text:
01.042},
  journal = {Applied Soft Computing},
  bibtexkey = {VERBEKE2017858},
  author = {Wouter Verbeke, David Martens, Bart Baesens},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2003},
  title = {Chapter 9 - Getting Started},
  abstract = {This chapter is an approach to get raw data into a state that is appropriate for mining purposes. Any miner uses these basic techniques for addressing almost any business problem. Miners need to note preparation techniques when mining data in other domains, such as biomedical data, industrial automation data, telemetry data, geophysical data, time domain data, and so on. Developing mined models requires a hard work. It involves working with data, including making changes and taking actions that depend on the needs of the business problem and the miner's data discovery. Mining data is not magic, and it is not something that computer software will do for any one. Basically, data mining is an organized way of working with data, digging out useful information, and application of that useful information in solving the business problems. Most of the tools that are currently in use were developed from three main areas including statistics, artificial intelligence, and machine learning. Despite apparently different roots, these tools essentially do only one thing that is related to the discovery of a relationship, which more or less maps measurements in one part of a data set to measurements in another linked part of the data set.},
  doi = {https://doi.org/10.1016/B978-155860653-1.50012-9},
  bibtexkey = {PYLE2003275},
  author = {Dorian Pyle},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Customer churn prediction in the telecommunication sector using a rough set approach},
  abstract = {Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitioners, interested in forecasting the behavior of customers in order to differentiate the churn from non-churn customers. The primary motivation is the dire need of businesses to retain existing customers, coupled with the high cost associated with acquiring new ones. A review of the field has revealed a lack of efficient, rule-based Customer Churn Prediction (CCP) approaches in the telecommunication sector. This study proposes an intelligent rule-based decision-making technique, based on rough set theory (RST), to extract important decision rules related to customer churn and non-churn. The proposed approach effectively performs classification of churn from non-churn customers, along with prediction of those customers who will churn or may possibly churn in the near future. Extensive simulation experiments are carried out to evaluate the performance of our proposed RST based CCP approach using four rule-generation mechanisms, namely, the Exhaustive Algorithm (EA), Genetic Algorithm (GA), Covering Algorithm (CA) and the LEM2 algorithm (LA). Empirical results show that RST based on GA is the most efficient technique for extracting implicit knowledge in the form of decision rules from the publicly available, benchmark telecom dataset. Further, comparative results demonstrate that our proposed approach offers a globally optimal solution for CCP in the telecom sector, when benchmarked against several state-of-the-art methods. Finally, we show how attribute-level analysis can pave the way for developing a successful customer retention policy that could form an indispensable part of strategic decision making and planning process in the telecom sector.},
  doi = {https://doi.org/10.1016/j.neucom.2016.12.009},
  journal = {Neurocomputing},
  bibtexkey = {AMIN2017242},
  author = {Adnan Amin, Sajid Anwar, Awais Adnan, Muhammad Nawaz, Khalid Alawfi, Amir Hussain, Kaizhu Huang},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Integrated framework for profit-based feature selection and SVM classification in credit scoring},
  abstract = {In this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The main goal is to incorporate business-related information such as the variable acquisition costs, the Types I and II error costs, and the profit generated by correctly classified instances, into the modeling process. Our proposal incorporates a group penalty function in the SVM formulation in order to penalize the variables simultaneously that belong to the same group, assuming that companies often acquire groups of related variables for a given cost rather than acquiring them individually. The proposed framework was studied in a credit scoring problem for a Chilean bank, and led to superior performance with respect to business-related goals.},
  doi = {https://doi.org/10.1016/j.dss.2017.10.007},
  journal = {Decision Support Systems},
  bibtexkey = {MALDONADO2017113},
  author = {SebastiÃ¡n Maldonado, CristiÃ¡n Bravo, Julio LÃ³pez, Juan PÃ©rez},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Mining social networks for anomalies: Methods and challenges},
  abstract = {Online social networks have received a dramatic increase of interest in the last decade due to the growth of Internet and Web 2.0. They are among the most popular sites on the Internet that are being used in almost all areas of life including education, medical, entertainment, business, and telemarketing. Unfortunately, they have become primary targets for malicious users who attempt to perform illegal activities and cause harm to other users. The unusual behavior of such users can be identified by using anomaly detection techniques. Anomaly detection in social networks refers to the problem of identifying the strange and unexpected behavior of users by exploring the patterns hidden in the networks, as the patterns of interaction of such users deviate significantly from the normal users of the networks. Even though a multitude of anomaly detection methods have been developed for different problem settings, this field is still relatively young and rapidly growing. Hence, there is a growing need for an organized study of the work done in the area of anomaly detection in social networks. In this paper, we provide a comprehensive review of a large set of methods for mining social networks for anomalies by providing a multi-level taxonomy to categorize the existing techniques based on the nature of input network, the type of anomalies they detect, and the underlying anomaly detection approach. In addition, this paper highlights the various application scenarios where these methods have been used, and explores the research challenges and open issues in this field.},
  doi = {https://doi.org/10.1016/j.jnca.2016.02.021},
  journal = {Journal of Network and Computer Applications},
  bibtexkey = {BINDU2016213},
  author = {P.V. Bindu, P. Santhi Thilagam},
  source = {SCIENCE}
}

@{,
  category = {BOOK},
  year = {2001},
  title = {Microsoft Data Mining: Integrated Business Intelligence for e-Commerce and Knowledge Management},
  abstract = {Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes.Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management.},
  doi = {https://doi.org/10.1016/B978-155558242-5/50016-4},
  bibtexkey = {ville_microsoft_2001},
  author = {Ville, Barry de},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2007},
  title = {Improved customer choice predictions using ensemble methods},
  abstract = {In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression. Next, an additive decomposition of the prediction error of a model is considered known as the bias/variance decomposition. A model with a high bias lacks the flexibility to fit the data well. A high variance indicates that a model is instable with respect to different datasets. Decision trees have a high variance component and a low bias component in the prediction error, whereas logistic regression has a high bias component and a low variance component. It is shown that ensemble methods aim at minimizing the variance component in the prediction error while leaving the bias component unaltered. Bias/variance decompositions for all models for both customer choice datasets are given to illustrate these concepts.},
  doi = {https://doi.org/10.1016/j.ejor.2006.05.029},
  journal = {European Journal of Operational Research},
  bibtexkey = {VANWEZEL2007436},
  author = {Michiel van Wezel, Rob Potharst},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks},
  abstract = {Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residualsâ€”to decrease the likelihood of false alarmsâ€”is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach.},
  doi = {https://doi.org/10.1016/j.ins.2020.06.034},
  journal = {Information Sciences},
  bibtexkey = {LUGHOFER2020425},
  author = {Edwin Lughofer, Alexandru-Ciprian Zavoianu, Robert Pollak, Mahardhika Pratama, Pauline Meyer-Heye, Helmut ZÃ¶rrer, Christian Eitzinger, Thomas Radauer},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Do the pieces fit? Assessing the configuration effects of promotion attributes},
  abstract = {Despite the extensive resources allocated to sales promotions, managers are still often unsure which combinations of promotion attributes, in what circumstances, will be most effective in achieving their marketing objectives. This problem has intensified as more firms move online and engage in frequent promotions. We conduct two empirical studies using field data from hundreds of online campaigns to identify the significant interactions that lead to an immediate sales effect. We adopt a boosted tree (BT) approach to investigate how promotional attributes can be aligned with one another and with other contextual variables to achieve synergy, and propose a parametric test to assess the statistical significance of these interactions. Our findings provide valuable insights into effective promotional designs. We also compare the proposed approach with various machine learning methods to demonstrate the merit of the BT approach and its potential applications for strategic configurations in business and marketing.},
  doi = {https://doi.org/10.1016/j.jbusres.2019.11.081},
  journal = {Journal of Business Research},
  bibtexkey = {PENG2020337},
  author = {Ling Peng, Geng Cui, Yuho Chung},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction},
  abstract = {Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear windâ€“wave and waveâ€“wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable.},
  doi = {https://doi.org/10.1016/j.oceaneng.2010.11.020},
  journal = {Ocean Engineering},
  bibtexkey = {MALEKMOHAMADI2011487},
  author = {Iman Malekmohamadi, Mohammad Reza Bazargan-Lari, Reza Kerachian, Mohammad Reza Nikoo, Mahsa Fallahnia},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {The analytics paradigm in business research},
  abstract = {The availability of data in massive collections in recent past not only has enabled data-driven decision-making, but also has created new questions that cannot be addressed effectively with the traditional statistical analysis methods. The traditional scientific research not only has prevented business scholars from working on emerging problems with big and rich data-sets, but also has resulted in irrelevant theory and questionable conclusions; mostly because the traditional method has mainly focused on modeling and analysis/explanation than on the real/practical problem and the data. We believe the lack of due attention to the analytics paradigm can to some extent be attributed to the business scholars' unfamiliarity with the analytics methods/methodologies and the type of questions it can answer. Therefore, our purpose in this paper is to illustrate how analytics, as a complement, rather than a successor, to the traditional research paradigm, can be used to address interesting emerging business research questions.},
  doi = {https://doi.org/10.1016/j.jbusres.2018.05.013},
  journal = {Journal of Business Research},
  bibtexkey = {DELEN2018186},
  author = {Dursun Delen, Hamed M. Zolbanin},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2020},
  title = {Chapter 3 - Social media data analytics using feature engineering},
  abstract = {In this fast-growing digital world, social media analytics is gaining attention in the field of big data. Big data is the collection of huge amounts of raw digital data that is difficult to analyze with conventional analysis methods. Due to the popularity of social media sites such as Twitter and Facebook, a vast amount of public unstructured data is generated by millions of users every day. This raw bulk data cannot be directly used in decision making and prediction tasks. Therefore many researchers have been working on converting this huge unstructured information into meaningful information through big data analytics. Developing an efficient data analytics tool is essential to understand the multimodal social media data that improves the performance of decision-making and prediction systems. This chapter overviews the strengths of the current state-of-the-art feature engineering approaches that have been developed to represent the characteristics of social media data. In addition, this chapter outlines the proposed framework for analyzing the social media data including the shared images, tags, associated comments, and its social relationship.},
  doi = {https://doi.org/10.1016/B978-0-12-819779-0.00003-4},
  bibtexkey = {ANITHA202029},
  author = {J. Anitha, I-Hsien Ting, S. Akila Agnes, S. Immanuel Alex Pandian, R.V. Belfin},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Why you should stop predicting customer churn and start using uplift models},
  abstract = {Uplift modeling has received increasing interest in both the business analytics research community and the industry as an improved paradigm for predictive analytics for data-driven operational decision-making. The literature, however, does not provide conclusive empirical evidence that uplift modeling outperforms predictive modeling. Case studies that directly compare both approaches are lacking, and the performance of predictive models and uplift models as reported in various experimental studies cannot be compared indirectly since different evaluation measures are used to assess their performance. Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift (MPU) measure that allows assessing the performance in terms of the maximum potential profit that can be achieved by adopting an uplift model. This measure, developed for evaluating customer churn uplift models, extends the maximum profit measure for evaluating customer churn prediction models. While introducing the MPU measure, we describe the generally applicable liftup curve and liftup measure for evaluating uplift models as counterparts of the lift curve and lift measure that are broadly used to evaluate predictive models. These measures are subsequently applied to assess and compare the performance of customer churn prediction and uplift models in a case study that applies uplift modeling to customer retention in the financial industry. We observe that uplift models outperform predictive models and lead to improved profitability of retention campaigns.},
  doi = {https://doi.org/10.1016/j.ins.2019.12.075},
  journal = {Information Sciences},
  bibtexkey = {DEVRIENDT2019},
  author = {Floris Devriendt, Jeroen Berrevoets, Wouter Verbeke},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Using data mining to improve assessment of credit worthiness via credit scoring models},
  abstract = {Credit scoring model have been developed by banks and researchers to improve the process of assessing credit worthiness during the credit evaluation process. The objective of credit scoring models is to assign credit risk to either a â€œgood riskâ€  group that is likely to repay financial obligation or a â€œbad riskâ€  group who has high possibility of defaulting on the financial obligation. Construction of credit scoring models requires data mining techniques. Using historical data on payments, demographic characteristics and statistical techniques, credit scoring models can help identify the important demographic characteristics related to credit risk and provide a score for each customer. This paper illustrates using data mining to improve assessment of credit worthiness using credit scoring models. Due to privacy concerns and unavailability of real financial data from banks this study applies the credit scoring techniques using data of payment history of members from a recreational club. The club has been facing a problem of rising number in defaulters in their monthly club subscription payments. The management would like to have a model which they can deploy to identify potential defaulters. The classification performance of credit scorecard model, logistic regression model and decision tree model were compared. The classification error rates for credit scorecard model, logistic regression and decision tree were 27.9%, 28.8% and 28.1%, respectively. Although no model outperforms the other, scorecards are relatively much easier to deploy in practical applications.},
  doi = {https://doi.org/10.1016/j.eswa.2011.04.147},
  journal = {Expert Systems with Applications},
  bibtexkey = {YAP201113274},
  author = {Bee Wah Yap, Seng Huat Ong, Nor Huselina Mohamed Husain},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Improving the performance of inductive learning classifiers through the presentation order of the training patterns},
  abstract = {Although the development of new supervised learning algorithms for machine learning techniques are mostly oriented to improve the predictive power or classification accuracy, the capacity to understand how the classification process is carried out is of great interest for many applications in business and industry. Inductive learning algorithms, like the Rules family, induce semantically interpretable classification rules in the form of if-then rules. Although the effectiveness of the Rules family has been studied thoroughly and new and improved versions are constantly been developed, one important drawback is the effect of the presentation order of the training patterns which has not been studied in depth previously. In this paper this issue is addressed, first by studying empirically the effect of random presentation orders in the number of rules and the generalization power of the resulting classifier. Then a presentation order method for the training examples is proposed which combines a clustering stage with a new density measure developed specifically for this problem. The results using benchmark datasets and a real application of wood defect classification show the effectiveness of the proposed method. Also, since the presentation order method is employed as a preprocessing stage, the simplicity of the Rules family is not affected but instead it enables the generation of fewer and more accurate rules, which can have a direct impact in the performance and usefulness of the Rules family in an expert system context.},
  doi = {https://doi.org/10.1016/j.eswa.2016.04.003},
  journal = {Expert Systems with Applications},
  bibtexkey = {RUZ20161},
  author = {Gonzalo A. Ruz},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Edge sensing data-imaging conversion scheme of load forecasting in smart grid},
  abstract = {Edge sensing data in smart grid provides vast valuable information, which promotes further innovated smart power applications in Internet of things (IoT) oriented smart cities and society. While in power load prediction, the potential relationships between the time series of power load data and the characteristics of temperature, weather and date, have not been explored comprehensively, which degrades the accuracy of load prediction in smart grid. In order to extract the generalized features and latent relationships in power load related edge sensing data, a power load prediction scheme based on edge sensing data-imaging conversion (DIC) is proposed to improve the forecasting accuracy in smart cites and society. DIC employs empirical mode decomposition (EMD) for power load time series data and combines it with characteristic time series including temperature, weather and date to form an image-like structure. And a DIC-based convolutional neural network (DI-CNN) is presented to implement convolution. Experimental results show that, compared with long short-term memory (LSTM), support vector machines (SVM), and CNN, the proposed DIC scheme improves the training speed by 61.7 %, reduces root mean square error (RMSE) by 32.9 % at least, and enhances the prediction accuracy by 1.4 %.},
  doi = {https://doi.org/10.1016/j.scs.2020.102363},
  journal = {Sustainable Cities and Society},
  bibtexkey = {LIU2020102363},
  author = {Xiaozhu Liu, Zhiyang Xiao, Rongbo Zhu, Jun Wang, Lu Liu, Maode Ma},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {A fuzzy linguistic supported framework to increase Artificial Intelligence intelligibility for subject matter experts},
  abstract = {The application of artificial intelligence (AI) techniques in the decision making processes is more widespread in the industry than ever before. Yet, one of the most critical show-stoppers is the communication gap between the machine learning (ML) models and the experts community. On one hand, the output of ML is often not intelligible for experts, in spite of the latest advances in explainable AI. On the other hand, the expert knowledge, rarely completely present in the available data, but rather in the heads of the experts, needs to be connected to the data-driven insights created by the ML model. In this paper we first identify the most critical situations with a manifest intelligibility gap and then propose a framework supported by fuzzy linguistic modelling techniques to close this gap. In addition, we present its integration into the end-to-end decision making flow, from data gathering to the execution and evaluation and we show the output of our approach with practical examples.},
  doi = {https://doi.org/10.1016/j.procs.2019.12.061},
  journal = {Procedia Computer Science},
  bibtexkey = {BERNABEMORENO2019865},
  author = {Juan BernabÃ©-Moreno, Karsten Wildberger},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Combining machine-based and econometrics methods for policy analytics insights},
  abstract = {Computational Social Science (CSS) has become a mainstream approach in the empirical study of policy analytics issues in various domains of e-commerce research. This article is intended to represent recent advances that have been made for the discovery of new policy-related insights in business, consumer and social settings. The approach discussed is fusion analytics, which combines machine-based methods from Computer Science (CS) and explanatory empiricism involving advanced Econometrics and Statistics. It explores several efforts to conduct research inquiry in different functional areas of Electronic Commerce and Information Systems (IS), with applications that represent different functional areas of business, as well as individual consumer, social and public issues. Recent developments and shifts in the scientific study of technology-related phenomena and Social Science issues in the presence of historically-large datasets prompt new forms of research inquiry. They include blended approaches to research methodology, and more interest in the production of research results that have direct application to industry contexts. This article showcases the methods shifts and several contemporary applications. They discuss: (1) feedback effects in mobile phone-based stock trading; (2) sustainability of top-rank chart popularity of music tracks; (3) household TV viewing patterns; and (4) household sampling and purchases of video-on-demand (VoD) services. The range of applicability of the ideas goes beyond the scope of these illustrations, to include issues in public services, healthcare, product and service deployment, public opinion and elections, electronic auctions, and travel and tourism services. In fact, the coverage is as broad as for-profit and for-non-profit, private and public, and governmental and non-governmental institutions.},
  doi = {https://doi.org/10.1016/j.elerap.2017.04.004},
  journal = {Electronic Commerce Research and Applications},
  bibtexkey = {KAUFFMAN2017115},
  author = {Robert J. Kauffman, Kwansoo Kim, Sang-Yong Tom Lee, Ai-Phuong Hoang, Jing Ren},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Improved market segmentation by fuzzifying crisp clusters: A case study of the energy market in Spain},
  abstract = {This paper provides an innovative segmentation approach stemming from the combination of cluster analyses and fuzzy learning techniques. Our research provides a real case solution in the Spanish energy market to respond to the increasing number of requests from industry managers to be able to interpret ambiguous market information as realistically as possible. The learning stage is based on the segments created from a non-hierarchical cluster analysis. This results in fuzzy segmentation which permits patterns to be assigned to more than one segment. This in turn reveals that â€œfuzzifyingâ€  an excluding attitudinal segmentation offers more interpretable and acceptable results for managers. Our results demonstrate that 30% of the individuals show plural patterns of behaviour because they have a significant degree of adequacy to more than one segment. In such a rational market, this fact enables sales forces to develop more precise approaches to capture new customers and/or retain existing ones.},
  doi = {https://doi.org/10.1016/j.eswa.2014.09.044},
  journal = {Expert Systems with Applications},
  bibtexkey = {CASABAYO20151637},
  author = {MÃ²nica CasabayÃ³, NÃºria Agell, GermÃ¡n SÃ¡nchez-HernÃ¡ndez},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Integrated Rough Fuzzy Clustering for Categorical data Analysis},
  abstract = {In recent times, advanced data mining research has been mostly focusing on clustering of categorical data, where a natural ordering in attribute values is missing. To address this fact the Rough Fuzzy K-Modes clustering technique has been recently developed in order to handle imperfect information, i.e. indiscernibility (coarseness) and vagueness within the dataset. However, it has been observed that the said technique suffers from the problem of local optima due to the random choice of initial cluster modes. Hence, in this paper, we have proposed an integrated clustering technique using multi-phase learning. In this regard, first, Simulated Annealing based Rough Fuzzy K-Modes and Genetic Algorithm based Rough Fuzzy K-Modes are proposed in order to perform the clustering better by considering clustering as an underlying optimization problem. These clustering methods individually produce clusters having set of central and peripheral points. Thereafter, for each case, final improved clustering results are obtained by assigning peripheral points to a particular crisp cluster using Random Forest, where central points are used as training set. Second, the varying cardinality of the training and testing sets produced by each clustering method further motivated us to propose a generalized technique called Integrated Rough Fuzzy Clustering using Random Forest, where, results of three aforementioned clustering techniques are used to compute the roughness measure. Based on this measure, three different sets namely best central points, semi-best central points and pure peripheral points are determined. Thereafter, using multi-phase learning, best central points are used to classify the semi-best central points and then using both of them, pure peripheral points are classified by Random Forest. Experimental results are reported quantitatively and visually to demonstrate the effectiveness of the proposed methods in comparison with well-known state-of-the-art methods for six synthetic and five real-life datasets. Finally, statistical significance tests are conducted to establish the superiority of the results produced by the proposed methods.},
  doi = {https://doi.org/10.1016/j.fss.2018.02.007},
  journal = {Fuzzy Sets and Systems},
  bibtexkey = {SAHA20191},
  author = {Indrajit Saha, Jnanendra Prasad Sarkar, Ujjwal Maulik},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Forecasting client retention â€” A machine-learning approach},
  abstract = {In the age of big data, companies store practically all data on any client transaction. Making use of this data is commonly done with machine-learning techniques so as to turn it into information that can be used to drive business decisions. Our interest lies in using data on prepaid unitary services in a business-to-business setting to forecast client retention: whether a particular client is at risk of being lost before they cease being clients. The purpose of such a forecast is to provide the company with an opportunity to reach out to such clients as an effort to ensure their retention. We work with monthly records of client transactions: each client is represented as a series of purchases and consumptions. We vary (1) the length of the time period used to make the forecast, (2) the length of a period of inactivity after which a client is assumed to be lost, and (3) how far in advance the forecast is made. Our experimental work finds that current machine-learning techniques able to adequately predict, well in advance, which clients will be lost. This knowledge permits a company to focus marketing efforts on such clients as early as three months in advance.},
  doi = {https://doi.org/10.1016/j.jretconser.2019.101918},
  journal = {Journal of Retailing and Consumer Services},
  bibtexkey = {SCHAEFFER2020101918},
  author = {Satu Elisa Schaeffer, Sara Veronica Rodriguez Sanchez},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {IBRIDIA: A hybrid solution for processing big logistics data},
  abstract = {Internet of Things (IoT) is leading to a paradigm shift within the logistics industry. Logistics services providers use sensor technologies such as GPS or telemetry to track and manage their shipment processes. Additionally, they use external data that contain critical information about events such as traffic, accidents, and natural disasters. Correlating data from different sensors and social media and performing analysis in real-time provide opportunities to predict events and prevent unexpected delivery delay at run-time. However, collecting and processing data from heterogeneous sources foster problems due to the variety and velocity of data. In addition, processing data in real-time is heavily challenging that it cannot be dealt with using conventional logistics information systems. In this paper, we present a hybrid framework for processing massive volume of data in batch style and real-time. Our framework is built upon Johnsonâ€™s hierarchical clustering (HCL) algorithm which produces a dendrogram that represents different clusters of data objects.},
  doi = {https://doi.org/10.1016/j.future.2019.02.044},
  journal = {Future Generation Computer Systems},
  bibtexkey = {ALSHAER2019792},
  author = {Mohammed AlShaer, Yehia Taher, Rafiqul Haque, Mohand-SaÃ¯d Hacid, Mohamed Dbouk},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Big data analytics for smart factories of the future},
  abstract = {Continued advancement of sensors has led to an ever-increasing amount of data of various physical nature to be acquired from production lines. As rich information relevant to the machines and processes are embedded within these â€œbig dataâ€ , how to effectively and efficiently discover patterns in the big data to enhance productivity and economy has become both a challenge and an opportunity. This paper discusses essential elements of and promising solutions enabled by data science that are critical to processing data of high volume, velocity, variety, and low veracity, towards the creation of added-value in smart factories of the future.},
  doi = {https://doi.org/10.1016/j.cirp.2020.05.002},
  journal = {CIRP Annals},
  bibtexkey = {GAO2020},
  author = {Robert X. Gao, Lihui Wang, Moneer Helu, Roberto Teti},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Dynamic customer lifetime value prediction using longitudinal data: An improved multiple kernel SVR approach},
  abstract = {Customer lifetime value (CLV), as an important metric in customer relationship management (CRM), has attracted widespread attention over the last decade. Most CLV prediction models do not take into consideration the dynamics of the customer purchase behavior and changes of the marketing environment such as the adoption of different promotion policies. In this study, a framework for the dynamic CLV prediction using longitudinal data is presented. In the framework, both the dynamic customer purchase behavior and customized promotions are considered. An improved multiple kernel support vector regression (MK-SVR) approach is developed to predict the future CLV and select the best promotion using both the customer behavioral variables and controlled variable about multiple promotions. Computational experiments using two databases show that the MK-SVR exhibits good prediction performance and the usage of longitudinal data in the MK-SVR facilitate the dynamic prediction and promotion optimization.},
  doi = {https://doi.org/10.1016/j.knosys.2013.01.022},
  journal = {Knowledge-Based Systems},
  bibtexkey = {CHEN2013123},
  author = {Zhen-Yu Chen, Zhi-Ping Fan},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Classifying and Understanding Prospective Customers via Heterogeneity of Supermarket Stores},
  abstract = {In recent years, the supermarket industry in Japan is in a state of declining sales over the long term, and market contraction is expected to continue due to environmental changes such as demographic changes. In this research, as a support for supermarket managers placed under such circumstances we suggest a new method to classify good customers who should be kept top priority. We define good customers on the basis of not only current good customers but also customers who generate most of sales in the future and classify good customers in advance from the current information. Additionally We provide goods information for outstanding purchase by good customers to supermarkets. It can be expected to contribute to the improvement of management efficiency by utilizing it for sales promotion activities.},
  doi = {https://doi.org/10.1016/j.procs.2017.08.133},
  journal = {Procedia Computer Science},
  bibtexkey = {TANAKA2017956},
  author = {Takamasa Tanaka, Tomohiro Hamaguchi, Takumi Saigo, Kazuhiko Tsuda},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2006},
  title = {Applying data mining to telecom churn management},
  abstract = {Taiwan deregulated its wireless telecommunication services in 1997. Fierce competition followed, and churn management becomes a major focus of mobile operators to retain subscribers via satisfying their needs under resource constraints. One of the challenges is churner prediction. Through empirical evaluation, this study compares various data mining techniques that can assign a â€˜propensity-to-churnâ€™ score periodically to each subscriber of a mobile operator. The results indicate that both decision tree and neural network techniques can deliver accurate churn prediction models by using customer demographics, billing information, contract/service status, call detail records, and service change log.},
  doi = {https://doi.org/10.1016/j.eswa.2005.09.080},
  journal = {Expert Systems with Applications},
  bibtexkey = {HUNG2006515},
  author = {Shin-Yuan Hung, David C. Yen, Hsiu-Yu Wang},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {A data mining framework for detecting subscription fraud in telecommunication},
  abstract = {Service providing companies including telecommunication companies often receive substantial damage from customersâ€™ fraudulent behaviors. One of the common types of fraud is subscription fraud in which usage type is in contradiction with subscription type. This study aimed at identifying customersâ€™ subscription fraud by employing data mining techniques and adopting knowledge discovery process. To this end, a hybrid approach consisting of preprocessing, clustering, and classification phases was applied, and appropriate tools were employed commensurate to each phase. Specifically, in the clustering phase SOM and K-means were combined, and in the classification phase decision tree (C4.5), neural networks, and support vector machines as single classifiers and bagging, boosting, stacking, majority and consensus voting as ensembles were examined. In addition to using clustering to identify outlier cases, it was also possible â€“ by defining new features â€“ to maintain the results of clustering phase for the classification phase. This, in turn, contributed to better classification results. A real dataset provided by Telecommunication Company of Tehran was applied to demonstrate the effectiveness of the proposed method. The efficient use of synergy among these techniques significantly increased prediction accuracy. The performance of all single and ensemble classifiers is evaluated based on various metrics and compared by statistical tests. The results showed that support vector machines among single classifiers and boosted trees among all classifiers have the best performance in terms of various metrics. The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically.},
  doi = {https://doi.org/10.1016/j.engappai.2010.05.009},
  journal = {Engineering Applications of Artificial Intelligence},
  bibtexkey = {FARVARESH2011182},
  author = {Hamid Farvaresh, Mohammad Mehdi Sepehri},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Gated Mixture Variational Autoencoders for Value Added Tax audit case selection},
  abstract = {In this work, we address the problem of targeted Value Added Tax (VAT) audit case selection by means of machine learning. This is a challenging problem that has remained rather elusive for EU-based Tax Departments, due to the inadequate quantity of tax audits that can be used for conventional supervised model training. To this end, we devise a novel Gated Mixture Variational Autoencoder deep network, that can be effectively trained with data from a limited number of audited taxpayers, combined with a large corpus of filed VAT returns. This gives rise to a semi-supervised learning framework that leverages the latest advances in deep learning and robust regularization using variational inference. We developed our approach in collaboration with the Cyprus Tax Department and experimentally deployed it to facilitate its audit selection process; to this end, we used actual VAT data from Cyprus-based taxpayers. This way, we obtained strong empirical evidence that our approach can greatly facilitate the VAT audit case selection process. Specifically, we obtained up to 76% out-of-sample accuracy in detecting whether a significant tax yield will be generated from a specific prospective VAT audit.},
  doi = {https://doi.org/10.1016/j.knosys.2019.105048},
  journal = {Knowledge-Based Systems},
  bibtexkey = {KLEANTHOUS2020105048},
  author = {Christos Kleanthous, Sotirios Chatzis},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Predicting microblog usersâ€™ lifetime activities â€“ A user-based analysis},
  abstract = {With the rapid development of online social media, social networking services have become an important research area in recent years. In particular, microblogging as a new social media platform draws much attention from both researchers and practitioners. Although most current studies focus on the effect of social networks on the diffusion of services or information, most are descriptions or explanations of what has already happened. This study focuses on future activity by employing probability models such as the Pareto/NBD and BG/NBD models to predict user lifetime vitality. Three experiments were implemented to test the two models. Our results showed that both the Pareto/NBD model and the BG/NBD model were effective in predicting SNS user usage behavior on microblogging websites. It was found that tweeting behavior is more suitable for such probability models than retweeting behavior and user segmentation can improve prediction accuracy by distinguishing between currently active and inactive users.},
  doi = {https://doi.org/10.1016/j.elerap.2014.06.001},
  journal = {Electronic Commerce Research and Applications},
  bibtexkey = {CHEN2015150},
  author = {Xi Chen, Ruibin Geng, Shun Cai},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study},
  abstract = {Since the increased importance is placed on customer equity in todayâ€™s business environment, many firms are focusing on the notion of customer loyalty and profitability to increasing market share. Building successful customer relationship management (CRM), a firm starts from identifying customersâ€™ true value and loyalty since customer value can provide basic information to deploy more targeted and personalized marketing. In this paper, customer lifetime value (CLV) is used to customer segmentation of a health and beauty company. Two approaches are used: in the first approach, RFM (Recency, Frequency, and Monetary) marketing analysis method is used in order to segmentation of customers and in the second approach, the proposed extended RFM analysis method with one additional parameterâ€”called Count Itemâ€”is used. Comparing results of these approaches, shows that adding count Item as a new parameter to RFM method makes no difference to clustering result, so CLV is calculated based on weighted RFM method for each segment. The results of calculated CLV for different segments can be used to explain marketing and sales strategies by the company.},
  doi = {https://doi.org/10.1016/j.procs.2010.12.011},
  journal = {Procedia Computer Science},
  bibtexkey = {KHAJVAND201157},
  author = {Mahboubeh Khajvand, Kiyana Zolfaghar, Sarah Ashoori, Somayeh Alizadeh},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {A survey on opinion mining and sentiment analysis: Tasks, approaches and applications},
  abstract = {With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002â€“2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.},
  doi = {https://doi.org/10.1016/j.knosys.2015.06.015},
  journal = {Knowledge-Based Systems},
  bibtexkey = {RAVI201514},
  author = {Kumar Ravi, Vadlamani Ravi},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Bank misconduct and online lending},
  abstract = {We introduce a high quality proxy for bank misconduct that is constructed from Consumer Financial Protection Bureau (CFPB) complaint data. We employ this proxy to measure the impact of bank misconduct on the expansion of online lending in the United States. Using nearly complete loan and application data from the online lending market, we demonstrate that bank misconduct is associated with a statistically and economically significant increase in online lending demand at the state and county levels. This result is robust to the inclusion of bank credit supply shocks and holds for both broader and more narrowly-defined bank misconductâ€˜ measures. Furthermore, we show that this effect is strongest for lower rated borrowers and weakest in states with high levels of generalized trust.},
  doi = {https://doi.org/10.1016/j.jbankfin.2020.105822},
  journal = {Journal of Banking & Finance},
  bibtexkey = {BERTSCH2020105822},
  author = {Christoph Bertsch, Isaiah Hull, Yingjie Qi, Xin Zhang},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks},
  abstract = {Much has been written about word of mouth and customer behavior. Telephone call detail records provide a novel way to understand the strength of the relationship between individuals. In this paper, we predict using call detail records the impact that the behavior of one customer has on another customer's decisions. We study this in the context of churn (a decision to leave a communication service provider) and cross-buying decisions based on an anonymized data set from a telecommunications provider. Call detail records are represented as a weighted graph and a novel statistical learning technique, Markov logic networks, is used in conjunction with logit models based on lagged neighborhood variables to develop the predictive model. In addition, we propose an approach to propositionalization tailored to predictive modeling with social network data. The results show that information on the churn of network neighbors has a significant positive impact on the predictive accuracy and in particular the sensitivity of churn models. The results provide evidence that word of mouth has a considerable impact on customers' churn decisions and also on the purchase decisions, leading to a 19.5% and 8.4% increase in sensitivity of predictive models.},
  doi = {https://doi.org/10.1016/j.dss.2011.01.002},
  journal = {Decision Support Systems},
  bibtexkey = {DIERKES2011361},
  author = {Torsten Dierkes, Martin Bichler, Ramayya Krishnan},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Emotional responses to energy projects: A new method for modeling and prediction beyond self-reported emotion measure},
  abstract = {A considerable amount of studies report that negative emotions evoked by Wind Energy, Nuclear Energy and CO2 Capture and Storage (CCS) can lead to cancellation of the energy project or a delay in policy decisions for its implementation if not adequately addressed. Earlier studies have attempted to study this problem using self-reported emotion measurements to identify the emotions the participants felt. As an alternative, we propose the use of an emotional artificial intelligence (AI) algorithm for improved modeling and prediction of the participantsâ€™ emotional behaviour to guide decision-making. We have validated the system using emotional responses to a hypothetical CCS project as a case study. Running our simulation on the experimental dataset (thus 40% of the 72,105), we obtained an average validation accuracy of 98.81%. We challenged the algorithm further with 84 test samples (unseen cases), and it predicted 75 feelings correctly when the stakeholders took a definite position on how they felt. Although there are few limitations to this study, we did find, in a sensitivity experiment, that it was challenging for the algorithm to predict indecisive feelings. The method is adaptable to study emotional responses to other projects, including Wind Energy, Nuclear Energy and Hydrogen Technology.},
  doi = {https://doi.org/10.1016/j.energy.2019.116210},
  journal = {Energy},
  bibtexkey = {BUAH2020116210},
  author = {Eric Buah, Lassi Linnanen, Huapeng Wu},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2019},
  title = {Chapter 17 - Alternative Exit and Restructuring Strategies: Bankruptcy, Reorganization, and Liquidation},
  abstract = {The focus of this chapter is on bankruptcy and liquidation as alternative restructuring or exit strategies for failing firms, on the characteristics of such firms, and the bankruptcy process. This chapter discusses in detail the major motivations for bankruptcy and the conditions under which a firm is considered to be bankrupt. Bankruptcy enables a failing firm to reorganize, while protected from its creditors, or to cease operation by selling its assets to satisfy all or a portion of the firmâ€™s outstanding debt. How reorganization and liquidation take place both inside and outside the protection of the bankruptcy court are examined in detail, as are Section 363 sales, prepackaged bankruptcy, and cross-border bankruptcy. Common tactics, such as debt for equity swaps, compositions, and extensions, used by debtor firms to negotiate with creditors and the circumstances in which they are used are analyzed. This chapter also discusses the advantages and disadvantages of strategic options for failing firms ranging from merging with another firm to reaching an out-of-court voluntary settlement to voluntary and involuntary liquidations. The chapter also describes the increasing role of hedge funds in the bankruptcy process. The current state of bankruptcy prediction models and their limitations as well as the results of recent empirical studies of the performance of firms experiencing financial distress also are addressed.},
  doi = {https://doi.org/10.1016/B978-0-12-815075-7.00017-6},
  bibtexkey = {DEPAMPHILIS2019471},
  author = {Donald M. DePamphilis},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Neural networks in wireless networks: Techniques, applications and guidelines},
  abstract = {The design of modern wireless networks, which involves decision making and parameter optimization, is quite challenging due to the highly dynamic, and often unknown, environmental conditions that characterize wireless networks. There is a common trend in modern networks to incorporate artificial intelligence (AI) techniques to cope with this design complexity. While a number of AI techniques have been profitably employed in the wireless networks community, the well-established AI framework of neural networks (NNs), well known for their remarkable generality and versatility, has been applied in a wide variety of settings in wireless networks. In particular, NNs are especially popular for tasks involving classification, learning, or optimization. In this paper, we provide both an exposition of common NN models and a comprehensive survey of the applications of NNs in wireless networks. We also identify pitfalls and challenges of implementing NNs especially when we consider alternative AI models and techniques. While various surveys on NNs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on the applications of NNs in wireless networks.},
  doi = {https://doi.org/10.1016/j.jnca.2016.04.006},
  journal = {Journal of Network and Computer Applications},
  bibtexkey = {AHAD20161},
  author = {Nauman Ahad, Junaid Qadir, Nasir Ahsan},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2015},
  title = {Chapter 4 - Classification},
  abstract = {In classification or class prediction, we try to use the information from the predictors or independent variables to sort the data samples into two or more distinct classes or buckets. Classification is the most widely used data mining task in business. There are several ways to build classification models. In this chapter, we will discuss and show the implementation of six of the most commonly used classification algorithms: decision trees, rule induction, k-nearest neighbors, naÃ¯ve Bayesian, artificial neural networks, and support vector machines. We conclude this chapter with building ensemble classification models and a discussion on bagging, boosting, and random forests.},
  doi = {https://doi.org/10.1016/B978-0-12-801460-8.00004-5},
  bibtexkey = {KOTU201563},
  author = {Vijay Kotu, Bala Deshpande},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Contextual motivation in physical activity by means of association rule mining},
  abstract = {The primary thrust of this work is to demonstrate the applicability of association rule mining in public health domain, focusing on physical activity and exercising. In this paper, the concept of association rule mining is shown assisting to promote the physical exercise as regular human activity. Specifically, similar to the prototypical example of association rule mining, market basket analysis, our proposed novel approach considers two events â€“ exercise (sporadic) and sleep (regular) as the two items of the frequent set; and associating the former, exercise event, with latter, the daily occurring activity sleep at night, helps strengthening the frequency of the exercise patterns. The regularity can further be enhanced, if the exercising instruments are kept in the vicinity of the bed and are within easy reach.},
  doi = {https://doi.org/10.1016/j.eij.2015.06.003},
  journal = {Egyptian Informatics Journal},
  bibtexkey = {SHARMA2015243},
  author = {Sugam Sharma, Udoyara Sunday Tim, Marinelle Payton, Hari Cohly, Shashi Gadia, Johnny Wong, Sudharshanam Karakala},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Unsupervised pre-trained filter learning approach for efficient convolution neural network},
  abstract = {The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from the animal visual cortex. Since humans can learn through experience, similarly, ConvNet changes its weight accordingly to accomplish the desired output through backpropagation. In this paper, we provide a comprehensive survey of the relationship between ConvNet with different pre-trained learning methodologies and its optimization effects. These hybrid networks further develop the state-of-the-art algorithms in recognition, classification, and detection of images, speeches, texts, and videos. Furthermore, some task-specific applications of ConvNet have been introduced in computer vision. To validate the survey, we also perform some experiments on a public face and skin detection dataset to provide an authentic solution. The experimental results on the benchmark dataset highlight the merit of efficient pre-trained learning algorithms for optimized ConvNet. To motivate the follow-up research, we identify open problems and present future directions with regards to optimized ConvNet system design parameters and unsupervised learning.},
  doi = {https://doi.org/10.1016/j.neucom.2019.06.084},
  journal = {Neurocomputing},
  bibtexkey = {REHMAN2019171},
  author = {Sadaqat ur Rehman, Shanshan Tu, Muhammad Waqas, Yongfeng Huang, Obaid ur Rehman, Basharat Ahmad, Salman Ahmad},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {The future decisions of RoboJudge HHJ Arthur Ian Blockchain: Dread, delight or derision?},
  abstract = {Steve Saxby's prescient founding of CLSR, two hundred issues ago, encouraged and resonated with my own digital visionary thinking and professional activity in the evolving field of ICT and the Law. From Infolex, the UK's first commercially-available computer-assisted legal information retrieval service, and my APPEAL Report (on the admissibility of computer evidence in court and the legal reliability/security of IT systems), via my Forensic Systems Analysis expert methodology, to the nascent CryptoBlockTV, Steve's scholarly foresight in promoting adventurous exploration of â€˜digilawâ€™ high-ground topics and issues has presented me with opportunities to generate a stream of prescient material, for which I am immensely grateful. And what is beyond prescient today is that the Coming of the Robots is unstoppable. The Artificial Intelligence (AI) Age is upon us; RoboJudge has all but already arrived. While many are concerned about defining and developing Machine Ethics, Castell's Second Dictum: â€œYou cannot construct an algorithm that will reliably decide whether or not any algorithm is ethicalâ€  reveals that this is a futile exercise. Algorithms are also pivotal to the current mania for Crypto-Algorithmic Blockchain Technology Initial Coin Offerings (ICOs), with a â€˜Crypto Tribeâ€™ of Millennials relentlessly raising billions in real money thereby, to the extent that I have dubbed Crypto the Millennialsâ€™ Rock'nâ€™Roll. The seasoned ICT expert professional however bears in mind that there are as yet no ISO standards for blockchain, and there is far more to creating and delivering a complete quality-assured system than just the blockchain component. Furthermore, the legal status of cryptocurrency, smart contract and distributed ledger technology is not clear or uncontentious â€“ and there is already ICO litigation on foot. Nevertheless, taking my limerick-writing Castell GhostWriteBotâ€™s advice, it is perhaps time for my own asset-linked ICO, to launch my CapChere.com concept designed to reboot Capitalism and achieve ubiquitous universal share and wealth ownership. Look out for Castell GhostWriteBotâ€™s account (with or without limericks) of how I fared, in the 400th issue of CLSR.},
  doi = {https://doi.org/10.1016/j.clsr.2018.05.011},
  journal = {Computer Law & Security Review},
  bibtexkey = {CASTELL2018739},
  author = {Stephen Castell},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2015},
  title = {Chapter 12 - User-Generated Content and Social Media},
  abstract = {This chapter documents what economists have learned about user-generated content (UGC) and social media. A growing body of evidence suggests that UGC on platforms ranging from Yelp to Facebook has a large causal impact on economic and social outcomes ranging from restaurant decisions to voting behavior. These findings often leverage unique datasets and methods ranging from regression discontinuity to field experiments, and researchers often work directly with the companies they study. I then survey the factors that influence the quality of UGC. Quality is influenced by factors including promotional content, peer effects between contributors, biases of contributors, and self-selection into the decision to contribute. Non-pecuniary incentives, such as â€œbadgesâ€  and social status on a platform, are often used to encourage and steer contributions. I then discuss other issues including business models, network effects, and privacy. Throughout the chapter, I discuss open questions in this area.},
  doi = {https://doi.org/10.1016/B978-0-444-63685-0.00012-7},
  bibtexkey = {LUCA2015563},
  author = {Michael Luca},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {The next stage of U.S. communications policy: The emerging embedded infosphere},
  abstract = {The United States needs to reimagine the basic principles of its telecommunications and information policy to fit an emerging society in which networking and intelligence are embedded into an increasing number of everyday things which constantly monitor and measure our lives. This emerging environment is an always-on, ubiquitous, integrated system comprised of the Internet of Things, Big Data, Artificial Intelligence/Intelligent Systems and the Intercloud, which act together as a single system, referred to here as the â€œEmbedded Infosphereâ€  (EI). This development is driving the latest stage â€“ the third â€“ in the evolution of U.S. communications policy. Each of the components of the EI presents unique challenges, but the greater concern is all of them acting in concert. These developments bring into focus many topics that have been outside the traditional communications policy envelope, and exceed the portfolios of existing agencies and institutions. This article envisions a new â€œEI policy space,â€  grounded in established societal values, and built on the experience of the previous stages. There are appropriate policy responses to each of the challenges, but these responses need to be seen in a holistic perspective, as they are all interconnected. Many of the issues such as privacy, security, consumer protection, and data stewardship are common across several elements. The larger goal is to establish a framework for an integrated policy structure which can address unpredictable emergent conditions, while allowing markets to flourish without unduly burdensome regulations, restrictions or uncertainties. This articles suggests a high-level analytical framework of criteria against which proposed EI policies can be measured. While there may be no â€œperfectâ€  policies, some may be better (or worse) than others. It also offers a political process designed to incorporate the concept of the EI into national policy thinking. This approach should be implemented through a series of steps and should provide flexibility for development. The initial step is a process by which the EI can be acknowledged, its development analyzed, and the national interests institutionalized. The U.S. Senate has already initiated this process with the pending â€œDIGITâ€  Act, designed to bring together the core federal stakeholders and open a policy discourse which will be expanded over time to other key stakeholders. This should lead to the development of a national EI strategy. Since the EI is progressively global, the article suggests how both the normative and regulatory dimensions can be approached in the global context. It raises the possibility of a restructured ICANN â€œEmpowered Communityâ€  as a possible venue for developing policies and recommendations in this area. It notes that since global unanimity is unlikely, non-governmental regimes will likely develop to address the unresolved policy interstices.},
  doi = {https://doi.org/10.1016/j.telpol.2016.11.007},
  journal = {Telecommunications Policy},
  bibtexkey = {TAYLOR20171039},
  author = {Richard D. Taylor},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2019},
  title = {3 - Ideation},
  abstract = {Engineering design relies upon the generation of alternative ideas and their effective evaluation to ensure that attention is directed in a worthwhile manner. This chapter explores a number of creativity tools that are widely used for the generation of ideas including flip chart; post-it notes; alphabet and grid brainstorming; the creative problem solving process, also known as CPS; morphological analysis, a matrix combinational creativity tool; and TRIZ the theory of inventive problem solving. The chapter includes consideration of group dynamics and the evaluation of ideas as well as approaches for encouraging the realisation of value from creativity through innovation.},
  doi = {https://doi.org/10.1016/B978-0-08-102367-9.00003-2},
  bibtexkey = {CHILDS201975},
  author = {Peter R.N. Childs},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2004},
  title = {Subject Index},
  doi = {https://doi.org/10.1016/B0-08-043076-7/99109-5},
  bibtexkey = {200483},
  author = {NA},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Deep Learning vs. Wise Learning: A Critical and Challenging Overview},
  abstract = {Learning is the most important thing that living creatures do. An organism cannot properly animate itself without first learning how to. Knowledge is the basis for all natural and human made systems. Using knowledge to model and control a complex dynamic system (CDS) is considered. Wisdom is carefully reviewed and related to knowledge. Deep Learning (DL) and Wise Learning (WL) are presented and analyzed as two approaches to address the challenging problem of modelling and controlling CDS. Strong and weak issues of both methods are discussed. Future research directions are provided.},
  doi = {https://doi.org/10.1016/j.ifacol.2016.11.099},
  journal = {IFAC-PapersOnLine},
  bibtexkey = {GROUMPOS2016180},
  author = {Peter P. Groumpos},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2001},
  title = {5 - 110 manufacturing methods},
  abstract = {This chapter briefly describes each of the 110 manufacturing methods, such as agent-driven approach, activity-based costing (ABC), agile manufacturing, and so on. Activity-based costing is an information system that maintains and processes data on a firm's activities and products/services. It identifies the activities performed, traces costs to these activities, and then uses various cost drivers to trace the cost of activities to the final products/services. Agent-driven manufacturing systems are designed to solve shop-floor-control problems in manufacturing systems. Agile manufacturing can be defined as the capability of reacting quickly to changing markets, to produce high quality products, to reduce lead times, and to provide superior service. These are achieved by improving enterprise communications among all disciplines engaged in the manufacturing process.},
  doi = {https://doi.org/10.1016/B978-075065088-5/50005-4},
  bibtexkey = {HALEVI200159},
  author = {Gideon Halevi},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2004},
  title = {Data mining and the search for security: Challenges for connecting the dots and databases},
  abstract = {Data mining is emerging as one of the key features of many homeland security initiatives. Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. In the context of homeland security, data mining is often viewed as a potential means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists themselves, such as through travel and immigration records. However, compared to earlier uses of data mining by government, some of the homeland security data mining applications represent a significant expansion in the quantity and scope of data to be analyzed. Three of the higher profile initiatives include the now defunct Terrorism Information Awareness (TIA) project, the recently canceled Computer-Assisted Passenger Prescreening System II (CAPPS II), and the Multistate Anti-Terrorism Information Exchange (MATRIX) pilot project. This article examines the evolving nature of data mining for homeland security purposes, the limitations of data mining, and some of the issues raised by its expanding use, including data quality, interoperability, mission creep, and privacy.},
  doi = {https://doi.org/10.1016/j.giq.2004.08.006},
  journal = {Government Information Quarterly},
  bibtexkey = {SEIFERT2004461},
  author = {Jeffrey W. Seifert},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2016},
  title = {Expanded cloud plumes hiding Big Data ecosystem},
  abstract = {Today, a paradigm shift is being observed in science, where the focus is gradually shifting away from operation to data, which is greatly influencing the decision making also. The data is being inundated proactively from several sources in various forms; especially social media and in modern data science vocabulary is being recognized as Big Data. Today, Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, Xâ€¦X}classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term â€œcloudâ€  in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed in the past couple of years.},
  doi = {https://doi.org/10.1016/j.future.2016.01.003},
  journal = {Future Generation Computer Systems},
  bibtexkey = {SHARMA201663},
  author = {Sugam Sharma},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Big Data with deep learning for benchmarking profitability performance in project tendering},
  abstract = {A reliable benchmarking system is crucial for the contractors to evaluate the profitability performance of project tenders. Existing benchmarks are ineffective in the tender evaluation task for three reasons. Firstly, these benchmarks are mostly based on the profit margins as the only key performance indicator (KPI) while there are other KPIs fit to drive the evaluation process. Secondly, these benchmarks donâ€™t take project context into account, thereby restricts their predictive accuracy. And finally, these benchmarks are obtained from small subsets of data, making it hard to generalise. As a result, estimators cannot probe into tenders to judge the strengths and weaknesses of their bids. This advancement is critical for not only choosing more lucrative opportunities but also driving negotiations during the tendering process. This study aims to develop a benchmarking system for tender evaluation using Big Data of 1.2 terabytes, comprising 5.7 million cells. A holistic list of seventeen (17) KPIs is identified from the email data using Text Mining approaches. Besides, eight (8) key project attributes are chosen for ensuring context-aware benchmarking using Focused Group Interviews (FGIs). At the crux of this work lies the proposition of a deep ensemble learner based on the decomposition-integration methodology. In the decomposition stage, the model predicts several attribute-specific benchmarks for each KPI using our proposed context-aware algorithm. In the integration stage, deep neural network-based learners are trained to generate final project-sensitive KPI benchmark. The learner is deployed in the Spring tool to support the tender evaluation of power infrastructure projects. A tender of 60km underground cabling project is evaluated using the proposed learner. The system spontaneously identified KPIs in the tender that require further attention to achieve greater profitability performance.},
  doi = {https://doi.org/10.1016/j.eswa.2020.113194},
  journal = {Expert Systems with Applications},
  bibtexkey = {BILAL2020113194},
  author = {Muhammad Bilal, Lukumon O. Oyedele},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Soft Computing based object detection and tracking approaches: State-of-the-Art survey},
  abstract = {In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches.},
  doi = {https://doi.org/10.1016/j.asoc.2018.05.023},
  journal = {Applied Soft Computing},
  bibtexkey = {KAUSHAL2018423},
  author = {Manisha Kaushal, Baljit S. Khehra, Akashdeep Sharma},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Dynamic behavior based churn prediction in mobile telecom},
  abstract = {Customer churn is one of the most challenging problems that affects revenue and customer base in mobile telecom operators. The success of retention campaigns depends not only on the accuracy of predicting potential churners, but with equal importance, it depends on the timing when the prediction is done. Previous works related to churn prediction presented models to predict churn monthly with a focus on the static behavior of customers, and even the studies that considered the dynamic behavior of the customer, looked mainly at the monthly level behavior. However, customer behavior is susceptible to changes over days of month, and during the time leading up to a customer decision to churn, he/she starts behaving differently. Therefore, considering monthly behavioral features negatively affects the predictive performance, because it ignores changes in behavior over days of month. Moreover, predicting churners on monthly basis will be late for customers who decided to leave at the beginning of the month because they will not be detected as churners until the next month. To address these issues, in this paper, we propose daily churn prediction instead of monthly based on the daily dynamic behavior of customer instead of his monthly one. More precisely, we represent customer's daily behavior as multivariate time series and propose four models to predict churn daily based on this representation. Two models depend on features extracted from the multivariate time series, namely RFM-based model and statistics-based model. While the other models, exploit deep learning techniques for automatic feature extraction, namely LSTM-based model and CNN-based model. The predictive performance of the proposed models were investigated by evaluating them using a 150-day-long dataset collected from MTN operator in the country. The results showed that the daily models significantly outperform the monthly models in terms of predicting churners earlier and more accurately. Furthermore, the LSTM-based model significantly outperforms the CNN-based model. However, the prediction performances of the LSTM-based and the CNN-based models are equal to the prediction performance of the RFM-based model. Moreover, all of these three models significantly outperform the Statistics-based model},
  doi = {10.1016/j.eswa.2020.113779},
  journal = {Expert Systems with Applications},
  bibtexkey = {Alboukaey2020},
  author = {Alboukaey, N., Joukhadar, A., Ghneim, N.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities},
  abstract = {Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily. Therefore, it is necessary to identify which are the appropriate algorithms for a certain problem. In this paper, we aim to help practitioners to choose the best ensemble technique according to their problem characteristics and their workflow. To do so, we revise the most renowned bagging and boosting algorithms and their software tools. These ensembles are described in detail within their variants and improvements available in the literature. Their online-available software tools are reviewed attending to the implemented versions and features. They are categorized according to their supported programming languages and computing paradigms. The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency. This comparison is done under the same software environment with 76 different classification tasks. Their predictive capabilities are evaluated with a wide variety of scenarios, such as standard multi-class problems, scenarios with categorical features and big size data. The efficiency of these methods is analyzed with considerably large data-sets. Several practical perspectives and opportunities are also exposed for ensemble learning},
  doi = {10.1016/j.inffus.2020.07.007},
  journal = {Information Fusion},
  bibtexkey = {GonzÃ¡lez2020205},
  author = {GonzÃ¡lez, S., GarcÃa, S., Del Ser, J., Rokach, L., Herrera, F.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Social media analytics and business intelligence research: A systematic review},
  abstract = {Evidently, online voice of customers (VoC) expressed in social media has emerged as quality data for researchers who are willing to conduct customer-driven business intelligence (BI) research. Nevertheless, to the best of authors’ knowledge, there is still a dearth of studies that deal with such remarkable research stream and address various open data (e.g., social media, intellectual property) from a BI research perspective. Therefore, this study has attempted to evaluate the applicability of social media data in BI research and provide a systematic review on the primary research articles in the domain. This study compared social media data with the other open data (e.g., gray literature, public government data) in terms of data content, collection, updatability and structure, which are determined through a thorough discussion with experts. Next, this study selected 57 social media-based BI research articles from the Web of Science (WoS) database and analyzed them with three research questions about the data, methodologies, and results to understand this research domain. Our findings are expected to inform the existing researchers in the research domain about the future research directions, enable newcomers to understand the overall process of analyzing social media data, and provide the practitioners with social media analysis approaches suitable for their environment. © 2020 Elsevier Ltd},
  doi = {10.1016/j.ipm.2020.102279},
  journal = {Information Processing and Management},
  bibtexkey = {Choi2020},
  author = {Choi, J., Yoon, J., Chung, J., Coh, B.-Y., Lee, J.-M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Instance-based entropy fuzzy support vector machine for imbalanced data},
  abstract = {Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results toward the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample’s neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM, and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem. © 2019, Springer-Verlag London Ltd., part of Springer Nature.},
  doi = {10.1007/s10044-019-00851-x},
  journal = {Pattern Analysis and Applications},
  bibtexkey = {Cho20201183},
  author = {Cho, P., Lee, M., Chang, W.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {The Portrait Depiction of the Market Members Based on Data Mining},
  abstract = {Aiming at the problem of portrait of members in shopping malls, this paper analyzes the similarities and differences of consumption behaviors between member groups and nonmember groups, and constructs the LRFMC model with k-means algorithm to analyze the value of membership. Second, active states of members are divided according to the consumption time interval, and KNN algorithm model is established to predict member states and used to predict the membership status. Finally, it discusses which types of goods are more suitable for promotional activities and can bring more profits to the shopping mall. © 2020 World Scientific Publishing Company.},
  doi = {10.1142/S0218001420590247},
  journal = {International Journal of Pattern Recognition and Artificial Intelligence},
  bibtexkey = {Guan2020},
  author = {Guan, J., Tang, C., Ou, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A CUSUM tool for retrospectively evaluating team performance: the case of the English Premier League},
  abstract = {Purpose: Despite being a widely used management technique, cumulative sum (CUSUM) analysis remains almost unheard of in professional sport. To address this, CUSUM analysis of soccer match data from the English Premier League (EPL) was performed. The primary objective of the study was to evaluate CUSUM as a tool for assessing “on-field” team performance. As a secondary objective, the association between managerial change and team performance was evaluated. Design/methodology/approach: CUSUM was applied retrospectively to goal difference data for six EPL teams (Arsenal, Chelsea, Everton, Liverpool, Manchester United and Tottenham) over 23 consecutive seasons from 1995 to 2018. This was supplemented with change point analysis to identify structural changes in mean goal difference. Succession was evaluated by mapping historical managerial changes onto the CUSUM plots for the respective clubs. Findings: CUSUM analysis revealed the presence of structural changes in four clubs. Two structural change points were identified for both Chelsea and Everton, one for Manchester United and Tottenham and none for Arsenal and Liverpool. Relatively few managerial changes coincided temporally with structural changes in “on-field” performance, with most appointments having minimal impact on long-term team performance. Other factors (e.g. changes in ownership) appear to have been influential. Research limitations/implications: The study was limited by the fact that only successful teams were investigated. Practical implications: CUSUM analysis appears to have potential as a tool for executive decision-makers to evaluate performance outcomes in professional soccer. Originality/value: The study is the first of its kind to use CUSUM analysis to evaluate team performance in professional soccer. © 2020, Emerald Publishing Limited.},
  doi = {10.1108/SBM-03-2019-0025},
  journal = {Sport, Business and Management: An International Journal},
  bibtexkey = {Beggs2020263},
  author = {Beggs, C., Bond, A.J.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2020},
  title = {Customer Churn Prediction for a Software-as-a-Service Inventory Management Software Company: A Case Study in Thailand},
  abstract = {Software-as-a-Service is the fast growth and high market values as a new emerging online business. Customer churn is a critical measure for this business. Thus, this paper focuses on seeking a customer churn prediction model for a Software-as-a-Service inventory management software company in Thailand which is facing a high churn rate. This paper executes the prediction models with four machine learning algorithms: logistic regression, support vector machine, decision tree and random forest. The random forest model is capable to provide lowest error with 10-fold cross validation average scores of 91.6% recall and 92.6% F1-score. Moreover, feature importance scores can highlight useful insights of case-study that business metrics are significantly related to churn behavior. As a result, this paper is beneficial to the case-study company to help indicate real churn customer and enhance the effectiveness in executive decision and marketing campaign. © 2020 IEEE.},
  doi = {10.1109/ICIEA49774.2020.9102099},
  journal = {2020 IEEE 7th International Conference on Industrial Engineering and Applications, ICIEA 2020},
  bibtexkey = {Amornvetchayakul2020514},
  author = {Amornvetchayakul, P., Phumchusri, N.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Micro- and macro-level churn analysis of large-scale mobile games},
  abstract = {As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenue. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro-level (between an app and a specific user) and macro-level (between an app and all its users). Accurate micro-level churn prediction and macro-level churn ranking will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking. For micro-level churn prediction, in view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user–app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To address macro-level churn ranking, we propose to construct a relationship graph with estimated micro-level churn probabilities as edge weights and adapt link analysis algorithms on the graph. We devise a simple algorithm SimSum and adapt two more advanced algorithms PageRank and HITS. The performance of our solutions to the two-level churn analysis problem is evaluated on real-world data collected from the Samsung Game Launcher platform. The data includes tens of thousands of mobile games and hundreds of millions of user–app interactions. The experimental results with this data demonstrate the superiority of our proposed models against existing state-of-the-art methods. © 2019, Springer-Verlag London Ltd., part of Springer Nature.},
  doi = {10.1007/s10115-019-01394-7},
  journal = {Knowledge and Information Systems},
  bibtexkey = {Liu20201465},
  author = {Liu, X., Xie, M., Wen, X., Chen, R., Ge, Y., Duffield, N., Wang, N.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Leveraging fine-grained transaction data for customer life event predictions},
  abstract = {This real-world study with a large European financial services provider combines aggregated customer data including customer demographics, behavior and contact with the firm, with fine-grained transaction data to predict four different customer life events: moving, birth of a child, new relationship, and end of a relationship. The fine-grained transaction data—approximately 60 million debit transactions involving around 132,000 customers to >1.5 million different counterparties over a one-year period—reveal a pseudo-social network that supports the derivation of behavioral similarity measures. To advance decision support systems literature, this study validates the proposed customer life event prediction model in a real-world setting in the financial services industry; compares models that rely on aggregated data, fine-grained transaction data, and their combination; and extends existing methods to incorporate fine-grained data that preserve recency, frequency, and monetary value information of the transactions. The results show that the proposed model predicts life events significantly better than random guessing, especially with the combination of fine-grained transaction and aggregated data. Incorporating recency, frequency, and monetary value information of fine-grained transaction data also significantly improves performance compared with models based on binary logs. Fine-grained transaction data accounts for the largest part of the total variable importance, for all but one of the life events. © 2019 Elsevier B.V.},
  doi = {10.1016/j.dss.2019.113232},
  journal = {Decision Support Systems},
  bibtexkey = {DeCaigny2020},
  author = {De Caigny, A., Coussement, K., De Bock, K.W.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A novel approach to generate a large scale of supervised data for short text sentiment analysis},
  abstract = {As for the complexity of language structure, the semantic structure, and the relative scarcity of labeled data and context information, sentiment analysis has been regarded as a challenging task in Natural Language Processing especially in the field of short-text processing. Deep learning model need a large scale of training data to overcome data sparseness and the over-fitting problem, we propose multi-granularity text-oriented data augmentation technologies to generate large-scale artificial data for training model, which is compared with Generative adversarial network(GAN). In this paper, a novel hybrid neural network model architecture(LSCNN) was proposed with our data augmentation technology, which is can outperforms many single neural network models. The proposed data augmentation method enhances the generalization ability of the proposed model. Experiment results show that the proposed data augmentation method in combination with the neural networks model can achieve astonishing performance without any handcrafted features on sentiment analysis or short text classification. It was validated on a Chinese on-line comment dataset and Chinese news headline corpus, and outperforms many state-of-the-art models. Evidence shows that the proposed data argumentation technology can obtain more accurate distribution representation from data for deep learning, which improves the generalization characteristics of the extracted features. The combination of the data argumentation technology and LSCNN fusion model is well suited to short text sentiment analysis, especially on small scale corpus. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
  doi = {10.1007/s11042-018-5748-4},
  journal = {Multimedia Tools and Applications},
  bibtexkey = {Sun20205439},
  author = {Sun, X., He, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Water evaporation algorithm with probabilistic neural network for solving classification problems},
  abstract = {Classification is a crucial step in data mining as it facilitates decision-making in many areas of human activity, such as scientific endeavors, marketing campaigns, biomedical research and industrial applications. The probabilistic neural network (PNN) is widely utilized to solve classification and pattern recognition problems and is considered an effective method for solving such problems. In this paper, we propose an improved PNN model that employs the water evaporation algorithm (WEA) in order to solve classification problems more efficiently. The proposed method is able to obtain classification accuracies that are close to each other across all 11 benchmark tested datasets from the UCI machine-learning repository, which demonstrates the validity of this method (with respect to classification accuracy). The results show that the WEA is better than the firefly algorithm (FA) and biogeography-based optimization (BBO) in terms of both classification accuracy and convergence speed. © 2020, Scientific Research Support Fund of Jordan. All rights reserved.},
  doi = {10.5455/jjcit.71-1566466063},
  journal = {Jordanian Journal of Computers and Information Technology},
  bibtexkey = {Alweshah20201},
  author = {Alweshah, M., Ramadan, E., Ryalat, M.H., Almi'ani, M., Hammouri, A.I.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A Discriminative Approach to Sentiment Classification},
  abstract = {Due to the explosive growth of user-generated contents, understanding opinions (such as reviews on products) generated by Internet users is important for optimizing business decision. To achieve such understanding, this paper investigates a discriminative approach to classifying opinions according to sentiments. The discriminative approach builds a model with the prior knowledge of the categorization information in order to extract meaningful features from the unstructured texts. The prior knowledge includes ratio factors to reinforce terms’ sentiment polarity by using TF-IDF, short for term frequency-inverse document frequency. Experimental results with four datasets show the proposed approach is very competitive, compared with some of the previous works. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.},
  doi = {10.1007/s11063-019-10108-7},
  journal = {Neural Processing Letters},
  bibtexkey = {Li2020749},
  author = {Li, G., Lin, Z., Wang, H., Wei, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Systematic comparisons of customer base prediction accuracy: Pareto/NBD versus neural network},
  abstract = {Purpose: Predicting the inactivity and the repeat transaction frequency of a firm's customer base is critical for customer relationship management. The literature offers two main approaches to such predictions: stochastic modeling efforts represented by Pareto/NBD and machine learning represented by neural network analysis. As these two approaches have been developed and applied in parallel, this study systematically compares the two approaches in their prediction accuracy and defines the relatively appropriate implementation scenarios of each model. Design/methodology/approach: By designing a rolling exploration scheme with moving calibration/holdout combinations of customer data, this research explores the two approaches' relative performance by first utilizing three real world datasets and then a wide range of simulated datasets. Findings: The empirical result indicates that neither approach is dominant and identifies patterns of relative applicability between the two. Such patterns are consistent across the empirical and the simulated datasets. Originality/value: This study contributes to the literature by bridging two previously parallel analytical approaches applicable to customer base predictions. No prior research has rendered a comprehensive comparison on the two approaches' relative performance in customer base predictions as this study has done. The patterns identified in the two approaches' relative prediction performance provide practitioners with a clear-cut menu upon selecting approaches for customer base predictions. The findings further urge marketing scientists to reevaluate prior modeling efforts during the past half century by assessing what can be replaced by black boxes such as NNA and what cannot. © 2020, Emerald Publishing Limited.},
  doi = {10.1108/APJML-09-2019-0520},
  journal = {Asia Pacific Journal of Marketing and Logistics},
  bibtexkey = {Xie2020},
  author = {Xie, S.-M., Huang, C.-Y.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Estimating customer churn under competing risks},
  abstract = {Customer churn management focuses on identifying potential churners and implementing incentives that can cure churn. The success of a churn management program depends on accurately identifying potential churners and understanding what conditions contribute to churn. However, in the presence of uncertainties in the process of churn, such as competing risks and unpredictable customer behaviour, the accuracy of the prediction models can be limited. To overcome this, we employ a competing risk methodology within a random survival forest framework that accurately computes the risks of churn and identifies relationships between the risks and customer behaviour. In contrast to existing methods, the proposed model does not rely on a specific functional form to model the relationships between risk and behaviour, and does not have underlying distributional assumptions, both of which are limitations faced in practice. The performance of the method is evaluated using data from a membership-based firm in the hospitality industry, where customers face two competing churning events. The proposed model improves prediction accuracy by up to 20%, compared to conventional models. The findings from this work can allow marketers to identify and understand churners, and develop strategies on how to design and implement incentives. © Operational Research Society 2020.},
  doi = {10.1080/01605682.2020.1776166},
  journal = {Journal of the Operational Research Society},
  bibtexkey = {Routh2020},
  author = {Routh, P., Roy, A., Meyer, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A Comparative Study on Contract Recommendation Model: Using Macao Mobile Phone Datasets},
  abstract = {Bordering on the Mainland of China, Macao is a city with diverse population and is also a world-famous gambling city. The residents living in Macao are not only localresidents, but a large-scale of residents are from the Mainland of China. There are diversified living habits among different resident groups. Some of them live and work in Macao most of the time, while others cross the border between Macao and the Mainland of China frequently. These diversities result in different demands for mobile phone contract services. Machine learning and data mining are powerful tools used by telecom companies to monitor the behavior of their customers. This paper aims to build a contract service recommendation model suitable for Macao telecom companies, which can accurately recommend the best fit contract services according to call data records (CDR). Based on data mining algorithm and large number of comparative experiments, this study conducts a study on variety of factors that have effect on the contract recommendation for obtaining a composition structure of factors that have the greatest impact on contract recommendation accuracy so as to improve the operation efficiency of the model. In addition, this study makes comparisons among five classification algorithms, including Bayesian, logistic, Random trees, Decision tree (C5.0), and KNN (k-nearest neighbor). The results are analyzed by different metrics such as Gain, Lift, ROI (region of interest), Response, and Profile. Experimental results demonstrate that the best classifier is decision trees (C5.0), and the contract service obtained by this method can achieve a recommendation accuracy close to 90%, which successfully reaches the expected goal. © 2013 IEEE.},
  doi = {10.1109/ACCESS.2020.2975029},
  journal = {IEEE Access},
  bibtexkey = {Wang202039747},
  author = {Wang, G., Wu, N.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Brave New World? On AI and the Management of Customer Relationships},
  abstract = {In light of the emerging discourse on AI systems' effect on society, whose perception swings widely between utopian and dystopian, we conduct herein a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM). To do so, we survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development, and retention. We highlight in particular how AI-CRM's improving ability to predict customer lifetime value will generate an inexorable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. We further consider the consequences for firms and the challenges to regulators. © 2020 Marketing EDGE.org.},
  doi = {10.1016/j.intmar.2020.04.002},
  journal = {Journal of Interactive Marketing},
  bibtexkey = {Libai2020},
  author = {Libai, B., Bart, Y., Gensler, S., Hofacker, C.F., Kaplan, A., KÃ¶tterheinrich, K., Kroll, E.B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Multi-objective evolutionary approach for the performance improvement of learners using ensembling feature selection and discretization technique on medical data},
  abstract = {Background: Biomedical data is filled with continuous real values; these values in the feature set tend to create problems like underfitting, the curse of dimensionality and increase in misclassification rate because of higher variance. In response, pre-processing techniques on dataset minimizes the side effects and have shown success in maintaining the adequate accuracy. Aims: Feature selection and discretization are the two necessary preprocessing steps that were effectively employed to handle the data redundancies in the biomedical data. However, in the previous works, the absence of unified effort by integrating feature selection and discretization together in solving the data redundancy problem leads to the disjoint and fragmented field. This paper proposes a novel multi-objective based dimensionality reduction framework, which incorporates both discretization and feature reduction as an ensemble model for performing feature selection and discretization. Selection of optimal features and the categorization of discretized and non-discretized features from the feature subset is governed by the multi-objective genetic algorithm (NSGA-II). The two objectives, minimizing the error rate during the feature selection and maximizing the information gain, while discretization is considered as fitness criteria. Methods: The proposed model used wrapper-based feature selection algorithm to select the optimal features and categorized these selected features into two blocks namely discretized and non-discretized blocks. The feature belongs to the discretized block will participate in the binary discretization while the second block features will not be discretized and used in its original form. Results: For the establishment and acceptability of the proposed ensemble model, the experiment is conducted on the fifteen medical datasets, and the metric such as accuracy, mean and standard deviation are computed for the performance evaluation of the classifiers. Conclusion: After an extensive experiment conducted on the dataset, it can be said that the proposed model improves the classification rate and outperform the base learner. © 2020 Bentham Science Publishers.},
  doi = {10.2174/1573405614666180903114534},
  journal = {Current Medical Imaging Reviews},
  bibtexkey = {Singh2020355},
  author = {Singh, D., Sisodia, D.S., Singh, P.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {An 'Outside the Box' Solution for Imbalanced Data Classification},
  abstract = {A common problem of the real-world data sets is the class imbalance, which can significantly affect the classification abilities of classifiers. Numerous methods have been proposed to cope with this problem; however, even state-of-the-art methods offer a limited improvement (if any) for data sets with critically under-represented minority classes. For such problematic cases, an 'outside the box' solution is required. Therefore, we propose a novel technique, called enrichment, which uses the information (observations) from the external data set(s). We present three approaches to implement the enrichment technique: (1) selecting observations randomly, (2) iteratively choosing observations that improve the classification result, (3) adding observations that help the classifier to determine the border between classes better. We then thoroughly analyze developed solutions on ten real-world data sets to experimentally validate their usefulness. On average, our best approach improves the classification quality by 27%, and in the best case, by outstanding 66%. We also compare our technique with the state-of-the-art methods. We find that our technique surpasses the existing methods performing, on average, 21% better. The advantage is especially noticeable for the smallest data sets, for which existing methods failed, while our solutions achieved the best results. Additionally, the enrichment technique applies to both the multi-class and binary classification tasks. It can also be combined with other techniques dealing with the class imbalance problem. © 2013 IEEE.},
  doi = {10.1109/ACCESS.2020.3007801},
  journal = {IEEE Access},
  bibtexkey = {Jegierski2020125191},
  author = {Jegierski, H., Saganowski, S.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {User Classification in Electronic Devices Using Machine Learning Methods},
  abstract = {User classification is a major concern for electronic device providers, because accurate and efficient classification can cut operating cost of company significantly. To deal with this problem, manufacturers try to classify customer into several categories and recognize the characteristic of users, then adopt different promotion strategies to improve sales revenue. This study aims to build models to divide users into several categories, and identify critical and controllable features which dramatically affects classification results. We test proposed models with real data coming from an electronic device producer. Results shows that random forest model performs best. Our main contributions are: 1) we focus on user classification of electronic devices; although many existing studies have discussed similar problem, few of them focus on applications in electronic devices; 2) we consider imbalance sample, and datasets are from real company. This work will be helpful for electronic device producers to improve operation and enhance marketing competitiveness. © 2019 IEEE.},
  doi = {10.1109/IEEM44572.2019.8978567},
  journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
  bibtexkey = {Liu20191553},
  author = {Liu, X., Wang, W., Victor Chan, W.K., Kuan, C.Y., Lee, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Disaster damage assessment from the tweets using the combination of statistical features and informative words},
  abstract = {Nowadays, Twitter has become more popular among the users for communicating the information, especially during disaster. Identifying tweets related to the target event during disaster is a challenging task. Many prior studies discussed situational and non-situational information related to disaster. The detection of tweets related to damage assessment is a very difficult task in social media because it is a subset of situational information. One of the following drawbacks has been present in the existing damage assessment works: (1) focused only on infrastructure damage but does not include human damage in the assessment, (2) focused only on social media image data for damage assessment and (3) focused only on regional language tweets. To overcome these issues, Stacking-based Ensemble using Statistical features and Informative Words (SESIW) is proposed for detecting the tweets related to damage assessment. It uses proposed features, namely frequency of hashtags, user mentions, wh-words, URLs, count of numerals and informative words. Informative words are mined using term frequency and inverse document frequency technique. The SESIW method is tested on different Twitter disaster datasets, and it outperforms the baseline SVM with Bag-of-Words model. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.},
  doi = {10.1007/s13278-019-0579-5},
  journal = {Social Network Analysis and Mining},
  bibtexkey = {Madichetty2019},
  author = {Madichetty, S., Sridevi, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Fuzzy neural network with support vector-based learning for classification and regression},
  abstract = {Fuzzy neural network (FNN) and support vector machine (SVM) are two prominent and powerful learning models broadly used for classification and regression. FNN has a significant local representation and human reasoning advantage. However, the drawback of such a network is that the focal point of the learning algorithms is minimizing empirical risk. In contrary to FNN, SVM emphasizes simultaneously on minimizing empirical and expected risks, which theoretically leads to an excellent generalization performance power. In this paper, we show that a Takagi–Sugeno–Kang (TSK)-type-based fuzzy neural network is, in fact, equivalent to an SVM with an adaptive kernel based on fuzzy rules generated in this FNN. Consequently, it is possible to learn the last layer of the FNN using the concepts of SVM and thus taking the advantage of SVM in generalization. In fact, on the one side, the proposed method is an SVM with an adaptive kernel based on fuzzy rules and, on the other side, it is a TSK–FNN with SVM-based learning. As a matter of fact, the defined kernel in FNN is an adaptive kernel based on data characteristics, which is derived from the fuzzy rules generated by the FNN itself. The results obtained from the proposed method for classification and regression outperform the results of SVM with the conventional kernels and fuzzy neural network trained in the traditional way. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.},
  doi = {10.1007/s00500-019-04116-x},
  journal = {Soft Computing},
  bibtexkey = {Khodabandelou201912153},
  author = {Khodabandelou, G., Ebadzadeh, M.M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications},
  abstract = {Purpose: The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems. Design/methodology/approach: The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I. Findings: Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature. Research limitations/implications: The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends. Practical implications: The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum. Originality/value: The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative. © 2019, Emerald Publishing Limited.},
  doi = {10.1108/IMDS-06-2019-0351},
  journal = {Industrial Management and Data Systems},
  bibtexkey = {Khan2019128},
  author = {Khan, W.A., Chung, S.H., Awan, M.U., Wen, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Can artificial neural network models be used to improve the analysis of B2B marketing research data?},
  abstract = {Purpose: Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models. Design/methodology/approach: A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples. Findings: ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples. Research limitations/implications: Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications. Practical implications: ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike. Originality/value: The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before. © 2019, Emerald Publishing Limited.},
  doi = {10.1108/JBIM-01-2019-0060},
  journal = {Journal of Business and Industrial Marketing},
  bibtexkey = {Wilson2019495},
  author = {Wilson, R.D., Bettis-Outland, H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {How Do Usage and Payment Behavior Interact in Rent-to-Own Business Models? Evidence from Developing Economies},
  abstract = {The diffusion of technological innovations in developing economies has been facilitated by the use of rent-to-own business models, which give flexibility to customers by allowing them to make incremental payments over time. Understanding the implications of this flexibility is a fundamental problem for an increasing number of firms operating in these markets. In this study, we empirically analyze how consumer usage and payment behaviors interact in an application of rent-to-own to the distribution of solar lamps in developing countries. By exploiting the longitudinal variation in the data—and hence accounting for intrinsic differences between customers—the analysis led to three main insights. First, higher usage rates lowered the probability of late payments by customers. Our characterization of this engagement effect enhances existing knowledge of the drivers of payment behavior in these environments. Second, customers often “bundled” payments, making advance payments for future product access. We showed that bundling the initial payment led to lower usage rates (bundling effect), suggesting that firms may not benefit from advance payments upfront, and that they should closely track usage patterns from these customers. Finally, we showed that first-period usage information can improve the accuracy of predictive models of default and that observing usage rates in subsequent periods does not lead to further improvements. Overall, the analysis highlights the importance for firms of jointly tracking and analyzing payment and usage behavior by customers, particularly in initial stages of the adoption process. © 2019 The Authors. Production and Operations Management published by Wiley Periodicals, Inc. on behalf of Production and Operations Management Society},
  doi = {10.1111/poms.13067},
  journal = {Production and Operations Management},
  bibtexkey = {Guajardo20192808},
  author = {Guajardo, J.A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Prediction of cooling efficiency of forced-air precooling systems based on optimized differential evolution and improved BP neural network},
  abstract = {This paper proposes an approach to predict the efficiency of forced-air cooling of fresh apples that combines the optimized differential evolution (DE) algorithm and the back-propagation (BP) neural network algorithm. First, to balance population diversity and fast convergence, the individual mutation operation of the basic DE algorithm was optimized by dividing the entire population into two equal parts according to the fitness value of individuals, and DE-best-1 and DE-current-to-rand-1 are used as individual mutation operations for the superior- and inferior-part individuals, respectively. Moreover, the selection operation of basic DE was also changed by using a crowding scheme, which helps maintain population diversity and discover more regions containing the global optima. Second, an optimized DE-BP neural network model was established by using the optimized DE to determine the initial weights and thresholds of the BP neural network to avoid being trapped in local minima, following which the effect of input parameters on the network output was subjected to a comprehensive sensitivity analysis based on the trained neural network. The results show that the optimized DE-BP model accurately predicts the efficiency with which apples are cooled. Furthermore, the airflow velocity and total opening area have a significant negative correlation with the average apple temperature and a positive correlation with the cooling rate of the apples. Finally, the most important factor influencing the cooling efficiency of the pre-cooling system is the total opening area of the ventilated packaging. © 2019 Elsevier B.V.},
  doi = {10.1016/j.asoc.2019.105733},
  journal = {Applied Soft Computing Journal},
  bibtexkey = {Han2019},
  author = {Han, J.-W., Li, Q.-X., Wu, H.-R., Zhu, H.-J., Song, Y.-L.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Topology-based clusterwise regression for user segmentation and demand forecasting},
  abstract = {Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing accurate forecasts remain active topics of discussion both for researchers and managers. Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level with significantly higher accuracy than a state of the art baseline. This work thus seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner. © 2019 IEEE.},
  doi = {10.1109/DSAA.2019.00048},
  journal = {Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019},
  bibtexkey = {Rivera-Castro2019326},
  author = {Rivera-Castro, R., Pletnev, A., Pilyugina, P., Diaz, G., Nazarov, I., Zhu, W., Burnaev, E.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing},
  abstract = {Churn prediction in telecom has gained a huge prominence in the recent times due to the extensive interests exhibited by the stakeholders, large number of competitors and huge revenue losses incurred due to churn. Predicting telecom churn is challenging due to the voluminous and sparse nature of the data. This paper presents a technique for the telecom churn prediction that employs particle swarm optimization (PSO) and proposes three variants of PSO for churn prediction namely, PSO incorporated with feature selection as its pre-processing mechanism, PSO embedded with simulated annealing and finally PSO with a combination of both feature selection and simulated annealing. The proposed classifiers were compared with decision tree, naive bayes, K-nearest neighbor, support vector machine, random forest and three hybrid models to analyze their predictability levels and performance aspects. Accuracy, true positive rate, true negative rate, false positive rate, Precision, F-Measures, receiver operating characteristic and precision-recall plots were used as performance metrics. Experiments reveal that the performance of metaheuristics was more efficient and they also exhibited better predictability levels. © 2017, Springer Science+Business Media, LLC.},
  doi = {10.1007/s10586-017-1172-1},
  journal = {Cluster Computing},
  bibtexkey = {Vijaya201910757},
  author = {Vijaya, J., Sivasankar, E.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {From non-paying to premium: Predicting user conversion in video games with ensemble learning},
  abstract = {Retaining premium players is key to the success of free-to-play games, but most of them do not start purchasing right after joining the game. By exploiting the exceptionally rich datasets recorded by modern video games - -which provide information on the individual behavior of each and every player - -survival analysis techniques can be used to predict what players are more likely to become paying (or even premium) users and when, both in terms of time and game level, the conversion will take place. Here we show that a traditional semi-parametric model (Cox regression), a random survival forest (RSF) technique and a method based on conditional inference survival ensembles all yield very promising results. However, the last approach has the advantage of being able to correct the inherent bias in RSF models by dividing the procedure into two steps: first selecting the best predictor to perform the splitting and then the best split point for that covariate. The proposed conditional inference survival ensembles method could be readily used in operational environments for early identification of premium players and the parts of the game that may prompt them to become paying users. Such knowledge would allow developers to induce their conversion and, more generally, to better understand the needs of their players and provide them with a personalized experience, thereby increasing their engagement and paving the way to higher monetization. © 2019 ACM.},
  doi = {10.1145/3337722.3341855},
  journal = {ACM International Conference Proceeding Series},
  bibtexkey = {Guitart2019},
  author = {Guitart, A., Tan, S.H., RÃo, A.F.D., Chen, P.P., PeriÃ¡Ã±ez, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Fuzzy One-Class Extreme Auto-encoder},
  abstract = {A novel one class classification (OCC) algorithm called fuzzy one class extreme auto-encoder (FOCEAE) is presented in this article. The algorithm combines the precision of probability density estimation and the generalization of neural networks to accurately generate the compact bound for the target class cases. Firstly, a K-nearest-neighbors non-parametric probability density estimation-alike strategy is used to estimate the relative densities of all target class training objects, then the relative densities are transformed to be the fuzzy coefficients for further training fuzzy extreme learning machine (FELM) model. Specifically, considering there are only one-class instances, FELM is trained in the form of auto-encoder, i.e., each input equals to be the expected output of the network. Finally, the bound (i.e., the threshold) of the target class cases is determined by calculating and ranking the reconstructed errors of all training instances. We show the effectiveness and superiority of the proposed FOCEAE algorithm by comparing it with some benchmark OCC algorithms on a mass of data sets in terms of both F-measure and G-mean metrics. The statistical results also indicate that the proposed algorithm performs significantly better than some conventional ones. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
  doi = {10.1007/s11063-018-9952-z},
  journal = {Neural Processing Letters},
  bibtexkey = {Yu2019701},
  author = {Yu, H., Sun, D., Xi, X., Yang, X., Zheng, S., Wang, Q.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {A criterion for the comparison of binary classifiers based on a stochastic dominance with an application to the sale of home insurances},
  abstract = {Binary classification is an essential matter in multiple real-life problems, and so, the comparison of the performance of classifiers is a key issue. A criterion for that purpose is introduced in this manuscript. That criterion is based on a stochastic dominance, and permits to compare classifiers in subgroups of the population with the same size. By means of the new criterion, the alteration of the size of the subgroups where classifiers are compared, does not entail the modification of the suitable classifier. Characterization results of the criterion are proved. For that purpose, connections of the criterion with the theory of copulas, and with a tool introduced in the manuscript, the so-called continuity modelling vector, are essential. An application to the comparison of some classifiers for the detection of purchasers of home insurances is developed. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
  doi = {10.1080/03461238.2019.1574237},
  journal = {Scandinavian Actuarial Journal},
  bibtexkey = {LÃ³pez-DÃaz2019453},
  author = {LÃ³pez-DÃaz, M.C., LÃ³pez-DÃaz, M., MartÃnez-FernÃ¡ndez, S.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Churn Prediction in Banking System using K-Means, LOF, and CBLOF},
  abstract = {Customer churn prediction helps in identifying those customers who are probable to stop a subscriptions, products or services, and is therefore very essential for any business. Churn predictions can be very valuable for customers retentions, as it helps in predicting customer that are at risks of sendoff. It is more challenging to put forth churn prediction in banking sector, as there are no contractual agreements between a customer and a bank regarding the duration of services. Loss of customers can be very costly as it is very expensive to obtain new customers in this age of competition. There are many churn prediction techniques however; K-Means, Local Outlier Factors (LOF) and Cluster-Based Local Outlier Factors (CBLOF) have not been used so far for this purpose. In this research, we apply these techniques for customer churn prediction. The results are evaluated and analyzed using Precision (Pr), Recall (Re) and F1-Measures to justify the efficiency and effectiveness of this research. © 2019 IEEE.},
  doi = {10.1109/ICECCE47252.2019.8940667},
  journal = {1st International Conference on Electrical, Communication and Computer Engineering, ICECCE 2019},
  bibtexkey = {Ullah2019},
  author = {Ullah, I., Hussain, H., Ali, I., Liaquat, A.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Emotion Intensity Estimation from Video Frames using Deep Hybrid Convolutional Neural Networks},
  abstract = {Detecting emotional states of human from videos is essential in order to automate the process of profiling human behaviour, which has applications in a variety of domains, such as social, medical and behavioural science. Considerable research has been carried out for binary classification of emotions using facial expressions. However, a challenge exists to automate the feature extraction process to recognise the various intensities or levels of emotions. The intensity information of emotions is essential for tasks such as sentiment analysis. In this work, we propose a metric-based intensity estimation mechanism for primary emotions, and a deep hybrid convolutional neural network-based approach to recognise the defined intensities of the primary emotions from spontaneous and posed sequences. Further, we extend the intensity estimation approach to detect the basic emotions. The frame level facial action coding system annotations and the intensities of action units associated with each primary emotion are considered for deriving the various intensity levels of emotions. The evaluation on benchmark datasets demonstrates that our proposed approach is capable of correctly classifying the various intensity levels of emotions as well as detecting them. © 2019 IEEE.},
  doi = {10.1109/IJCNN.2019.8852365},
  journal = {Proceedings of the International Joint Conference on Neural Networks},
  bibtexkey = {Thuseethan2019},
  author = {Thuseethan, S., Rajasegarar, S., Yearwood, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Analysis of technological knowledge stock and prediction of its future development potential: The case of lithium-ion batteries},
  abstract = {Amidst growing environmental challenges and increasing penetration of sustainable energy production, the development of efficient energy storage system (EES) is indispensable to fully benefit from the use of intermittent renewable energy sources. Although lithium-ion batteries (LIBs) have been the dominant energy storage technology for applications in consumer electronics, electrified transportation and stationary electricity storage, further technological progress is required for achieving the climate protection objectives. In this context, continuous exploration of new technological opportunities is favorable to advance the global energy transition towards sustainability to success. To facilitate such progress and to avoid potential technological lock-in situation, understanding the underlying latent knowledge construct revealed in patents may prove beneficial. However, previous studies have either focused on analyzing the technological improvements along a particular development path or emphasizing the necessity for improvements in environmental-economic dimensions. Accordingly, a novel patent-based analysis framework is proposed to obtain an inclusive view on the continuing path of technology development concerning the research of LIBs as well as to predict its future development potential based on two machine learning algorithms: Principal component analysis (PCA) and random forest classifier (RFC). In this study, PCA is firstly applied to group various knowledge areas into key technological knowledge stocks. Building upon these results, RFC is adopted to forecast the future developmental potential of technological knowledge areas. The findings show that the research landscape of LIBs is a dynamic environment where new knowledge stocks emerged overtime and the identified knowledge stocks within a defined time interval play an orchestrating role in the advancement of lithium-ion battery technology. In particular, knowledge stocks related to battery management system and hybrid capacitors have gained relevance. The proposed analysis framework can be used to track the past chronology of knowledge accumulation trajectory and can proactively enhance the knowledge management capabilities of experts, who wish to create a path towards a low-carbon economy. © 2019 Elsevier Ltd},
  doi = {10.1016/j.jclepro.2019.03.174},
  journal = {Journal of Cleaner Production},
  bibtexkey = {Aaldering2019301},
  author = {Aaldering, L.J., Leker, J., Song, C.H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations},
  abstract = {Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world. Research linking BDA to improved organizational performance seems scarce and widely dispersed though, with the majority focused on specific domains and/or macro-level relationships. In order to synthesize past research and advance knowledge of the potential organizational value of BDA, the authors obtained a data set of 327 primary studies and 1252 secondary cited papers. This paper reviews this body of research, using three bibliometric methods. First, it elucidates its intellectual foundations via co-citation analysis. Second, it visualizes the historical evolution of BDA and performance research and its substreams through algorithmic historiography. Third, it provides insights into the field's potential evolution via bibliographic coupling. The results reveal that the academic attention for the BDA–performance link has been increasing rapidly. The study uncovered ten research clusters that form the field's foundation. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross-disciplinary collaborations. Moreover, the study identified several research topics undergoing focused development, including financial and customer risk management, text mining and evolutionary algorithms. The review concludes with a discussion of the implications for different functional management domains and the gaps for both research and practice. © 2019 British Academy of Management},
  doi = {10.1111/1467-8551.12340},
  journal = {British Journal of Management},
  bibtexkey = {BatistiÄ 2019229},
  author = {BatistiÄ , S., van der Laken, P.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Customer Churn Prediction with Feature Embedded Convolutional Neural Network: An Empirical Study in the Internet Funds Industry},
  abstract = {In this paper, we investigated the customer churn prediction problem in the Internet funds industry. We designed a novel feature embedded convolutional neural networks (FE-CNN) method that can automatically learn features from both the dynamic customer behavioral data and static customer demographic data and can utilize the advantage of convolutional neural networks to automatically learn features that capture the structured information. Our results show that our FE-CNN model outperforms the other traditional machine learning models with hand-crafted features, such as logistic regression (LR), support vector machines (SVM), random forests (RF) and neural networks (NN) in terms of accuracy, area under the receiver operating characteristics curve (AUC) and top-decile lift. Furthermore, we found that after adding the demographic data feature to the basic CNN model, the performance of the FE-CNN model improved. Overall, we found that the FE-CNN is the most powerful way to solve the problem of customer churn prediction in the Internet funds industry. Our FE-CNN method can also be applied to other fields that have both dynamic data and static data. © 2019 World

license: MIT

license_type: permissive

branch_name: refs/heads/master