respect to a superfamily. The study performed in this paper is aimed to provide better insights of proteins structural similarity, with the broader goal of learning to predict proteins conformational transitions.

This paper analyses different multi-objective evolutionary algorithms to deal with the Environmental/Economic Load Dispatch (EELD). EELD is formulated as a multi-objective optimization problem in which two competing objectives (fuel cost and pollutants emission) should be optimized simultaneously while fulfilling constraints. Due to the typical process of an evolutionary algorithm (EA), the use of operators applied to individuals of the population might violate constraint rules of the problem. The way in which EAs deal with such constraint rules is an important point and it is directly related to the quality of the generated solutions. One of the contributions of this paper is the analysis of the impact of a repair procedure in four multi-objective EAs. The analyzed approaches are evaluated in eight known instances (with 3, 6, 10, 20 and 40 generators) of the multi-objective EELD. Furthermore, two new instances (with 80 and 120 generators) are proposed and evaluated in this work. Experiments were applied using Dominance Ranking, hypervolume and unary-epsilon indicators, empirical attainment functions and statistical tests, in order to evaluate the algorithms performances. The results point to the consistency of the NSGA-II with repair procedure compared to the literature algorithms, and it outperforms other approaches in most of the considered instances.

The excellent generalization performance of time series ensemble forecasting depends on the accuracy and diversity of the individual models. In this paper, a heterogeneous ensemble forecasting model with multi-objective programming for nonlinear time series is proposed. Accordingly, an improved multi-objective particle swarm optimization (MOPSO) algorithm integrated with a dynamic heterogeneous mutation operator is designed. The nonlinear time series of the Baltic Dry Index (BDI) is selected as the forecasting object to train, validate and test the ensemble forecasting model established in this paper. To verify the superior forecasting performance of the proposed model, 20 forecasting models including statistical models, machine learning models, and optimization algorithm–based ensemble models are utilized and compared. The experimental results under different lead times revealed that: 1) the forecasting approach with multi-objective programming has excellent robustness and can effectively exert out-of-sample prediction under different lead times for nonlinear time series; 2) with the increase of lead time, the out-of-sample forecasting performance would gradually decrease for all models, and the precision of the ensemble forecasting model is better than that of the individual forecasting model; 3) the forecasting performance of the MOPSO with crowding distance (MOPSOCD)-based ensemble forecasting model is better than that of benchmark machine learning models and other optimal ensemble forecasting models in terms of the prediction accuracy and statistical test results.

Many intelligent systems in business, government or academy process natural language as an input during inference or they might even communicate with users in natural language. The natural language processing is currently often done with machine learning models. However, machine learning needs training data and such data are often scarce for low-resource languages. The lack of data and resulting poor performance of natural language processing can be solved with cross-lingual learning. Cross-lingual learning is a paradigm for transferring knowledge from one natural language to another. The transfer of knowledge can help us overcome the lack of data in the target languages and create intelligent systems and machine learning models for languages, where it was not possible previously. Despite its increasing popularity and potential, no comprehensive survey on cross-lingual learning was conducted so far. We survey 173 text processing cross-lingual learning papers and examine tasks, datasets and languages that were used. The most important contribution of our work is that we identify and analyze four types of cross-lingual transfer based on “what” is being transferred. Such insight might help other NLP researchers and practitioners to understand how to use cross-lingual learning for wide range of problems. In addition, we identify what we consider to be the most important research directions that might help the community to focus their future work in cross-lingual learning. We present a comprehensive table of all the surveyed papers with various data related to the cross-lingual learning techniques they use. The table can be used to find relevant papers and compare the approaches to cross-lingual learning. To the best of our knowledge, no survey of cross-lingual text processing techniques was done in this scope before.

Social media has recently become popular due to its vast applications. The common people all over the world uses its diverse channels to express personal views, experiences and opinions regarding diverse topics. Social media has revolutionized the way people interact and communicate with each other and overall, it has changed the methods and approaches in about all the aspects of life such as social issue, business, education, health, etc. Thus, sales and marketing departments of multinational industries are focusing on social media trends to analyze current trends and predict future trends by analyzing user generated content on Facebook, Flickr, Twitter, etc. However, the prediction process becomes challenging as the multiplicity of factors affect the popular elements in the social media content. This research paper aims to work on Twitter trend analysis and proposes a trend detection process over streams of tweets. The proposed approach detects the trending topics of the real-time Twitter trends along with ranking the top terms and hashtags. The paper further discusses the motivation for trend prediction over the social media; In addition to exploratory data analysis, the research paper explores the Term Frequency-Inverse Document Frequency (Tf-IDF), Combined Component Approach (CCA) and Biterm Topic Model (BTM) approaches for finding the topics and terms within given topics. In modern competitive world, this research provides investors, advertisers, industries and all the stakeholders. A detailed and comprehensive data analysis which may help them to focus their investment, area of work, marketing, and product.

Pythagorean fuzzy (PF) sets involving Pythagorean membership grades can befittingly manipulate inexact and equivocal information in real-life problems involving multiple criteria decision analysis (MCDA). The linear programming technique for multidimensional analysis of preference (LINMAP) is a prototypical compromising model, and it is widely used to carry on decision-making problems in many down-to-earth applications. In LINMAP, the employment of squares of Euclidean distances is a significant technique that is an effective approach to fit measurements. Taking the advantages of a newly developed Euclidean distance model on the grounds of PF sets, this paper initiates a beneficial concept of squared PF Euclidean distances and studies its valuable and desirable properties. This paper aims to establish a squared Euclidean distance (SED)-based outranking approach and develop a novel PF LINMAP methodology for handling an MCDA problem under PF uncertainty. In the SED-based outranking approach, a novel SED-based dominance index is proposed to reflect an overall balance of a PF evaluative rating between the connection and remotest connection with positive- and negative-ideal ratings, respectively. The properties of the proposed index are also analyzed to exhibit its efficaciousness in determining the dominance relations for intracriterion comparisons. Moreover, this paper derives the comprehensive dominance index to determine the overall dominance relation and defines measurements of rank consistency for goodness of fit and rank inconsistency for poorness of fit. The PF LINMAP model is formulated to seek to ascertain the optimal weight vector that maximizes the total comprehensive dominance index and minimizes the poorness of fit under consideration of the lowest acceptable level and specialized degenerate weighting issues. The practical application concerning bridge-superstructure construction methods is conducted to test the feasibility and practicability of the PF LINMAP model. Over and above that, a generalization of the proposed methodology, along with applications to green supplier selection and railway project investment, is investigated to deal with group decision-making issues. Several comparative studies are implemented to further validate its usefulness and advantages. The application and comparison results display the effectuality and flexibility of the developed PF LINMAP methodology. In the end, the directions for future research of this work are represented in the conclusion.

With the rising share of electric vehicles used in the service industry, the optimization of their specific constraints is gaining importance. Lowering energy consumption, time of charging and the strain on the electric grid are just some of the issues that must be tackled, to ensure a cleaner and more efficient industry. This paper presents a Two-Layer Genetic Algorithm (TLGA) for solving the capacitated Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW) and Electric Vehicles (EV) with partial nonlinear recharging times (NL) – E-MDVRPTW-NL. Here, the optimization goal is to minimize driving times, number of stops at electric charging stations and time of recharging while taking the nonlinear recharging times into account. This routing problem closes the gap between electric vehicle routing problem research on the one hand and its applications to several problems with numerous real-world constraints of electric vehicles on the other. Next to the definition and the formulation of the E-MDVRPTW-NL, this paper presents the evolutionary method for solving this problem using the Genetic Algorithm (GA), where a novel two-layer genotype with multiple crossover operators is considered. This allows the GA to not only solve the order of the routes but also the visits to electric charging stations and the electric battery recharging times. Various settings of the proposed method are presented, tested and compared to competing meta-heuristics using well-known benchmarks with the addition of charging stations.

Several algorithms have been proposed towards discovering the graphical structure of Bayesian networks. Most of these algorithms are restricted to observational data and some enable us to incorporate knowledge as constraints in terms of what can and cannot be discovered by an algorithm. A common type of such knowledge involves the temporal order of the variables in the data. For example, knowledge that event B occurs after observing A and hence, the constraint that B cannot cause A . This paper investigates real-world case studies that incorporate interesting properties of objective temporal variable order, and the impact these temporal constraints have on the learnt graph. The results show that most of the learnt graphs are subject to major modifications after incorporating incomplete temporal objective information. Because temporal information is widely viewed as a form of knowledge that is subjective, rather than as a form of data that tends to be objective, it is generally disregarded and reduced to an optional piece of information that only few of the structure learning algorithms may consider. The paper argues that objective temporal information should form part of observational data, to reduce the risk of disregarding such information when available and to encourage its reusability across related studies.

Indoor localization systems have the capability to change the way of providing location-based services in a closed environment. Though there is no agreed-upon technology that works best in indoor, WiFi signal is an important alternative as most of such places are covered by WiFi Access Points (APs). In this paper, the problem of indoor localization is investigated from the perspective of expert systems through applying machine learning techniques. The significant variation of WiFi signal strength with ambient conditions as well as device configuration badly affects the localization accuracy. Thus, the fingerprinting effort required to train a localization system subject to context heterogeneity is huge. The uncertainty in localization performance due to varying contexts is hardly investigated in the literature. Consequently, the main contribution of this paper is to propose a weighted ensemble classifier based on Dempster–Shafer belief theory to efficiently handle context heterogeneity. Here, the context is defined in terms of different smartphone configurations used for training and testing the system as well as temporal variation of signals. The method presented here utilizes the Dempster–Shafer theory of belief functions to calculate the weights of the base learners in the decision of the ensemble. Belief theory is applied here to handle the inherent uncertainty in WiFi signal variations due to heterogeneous context. Real life experiments are conducted for two datasets, JUIndoorLoc and UJIIndoorLoc at different granularity levels. For JUIndoorLoc, with state-of-the-art classifiers, 86–97% accuracy can be achieved for 10-fold cross-validation. However, when the training context differs from the test conditions, accuracy drops to 62–87%. In such a scenario, the proposed weighted ensemble technique is found to achieve almost 98% localization accuracy when RSSIs, mean and variance of RSSIs are considered as features. The technique can lead to an effective expert system for indoor localization at varying granularity levels. Such systems would be beneficial for pervasive indoor positioning applications as no dedicated infrastructure is needed for positioning.

This paper examines the option pricing performance of the most popular Machine Learning algorithms. The classic parametrical models suffer from several limitations in term of computational power required for parametric calibration and unrealistic economical and statistical assumptions. Therefore, a data driven approach based on non-parametric models is are well justified. Most of the previous researchers focus especially on the neural networks method (NN), the other algorithms being unexplored. Beside NN, this paper also analyses the performance of the Support Vector Regressions and Genetic Algorithms and propose three other Decision Tree methods, respectively Random Forest, XGBoost and LightGMB. In order to emphasize the power of this algorithms, a comparison with classical methods like Black-Scholes and Corrado-Su with both historical and implied parameters have been conducted. The analyzes were performed on European call options who have as underlying asset the WTI crude oil future contracts. Machine Learning algorithms outperform by a great margin the classical approaches regardless of the moneyness and the maturity of the contracts.

Decentralized nature of cryptocurrencies, irrational cryptocurrency valuations and severe price volatility in cryptocurrency market makes it a formidable task for investors to pick individual coins, and rather investors would prefer to invest money on the entire cryptocurrency market accurately represented by a cryptocurrency index. This paper proposes the design of a Principal Component Analysis based methodology to construct a dynamic cryptocurrency index that accurately tracks the movement of the entire cryptocurrency market. Our analysis on real market data shows why first component derived from PCA is sufficient to construct the cryptocurrency index and how to determine the number of constituents while building the index. Proposed PCA based tool, tested on actual historical cryptocurrency data, takes into account the changing dynamics of the cryptocurrency market by regularly shifting the number of constituents as well as the weights and is able to capture the evolving pattern in the cryptocurrency market. The proposed index has been validated by three-factor pricing model, consisting of market, size and momentum factors. Subsequently, the proposed index has been compared with other existing indexes based on the results of three-factor model. In summary, the paper presents a robust mathematical model for construction of a dynamic cryptocurrency index that can be used as a tool to analyze the return on investments as well as to study the fluctuations present in the cryptocurrency market.

Hundreds of millions of people in low-income economies do not have a credit or bank account because they have insufficient credit history for a credit score to be ascribed to them. In this paper we evaluate the predictive accuracy of models using alternative data, that may be used instead of credit history, to predict the credit risk of a new account. Without alternative data, the type of data that is typically available is demographic data. We show that a model that contains email usage and psychometric variables, as well as demographic variables, can give greater predictive accuracy than a model that uses demographic data only and that the predictive accuracy is sufficiently high for the demographic and email data to be used when conventional credit history data is unavailable. The same applies if merely psychometric data is included together with demographic data. However, we show that different randomly selected training: test sample splits give a wide range of predictive accuracies. In the second part of the paper, using two datasets that include only email usage as a predictor, we compare the predictive performances of a wide range of machine learning and statistical classifiers. We find that some classifiers applied to these alternative predictors give sufficiently accurate predictions for these variables to be used when no other data is available.

Dandelion Algorithm (DA) is a novel swarm intelligent optimization algorithm. In evolutionary process of DA, the quality of the seeds generated by dandelions is uneven, and the excellent seeds are expected to be retained and evaluated, while the poor seeds should be discarded without evaluation. In order to determine whether a seed is excellent or not, an improvement of evolution process of dandelion algorithm with extreme learning machine (ELMDA) is proposed in this paper. In ELMDA, firstly, the dandelion population can be partitioned into excellent dandelions and poor dandelions based on fitness values. Then, the excellent dandelions and poor dandelions are assigned corresponding labels (i.e. +1 if excellent or -1 if poor), which can be regarded as a training set, and the training model is built based on ELM. Finally, the model is applied to classify the seeds as excellent or poor, and the excellent seeds are chosen to participate in evolution process. Meanwhile, the robustness of the proposed algorithm is analyzed in this paper. Experimental results performed on test functions show that the proposed algorithm is competitive to its peers. Moreover, the proposed algorithm is demonstrated on three engineering designed problems, and the results indicate that the proposed algorithm has better performance in solving them.

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

A novel approach is provided to specify and verify service compositions contracts.BPEL is extended to represent specifications and mark the points to be verified.Properties are derived automatically from composition implementations.The verification process relies on commitment logic and its model checking.Web services are verified from the perspectives of compliance and violations. The paper proposes a novel model checking-based approach towards verifying the compliance of intelligent agent-based web services with contracts regulating their compositions specified in the Business Process Execution Language (BPEL). Unlike the existing approaches in the literature, the main contribution and impact of the introduced approach is the ability to verify intelligent and autonomous composite web services by capturing and describing in details both compliance and violation behaviors, how the system can distinguish between them, and how the system reacts and can be recovered after each violation. The approach encompasses three contributing parts, namely: 1) the marking process of an extended BPEL; 2) the transformation of the extended and marked BPEL to an automata model; and 3) the encoding of the resulting automata model into the Interpreted Systems Programming Language (ISPL), the input language of the MCMAS model checker for intelligent and autonomous multi-agent systems. In the first part, we extend BPEL that specifies the business process of the composition by creating custom activities called labels. We use those labels as means to represent the specifications and mark the points the developer aims to verify. A significant advantage of this labeling is the ability to highlight specific points in the design to be verified and to distinguish compliance behaviors from violations, which makes this verification focused and highly efficient. In the second part, we introduce new transformation rules to transform the extended and marked BPEL to an automata model. This transformation requires a prior modeling of agent-based web services composition using automata definitions. In the third part, we introduce algorithmic translation rules encoding the resulting automata model into ISPL. This translation makes model checking the behavior of our contract-driven compositions possible. A novel characteristic of the proposed approach is the automatic generation of the properties against which the system is verified from the composition's implementation, which is technically challenging. The verification properties are expressed in the Computation Tree Logic of Commitments (CTLC). Technically, CTLC provides a powerful representation to formally model 1) interactions among multi-agent based web services and 2) compliance and violation behaviors within composite business contracts by making use of communicative commitment operators. CTLC also includes a fulfillment operator which helps formally check the compliance with business contracts and specify the system recovery. A detailed case study from expert and intelligent systems domain along with experimental results are also reported in the paper. Finally, the main impact and significance of the paper on expert and intelligent systems is the ability to use these systems safely since there is a way to verify if the intelligent components behave according to and in compliance with the underlying regulating contracts.

SAM provides enhanced visual aids for representing the many-to-many association rules in large transaction data.The performance of SAM can be numerically evaluated by using S2C measure.SAM enables users to conveniently identify the interesting areas that might contain interesting association rules.SAMs with higher S2C values are more useful for visual exploration of association rules. The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules.

A theorem characterizing the property of the optimal solution is presented.A hybrid evolutionary algorithm (HEA) is proposed.Extreme machine learning is used to approximate the fitness of most individuals.Two heuristics are incorporated to generate a good initial population.The proposed algorithm is computationally effective. This paper considers the two-stage capacitated facility location problem (TSCFLP) in which products manufactured in plants are delivered to customers via storage depots. Customer demands are satisfied subject to limited plant production and limited depot storage capacity. The objective is to determine the locations of plants and depots in order to minimize the total cost including the fixed cost and transportation cost. However, the problem is known to be NP-hard. A practicable exact algorithm is impossible to be developed. In order to solve large-sized problems encountered in the practical decision process, an efficient alternative approximate method becomes more valuable. This paper aims to propose a hybrid evolutionary algorithm framework with machine learning fitness approximation for delivering better solutions in a reasonable amount of computational time. In our study, genetic operators are adopted to perform the search process and a local search strategy is used to refine the best solution found in the population. To avoid the expensive consumption of computational time during the fitness evaluating process, the framework uses extreme machine learning to approximate the fitness of most individuals. Moreover, two heuristics based on the characteristics of the problem is incorporated to generate a good initial population. Computational experiments are performed on two sets of test instances from the recent literature. The performance of the proposed algorithm is evaluated and analyzed. Compared with other algorithms in the literature, the proposed algorithm can find the optimal or near-optimal solutions in a reasonable amount of computational time. By employing the proposed algorithm, facilities can be positioned more efficiently, which means the fixed cost and the transportation cost can be decreased significantly, and organizations can enhance competitiveness by using the optimized facility location scheme.

The uncertain quantity of drugs for patients in home health care service is considered.A fuzzy chance constraint programming for vehicle scheduling problem is proposed.A hybrid genetic algorithm integrated with stochastic simulation method is designed.3 series of experiments are conducted to test the efficiency of the algorithm.Cost changes for difference value of Dispatcher Preference Index are discussed. Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs are among the biggest sources of expenditure in company activities, it is of great significance to optimize this in the Home Health Care industry. From the perspective of optimizing the cost of transportation, this paper studies the Vehicle Routing Scheduling problem as it applies to HHC companies. According to a survey of the HHC companies, during the process of delivering medication drugs, the quantity of drugs required for each patient is non-deterministic when the company makes planned routes. This paper considers uncertain demand as a fuzzy variable, which is closer to a potential real life scenario. A Home Health Care Scheduling Problem with fuzzy demand is considered and a fuzzy chance constraint model is designed. We propose a hybrid genetic algorithm integrated with stochastic simulation methods to solve the proposed model. Firstly, the problem is reduced to the classical vehicle routing problem within a time window. Experimental results for Solomons and Hombergers benchmark instances show that the proposed algorithm performs efficiently. Then other experiments on the fuzzy version model are undertaken with the variable value of the Dispatcher Preference Index (DPI) parameter between [0, 1]. Finally, the influence of DPI on the final objective and the indicators of the problem are discussed using stochastic simulation, and the best value of DPI is obtained. This research will help HHC companies to make appropriate decisions when arranging their vehicle scheduling routes.

Automatic generation of user interfaces from domain knowledge: ARBUI.Practical implementation of the proposed architecture (ARBUI): the Semantic MVC.Tested our approach in a pilot project at the Oncology Center in Warsaw. Motivation: The ability to directly trace how requirements are implemented in a software system is crucial in domains that require a high level of trust (e.g. medicine, law, crisis management). This paper describes an approach that allows a high level of traceability to be achieved with model-driven engineering supported by automated reasoning. The paper gives an introduction to the novel, automated user interface synthesis in which a set of requirements is automatically translated into a working application. It is presented as a generalization of the current state of the art model-driven approaches both from the conceptual perspective as well as the concrete implementation is discussed together with its advantages like the alignment of business logic with the application and ease of adaptability. It also presents how a high level of traceability can be obtained if runtime support of automated reasoning over models is applied.Results: We have defined the Automated Reasoning-Based User Interface (ARBUI) approach and implemented a framework for application programming that follows our definition. The framework, called Semantic MVC, is based on model-driven engineering principles enhanced with W3C standards for the semantic web. We will present the general architecture and main ideas underlying our approach and framework. Finally, we will present a practical application of the Semantic MVC that we created in the medical domain as a Clinical Decision Support System for GIST cancer in cooperation with the Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology in Warsaw. The discussed expert system allows the expert to directly modify the executable knowledge on the fly, making the overall system cost effective.

Novel fuzzy rule-based system (FRBS) for medical data classification tasks is proposed.FRBSs are designed using multi-objective evolutionary optimization algorithms.Set of FRBSs with various levels of accuracy-interpretability trade-off is generated.Benchmark medical data sets are used to evaluate the effectiveness of the system.Highly interpretable and accurate medical decision support is provided by our approach. In medical decision support systems, both the accuracy (i.e., the ability to adequately represent the decision making processes) as well as the transparency and interpretability (i.e., the ability to provide a domain user with compact and understandable explanation and justification of the proposed decisions) play essential roles. This paper presents an approach for automatic design of fuzzy rule-based classification systems (FRBCSs) from medical data using multi-objective evolutionary optimization algorithms (MOEOAs). Our approach generates, in a single run, a collection of solutions (medical FRBCSs) characterized by various levels of accuracy-interpretability trade-off. We propose a new complexity-related interpretability measure and we address the semantics-related interpretability issue by means of efficient implementation of the so-called strong fuzzy partitions of attribute domains. We also introduce a special-coding-free representation of the rule base and original genetic operators for its processing as well as we implement our ideas in the context of well-known and one of the presently most advanced MOEOAs, i.e., Non-dominated Sorting Genetic Algorithm II (NSGA-II). An important part of the paper is devoted to a broad comparative analysis of our approach and as many as 26 alternative techniques arranged in 32 experimental set-ups and applied to three well-known benchmark medical data sets (Breast Cancer Wisconsin (Original), Pima Indians Diabetes, and Heart Disease (Cleveland)) available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml). A number of useful in medical applications performance measures including accuracy, sensitivity, specificity, and several interpretability measures are employed. The results of such a broad comparative analysis demonstrate that our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained FRBCSs while remaining either competitive or superior in terms of their accuracy. It is worth stressing that the overwhelming majority of the existing medical classification methods concentrate almost exclusively on the accuracy issues.

Multi-criteria decision support for eco-friendly routing solutions.Emission minimization and reverse logistics.Efficient unification of multi-objective and multi-attribute VRPs.Efficient handling of non-uniform traffic conditions.Demonstrated utility in real-world VRP manifestations. In this paper, we present a multi-objective shortest path evolutionary algorithm for comprehensive solutions to real-world manifestations of the classical vehicle routing problem. The shift from being a purely academic pursuit is highlighted by the introduction of a generic optimization framework which accommodates a variety of attributes that commonly occur in industrial applications. Specifically, the paper's main contribution are as follows: (1) consideration for the following real-world constraints: (a) time windows at customer locations, (b) simultaneous pickup and delivery demands, (c) a heterogeneous fleet of vehicles, and (d) the heterogeneity of traffic congestion levels in urban transportation networks; (2) assimilation of all the above attributes into a multi-objective program which aims to minimize environmental impact, while simultaneously addressing the overall operational costs of the routing solution and service quality concerns; a feat that has not been fully realized by known intelligent systems according to the authors' best knowledge. In order to showcase the efficacy of the proposed algorithm, it is first tested on existing benchmark instances and then applied on a pair of real-world industrial examples from Singapore. These industrial examples serve as a source of new benchmarks which facilitate the study of different routing constraints and their effects on the economic and environmental viability of urban logistics systems.

Random forests are grown on convolutional neural networks for scene categorization.Features from multi-layers of deep convolutional neural networks are utilized.A feature selection method is proposed to use random forests to categorize scenes. Breakthrough performances have been achieved in computer vision by utilizing deep neural networks. In this paper we propose to use random forest to classify image representations obtained by concatenating multiple layers of learned features of deep convolutional neural networks for scene classification. Specifically, we first use deep convolutional neural networks pre-trained on the large-scale image database Places to extract features from scene images. Then, we concatenate multiple layers of features of the deep neural networks as image representations. After that, we use random forest as the classifier for scene classification. Moreover, to reduce feature redundancy in image representations we derived a novel feature selection method for selecting features that are suitable for random forest classification. Extensive experiments are conducted on two benchmark datasets, i.e. MIT-Indoor and UIUC-Sports. Obtained results demonstrated the effectiveness of the proposed method. The contributions of the paper are as follows. First, by extracting multiple layers of deep neural networks, we can explore more information of image contents for determining their categories. Second, we proposed a novel feature selection method that can be used to reduce redundancy in features obtained by deep neural networks for classification based on random forest. In particular, since deep learning methods can be used to augment expert systems by having the systems essentially training themselves, and the proposed framework is general, which can be easily extended to other intelligent systems that utilize deep learning methods, the proposed method provide a potential way for improving performances of other expert and intelligent systems.

Scientific stakeholders identifications using mobile agents.Tool for improving HR efficiency.Most influential (cited) researcher detection using a relationship graph.Distributed frameworks on top of JADE.Inference rules used in the process. In the business process of reengineering, one of the main problems consists in the organizational reintegration of the employee. New instruments for the automatic identification of stakeholders among existing staff evaluation are needed. It is difficult to identify a stakeholder, especially if we refer to science stakeholders, and the proper evaluation of a researcher's value and position at international level is needed. There are large contradictions concerning the validity of various types of indexing services and other techniques used to classify the researchers. This paper proposes the use of corporate Human Resource Management (HRM) specific instruments when it comes to the researcher as informational stakeholder identification. The main idea behind the method is to use some form of information retrieval techniques over a set of research papers which contain, mostly, the same key words and to extract the hierarchy of the researchers involved/referenced over that set. A distributed approach was used due to its inherent scalability. The use of an inference engine was also deemed necessary due to the complexity of the problem. The system may be used in the automatic assessment of research performance in public or private institutions. It can be easily modified to support complex sets of rules depending on each user's needs in personnel evaluation. The main research field of this papers concerns distributed artificial intelligence.

The work proposes an Event Popularity Algorithm for Financial Trading.The approach is based on sentiment analysis to the social network Twitter.Planning and performing a financial community for the extraction of analyzed tweets.The events are focused on the thirty companies that compose the Dow Jones index. The growing number of Twitter users makes it a valuable source of information to study what is happening right now. Users often use Twitter to report real-life events. Here we are only interested in following the financial community. This paper focuses on detecting events popularity through sentiment analysis of tweets published by the financial community on the Twitter universe. The detection of events popularity on Twitter makes this a non-trivial task due to noisy content that often are the tweets. This work aims to filter out all the noisy tweets in order to analyze only the tweets that influence the financial market, more specifically the thirty companies that compose the Dow Jones Average. To perform these tasks, in this paper it is proposed a methodology that starts from the financial community of Twitter and then filters the collected tweets, makes the sentiment analysis of the tweets and finally detects the important events in the life of companies.

A practical solution of a ships safe trajectory planning problem is proposed.A deterministic method utilizing a concept of a database of trajectories.The method considers COLREGs, ships dynamic properties, static and dynamic obstacles.The algorithm has short computational time and returns optimal solutions.The method is applicable in commercial systems due to simplicity and efficiency. The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems.

Focus is on computational intelligence methods in prostate cancer predictive modeling.We survey metaheuristic optimisation methods.We review machine learning methods.We consider cancer data of different modalities.We discuss recent advances, challenges and provide future directions. Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.

We propose an approach for selecting appropriate defuzzification methods that fulfills the requirements of a group of experts.We propose a sensitivity analysis of defuzzification methods.We apply this approach to the assessment of dam performance.Methods were chosen in relation to the objectives (decision-support and communication) defined by a group of dam experts.The approach is applied to a real case. The performance assessment of dams is of major importance for optimizing maintenance management. A methodology is provided here to assess the risk of dam failure using data collected during in-situ inspections and from the design and follow-up files. This assessment takes into account uncertainty associated with the data and the assessment process. The result of this assessment is introduced in a fuzzy frame. This paper presents the approach taken to choose defuzzification methods that allow extracting the information necessary from this possibility distribution so that dam experts can both rank dam maintenance actions and communicate the results of this assessment.This paper first presents the process used to assess dam performance after which it presents the defuzzification methods available. A sensitivity analysis is then performed to select the methods most relevant for our case study. The last part presents the development of a tool useful for the application of defuzzification methods to our problematic and describes a real case study in which the selected defuzzification methods are used.

Four methods are developed for data mining discrete multi-objective optimization datasets.Two of the methods are unsupervised, one is supervised and the other is hybrid.Knowledge is represented as patterns in one method, and as rules in other methods.Methods are applied to three real-world production system optimization problems.Extracted knowledge is compared across methods and provides new insights. The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision makers preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences.

We are concerned with an unequal area facility layout problem.This paper is the first work to investigate the use of PGAs for solving the UA-FLP.We present a novel island model genetic algorithm for the UA-FLP.Our novel approach has been tested with a set of well-known problems. The Unequal Area Facility Layout Problem (UA-FLP) has been addressed by various methods, including mathematical modelling, heuristic and metaheuristic approaches. Nevertheless, each type of approach presents problems such as premature convergence, lack of diversity, or high computational cost. In this paper, for the first time, an Island Model Genetic Algorithm (IMGA) is proposed to solve these subjects in the UA-FLP. The parallel evolution of several populations is used to maintain the population diversity and to obtain a wider sampling of the search space to obtain better quality solutions in fewer generations. Our novel approach was tested with a well-known set of problems taken from the literature and the results were compared with those of previous reports. In most cases, the results obtained by our novel approach improved on the previous results. Additionally, the proposed approach is able to reach good solutions with a wide range of problem sizes and in a reasonable computational time.

In this research UCI Indian Liver Patient Dataset (ILPD) used.Boosted C5.0 and CHAID algorithms are used to identify liver disease risk factors.This research shows females have more chance of liver disease than males.Common risk factors of liver disease were extracted by data mining.This research produced quite simple rules. 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.

Because the most dangerous type of skin cancer, melanoma, is very difficult for dermatologists to detect because of the low contrast between the lesion and the adjacent skin, the automatic application of skin lesion segmentation is regarded as very challenging. This paper proposes the implementation of a medical image segmentation that will accelerate a melanoma diagnosis by dermatologists. In the implementation, Fully Convolutional Network (FCN) architectures generated by modifying Convolutional Neural Network (CNN) architectures are used. The proposed algorithm for an automatic semantic segmentation of skin lesions utilizes four different FCN architectures, FCN-AlexNet, FCN-8s, FCN-16s, and FCN-32s. The experimental studies in this paper are constructed on the ISIC 2017 dataset, and the evaluations of these architectures on the dataset are carried out for the first time with this study. In the experimental studies, once the images in the dataset are preprocessed, the FCNs are first trained separately. Secondly, the accuracies and Dice coefficients on the validation dataset are calculated by using these trained FCN architectures. Thirdly, the obtained results are compared. Finally, the inferences of lesion segmentation are visualized in order to exhibit how exactly the FCN architectures can segment the lesions. The experimental results show that the FCNs in the proposed algorithm are suitable for skin lesion segmentation. In addition, it is thought that the experimental results will contribute to the scientific literature and assist the researchers who are working on medical image segmentation.

Under the Internet background, group recommendation has become a major interest in the study of recommendation systems. In the method of group recommendation, the existing researches are mostly conducted using cluster analysis and similarity analysis. The group characteristics studied are relatively generalized, and the group objects studied are mostly fixed, so the group cannot change in real time according to the different attributes and characteristics of the different products. At the same time, for the research object of group recommendation, the existing research mainly consider the recommended project group or user group, but seldom consider recommending the appropriate project group to the appropriate user group to improve the recommendation efficiency and user satisfaction. In view of these problems, this paper proposes a deep neural network that integrates the attention mechanism for group users’ dynamic identification and recommendation on the Internet platform. This paper uses an attention mechanism and deep neural networks to generate the attention preference weights for the group users according to the product attributes. Doing so achieves the purpose of recommending many types of projects to different groups to adapt to their preferences. We compare this method with other baseline methods on two public datasets to validate the effectiveness of the proposed method, which achieves better performance than the most advanced methods.

In this paper, a case study on the role of fuzzy logic (FL) in data mining and machine learning is carried out. It is outlined that, in order to draw more attention of data-mining and machine-learning communities to FL, studies on FL could be more focused not on the activities that fuzzy methods can perform better but rather on the activities that fuzzy methods can perform and the non-fuzzy ones can’t. Such approach takes us away from discussing quantitative differences between fuzzy and non-fuzzy methods to discussing qualitative differences, which are possibly more favorable objects of scientific curiosity. Following the outlined suggestion, a novel speed-up technique is proposed in this paper to support association rule mining (ARM). The proposed technique is a clustering-based one and provides fusion of clustering and ARM. The catchy feature of this technique is that it works well if applied in fuzzy ARM and doesn’t work well if applied in non-fuzzy ARM. The proposed technique is put through experimental verification involving several real-world datasets, and the results substantiate its effectiveness.

The paper addresses the question of ensuring comparability in data envelopment analysis (DEA) in situations when units are organized in an ordered hierarchy with functions shared at different levels. In such a case, although units may have identical input-output sets that they put to use in similar production, they are not an ideally homogeneous group and their comparability in a benchmarking context is limited. The paper proposes to control explicitly the degree of comparability by a fairly flexible comparability constraint in order to obtain more informative technical efficiency scores and economically feasible targets. Furthermore, the paper develops a methodology to identify closest targets under the comparability constraint that are more attainable for inefficient units than traditional targets. These ideas are demonstrated in a case study located in the area of bank branch performance assessment from which the motivation of the paper sprouted. The case study shows for three hierarchical branch categories of a Slovak commercial bank that the comparability constraint renders closest targets more apposite, but they depend on how slacks are handled, i.e. whether they are summarized by a normalized sum or by means of a slacks-based measure.

This paper proposes a novel approach to the portfolio management using an AutoEncoder. In particular, features learned by an AutoEncoder with ReLU are directly exploited to portfolio constructions. Since the AutoEncoder extracts characteristics of data through a non-linear activation function ReLU, its realization is generally difficult due to the non-linear transformation procedure. In the current paper, we solve this problem by taking full advantage of the similarity of ReLU and an option payoff. Especially, this paper shows that the features are successfully replicated by applying so-called dynamic delta hedging strategy. An out of sample simulation with crypto currency dataset shows the effectiveness of our proposed strategy.

To develop an advanced CBR system to well adapt to the intelligence implementation of new engineering process in the big data environment, Bayesian network (BN) model is introduced to CBR system for knowledge reasoning. However, as engineering application is becoming more and more complicated, the number of parameters used to define engineering application grows larger and larger, leading to the seriously reduced efficiency as well as the accuracy of the integrated model. For the problem of reduced efficiency, this paper proposes In-External (IE) algorithm to perform the assignment of big data distribution for parallel data processing, which can fully utilize the capacity of Hadoop system and attain the best efficiency of knowledge reasoning. For the problem of reduced accuracy, in view of the fact that traditional probability learning methods are unfit for the proposed CBR system, this paper proposes Discount Exponential Coefficients of Multivariate Beta Distribution (DECMBD) algorithm to conduct the probability learning of proposed system. In DECMBD algorithm, a discount ratio is given to each exponential coefficient of multivariate Beta distribution to improve the occurrence times counting of all problem features and then gain better effect of probability learning. Finally, lots of experiments are performed to validate the effectiveness of the proposed advanced CBR system.

Smart shared mobility is an emerging transportation strategy that promotes sustainable and intelligent transportation. As one mode of smart shared mobility bike sharing is gaining popularity in recent years. A daily rebalancing operation is commonly carried out to keep high level service of bike-sharing systems (BSSs). The static bike-sharing rebalancing problems (SBRPs) studied in existing papers focus on determining the vehicle routes with minimal traveling cost. However, the depot inventory is rarely considered during the relocation. Thus, this paper researches the integration of the depot inventory and vehicle routing problems, with the aim of minimizing the daily operational cost including the depot inventory cost (DIC) and the traveling cost. First, two mixed integer programming (MIP) formulations are proposed to find the daily optimal decision on the vehicle routes and the numbers of bikes and vehicles employed from the depot. Based on the models, an improved general variable neighborhood search (IGVNS) algorithm is developed with a variety of neighborhood structures and a hybrid strategy. Finally, we apply a set of benchmark instances to test our proposed model and approach, and the computational results demonstrate that IGVNS can efficiently compute the SBRP and achieve lower operational cost than the existing solutions.

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.