text:
@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Construction of a Bank Customer Data Warehouse and an Application of Data Mining},
  abstract = {In this era of strong competition, data mining can provide effective support to bank operators in their effort to analyze and forecast the real needs of their customers. Applying bank data mining results to the actual business enables banks to develop products that not only meet customer needs but also deliver maximum bank profitability. In this paper, a data mining model for bank customers is established, and after extraction and transformation, the data are loaded into a data warehouse specifically built for that purpose. Using the data mining platform, we conduct multidimensional analysis of the customer information to uncover characteristics of their preferences. The goal of this research is to help facilitate new ideas and methods for the analysis and prediction of bank customer data.},
  doi = {10.1145/3195106.3195178},
  bibtexkey = {10.1145/3195106.3195178},
  author = {Cui, Shaoying, Ding, Ning},
  source = {ACM}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Scalable Clustering and Applications},
  abstract = {Large scale machine learning is becoming an active research area recently. Most of the existing clustering algorithms cannot handle big data due to its high time and space complexity. Among the clustering algorithms, eigen vector based clustering, such as Spectral clustering, shows very good accuracy, but it has cubic time complexity. There are various methods proposed to reduce the time and space complexity for eigen decomposition such as Nystr\"{o}m method, Lanc-zos method etc. Nystr\"{o}m method has linear time complexity in terms of number of data points, but has cubic time complexity in terms of number of sampling points. To reduce this, various Rank k approximation methods also proposed, but which are less efficient compare to the normalized spectral clustering. In this paper we propose a two step algorithm for spectral clustering to reduce the time complexity toO(nmk + m2k'), by combining both Nystr\"{o}m and Lanczos method, where k is the number of clusters and k' is the rank k approximation of the sampling matrix (k < k' << m << n). It shows very good results, with various data sets, image segmentation problems and churn prediction of a telecommunication data set, even with very low sampling (for 10 Million \texttimes{} 10 Million matrix, sampled only 100 columns) with lesser time, which confirms the validity of the algorithm.},
  doi = {10.1145/3009977.3010073},
  bibtexkey = {10.1145/3009977.3010073},
  author = {I, Shahid K, Chaudhury, Santanu},
  source = {ACM}
}

@{,
  category = {INPROCEEDINGS},
  year = {2015},
  title = {A Case Study for the Churn Prediction in Turksat Internet Service Subscription},
  abstract = {Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. An acquaintance of a customer is more costly than retainment of an existing customer. Churn predictions shed light on members about to leave the service and support promotion activities. These attempts are utilized to avoid subscription cancellation of existing customers. Nowadays, telecommunication companies take churn prediction very serious. They strive for monitoring customers in the business by using various applications in systematic approach. Our study is based on leading internet service providing company, Turksat Satellite Communications and Cable TV Operations Company's customer behavior analysis. It is the leading internet service provider of Turkey operating in telecommunications sector. We have created a two-phase solution utilizing data mining techniques. These are time series clustering and classification techniques.},
  doi = {10.1145/2808797.2808821},
  bibtexkey = {10.1145/2808797.2808821},
  author = {G\"ok, Mehmet, \"Ozyer, Tansel, Jida, Jamal},
  source = {ACM}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Comprehensible Classification Models: A Position Paper},
  abstract = {The vast majority of the literature evaluates the performance of classification models using only the criterion of predictive accuracy. This paper reviews the case for considering also the comprehensibility (interpretability) of classification models, and discusses the interpretability of five types of classification models, namely decision trees, classification rules, decision tables, nearest neighbors and Bayesian network classifiers. We discuss both interpretability issues which are specific to each of those model types and more generic interpretability issues, namely the drawbacks of using model size as the only criterion to evaluate the comprehensibility of a model, and the use of monotonicity constraints to improve the comprehensibility and acceptance of classification models by users.},
  doi = {10.1145/2594473.2594475},
  journal = {SIGKDD Explor. Newsl.},
  bibtexkey = {10.1145/2594473.2594475},
  author = {Freitas, Alex A.},
  source = {ACM}
}

@{,
  category = {ARTICLE},
  year = {2008},
  title = {Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters},
  abstract = {In classical clustering, each data point is assigned to at least one cluster. However, in many applications only a small subset of the available data is relevant for the problem and the rest needs to be ignored in order to obtain good clusters. Certain nonparametric density-based clustering methods find the most relevant data as multiple dense regions, but such methods are generally limited to low-dimensional data and do not scale well to large, high-dimensional datasets. Also, they use a specific notion of â€œdistanceâ€ , typically Euclidean or Mahalanobis distance, which further limits their applicability. On the other hand, the recent One Class Information Bottleneck (OC-IB) method is fast and works on a large class of distortion measures known as Bregman Divergences, but can only find a single dense region. This article presents a broad framework for finding k dense clusters while ignoring the rest of the data. It includes a seeding algorithm that can automatically determine a suitable value for k. When k is forced to 1, our method gives rise to an improved version of OC-IB with optimality guarantees. We provide a generative model that yields the proposed iterative algorithm for finding k dense regions as a special case. Our analysis reveals an interesting and novel connection between the problem of finding dense regions and exponential mixture models; a hard model corresponding to k exponential mixtures with a uniform background results in a set of k dense clusters. The proposed method describes a highly scalable algorithm for finding multiple dense regions that works with any Bregman Divergence, thus extending density based clustering to a variety of non-Euclidean problems not addressable by earlier methods. We present empirical results on three artificial, two microarray and one text dataset to show the relevance and effectiveness of our methods.},
  doi = {10.1145/1376815.1376817},
  journal = {ACM Trans. Knowl. Discov. Data},
  bibtexkey = {10.1145/1376815.1376817},
  author = {Gupta, Gunjan, Ghosh, Joydeep},
  source = {ACM}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {The State-of-the-Art in Twitter Sentiment Analysis: A Review and Benchmark Evaluation},
  abstract = {Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique challenges presented by Twitter sentiment analysis and review the literature to determine how the devised approaches have addressed these challenges. To assess the state-of-the-art in Twitter sentiment analysis, we conduct a benchmark evaluation of 28 top academic and commercial systems in tweet sentiment classification across five distinctive data sets. We perform an error analysis to uncover the causes of commonly occurring classification errors. To further the evaluation, we apply select systems in an event detection case study. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of the next generation of approaches.},
  doi = {10.1145/3185045},
  journal = {ACM Trans. Manage. Inf. Syst.},
  bibtexkey = {10.1145/3185045},
  author = {Zimbra, David, Abbasi, Ahmed, Zeng, Daniel, Chen, Hsinchun},
  source = {ACM}
}

@{,
  category = {INPROCEEDINGS},
  year = {2013},
  title = {Nonparametric Hierarchal Bayesian Modeling in Non-Contractual Heterogeneous Survival Data},
  abstract = {An important problem in the non-contractual marketing domain is discovering the customer lifetime and assessing the impact of customer's characteristic variables on the lifetime. Unfortunately, the conventional hierarchical Bayes model cannot discern the impact of customer's characteristic variables for each customer. To overcome this problem, we present a new survival model using a non-parametric Bayes paradigm with MCMC. The assumption of a conventional model, logarithm of purchase rate and dropout rate with linear regression, is extended to include our assumption of the Dirichlet Process Mixture of regression. The extension assumes that each customer belongs probabilistically to different mixtures of regression, thereby permitting us to estimate a different impact of customer characteristic variables for each customer. Our model creates several customer groups to mirror the structure of the target data set.The effectiveness of our proposal is confirmed by a comparison involving a real e-commerce transaction dataset and an artificial dataset; it generally achieves higher predictive performance. In addition, we show that preselecting the actual number of customer groups does not always lead to higher predictive performance.},
  doi = {10.1145/2487575.2487590},
  bibtexkey = {10.1145/2487575.2487590},
  author = {Nagano, Shouichi, Ichikawa, Yusuke, Takaya, Noriko, Uchiyama, Tadasu, Abe, Makoto},
  source = {ACM}
}

@{,
  category = {INPROCEEDINGS},
  year = {2017},
  title = {Churn prediction model for effective gym customer retention},
  abstract = {In the fitness industry, rolling gym membership contracts allow customers to terminate a contract with little advanced notice. Customer churn prediction is a well known area in Machine Learning research. Many companies, however, face a data science skills gap when trying to translate this research onto their own datasets and IT infrastructure. In this paper we present a series of experiments that aim to predict customer behaviour, in order to increase gym utilisation and customer retention. We use two off-the-shelf machine learning platforms, so that we can evaluate whether these platforms, used by non ML experts, can help companies improve their services.},
  doi = {10.1109/BESC.2017.8256385},
  bibtexkey = {8256385},
  author = {J, Semrl, A, Matei},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2010},
  title = {Clustering analysis of broadband customers and its application in marketing},
  abstract = {The thesis briefly introduced data mining and clustering theories. Also through K-Means, ADSL voice multiplex customers are clustered, which caused over 240,000 customers divided into 8 clusters. Further analysis showed that the 8 clusters had distinct differences in basic characters and surfing the net. However, these differences could help X telecom Co. to design pointed sales strategies respectively, so as to improve customer's value, using perception and satisfaction and to reduce customer dropout.},
  doi = {10.1109/ICLSIM.2010.5461170},
  bibtexkey = {5461170},
  author = {Y, Zhang, C, Chen, R, Zhang},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2009},
  title = {A low dropout regulator with over current reminder circuits},
  abstract = {A low voltage, low dropout (LDO) regulator with an over current reminder circuit is proposed in this paper. Based on SMIC 0.18 Â¿m mix signal CMOS process, the circuit is able to drive a load up to 120 mA, and it can give an over current warning to the customer when the load current is over 120 mA, and when the load current drops down to 110 mA the warning will be recalled. By using the pole-zero tracking frequency compensation, the ripple of the output voltage between 28 mV~30 mV when a current pulse draws-out from the LDO.},
  doi = {10.1109/ASICON.2009.5351389},
  bibtexkey = {5351389},
  author = {W. Yong-sheng, L. Rui-xuan, Y. Ming-yan, L. Feng-chang},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2016},
  title = {Failure investigation of a die crack on a CMOS low dropout regulator device},
  abstract = {There are recent failures on a Low Dropout (LDO) Regulator where the customer is experiencing an out of specification output voltage. This paper aims to investigate the exact root cause of the failure by detailed failure analysis and relating the customer complaint to the die crack observed.},
  doi = {10.1109/IPFA.2016.7564273},
  bibtexkey = {7564273},
  author = {F. N. Lupena, S. M. Teo, R. Galarce},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2015},
  title = {Failure localization on a Low-Dropout Voltage Regulator with Reset Automotive Device due to wire bonding issue},
  abstract = {There is a recent failure on a Low-Dropout Voltage Regulator with Reset Automotive Device of which customer is experiencing an out of specification output voltage. This paper aims to pinpoint the exact failure mechanism for this automotive customer using detailed fault localization analysis and identify the root cause of broken stitch bond.},
  doi = {10.1109/IPFA.2015.7224392},
  bibtexkey = {7224392},
  author = {R. V. Galarce, L. C. Vagues},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Customer Review Analytics using Subjective Loss Function for Conceptual-based Learning},
  abstract = {Deep neural networks (DNNs) are currently among the most commonly used machine learning methods in content understanding such as computer vision and natural language understanding (NLU). One of the best characteristics of these methods is their modular design - the ability to change the connectivity patterns of layers, try different activation functions, inject different statistical approaches such as normalization and dropout in the network, and many other actions - in every aspect of deep learning networks. While the majority of deep learning applications simply use cross-entropy, L1, and L2 losses, subjective loss function can actually result in impressive performance improvement. In addition, architecting the last layer of DNNs - referred to as the prediction layer - according to the needs of the application increases the discriminative power of the DNNs. This paper aims to investigate how particular choices of loss functions and prediction layer architecture affect deep neural networks and their learning dynamics, as well as the robustness of various effects. Furthermore a real-life application to measure customer loyalty called Deep Net Promoter Score (DeepNPS) from online product reviews is also proposed. The results are promising for learning more latent features and matching the customer feedback with the NPS score.},
  doi = {10.1109/SYSOSE.2018.8428702},
  bibtexkey = {8428702},
  author = {S. A. Miraftabzadeh, P. Rad, M. Jamshidi, J. Prevost},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2013},
  title = {Microblog users' life time activity prediction},
  abstract = {As the fast development of online social media, social network services have become an important research area nowadays. Particularly, microblog as new social media needs more attention. Most of current studies are usually static descriptions or explanations of what already has happened. Limited study has been conducted focusing on SNS users and analysing their behaviors dynamically. In this paper, we firstly segment microblog users based on the recency and frequency of tweet and retweet behavior, then use probability models such as Pareto/NBD and BG/NBD to predict customer lifetime vitality. Our results showed that both Pareto/NBD model and BG/NBD model showed effective ability to fit and predict SNS users' usage behavior on microblog website. Tweet behaviors of sustainably active user base are more suitable for the probability models. Managerial implications of the two models should be highlighted as well. Interaction rate and dropout rate can be considered as the vitality index of the whole user base measuring how active users are and how likely a user is active. Managerial questions such as how active the users are in this platform now and how active the users will be in the future can be answered by applying those models.},
  doi = {10.1109/ICSSSM.2013.6602505},
  bibtexkey = {6602505},
  author = {J. Jin, X. Chen, R. Geng, S. Cai},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Aspect Extraction from Customer Reviews Using Convolutional Neural Networks},
  abstract = {Convolutional Neural Networks (CNN) have emerged as a viable solution for text classification, including aspect extraction. Yet the existing state-of-the-art CNN architectures used in aspect extraction do not incorporate improvements (e.g. non-static CNN, multi-kernel convolution layers, and dropout regularization) that have been identified as beneficial for general text classification tasks. In this research, we present an improved CNN architecture with these enhancements for aspect extraction. Our modifications to the basic CNN model improve feature extraction of the convolutional layer, while allowing the dense layers to represent complex non-linear relationships between selected features and the output layer. Furthermore, we show that using non-static CNN instead of static CNN further fine-tunes the word embedding features for the specific domain of customer reviews in the absence of domain-specific corpora to train word2Vec models. We show that using Skip-gram trained word2vec models opposed to Continuous bag of word (CBOW) architecture improves the quality of word2vec embedding features for aspect extraction and text classification tasks. This new CNN model and baseline CNN are evaluated using SemEval-2016 Task 5 Restaurant and Laptop domain datasets. Our CNN model shows significant improvements over the baseline CNN model for aspect extraction from customer reviews for both restaurant and laptop domains. It also outperforms the currently available state-of-the-art systems for restaurant domain.},
  doi = {10.1109/ICTER.2018.8615575},
  bibtexkey = {8615575},
  author = {N. Jihan, Y. Senarath, S. Ranathunga},
  source = {IEEE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Generate and Analyze Standard Testing for Power Supply Quality: Determining How Equipment Is Affected Enables Better Protection and Greater Customer Satisfaction},
  abstract = {Critical applications in aerospace, defense, and industrial segments demand reliable quality power sources. Test standards including IEC 61000-4-11, IEC 61000-4-14, IEC 610004-28, and MIL-STD-704F define power quality and the test procedures. IEC 61000-4-11 is the standard for testing immunity to voltage variations like dips, dropouts, sags, and interruptions; IEC 61000-4-14 tests for voltage fluctuations; and IEC 61000-4-28 addresses the immunity of devices to variations in the frequency of their ac power sources. MIL STD -704F defines power characteristics for aircraft systems. One solution to achieve stable power quality is to use a programmable ac power source that meets the parameters outlined by the test standards, satisfying the conditions for critical aerospace, defense, and industrial applications.},
  doi = {10.1109/MPEL.2019.2959099},
  journal = {IEEE Power Electronics Magazine},
  bibtexkey = {9003617},
  author = {B. Hsu, J. Velazquez},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2010},
  title = {Implementation of a integrated algorithm for voltage variation and flicker measurements},
  abstract = {Recently, voltage variation and flicker events in a power system are concerned by both of utility and customers. Actually, these two events are always measured separately, but in a certain cases they can happen successively. Voltage variation may cause equipments dropout or failure and result in huge economic losses; while voltage flicker will interfere human vision. In order to discriminate voltage variation from flicker in real time, this paper propose a virtual instrumentation system to implement a integrated algorithm which can complete the measurements of both events. The simulation results illustrate that the proposed method can effectively differentiate between voltage variation and flicker; besides, the field tests also validate the capability of the virtual instrument.},
  doi = {10.1109/IMTC.2010.5488654},
  bibtexkey = {5488654},
  author = {M. Chen, C. Lu, C. Lin},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2012},
  title = {LDO EMC susceptibility modeling with on-chip sensor measurements},
  abstract = {Low-dropout voltage regulator (LDO) is widely used in up-to-date electrical systems but with severe EMC problems. Its susceptibility prediction has become one of the major concerns both of IC suppliers before fabrication to avoid redesign cost and customers before real application to reduce incompatible. This paper presents a LDO susceptibility modeling study including IEC standard modeling flow and immunity test applied to a dedicated test chip. Two on-chip voltage sensors are used to characterize the radio frequency interference propagation inside LDO and thus validate the susceptibility modeling process.},
  doi = {10.1109/CEEM.2012.6410619},
  bibtexkey = {6410619},
  author = {J. Wu, J. Li, A. Boyer, H. Wang, X. Gu, R. Shen},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2002},
  title = {Power quality index meter},
  abstract = {The increased sensitivity of power electronics equipment results in high susceptibility of industrial customers to short term voltage variations such as sags and swells. Many utilities are publishing power quality index values to indicate overall quality of their distribution systems. In this study, a real time Power Quality Index Meter that combines the detection of disturbances and the calculation of required power quality indices is proposed. The real time index metering eliminates the need for extended memory and greatly reduces subsequent data processing efforts.},
  doi = {10.1109/IMTC.2002.1007098},
  bibtexkey = {1007098},
  author = {L. Eren, M. J. Devaney},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2019},
  title = {Course Experience Evaluation using Importance-Performance Analysis},
  abstract = {Measures of students' satisfaction are often used for the evaluation of course quality in tertiary education, aiming to improve students achievement and reduce dropout phenomena. In general, students' satisfaction rates are proportionally related to course quality and students' achievement. Satisfaction evaluation methods were traditionally used in marketing for the evaluation of services or products. The analysis of the influential factors offers practitioners the means to identify problems, as well as, the chance to prioritize the appropriate measures for improvement, based on expected performance and anticipated cost. Teaching instructors, on the other hand, can differentiate their teaching according to students' perceptions. In the present work, an adapted version of the Course Experience Questionnaire was used to measure students' satisfaction. The 36-item scale, widely used in tertiary education for course assessment, examines a variety of factors along with total satisfaction. Utilizing importance-performance analysis the strengths and the weaknesses of the course were highlighted. The methodology proposed, offers educators a tool to identify the critical factors that affect students' satisfaction, and prioritize necessary interventions for the improvement of course performance.},
  doi = {10.1109/EDUCON.2019.8725275},
  bibtexkey = {8725275},
  author = {I. Sitaridis, F. Kitsios},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2018},
  title = {Prediction of Cloud Resources Demand Based on Fuzzy Deep Neural Network},
  abstract = {In this information explosion age, processing and storing the vast data sometimes are intractable problems. To cope with the challenges, fog computing is proposed, which is good at handling the real-time tasks but the service price can be correspondingly more expensive. In order to harvest more profits and get more users, the companies that offer cloud services also carry out the reservation selling strategy, whose price is relatively cheaper. For the purpose of minimizing the cost of using cloud services, this paper proposes a fuzzy deep neural network (FDNN) based method to predict the demand of cloud resources. Besides the utilization of fuzzy logic, in the phase of network training, the back-propagation algorithm, adaptively varied learning rate and the dropout strategy are also employed. According to the network predictions, the customers are able to decide how many resources to reserve so as to minimize their expenses most. Simulation results based on the real data sets from Carnegie Mellon University show that the proposed method gives the economical predictions and outperforms the traditional deep neural network.},
  doi = {10.1109/GLOCOM.2018.8647765},
  bibtexkey = {8647765},
  author = {D. Chen, X. Zhang, L. Wang, Z. Han},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2020},
  title = {Real-Time Event-Based Dynamic Multi-Facility Vehicle Rerouting},
  abstract = {This paper presents a system architecture for real-time information transmission between the dispatcher and service vehicles allowing the logistic system the possibility of reacting to dynamic events such as new service requests and dropouts in the Internet of Things (IoT) paradigm. This paper focuses on a variant of the vehicle routing problem (VRP) called the capacitated multi-depot vehicle routing problem and takes into consideration the dynamic nature of the system. To obtain real-time solutions to the routing problem, a new two-stage algorithm is detailed that makes use of various heuristics and local search metaheuristics to generate high-quality solutions to facilitate real-time decision-making. The proposed methodology creates an initial route plan for the predetermined requests and then modifies the original route as new events (customer requests) are available over time. The performance of proposed rerouting algorithms demonstrated in a simulated framework with two depots and four vehicles.},
  doi = {10.1109/ISORC49007.2020.00034},
  bibtexkey = {9112988},
  author = {A. Madhu, R. Harikrishnakumar, S. Nannapaneni, K. Krishnan},
  source = {IEEE}
}

@{,
  category = {ARTICLE},
  year = {2005},
  title = {GPS/INS uses low-cost MEMS IMU},
  abstract = {A description of the design, operation, and test results of a miniature, low-cost, integrated GPS/inertial navigation system that uses commercial off-the-shelf micro-electro-mechanical system (MEMS) accelerometers and gyroscopes. The MEMS inertial measurement unit (EMU) is packaged in a small size and provides the raw EMU data through a serial interface to a processor board where the inertial navigation solution and integrated GPS/inertial Kalman filter is generated. The GPS/inertial software integration is performed using NAVSYS' modular InterNav software product. This allows integration with different low-cost GPS chip sets or receivers and also allows the integrated GPS/inertial navigation solution to be embedded as an application on a customer's host computer. This modular object-oriented architecture facilitates integration of the miniature MEMS GPS/INS navigation system for embedded navigation applications and is designed to handle the large errors characteristic of a low-grade MEMS IMU. Test results are presented showing the performance of the integrated MEMS GPS/inertial navigation system. Data is provided showing the position, velocity, and attitude accuracy when operating with GPS aiding and also for periods where GPS dropouts occur and alternative navigation update sources are used to bound the MEMS inertial navigation error growth.},
  doi = {10.1109/MAES.2005.1514768},
  journal = {IEEE Aerospace and Electronic Systems Magazine},
  bibtexkey = {1514768},
  author = {A. K. Brown},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2007},
  title = {Application of EMC Testing to Chip Level SOC (System on a CIp)},
  abstract = {The advent of customer requirements for module level electromagnetic compatibility (EMC) testing has an impact on the successful achievement of application specific integrated circuit (ASIC) design and qualification. This paper will describe the applicability of the following EMC test standards to a SOC (system on a chip) device: ISO 11452, SAE J1113, AEC Q100, SAE J1752/3 and Ford ES-XW7T-1A278-AB and -AC. The testing presented will focus on: 1) immunity to power supply voltage dropout, 2) radiated emissions, 3) spark over parallel wire, 4) coupled immunity, and 5) RF immunity. Best practice design techniques for module PCB layout will be reviewed. A recently performed battery of EMC test results, including high temperature testing, for a sensor signal conditioner SOC IC will be presented in this paper as an illustration of the overall approach.},
  doi = {10.1109/PSES.2007.4378480},
  bibtexkey = {4378480},
  author = {D. Petry},
  source = {IEEE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition},
  abstract = {Load forecasting can enhance the reliability and efficiency of operations in a home energy management system (HEMS). The rise of big data with machine learning in recent years makes it a potential solution. This paper proposes two new energy load forecasting methods, enhancing the traditional sequence to sequence long short-term memory (S2S-LSTM) model. Method 1 integrates S2S-LSTM with human behavior patterns recognition, implemented and compared by 3 types of algorithms: density based spatial clustering of applications with noise (DBSCAN), K-means and Pearson correlation coefficient (PCC). Among them, PCC is proven to be better than the others and suitable for a large number of residential customers. Method 2 further improves Method 1's performance with a modified multi-layer Neural Network architecture, which is constituted by fully-connected, dropout and stable improved softmax layers. It optimizes the process of supervised learning in LSTM and improves the stability and accuracy of the prediction model. The performances of both proposed methods are evaluated on a dataset of 8-week electricity consumptions from 2337 residential customers.},
  doi = {10.17775/CSEEJPES.2018.01130},
  journal = {CSEE Journal of Power and Energy Systems},
  bibtexkey = {9057003},
  author = {L, Fan, J, Li, X, Zhang},
  source = {IEEE}
}

@{,
  category = {INPROCEEDINGS},
  year = {2009},
  title = {Identifying student retention patterns using GIS technology},
  abstract = {Geographic information systems (GIS) have been used successfully by personnel in government, business, and industry to map natural resource data, determine socioeconomic boundaries, and manage fleet routing activities. The diverse applications of GIS have made its use attractive to people in many professions. However, educators have not used this technological tool as extensively even though they acquire a large amount of student data. Similar to business owners, it is helpful for administrators in higher education to understand their student/customer base in order to meet their needs and retain them. The author obtained student addresses at both a Midwestern and Western university and mapped them onto a GIS system. Spatial analysis was used to determine whether there were geographic differences among students who dropped out of school, remained enrolled, or successfully graduated within six years of matriculation. Differences were found among the student groups. Geographic distance did not make a difference for dropouts or graduates. However, living near the universities was significant for students who remained in school. Proximity to classes is important for many students to continue their education and ultimately graduate. Additional research using GIS can assist educators in finding off-site campus locations near the greatest concentration of students.},
  doi = {10.1109/PICMET.2009.5261844},
  bibtexkey = {5261844},
  author = {C. Hanewicz},
  source = {IEEE}
}

@{,
  category = {ARTICLE},
  year = {2006},
  title = {IEEE Recommended Practice for Powering and Grounding Electronic Equipment},
  abstract = {The IEEE Emerald Book(TM) presents a collection of consensus best practices for thepowering and grounding of electronic equipment used in commercial and industrial applications.The main objective is to provide consensus recommended practices in an area where conflictinginformation and conflicting design philosophies have dominated. The recommended practicesdescribed are intended to enhance equipment performance while maintaining a safe installation. Adescription of the nature and origin of power disturbances is provided, followed by theory on thevarious parameters that impact power quality. Information on quantifying and resolving power andgrounding related concerns using measurement and diagnostic instrumentation and standardizedinvestigative procedures are included. Recommended power protection equipment and wiring andgrounding system design practices are presented. Information on telecommunications systempower protection as well as grounding, industrial system grounding, and noise control is included.Finally a selection of case studies are presented to support the recommended practices presentedthroughout the book.},
  doi = {10.1109/IEEESTD.2006.216391},
  journal = {IEEE Std 1100-2005 (Revision of IEEE Std 1100-1999)},
  bibtexkey = {1638205},
  author = {},
  source = {IEEE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data},
  abstract = {Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20â€¯m2 (0.002â€¯ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracyâ€¯=â€¯93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index âˆ¼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.},
  doi = {https://doi.org/10.1016/j.jag.2020.102215},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  bibtexkey = {MARTINS2020102215},
  author = {Vitor S. Martins, Amy L. Kaleita, Brian K. Gelder, Gustavo W. Nagel, Daniel A. Maciel},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Designing an expert system for fraud detection in private telecommunications networks},
  abstract = {Telecommunications fraud not only burdens telecom providerâ€™s accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate userâ€™s account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firm or organization, superimposed fraud may go undetected for some time. The present paper deals with the detection of fraudulent telecom activity inside large organizationsâ€™ premises. Focus is given on superimposed fraud detection. The problem is attacked via the construction of an expert system which incorporates both the network administratorâ€™s expert knowledge and knowledge derived from the application of data mining techniques on real world data.},
  doi = {https://doi.org/10.1016/j.eswa.2009.03.031},
  journal = {Expert Systems with Applications},
  bibtexkey = {HILAS200911559},
  author = {Constantinos S. Hilas},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Fuzzy Inference Algorithm based on Quantitative Association Rules},
  abstract = {In order to develop a data mining system to extract the fuzzy inference rules from the data, in this paper a fuzzy inference algorithm based on quantitative association rule (FI-QAR) is proposed. First, a discretization algorithm based on an improved clustering for each dimension data is adopted, and then the quantitative results are represented in the form of a Nominal variables matrix to compute the support and confidence level in the Apriori algorithm for quantitative association rules mining. On the basis of this, the quantitative association fuzzy rules are reconstructed by combing with TS fuzzy model to realize fuzzy inference, which can be applied to predict the output class and precise output. Experiment results demonstrated that the proposed algorithm is feasible and practical.},
  doi = {https://doi.org/10.1016/j.procs.2015.09.166},
  journal = {Procedia Computer Science},
  bibtexkey = {WANG2015388},
  author = {Ling Wang, Ji-Yuan Dong, Shu-Lin Li},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Citizens as consumers: Profiling e-government servicesâ€™ users in Egypt via data mining techniques},
  abstract = {This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizensâ€™ use of e-government services. Data mining uses a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualization. Three artificial neural network models (multi-layer perceptron neural network [MLP], probabilistic neural network [PNN] and self-organizing maps neural network [SOM]) and three machine learning techniques (classification and regression trees [CART], multivariate adaptive regression splines [MARS], and support vector machines [SVM]) are compared to a standard statistical method (linear discriminant analysis [LDA]). The variable sets considered are sex, age, educational level, e-government services perceived usefulness, ease of use, compatibility, subjective norms, trust, civic mindedness, and attitudes. The study shows how it is possible to identify various dimensions of e-government services usage behavior by uncovering complex patterns in the dataset, and also shows the classification abilities of data mining techniques.},
  doi = {https://doi.org/10.1016/j.ijinfomgt.2013.03.007},
  journal = {International Journal of Information Management},
  bibtexkey = {MOSTAFA2013627},
  author = {Mohamed M. Mostafa, Ahmed A. El-Masry},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A machine learning framework for customer purchase prediction in the non-contractual setting},
  abstract = {Predicting future customer behavior provides key information for efficiently directing resources at sales and marketing departments. Such information supports planning the inventory at the warehouse and point of sales, as well strategic decisions during manufacturing processes. In this paper, we develop advanced analytics tools that predict future customer behavior in the non-contractual setting. We establish a dynamic and data driven framework for predicting whether a customer is going to make purchase at the company within a certain time frame in the near future. For that purpose, we propose a new set of customer relevant features that derives from times and values of previous purchases. These customer features are updated every month, and state of the art machine learning algorithms are applied for purchase prediction. In our studies, the gradient tree boosting method turns out to be the best performing method. Using a data set containing more than 10â€‰000 customers and a total number of 200â€‰000 purchases we obtain an accuracy score of 89% and an AUC value of 0.95 for predicting next moth purchases on the test data set.},
  doi = {https://doi.org/10.1016/j.ejor.2018.04.034},
  journal = {European Journal of Operational Research},
  bibtexkey = {MARTINEZ2020588},
  author = {AndrÃ©s Martinez, Claudia Schmuck, Sergiy Pereverzyev, Clemens Pirker, Markus Haltmeier},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Training trends: Macro, micro, and policy issues},
  abstract = {The scope of the training enterprise is vast, the field is dynamic, and multi-level issues confront training researchers. After identifying three â€œmega trendsâ€  â€“ globalization, technology, and demographic changes - this paper reviews training trends at the macro level, the micro level, and emerging policy issues and links each one to the mega trends. The macro-level trends - increasing demands for personal and professional development by job seekers and employees, the effects of digital technology on work, structural changes in labor markets, increasing training opportunities for non-standard workers, and training as an important aspect of an employer's brand - reflect broad trends in the economy. Micro-level trends - better understanding of requirements for effective learning; use of short, digital lessons; and options for optimizing learning and preventing skill and knowledge decay - each focus on improving the quality of training. Policy issues - training needs in small and medium-sized enterprises, the need for â€œmiddle skillsâ€ , and vocational education - raise vexing issues for all stakeholders. Together, macro, micro, and policy issues reflect ongoing challenges for researchers, practitioners, and policy makers everywhere.},
  doi = {https://doi.org/10.1016/j.hrmr.2017.11.001},
  journal = {Human Resource Management Review},
  bibtexkey = {CASCIO2019284},
  author = {Wayne F. Cascio},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Churn prediction using comprehensible support vector machine: An analytical CRM application},
  abstract = {Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates â€œblack boxâ€  model, i.e. it does not reveal the knowledge learnt during training in human comprehensible form. The process of converting such opaque models into a transparent model is often regarded as rule extraction. In this paper we proposed a hybrid approach for extracting rules from SVM for customer relationship management (CRM) purposes. The proposed hybrid approach consists of three phases. (i) During first phase; SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set. (ii) Dataset with reduced features is then used in the second phase to obtain SVM model and support vectors are extracted. (iii) Rules are then generated using Naive Bayes Tree (NBTree) in the final phase. The dataset analyzed in this research study is about Churn prediction in bank credit card customer (Business Intelligence Cup 2004) and it is highly unbalanced with 93.24% loyal and 6.76% churned customers. Further we employed various standard balancing approaches to balance the data and extracted rules. It is observed from the empirical results that the proposed hybrid outperformed all other techniques tested. As the reduced feature dataset is used, it is also observed that the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. The generated rules act as an early warning expert system to the bank management.},
  doi = {https://doi.org/10.1016/j.asoc.2014.01.031},
  journal = {Applied Soft Computing},
  bibtexkey = {FARQUAD201431},
  author = {M.A.H. Farquad, Vadlamani Ravi, S. Bapi Raju},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {A big data analytics model for customer churn prediction in the retiree segment},
  abstract = {Undoubtedly, the change in consumersâ€™ choices and expectations, stemming from the emerging technology and also significant availability of different products and services, created a highly competitive landscape in various customer service sectors, including the financial industry. Accordingly, the Canadian banking industry has also become highly competitive due to the threats and disruptions caused by not only direct competitors, but also new entrants to the market. The primary objective of this paper is to construct a predictive churn model by utilizing big data, including the structured archival data, integrated with unstructured data from sources such as online web pages, the number of website visits and phone conversation logs, for the first time in the financial industry. It also examines the effect of different aspects of customersâ€™ behavior on churning decisions. The Datameer big data analytics tool on the Hadoop platform and predictive techniques using the SAS business intelligence system were applied to study the client retirement journey path and to create a churn prediction model. By deploying the above systems, we were able to uncover a wealth of data and information associated with over 3 million customersâ€™ records within the retiree segment of the target bank, from 2011 to 2015.},
  doi = {https://doi.org/10.1016/j.ijinfomgt.2018.10.005},
  journal = {International Journal of Information Management},
  bibtexkey = {SHIRAZI2019238},
  author = {Farid Shirazi, Mahbobeh Mohammadi},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks},
  abstract = {In recent years, the concept of Local Climate Zones (LCZs) has become a new standard in the research of urban landscapes. LCZs outline a classification scheme, which is designed to categorize urban and rural surfaces according to their climate-relevant properties, irrespective of local building materials or cultural background. We present a novel workflow for a high-resolution derivation of LCZs using multi-temporal Sentinel 2 (S2) composites and supervised Convolutional Neural Networks (CNNs). We assume that CNNs, due to their potential invariance to size and illumination of objects, are best suited to predict the highly context-based LCZs on a large scale. As a first step, the proposed workflow includes a fully automated generation of cloud-free S2 composites. These composites serve as training data basis for the LCZ classifications carried out over eight German cities. Results show that by using a CNN, overall accuracies can be increased by an average of 16.5 and 4.8 percentage points when compared to a pixel-based and a texture-based Random Forest approach, respectively. If sufficient training data is available, CNN models proved to be robust in classifying unknown cities and achieved overall accuracies of up to 86.5%. The proposed method constitutes a feasible approach for automated, large scale mapping of LCZs, and could be the preferred alternative for LCZ classifications in upcoming studies.},
  doi = {https://doi.org/10.1016/j.rse.2019.111472},
  journal = {Remote Sensing of Environment},
  bibtexkey = {ROSENTRETER2020111472},
  author = {Johannes Rosentreter, Ron Hagensieker, BjÃ¶rn Waske},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Multidisciplinary Pattern Recognition applications: A review},
  abstract = {Pattern recognition (PR) is the study of how machines can examine the environment, learn to distinguish patterns of interest from their background, and make reliable and feasible decisions regarding the categories of the patterns. However, even after almost 70 years of research, the design of an application based on pattern recognizer remains an ambiguous goal. Moreover, currently, there are huge volumes of data that must be dealt with, which include image, video, text and web documents; DNA; microarray gene data; etc. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical and machine learning approaches have been most comprehensively studied and employed in practice. Recently, deep learning techniques and methods have been receiving increasing attention. The main objective of this review is to summarize PR applications, departing from the major algorithms used for their design. The PR approaches are subdivided into three main methods: machine learning, statistical, and deep learning. In order to evidence the multidisciplinary aspects of PR applications, attention has been focused on latest PR methods applied to five fields of research: biomedical and biology, retail, surveillance, social media intelligence, and digital cultural heritage. In this paper, we discuss in detail the recent advances of PR approaches and propose the main applications within each field. We also present challenges and benchmarks in terms of advantages and disadvantages of the selected method in each field. A wide set of examples of applications in various domains are also provided, along with the specific method applied.},
  doi = {https://doi.org/10.1016/j.cosrev.2020.100276},
  journal = {Computer Science Review},
  bibtexkey = {PAOLANTI2020100276},
  author = {Marina Paolanti, Emanuele Frontoni},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House},
  abstract = {CRM has traditionally referred to a company managing relationships with customers. The rise of social media, which has connected and empowered customers, challenges this fundamental raison d'etre. This paper examines how CRM needs to adapt to the rise of social media. The convergence of social media and CRM creates pitfalls and opportunities, which are explored. We organize this discussion around the new â€œsocial CRM house,â€  and discuss how social media engagement affects the house's core areas (i.e., acquisition, retention, and termination) and supporting business areas (i.e., people, IT, performance evaluation, metrics and overall marketing strategy). Pitfalls discussed include the organization's lack of control over message diffusion, big and unstructured data sets, privacy, data security, the shortage of qualified manpower, measuring the ROI of social media marketing initiatives, strategies for managing employees, integrating customer touch points, and content marketing.},
  doi = {https://doi.org/10.1016/j.intmar.2013.09.008},
  journal = {Journal of Interactive Marketing},
  bibtexkey = {MALTHOUSE2013270},
  author = {Edward C. Malthouse, Michael Haenlein, Bernd Skiera, Egbert Wege, Michael Zhang},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability},
  abstract = {Mobile internet usage has exploded with the mass popularity of smartphones that offer more convenient and efficient ways of doing anything from watching movies, playing games, and streaming music. Understanding the patterns of data usage is thus essential for strategy-focused data-driven business analytics. However, data usage has several unique stylized facts (such as high dimensionality, heteroscedasticity, and sparsity) due to a great variety of user behaviour. To manage these facts, we propose a novel density-based subspace clustering approach (i.e., a three-stage iterative optimization procedure) for intelligent segmentation of consumer data usage/demand. We discuss the characteristics of the proposed method and illustrate its performance in both simulation with synthetic data and business analytics with real data. In a field experiment of wireless mobile telecommunications for data-driven strategic design and managerial implementation, we show that our method is adequate for business analytics and plausible for sustainability in search of business value.},
  doi = {https://doi.org/10.1016/j.ejor.2019.02.046},
  journal = {European Journal of Operational Research},
  bibtexkey = {CHEN2020687},
  author = {Yi-Ting Chen, Edward W. Sun, Yi-Bing Lin},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Managing B2B customer churn, retention and profitability},
  abstract = {It is now widely accepted that firms should direct more effort into retaining existing customers than to attracting new ones. To achieve this, customers likely to defect need to be identified so that they can be approached with tailored incentives or other bespoke retention offers. Such strategies call for predictive models capable of identifying customers with higher probabilities of defecting in the relatively near future. A review of the extant literature on customer churn models reveals that although several predictive models have been developed to model churn in B2C contexts, the B2B context in general, and non-contractual settings in particular, have received less attention in this regard. Therefore, to address these gaps, this study proposes a data-mining approach to model non-contractual customer churn in B2B contexts. Several modeling techniques are compared in terms of their ability to predict true churners. The best performing data-mining technique (boosting) is then applied to develop a profit maximizing retention campaign. Results confirm that the model driven approach to churn prediction and developing retention strategies outperforms commonly used managerial heuristics.},
  doi = {https://doi.org/10.1016/j.indmarman.2014.06.016},
  journal = {Industrial Marketing Management},
  bibtexkey = {TAMADDONIJAHROMI20141258},
  author = {Ali Tamaddoni Jahromi, Stanislav Stakhovych, Michael Ewing},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Deep Learning loss model for large-scale low voltage smart grids},
  abstract = {Distribution systems operators (DSOs) encounter the challenge of managing network losses in large geographical areas with hundreds of secondary substations and thousands of customers and with an ever-increasing presence of renewable energy sources. This situation complicates the estimation process of power loss, which is paramount to improve the network energy efficiency level in the context of the European Union energy policies. Thus, this article presents a methodology to estimate power losses in large-scale low voltage (LV) smart grids. The methodology is based on a deep-learning loss model to infer the network technical losses considering a large rollout of smart meters, a high penetration of distributed generation (DG) and unbalanced operation, among other network characteristics. The methodology has been validated in a large-scale LV distribution area in Madrid (Spain). The proposed methodology has proven to be a potential network loss estimation tool to improve the energy efficiency level in large-scale smart grids with a high penetration of distributed resources. The accuracy of the proposed methodology outperforms that of the state-of-the-art loss estimation methods, exhibiting a rapid convergence which allows for its use in real-time operations.},
  doi = {https://doi.org/10.1016/j.ijepes.2020.106054},
  journal = {International Journal of Electrical Power & Energy Systems},
  bibtexkey = {VELASCO2020106054},
  author = {Jose Angel Velasco, Hortensia Amaris, Monica Alonso},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {An optimized model using LSTM network for demand forecasting},
  abstract = {In a business environment with strict competition among firms, accurate demand forecasting is not straightforward. In this paper, a forecasting method is proposed, which has a strong capability of predicting highly fluctuating demand data. Therefore, in this paper we propose a demand forecasting method based on multi-layer LSTM networks. The proposed method automatically selects the best forecasting model by considering different combinations of LSTM hyperparameters for a given time series using the grid search method. It has the ability to capture nonlinear patterns in time series data, while considering the inherent characteristics of non-stationary time series data. The proposed method is compared with some well-known time series forecasting techniques from both statistical and computational intelligence methods using demand data of a furniture company. These methods include autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), artificial neural network (ANN), K-nearest neighbors (KNN), recurrent neural network (RNN), support vector machines (SVM) and single layer LSTM. The experimental results indicate that the proposed method is superior among the tested methods in terms of performance measures.},
  doi = {https://doi.org/10.1016/j.cie.2020.106435},
  journal = {Computers & Industrial Engineering},
  bibtexkey = {ABBASIMEHR2020106435},
  author = {Hossein Abbasimehr, Mostafa Shabani, Mohsen Yousefi},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Quantitative models for direct marketing: A review from systems perspective},
  abstract = {In this paper, quantitative models for direct marketing models are reviewed from a systems perspective. A systems view consists of input, processing, and output and the six key activities of direct marketing that take place within these constituent parts. A discussion about inputs for direct marketing models is provided by describing the various types of data used, by determining the significance of the data, and by addressing the issue of selection of appropriate data. Two types of models, statistical and machine learning based, are popularly used for conducting direct marketing activities. The advantages and disadvantages of these two approaches are discussed along with enhancements to these models. The evaluation of output for direct marketing models is done on the basis of accuracy and profitability. Some challenges in conducting research in the area of quantitative direct marketing models are listed and some significant research questions are proposed.},
  doi = {https://doi.org/10.1016/j.ejor.2008.04.006},
  journal = {European Journal of Operational Research},
  bibtexkey = {BOSE20091},
  author = {Indranil Bose, Xi Chen},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Operational research from Taylorism to Terabytes: A research agenda for the analytics age},
  abstract = {The growing attention and prominence afforded to analytics presents a genuine challenge for the operational research community. Many in the community have recognised this growth and sought to align themselves with analytics. For instance, the US operational research society INFORMS now offers analytics related conferences, certification and a magazine. However, as shown in this research, the volume of analytics-orientated studies in journals associated with operational research is comparatively low. This paper seeks to address this paradox by seeking to better understand what analytics is, and how operational research is related to it. To do so literature from a range of academic disciplines is analysed, in what is conceived as concurrent histories in the shared tradition of a management paradigm spread over the last 100 years. The findings of this analysis reveal new insights as to how operational research exists within an ecosystem shared with several other disciplines, and how interactions and ripple effects diffuse knowledge and ideas between each. Whilst this ecosystem is developed and evolved through interdisciplinary collaborations, individual disciplines are cast into competition for the attention of the same business users. These findings are further explored by discussing the implication this has for operational research, as well as considering what directions future research may take to maximise the potential value of these relationships.},
  doi = {https://doi.org/10.1016/j.ejor.2014.08.029},
  journal = {European Journal of Operational Research},
  bibtexkey = {MORTENSON2015583},
  author = {Michael J. Mortenson, Neil F. Doherty, Stewart Robinson},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {A memetic approach to construct transductive discrete support vector machines},
  abstract = {Transductive learning involves the construction and application of prediction models to classify a fixed set of decision objects into discrete groups. It is a special case of classification analysis with important applications in web-mining, corporate planning and other areas. This paper proposes a novel transductive classifier that is based on the philosophy of discrete support vector machines. We formalize the task to estimate the class labels of decision objects as a mixed integer program. A memetic algorithm is developed to solve the mathematical program and to construct a transductive support vector machine classifier, respectively. Empirical experiments on synthetic and real-world data evidence the effectiveness of the new approach and demonstrate that it identifies high quality solutions in short time. Furthermore, the results suggest that the class predictions following from the memetic algorithm are significantly more accurate than the predictions of a CPLEX-based reference classifier. Comparisons to other transductive and inductive classifiers provide further support for our approach and suggest that it performs competitive with respect to several benchmarks.},
  doi = {https://doi.org/10.1016/j.ejor.2013.05.010},
  journal = {European Journal of Operational Research},
  bibtexkey = {BRANDNER2013581},
  author = {Hubertus Brandner, Stefan Lessmann, Stefan VoÃŸ},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Fuzzy rule based unsupervised sentiment analysis from social media posts},
  abstract = {In this paper, we compute the sentiment of social media posts using a novel set of fuzzy rules involving multiple lexicons and datasets. The proposed fuzzy system integrates Natural Language Processing techniques and Word Sense Disambiguation using a novel unsupervised nine fuzzy rule based system to classify the post into: positive, negative or neutral sentiment class. We perform a comparative analysis of our method on nine public twitter datasets, three sentiment lexicons, four state-of-the-art approaches for unsupervised Sentiment Analysis and one state-of-the-art method for supervised machine learning. Traditionally, Sentiment Analysis of twitter data is performed using a single lexicon. Our results can give an insight to researchers to choose which lexicon is best for social media. The fusion of fuzzy logic with lexicons for sentiment classification provides a new paradigm in Sentiment Analysis. Our method can be adapted to any lexicon and any dataset (two-class or three-class sentiment). The experiments on benchmark datasets yield higher performance for our approach as compared to the state-of-the-art.},
  doi = {https://doi.org/10.1016/j.eswa.2019.112834},
  journal = {Expert Systems with Applications},
  bibtexkey = {VASHISHTHA2019112834},
  author = {Srishti Vashishtha, Seba Susan},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Faults in smart grid systems: Monitoring, detection and classification},
  abstract = {Smart Grid (SG) is a multidisciplinary concept related to the power system update and improvement. SG implies real-time information with specific communication requirements. System reliability relies on the best capabilities for monitoring and controlling the grid. Among other aspects, SG applications involve three main challenges, sufficient real-time capable measurement units, managing large data sets, and two-way low-latency communications. Considering fault detection and classification a key factor to SG reliability, this work provides a systematic review of SG faults from the most significant research databases and state-of-the-art research papers aiming at creating a comprehensive classification framework on the relevant requirements. This paper includes in detail the classification of different fault scenarios in a comprehensive framework that involves system-level of application, e.g., transmission, distribution, commercial, DG, and EV. To this end, We analyze and indicate relevant topics for future developments related to the monitoring and fault detection and classification in SG systems.},
  doi = {https://doi.org/10.1016/j.epsr.2020.106602},
  journal = {Electric Power Systems Research},
  bibtexkey = {LABRADORRIVAS2020106602},
  author = {Angel Esteban Labrador Rivas, Taufik AbrÃ£o},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Adaptive intelligent techniques for microgrid control systems: A survey},
  abstract = {This paper introduces a survey on the adaptive and intelligent methods that have been applied to microgrids systems. Interestingly, the adaptive technique is effectively exercised in various control issues including stability, tracking error, and parameter uncertainties. Adaptive control has been extremely developed by using intelligent algorithms to automatically tune the control parameters namely fuzzy logic, particle swarm optimization, bacterial search algorithm, and etc. The objective is to evaluate and classify the design control methods and evaluation algorithms for the microgrid systems to maintain stability, reliability, and load variations by adjusting the controller parameters especially in standalone operation mode. The stability of islanded microgrids are constantly impacted by the related loads. A significant part of the research on an islanded microgrid involves droop control technique. In normal operation, distributed generation units and storage units provide power quality control. Once a shutdown is occurred, microgrid can be isolated from the main grid and operate in a local grid to support the local loads. Thus, distributed generations co-operate storage units to sustain the stability of the islanded microgrid.},
  doi = {https://doi.org/10.1016/j.ijepes.2017.02.008},
  journal = {International Journal of Electrical Power & Energy Systems},
  bibtexkey = {MAHMOUD2017292},
  author = {Magdi S. Mahmoud, Nezar M. Alyazidi, Mohamed I. Abouheaf},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A survey on hyperparameters optimization algorithms of forecasting models in smart grid},
  abstract = {Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between demand and supply of electricity, efficient energy management, better planning of energy generation units and renewable energy sources and their dispatching and scheduling. Existing forecasting models are being used and new models are developed for a wide range of SG applications. These algorithms have hyperparameters which need to be optimized carefully before forecasting. The optimized values of these algorithms increase the forecasting accuracy up to a significant level. In this paper, we present a brief literature review of forecasting models and the optimization methods used to tune their hyperparameters. In addition, we have also discussed the data preprocessing methods. A comparative analysis of these forecasting models, according to their hyperparameter optimization, error methods and preprocessing methods, is also presented. Besides, we have critically analyzed the existing optimization and data preprocessing models and highlighted the important findings. A survey of existing survey papers is also presented and their recency score is computed based on the number of recent papers reviewed in them. By recent, we mean that the year in which a survey paper is published and its previous three years. Finally, future research directions are discussed in detail.},
  doi = {https://doi.org/10.1016/j.scs.2020.102275},
  journal = {Sustainable Cities and Society},
  bibtexkey = {KHALID2020102275},
  author = {Rabiya Khalid, Nadeem Javaid},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2008},
  title = {Model of Customer Churn Prediction on Support Vector Machine},
  abstract = {To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural network, decision tree, logistic regression, and naive bayesian classifier. It is found that the method enjoys the best accuracy rate, hit rate, covering rate, and lift coefficient, and therefore, provides an effective measurement for customer churn prediction.},
  doi = {https://doi.org/10.1016/S1874-8651(09)60003-X},
  journal = {Systems Engineering - Theory & Practice},
  bibtexkey = {XIA200871},
  author = {Guo-en XIA, Wei-dong JIN},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Fuzzy c-means clustering using Jeffreys-divergence based similarity measure},
  abstract = {In clustering, similarity measure has been one of the major factors for discovering the natural grouping of a given dataset by identifying hidden patterns. To determine a suitable similarity measure is an open problem in clustering analysis for several years. The purpose of this study is to make known a divergence based similarity measure. The notion of the proposed similarity measure is derived from Jeffrey-divergence. Various features of the proposed similarity measure are explained. Afterwards we develop fuzzy c-means (FCM) by making use of the proposed similarity measure, which guarantees to converge to local minima. The various characteristics of the modified FCM algorithm are also addressed. Some well known real-world and synthetic datasets are considered for the experiments. In addition to that two remote sensing image datasets are also adopted in this work to illustrate the effectiveness of the proposed FCM over some existing methods. All the obtained results demonstrate that FCM with divergence based proposed similarity measure outperforms three latest FCM algorithms.},
  doi = {https://doi.org/10.1016/j.asoc.2019.106016},
  journal = {Applied Soft Computing},
  bibtexkey = {SEAL2020106016},
  author = {Ayan Seal, Aditya Karlekar, Ondrej Krejcar, Consuelo Gonzalo-Martin},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Detecting the migration of mobile service customers using fuzzy clustering},
  abstract = {Customer clustering is used to build customer profiles which make up the core of a customer centric information system. In this paper, we develop a method for extending the standard fuzzy c-means clustering algorithm using membership functions to detect how customers move between clusters over time. The study leads to the discovery of new usage and revenue patterns for customers, the identification of two groups of customers that exhibit migratory behavior over time, and the determination of specific usage and revenue attributes that impact customer migration. The findings provide insights to mobile services providers about how to detect temporal changes in customer behavior.},
  doi = {https://doi.org/10.1016/j.im.2014.11.001},
  journal = {Information & Management},
  bibtexkey = {BOSE2015227},
  author = {Indranil Bose, Xi Chen},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Can we vote with our tweet? On the perennial difficulty of election forecasting with social media},
  abstract = {Social media and other â€œbigâ€  data promise new sources of information for tracking and forecasting electoral contests in democratic societies. This paper discusses the use of social media, and Twitter in particular, for forecasting elections in the United States, Germany, and other democracies. All known forecasting methods based on social media have failed when subjected to the demands of true forward-looking electoral forecasting. These failures appear to be due to fundamental properties of social media, rather than to methodological or algorithmic difficulties. In short, social media do not, and probably never will, offer a stable, unbiased, representative picture of the electorate; and convenience samples of social media lack sufficient data to fix these problems post hoc. Hence, while these services may, as others in this volume discuss, offer new ways of reaching prospective voters, the data that they generate will not replace polling as a means of assessing the sentiment or intentions of the electorate.},
  doi = {https://doi.org/10.1016/j.ijforecast.2014.08.005},
  journal = {International Journal of Forecasting},
  bibtexkey = {HUBERTY2015992},
  author = {Mark Huberty},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Calling communities analysis and identification using machine learning techniques},
  abstract = {The analysis of social communities related logs has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In the customer analysis domain, identifying calling communities can be used for determining a particular customerâ€™s value according to the general pattern behavior of the community that the customer belongs to; this helps the effective targeted marketing design, which is significantly important for increasing profitability. In telecommunication industry, machine learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behavior such as churn prediction. In this paper, we pursue identifying the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the CDR data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply different machine learning techniques to build classifier models and compare them in terms of classification accuracy and computational performance. The reported test results demonstrate the applicability and effectiveness of the proposed approach.},
  doi = {https://doi.org/10.1016/j.eswa.2008.07.072},
  journal = {Expert Systems with Applications},
  bibtexkey = {KIANMEHR20096218},
  author = {Keivan Kianmehr, Reda Alhajj},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Applying behavioral economics in predictive analytics for B2B churn: Findings from service quality data},
  abstract = {Motivated by the long-standing debate on rationality in behavioral economics and the potential of theory-driven predictive analytics, this paper examines the link between service quality and B2B churn. Using longitudinal B2B transactional data with service quality indicators provided by a large company, we present evidence that both rationality and bounded-rationality assumptions play significant roles in predicting organizational decisions on churn. Specifically, variables that relate to the assumed rationality of organizations appear to provide accurate predictions while, at the same time, variables that capture boundedly rational decision rules appear to play a role through â€œsomatic statesâ€  that make organizations more sensitive to the rational variables. In addition to presenting a novel approach for predicting organizational decisions on churn, this paper offers theoretical and managerial insights as well as opportunities for future research at the intersection of behavioral economics and predictive analytics for decision-making.},
  doi = {https://doi.org/10.1016/j.dss.2017.06.006},
  journal = {Decision Support Systems},
  bibtexkey = {BARFAR2017115},
  author = {Arash Barfar, Balaji Padmanabhan, Alan Hevner},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2016},
  title = {Chapter 1 - The Automating of Open Source Intelligence},
  abstract = {Open source intelligence (OSINT) is intelligence that is synthesized using publicly available data. We will discuss the current state of OSINT and data science. The changes in the analysts and users will be explored. We will cover data analysis, automated data gathering, APIs, and tools; algorithms including supervised and unsupervised learning, geolocational methods, de-anonymization. How do all these things interact within OSINT including ethics and context? Now that open intelligence has become more open and playing fields are leveling, the need to ensure and encourage positive use is even stronger.},
  doi = {https://doi.org/10.1016/B978-0-12-802916-9.00001-4},
  bibtexkey = {PONDERSUTTON20161},
  author = {Agate M. Ponder-Sutton},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2018},
  title = {Chapter 11 - InsurTech and FinTech: Banking and Insurance Enablement},
  abstract = {This chapter surveys the landscape of insurance technology and its potential from the perspective of enablement for financial and insurance services. Digital revolution is occurring in a sector that has hardly changed over the last 300 years. Surprising to many, recent innovation in China's digital finance space has shown that emerging entities can and will disrupt the multitrillion-dollar industry. With big data and Blockchain, the impact on insurance sector is going to be a lot faster and more significant than most people will anticipate.},
  doi = {https://doi.org/10.1016/B978-0-12-810441-5.00011-7},
  bibtexkey = {YAN2018249},
  author = {Tan Choon Yan, Paul Schulte, David LEE Kuo Chuen},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach},
  abstract = {In the customer-centered marketplace, the understanding of customer behavior is a critical success factor. The big databases in an organization usually involve multiplex data such as static, time series, symbolic sequential and textual data which are separately stored in different databases of different sections. It poses a challenge to traditional centralized customer behavior prediction. In this study, a novel approach called collaborative multiple kernel support vector machine (C-MK-SVM) is developed for distributed customer behavior prediction using multiplex data. The alternating direction method of multipliers (ADMM) is used for the global optimization of the distributed sub-models in C-MK-SVM. Computational experiments on a practical retail dataset are reported. Computational results show that C-MK-SVM exhibits better customer behavior prediction performance and higher computational speed than support vector machine and multiple kernel support vector machine.},
  doi = {https://doi.org/10.1016/j.knosys.2012.04.023},
  journal = {Knowledge-Based Systems},
  bibtexkey = {CHEN2012111},
  author = {Zhen-Yu Chen, Zhi-Ping Fan},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Staying Power of Churn Prediction Models},
  abstract = {In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging procedure. Bagging consists of averaging the results of multiple models that have each been estimated on a bootstrap sample from the original sample. We test the models using customer data of two firms from different industries, namely the internet service provider and insurance markets. The results show that the classification tree in combination with a bagging procedure outperforms the other three methods. It is shown that the ability to identify high risk customers of this model is similar for the in-period and one-period-ahead forecasts. However, for all methods the staying power is rather low, as the predictive performance deteriorates considerably within a few periods after the estimation period. This is due to the fact that both the parameter estimates change over time and the fact that the variables that are significant differ between periods. Our findings indicate that churn models should be adapted regularly. We provide a framework for database analysts to reconsider their methods used for churn modeling and to assess for how long they can use an estimated model.},
  doi = {https://doi.org/10.1016/j.intmar.2010.04.002},
  journal = {Journal of Interactive Marketing},
  bibtexkey = {RISSELADA2010198},
  author = {Hans Risselada, Peter C. Verhoef, Tammo H.A. Bijmolt},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Configuring the new digital landscape in western Canadian agriculture},
  abstract = {Digital technologies are working to transform the global agricultural system. Farmers and firms are creating, adapting and adopting a range of new hardware, software, mobile apps, sensor technologies and big data applications, which is working to disrupt established structures within the farm machinery and associated data sectors. Focusing just on the extension of precision technologies to agriculture, this paper maps the competitive landscape using a 2â€¯Ã—â€¯2 typology that situates entities operating in Canada based on their strategies, distinguishing between top-down and bottom-up networks of competitors and collaborators and the degree of interoperability of their digital applications. We examine the emergence of four specific cases in western Canadian agriculture. The typology and the cases suggest global agri-food firms, industry collectives and a host of entrepreneurial start-ups and small and medium-sized enterprises are competing to both organize and disrupt the global agri-food value chain. It is not yet clear which strategy, if any, will prevail and provide the model for broad acre agriculture in Canada and around the world.},
  doi = {https://doi.org/10.1016/j.njas.2019.04.001},
  journal = {NJAS - Wageningen Journal of Life Sciences},
  bibtexkey = {PHILLIPS2019100295},
  author = {Peter W.B. Phillips, Jo-Anne Relf-Eckstein, Graeme Jobe, Brian Wixted},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2009},
  title = {Chapter 16 - Customer Response Modeling},
  abstract = {Customer Relationship Management (CRM) systems are built to manage call centers, how a business relates to its customers, and inform marketing and sales efforts. Companies try to build Customer Relationship Management programs that aim to create the same kinds of relationships with their customers. The key principle in this approach is that the most powerful predictors of customer behavior in the future are customer behavior patterns in the past. Other customer characteristics are important also in defining patterns of customer behavior, i.e., demographic and firmographic information. The key to successful customer response modeling is to associate with each customer a historical time-series of fields that in some way reflect motives and attitudes that cause the customer decision. Modeling customer behavior with temporal abstractions involves rearranging all the modeling variables to more clearly reflect patterns of change in the customer response variable. Customers are biological entities that respond in a biological manner. It seems reasonable to expect that the only way to create highly predictive models of customer behavior is to express some degree of this complex interaction in the design of the modeling methodology. Modeling customer behavior with temporal abstractions involves rearranging all the modeling variables to more clearly reflect patterns of change in the customer response variable.},
  doi = {https://doi.org/10.1016/B978-0-12-374765-5.00016-4},
  bibtexkey = {NISBET2009335},
  author = {Robert Nisbet, John Elder, Gary Miner},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry},
  abstract = {Data preparation is a process that aims to convert independent (categorical and continuous) variables into a form appropriate for further analysis. We examine data-preparation alternatives to enhance the prediction performance for the commonly-used logit model. This study, conducted in a churn prediction modeling context, benchmarks an optimized logit model against eight state-of-the-art data mining techniques that use standard input data, including real-world cross-sectional data from a large European telecommunication provider. The results lead to following conclusions. (i) Analysts better acknowledge that the data-preparation technique they choose actually affects churn prediction performance; we find improvements of up to 14.5% in the area under the receiving operating characteristics curve and 34% in the top decile lift. (ii) The enhanced logistic regression also is competitive with more advanced single and ensemble data mining algorithms. This article concludes with some managerial implications and suggestions for further research, including evidence of the generalizability of the results for other business settings.},
  doi = {https://doi.org/10.1016/j.dss.2016.11.007},
  journal = {Decision Support Systems},
  bibtexkey = {COUSSEMENT201727},
  author = {Kristof Coussement, Stefan Lessmann, Geert Verstraeten},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {An ANN-based model for learning individual customer behavior in response to electricity prices},
  abstract = {In this paper, we consider the problem of learning the electricity consumption patterns of an individual residential electricity customer, in response to electricity price signals in a demand response program. Two new methods are presented for predicting the hourly loads using the outdoor temperatures, electricity prices and previous loads. The proposed models are based respectively on a fully connected neural network and a Longâ€“Short-term memory network. Both models deal with the uncertainty of household devices and its indoor temperature. Numerical results show the high performance of the proposed methods in terms of accuracy of the predictions. Both models can learn the consumption patterns and are able to give a good approximation of the load profile given a set of prices and temperatures. The proposed architecture can be used to investigate the price elasticity of demand, which can be used in several applications such as optimal pricing, demand flexibility or carbon emission reduction.},
  doi = {https://doi.org/10.1016/j.segan.2019.100212},
  journal = {Sustainable Energy, Grids and Networks},
  bibtexkey = {NAKABI2019100212},
  author = {Taha Abdelhalim Nakabi, Pekka Toivanen},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Borrow from rich cousin: transfer learning for emotion detection using cross lingual embedding},
  abstract = {Performance of any natural language processing (NLP) system greatly depends on the amount of resources and tools available in a particular language or domain. Therefore, while solving any problem in low-resource setting, it is important to investigate techniques to leverage the resources and tools available in resource-rich languages. In this paper we propose an efficient technique to mitigate the problem of resource scarcity for emotion detection in Hindi by leveraging information from a resource-rich language like English. Our method follows a deep transfer learning framework which efficiently captures relevant information through the shared space of two languages, showing significantly better performance compared to the monolingual scenario that learns in the vector space of only one language. As base learning models, we use Convolution Neural Network (CNN) and Bi-Directional Long Short Term Memory (Bi-LSTM). As there are no available emotion labeled dataset for Hindi, we create a new dataset for emotion detection in disaster domain by annotating sentences of news documents with nine different classes based on PlutchikÃ¢;;s wheel of emotions. To improve the performance of emotion classification in Hindi, we employ transfer learning to exploit the resources available in the related domains. The core of our approach lies in generating a cross-lingual word embedding representation of words in the shared embedding space. The neural networks are trained on the existing datasets, and then weights are fine-tuned following the four different transfer learning strategies for emotion classification in Hindi. We obtain a significant performance gain in our our proposed transfer learning techniques, achieving an F1-score of 0.53 (compared to 0.47)-thereby implying that knowledge from a resource-rich language can be transferred across language and domains.11codes and data available at https://github.com/zishanahmad1821cs18/crosslingual_transfer},
  doi = {https://doi.org/10.1016/j.eswa.2019.112851},
  journal = {Expert Systems with Applications},
  bibtexkey = {AHMAD2020112851},
  author = {Zishan Ahmad, Raghav Jindal, Asif Ekbal, Pushpak Bhattachharyya},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Sales forecasts in clothing industry: The key success factor of the supply chain management},
  abstract = {Like many others, Textileâ€“apparel companies have to deal with a very competitive environment and have to manage consumers which become more demanding. Thus, to stay competitive, companies rely on sophisticated information systems and logistic skills, and especially accurate and reliable forecasting systems. However, forecasters have to deal with some singular constraints of the textileâ€“apparel market such as for instance the volatile demand, the strong seasonality of sales, the wide number of items with short life cycle or the lack of historical data. To respond to these constraints, companies have implemented specific forecasting systems often simple but robust. After the study of existing practices in the clothing industry, we propose different forecasting models which perform more accurate and more reliable sales forecasts. These models rely on advanced methods such as fuzzy logic, neural networks and data mining. In order to evaluate the benefits of these methods for the supply chain and more especially for the reduction of the bullwhip effect, a simulation based on real data of sourcing and forecasting processes is performed and analyzed.},
  doi = {https://doi.org/10.1016/j.ijpe.2010.07.018},
  journal = {International Journal of Production Economics},
  bibtexkey = {THOMASSEY2010470},
  author = {SÃ©bastien Thomassey},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Behavior-aware user response modeling in social media: Learning from diverse heterogeneous data},
  abstract = {With the rapid development of Web 2.0 applications, social media have increasingly become a major factor influencing the purchase decisions of customers. Longitudinal individual and engagement behavioral data generated on social media sites post challenges to integrate diverse heterogeneous data to improve prediction performance in customer response modeling. In this study, a hierarchical ensemble learning framework is proposed for behavior-aware user response modeling using diverse heterogeneous data. In the framework, a general-purpose data transformation and feature extraction strategy is developed to transform the heterogeneous high-dimensional multi-relational datasets into customer-centered high-order tensors and to extract attributes. An improved hierarchical multiple kernel support vector machine (H-MK-SVM) is developed to integrate the external, tag and keyword, individual behavioral and engagement behavioral data for feature selection from multiple correlated attributes and for ensemble learning in user response modeling. The subagging strategy is adopted to deal with large-scale imbalanced datasets. Computational experiments using a real-world microblog database were conducted to investigate the benefits of integrating diverse heterogeneous data. Computational results show that the improved H-MK-SVM using longitudinal individual behavioral data exhibits superior performance over some commonly used methods using aggregated behavioral data and the improved H-MK-SVM using engagement behavioral data performs better than using only the external and individual behavioral data.},
  doi = {https://doi.org/10.1016/j.ejor.2014.09.008},
  journal = {European Journal of Operational Research},
  bibtexkey = {CHEN2015422},
  author = {Zhen-Yu Chen, Zhi-Ping Fan, Minghe Sun},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Does country-of-origin brand personality generate retail customer lifetime value? A Big Data analytics approach},
  abstract = {Many retail firms have witnessed the erosion of customer loyalty with the rise of e-commerce and its resulting benefits to consumers, including increased choices, lower prices, and ease of brand switching. Retailers have long collected data to learn about customer purchasing habits; however, many currently do not use data-mining analytics to increase marketing effectiveness by predicting future buying patterns and potential customer lifetime value, particularly to important segments such as loyal and potential repeat customers. Data mining can efficiently analyze large amounts of business data (â€œBig Dataâ€ ) in an effort to forecast consumer needs and increase the lifetime value of customers (CLV). Previous studies on these topics primarily focus on conceptual assumptions and generally do not present empirically valid models. The present study sought to fill the research gap by using Big Data analytics to analyze approximately 44,000 point-of-sale transaction records for 26,000 customers of a Taiwanese retail store to understand how consumer personality traits relate to the country-of-origin (COO) traits (brand personality) of beer brands, and to predict potential customer lifetime value (CLV). The findings revealed that consumers tend to purchase and co-purchase brands with traits similar to their own personality traits (i.e., Japanâ€”peacefulness, Belgiumâ€”openness, Irelandâ€”excitement, etc.). Significantly, customers with the group of personality traits associated with â€œpeacefulnessâ€  and â€œopennessâ€  were the most profitable customers among the five analyzed clusters (CLV value=0.3149, 0.2635). The study provides valuable new insights into COO brand personality and consumer personality traits with co-purchase behaviors via data mining techniques, and highlights the value of extending CLV in developing useful marketing strategies.},
  doi = {https://doi.org/10.1016/j.techfore.2017.06.034},
  journal = {Technological Forecasting and Social Change},
  bibtexkey = {CHIANG2018177},
  author = {Lan-Lung (Luke) Chiang, Chin-Sheng Yang},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Modeling of steelmaking process with effective machine learning techniques},
  abstract = {Monitoring and control of the output yield of steel in a steelmaking shop plays a critical role in steel industry. The yield of steel determines how much percentage of hot metal, scrap, and iron ore are being converted into steel ingots. It represents the operational efficiency of the steelmaking shop and is considered as an important performance measure for producing a specific quantity of steel. Due to complexity of the steelmaking process and nonlinear relationship between the process parameters, modeling the inputâ€“output process parameters and accurately predicting the output yield in the steelmaking shop is very difficult and has been a major research issue. Statistical models and artificial neural networks (ANN) have been extensively studied by researchers and practitioners to model a variety of complex processes. In the present study, we consider random forests (RF), ANN, dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector regression (SVR) as competitive learning tools to verify the suitability of applications of these approaches and investigate their comparative predictive ability. In the present investigation, 0.00001 of MSE is set as a goal of learning during modeling. Based on real-life data, the computational results depict that the training and testing MSE values of SVR and DENFIS are close to 0.00001 indicating that they have higher prediction ability than ANN and RF. Also, mean absolute percentage prediction errors of the proposed models confirm that the predicted yield based on each method is in good agreement with the testing datasets. Overall, SVR performs best and DENFIS the next best followed by ANN and RF methods respectively. The results suggest that the prediction precision given by SVR can meet the requirement for the actual production of steel.},
  doi = {https://doi.org/10.1016/j.eswa.2015.01.030},
  journal = {Expert Systems with Applications},
  bibtexkey = {LAHA20154687},
  author = {Dipak Laha, Ye Ren, P.N. Suganthan},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2020},
  title = {Chapter 2 - Big data principles and paradigm},
  abstract = {Big data refers to the massive datasets that are collected from a variety of data sources for businessâ€™ needs to reveal new insights for optimized decision making. The ocean energy system is the modernization of electrical energy generation systems due to the pollution-free nature and the continuous advancement of ocean system technologies. In the ocean energy surroundings, the application of big data analysis-based decision-making and control are mainly in the following three aspects: data stream side management, storage side management, and load side management. The objective of this chapter is to present a technological framework for the management of large volumes and the variety and velocity of related information through big data tools such as NoSQL and Hadoop in order to support the assessment of ocean energy systems. This paper also includes the Market Basket model, an application of the MapReduce algorithm, and also provides light on data stream management and landscape ocean data systems.},
  doi = {https://doi.org/10.1016/B978-0-12-818904-7.00002-2},
  bibtexkey = {KHARE202049},
  author = {Vikas Khare, Savita Nema, Prashant Baredar},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Churn and Net Promoter Score forecasting for business decision-making through a new stepwise regression methodology},
  abstract = {Companies typically have to make relevant decisions regarding their clientsâ€™ fidelity and retention on the basis of analytical models developed to predict both their churn probability and Net Promoter Score (NPS). Although the predictive capability of these models is important, interpretability is a crucial factor to look for as well, because the decisions to be made from their results have to be properly justified. In this paper, a novel methodology to develop analytical models balancing predictive performance and interpretability is proposed, with the aim of enabling a better decision-making. It proceeds by fitting logistic regression models through a modified stepwise variable selection procedure, which automatically selects input variables while keeping their business logic, previously validated by an expert. In synergy with this procedure, a new method for transforming independent variables in order to better deal with ordinal targets and avoiding some logistic regression issues with outliers and missing data is also proposed. The combination of these two proposals with some competitive machine-learning methods earned the leading position in the NPS forecasting task of an international university talent challenge posed by a well-known global bank. The application of the proposed methodology and the results it obtained at this challenge are described as a case-study.},
  doi = {https://doi.org/10.1016/j.knosys.2020.105762},
  journal = {Knowledge-Based Systems},
  bibtexkey = {VELEZ2020105762},
  author = {D. VÃ©lez, A. Ayuso, C. Perales-GonzÃ¡lez, J. Tinguaro RodrÃguez},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review},
  abstract = {Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) â€” a branch of AI â€” have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.},
  doi = {https://doi.org/10.1016/j.rser.2020.109899},
  journal = {Renewable and Sustainable Energy Reviews},
  bibtexkey = {ANTONOPOULOS2020109899},
  author = {Ioannis Antonopoulos, Valentin Robu, Benoit Couraud, Desen Kirli, Sonam Norbu, Aristides Kiprakis, David Flynn, Sergio Elizondo-Gonzalez, Steve Wattam},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {An evidential reasoning-based decision support system for handling customer complaints in mobile telecommunications},
  abstract = {Handling customer complaints is a decision-making process that inherently involves a classification problem where each complaint should be classified exclusively to one of the complaint categories before a resolution is communicated to customers. Previous studies focus extensively on decision support systems (DSSs) to automate complaint handling, while few addresses the issue of classification imprecision when inaccurate or inconsistent information exists in customer complaint narratives. This research presents a novel DSS for handling customer complaints and develops an evidential reasoning (ER) rule-based classifier as the core component of the system to classify customer complaints with uncertain information. More specifically, textual and numeric features are firstly combined to generate evidence for formulating the relationship between customer complaint features and classification results. The ER rule is then applied to combine multiple pieces of evidence and classify customer complaints into different categories with probabilities. An empirical study is conducted in a telecommunication company. Results show that the proposed ER rule-based classification model provides high performance in comparison with other machine learning algorithms. The developed system offers telecommunication companies an informative and data-driven method for handling customer complaints in a systematic and automatic manner.},
  doi = {https://doi.org/10.1016/j.knosys.2018.09.029},
  journal = {Knowledge-Based Systems},
  bibtexkey = {YANG2018202},
  author = {Ying Yang, Dong-Ling Xu, Jian-Bo Yang, Yu-Wang Chen},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2015},
  title = {Random Balance: Ensembles of variable priors classifiers for imbalanced data},
  abstract = {In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Imbalanced data sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampled from the training set and augmented by artificial instances obtained using SMOTE. The novelty in the approach is that the proportions of the classes for each ensemble member are chosen randomly. The intuition behind the method is that the proposed diversity heuristic will ensure that the ensemble contains classifiers that are specialized for different operating points on the ROC space, thereby leading to larger AUC compared to other ensembles of classifiers. Experiments have been carried out to test the Random Balance approach by itself, and also in combination with standard ensemble methods. As a result, we propose a new ensemble creation method called RB-Boost which combines Random Balance with AdaBoost.M2. This combination involves enforcing random class proportions in addition to instance re-weighting. Experiments with 86 imbalanced data sets from two well known repositories demonstrate the advantage of the Random Balance approach.},
  doi = {https://doi.org/10.1016/j.knosys.2015.04.022},
  journal = {Knowledge-Based Systems},
  bibtexkey = {DIEZPASTOR201596},
  author = {JosÃ© F. DÃez-Pastor, Juan J. RodrÃguez, CÃ©sar GarcÃa-Osorio, Ludmila I. Kuncheva},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Deep learning based software defect prediction},
  abstract = {Software systems have become larger and more complex than ever. Such characteristics make it very challengeable to prevent software defects. Therefore, automatically predicting the number of defects in software modules is necessary and may help developers efficiently to allocate limited resources. Various approaches have been proposed to identify and fix such defects at minimal cost. However, the performance of these approaches require significant improvement. Therefore, in this paper, we propose a novel approach that leverages deep learning techniques to predict the number of defects in software systems. First, we preprocess a publicly available dataset, including log transformation and data normalization. Second, we perform data modeling to prepare the data input for the deep learning model. Third, we pass the modeled data to a specially designed deep neural network-based model to predict the number of defects. We also evaluate the proposed approach on two well-known datasets. The evaluation results illustrate that the proposed approach is accurate and can improve upon the state-of-the-art approaches. On average, the proposed method significantly reduces the mean square error by more than 14% and increases the squared correlation coefficient by more than 8%.},
  doi = {https://doi.org/10.1016/j.neucom.2019.11.067},
  journal = {Neurocomputing},
  bibtexkey = {QIAO2020100},
  author = {Lei Qiao, Xuesong Li, Qasim Umer, Ping Guo},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Marketing analytics: Methods, practice, implementation, and links to other fields},
  abstract = {Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, expert systems, statistics, and operations research. This paper provides an integrative review at the boundary of these areas. The aim is to give researchers in the intelligent and expert systems community the opportunity to gain a broad view of the marketing analytics area and provide a starting point for future interdisciplinary collaboration. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern, large, and complex â€œbig dataâ€  sets are described. Practical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques.},
  doi = {https://doi.org/10.1016/j.eswa.2018.11.002},
  journal = {Expert Systems with Applications},
  bibtexkey = {FRANCE2019456},
  author = {Stephen L. France, Sanjoy Ghose},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications},
  abstract = {Nowadays, the Internet network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providersâ€™ interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this work aims at finding new Internet traffic classification approaches to improving the QoS. Machine Learning (ML) and Deep Learning (DL) techniques will be studied and deployed to classify Internet traffic. All the necessary elements to couple an ML or DL solution over a well-known Satellite Communication and QoS management architecture will be evaluated. To develop this solution, a rich and complete set of Internet traffic is required. In this context, an emulated Satellite Communication platform will serve as a data generation environment in which different Internet communications will be launched and captured. The proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Finally, some experiments on a cloud emulated platform validates our proposal and set guidelines for its deployment over a Satellite architecture.},
  doi = {https://doi.org/10.1016/j.comnet.2020.107213},
  journal = {Computer Networks},
  bibtexkey = {PACHECO2020107213},
  author = {Fannia Pacheco, Ernesto Exposito, Mathieu Gineste},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {Learning from class-imbalanced data: Review of methods and applications},
  abstract = {Rare events, especially those that could potentially negatively impact society, often require humansâ€™ decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.},
  doi = {https://doi.org/10.1016/j.eswa.2016.12.035},
  journal = {Expert Systems with Applications},
  bibtexkey = {HAIXIANG2017220},
  author = {Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, Huang Yuanyue, Gong Bing},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Optimal selection of potential customer range through the union sequential pattern by using a response model},
  abstract = {Direct marketing is a common data mining application. Previous studies largely adopt the approach that, as the subjects for response model predictions, the entire customer population or filtered through certain attribute values is based on recommendations made from sales professionals. However, such methods may reduce response rates due to an oversized potential customer population, thus diminishing the accuracy of the prediction model. In resolve this problem, this work presents proposes a novel forecasting method that integrates the union sequential pattern with classification algorithms to facilitate the construction of customer response models. Based on use of a union sequential pattern, the potential customer size is established by identifying attributes with a high level of association. The prediction model is then constructed using classification algorithms such as support vector machines and logistic regression. Consequently, the problem involving the setting of range for potential customers can be solved, as well as the time spent on processing extended lists of customers during prediction. Finally, predicted potential Internet-phone customers and churning mobile-phone customers of a telecommunication company in Taiwan as are taken as an illustrative example, based on the proposed prediction model. The proposed method more accurately predicts potential customers than those of previous studies.},
  doi = {https://doi.org/10.1016/j.eswa.2010.12.078},
  journal = {Expert Systems with Applications},
  bibtexkey = {CHEN20117451},
  author = {Wen-Chin Chen, Chiun-Chieh Hsu, Jing-Ning Hsu},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud},
  abstract = {Automobile insurance fraud represents a pivotal percentage of property insurance companies' costs and affects the companies' pricing strategies and social economic benefits in the long term. Automobile insurance fraud detection has become critically important for reducing the costs of insurance companies. Previous studies on automobile insurance fraud detection examined various numeric factors, such as the time of the claim and the brand of the insured car. However, the textual information in the claims has rarely been studied to analyze insurance fraud. This paper proposes a novel deep learning model for automobile insurance fraud detection that uses Latent Dirichlet Allocation (LDA)-based text analytics. In our proposed method, LDA is first used to extract the text features hiding in the text descriptions of the accidents appearing in the claims, and deep neural networks then are trained on the data, which include the text features and traditional numeric features for detecting fraudulent claims. Based on the real-world insurance fraud dataset, our experimental results reveal that the proposed text analytics-based framework outperforms a traditional one. Furthermore, the experimental results show that the deep neural networks outperform widely used machine learning models, such as random forests and support vector machine. Therefore, our proposed framework that combines deep neural networks and LDA is a suitable potential tool for automobile insurance fraud detection.},
  doi = {https://doi.org/10.1016/j.dss.2017.11.001},
  journal = {Decision Support Systems},
  bibtexkey = {WANG201887},
  author = {Yibo Wang, Wei Xu},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2006},
  title = {Subscription fraud prevention in telecommunications using fuzzy rules and neural networks},
  abstract = {A system to prevent subscription fraud in fixed telecommunications with high impact on long-distance carriers is proposed. The system consists of a classification module and a prediction module. The classification module classifies subscribers according to their previous historical behavior into four different categories: subscription fraudulent, otherwise fraudulent, insolvent and normal. The prediction module allows us to identify potential fraudulent customers at the time of subscription. The classification module was implemented using fuzzy rules. It was applied to a database containing information of over 10,000 real subscribers of a major telecom company in Chile. In this database, a subscription fraud prevalence of 2.2% was found. The prediction module was implemented as a multilayer perceptron neural network. It was able to identify 56.2% of the true fraudsters, screening only 3.5% of all the subscribers in the test set. This study shows the feasibility of significantly preventing subscription fraud in telecommunications by analyzing the application information and the customer antecedents at the time of application.},
  doi = {https://doi.org/10.1016/j.eswa.2005.09.028},
  journal = {Expert Systems with Applications},
  bibtexkey = {ESTEVEZ2006337},
  author = {Pablo A. EstÃ©vez, Claudio M. Held, Claudio A. Perez},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2018},
  title = {Chapter 9 - Classification},
  abstract = {The first general set of data mining applications predicts to which category of the target variable each case belongs. This grouping activity is called classification. In this chapter, we will focus on the use of classification algorithms, rather than their descriptions.},
  doi = {https://doi.org/10.1016/B978-0-12-416632-5.00009-8},
  bibtexkey = {NISBET2018169},
  author = {Robert Nisbet, Gary Miner, Ken Yale},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction},
  abstract = {Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ensemble classifiers are proposed as modeling techniques for customer churn prediction. In Rotation Forests, feature extraction is applied to feature subsets in order to rotate the input data for training base classifiers, while RotBoost combines Rotation Forest with AdaBoost. In an experimental validation based on data sets from four real-life customer churn prediction projects, Rotation Forest and RotBoost are compared to a set of well-known benchmark classifiers. Moreover, variations of Rotation Forest and RotBoost are compared, implementing three alternative feature extraction algorithms: principal component analysis (PCA), independent component analysis (ICA) and sparse random projections (SRP). The performance of rotation-based ensemble classifier is found to depend upon: (i) the performance criterion used to measure classification performance, and (ii) the implemented feature extraction algorithm. In terms of accuracy, RotBoost outperforms Rotation Forest, but none of the considered variations offers a clear advantage over the benchmark algorithms. However, in terms of AUC and top-decile lift, results clearly demonstrate the competitive performance of Rotation Forests compared to the benchmark algorithms. Moreover, ICA-based Rotation Forests outperform all other considered classifiers and are therefore recommended as a well-suited alternative classification technique for the prediction of customer churn that allows for improved marketing decision making.},
  doi = {https://doi.org/10.1016/j.eswa.2011.04.007},
  journal = {Expert Systems with Applications},
  bibtexkey = {DEBOCK201112293},
  author = {Koen W. De Bock, Dirk Van den Poel},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty},
  abstract = {Detection of fraudulent transactions is a vital factor for financial institutions, and finding more effective and accurate methods is of tremendous importance. The use of supervised data mining techniques is not feasible in many cases due to the lack of access to labeled data. Fraud detection is a complex task, and unsupervised methods like clustering and outlier detection techniques employed alone do not yield satisfactory results. Another issue is epistemic uncertainty due to the absence of sufficient information on the behavioral aspects of different customers, which also leads to poorer results for fraud detection and makes the fraud detection system inapplicable in real world environment. In this paper, using multi criteria decision method, intuitionistic fuzzy set, and evidential reasoning, a new method for detection of fraud was introduced, which infuses several behavioral evidence of a transaction concerning the effect of uncertainty for them. Transactional behavior was modeled by considering the trends of different main and aggregated variables at different periods and the extent of deviation of the new arrived transaction from each of these trends were considered as behavioral evidence. The final belief, which is the result of the combination of much evidence using the proposed method, will determine the originality of a newly arrived transaction. Finally, using a real world dataset, the results of the new method were compared with the results of Dempsterâ€“Shafer method in terms of the number of frauds discovered and the number of erroneous alerts they issued. The findings showed that the method introduced in this study has higher accuracy and lower false alarms compared to Dempsterâ€“Shafer method while the computational complexity of this method makes its implementation time longer.},
  doi = {https://doi.org/10.1016/j.eswa.2018.11.039},
  journal = {Expert Systems with Applications},
  bibtexkey = {ESHGHI2019382},
  author = {Abdollah Eshghi, Mehrdad Kargari},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Targeting customers for profit: An ensemble learning framework to support marketing decision-making},
  abstract = {Marketing messages are most effective if they reach the right customers. Deciding which customers to contact is an important task in campaign planning. The paper focuses on empirical targeting models. We argue that common practices to develop such models do not account sufficiently for business goals. To remedy this, we propose profit-conscious ensemble selection, a modeling framework that integrates statistical learning principles and business objectives in the form of campaign profit maximization. Studying the interplay between data-driven learning methods and their business value in real-world application contexts, the paper contributes to the emerging field of profit analytics and provides original insights how to implement profit analytics in marketing. The paper also estimates the degree to which profit-concious modeling adds to the bottom line. The results of a comprehensive empirical study confirm the business value of the proposed ensemble learning framework in that it recommends substantially more profitable target groups than several benchmarks.},
  doi = {https://doi.org/10.1016/j.ins.2019.05.027},
  journal = {Information Sciences},
  bibtexkey = {LESSMANN2019},
  author = {Stefan Lessmann, Johannes Haupt, Kristof Coussement, Koen W. De Bock},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges},
  abstract = {Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societiesâ€™ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.},
  doi = {https://doi.org/10.1016/j.iot.2019.100118},
  journal = {Internet of Things},
  bibtexkey = {GILL2019100118},
  author = {Sukhpal Singh Gill, Shreshth Tuli, Minxian Xu, Inderpreet Singh, Karan Vijay Singh, Dominic Lindsay, Shikhar Tuli, Daria Smirnova, Manmeet Singh, Udit Jain, Haris Pervaiz, Bhanu Sehgal, Sukhwinder Singh Kaila, Sanjay Misra, Mohammad Sadegh Aslanpour, Harshit Mehta, Vlado Stankovski, Peter Garraghan},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {A bibliometric analysis and cutting-edge overview on fuzzy techniques in Big Data},
  abstract = {Over the last few years, Big Data has gained a tremendous attention from the research community. The data being generated in huge quantity from almost every field is unstructured and unprocessed. Extracting knowledge base and useful information from the big raw data is one of the major challenges, present today. Various computational intelligence and soft computing techniques have been proposed for efficient big data analytics. Fuzzy techniques are one of the soft computing approaches which can play a very crucial role in current big data challenges by pre-processing and reconstructing data. There is a wide spread application domains where traditional fuzzy sets (type-1 fuzzy sets) and higher order fuzzy sets (type-2 fuzzy sets) have shown remarkable outcomes. Although, this research domain of â€œfuzzy techniques in Big Dataâ€  is gaining some attention, there is a strong need for a motivation to encourage researchers to explore more in this area. In this paper, we have conducted bibliometric study on recent development in the field of â€œfuzzy techniques in big dataâ€ . In bibliometric study, various performance metrics including total papers, total citations, and citation per paper are calculated. Further, top 10 of most productive and highly cited authors, discipline, source journals, countries, institutions, and highly influential papers are also evaluated. Later, a comparative analysis is performed on the fuzzy techniques in big data after analysing the most influential works in this field.},
  doi = {https://doi.org/10.1016/j.engappai.2020.103625},
  journal = {Engineering Applications of Artificial Intelligence},
  bibtexkey = {SHUKLA2020103625},
  author = {Amit K. Shukla, Pranab K. Muhuri, Ajith Abraham},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {A reference model for customer-centric data mining with support vector machines},
  abstract = {Supervised classification is an important part of corporate data mining to support decision making in customer-centric planning tasks. The paper proposes a hierarchical reference model for support vector machine based classification within this discipline. The approach balances the conflicting goals of transparent yet accurate models and compares favourably to alternative classifiers in a large-scale empirical evaluation in real-world customer relationship management applications. Recent advances in support vector machine oriented research are incorporated to approach feature, instance and model selection in a unified framework.},
  doi = {https://doi.org/10.1016/j.ejor.2008.12.017},
  journal = {European Journal of Operational Research},
  bibtexkey = {LESSMANN2009520},
  author = {Stefan Lessmann, Stefan VoÃŸ},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning},
  abstract = {Credit card fraud detection is an essential part of screening fraudulent transactions in advance of their authorization by card issuers. Although credit card frauds occur extremely infrequently, they result in huge losses as most fraudulent transactions have large values. An adequate detection of fraud allows investigators to take timely actions that can potentially prevent additional fraud or financial losses. In practice, however, investigators can only check a few alerts per day since the investigation process can be long and tedious. Thus, the primary goal of the fraud detection model is to return accurate alerts with fewer false alarms and missed frauds. Conventional fraud detection is mainly based on the hybrid ensemble of diverse machine learning models. Recently, several studies have compared deep learning and traditional machine learning models including ensemble. However, these studies used evaluation methods without considering that the real-world fraud detection system operated with the constraints: (i) the number of investigators who check the high-risk transactions from the data-driven scoring models are limited and (ii) the two types of misclassification, false alarms and missed frauds, have different costs. In this study, we conducted an in-depth comparison between the hybrid ensemble and deep learning method to determine whether or not to adopt the latter in our partnerâ€™s system that currently operates with the hybrid ensemble model. To compare the two, we introduced the champion-challenger framework and the development process of the two models. After developing the two models, we evaluated them on large transaction data sets taken from our partner, a major card issuing company in South Korea. We used various practical evaluation metrics appropriate for this domain that has severe class and cost imbalances. Moreover, we deployed these models in a real-world fraud detection system to check the post-launch performance for one month. The challenger outperformed the champion on both in off-line and post-launch tests.},
  doi = {https://doi.org/10.1016/j.eswa.2019.03.042},
  journal = {Expert Systems with Applications},
  bibtexkey = {KIM2019214},
  author = {Eunji Kim, Jehyuk Lee, Hunsik Shin, Hoseong Yang, Sungzoon Cho, Seung-kwan Nam, Youngmi Song, Jeong-a Yoon, Jong-il Kim},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {A deep learning-based sports player evaluation model based on game statistics and news articles},
  abstract = {Player evaluation is a key component of the question-answering (QA) system in sports. Since existing player evaluation methods heavily rely on game statistics, they cannot capture the qualitative impact of each player during a game, which can be exploited using news articles after the game. In this paper, we propose a deep learning-based player evaluation model by combining both quantitative game statistics and the qualitative analyses provided by news articles. Players are classified as positive or negative based on their performance during certain periods, and news articles in the same period are annotated using the player's class. Then, the relationship between news articles and the annotated polarity is investigated by a deep neural network, which can deal with the high dimensionality of the text data. Since there is no explicit polarity label for news articles, we use the change in game statistics in target periods to annotate related sentences. The proposed system is applied to a Korean professional baseball league (KBO) and it is shown to be capable of understanding the sentence polarity of news articles on player performances.},
  doi = {https://doi.org/10.1016/j.knosys.2017.09.028},
  journal = {Knowledge-Based Systems},
  bibtexkey = {PARK201715},
  author = {Young Joon Park, Hyung Seok Kim, Donghwa Kim, Hankyu Lee, Seoung Bum Kim, Pilsung Kang},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Improved churn prediction in telecommunication industry using data mining techniques},
  abstract = {To survive in today's telecommunication business it is imperative to distinguish customers who are not reluctant to move toward a competitor. Therefore, customer churn prediction has become an essential issue in telecommunication business. In such competitive business a reliable customer predictor will be regarded priceless. This paper has employed data mining classification techniques including Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine so as to compare their performances. Using the data of an Iranian mobile company, not only were these techniques experienced and compared to one another, but also we have drawn a parallel between some different prominent data mining software. Analyzing the techniquesâ€™ behavior and coming to know their specialties, we proposed a hybrid methodology which made considerable improvements to the value of some of the evaluations metrics. The proposed methodology results showed that above 95% accuracy for Recall and Precision is easily achievable. Apart from that a new methodology for extracting influential features in dataset was introduced and experienced.},
  doi = {https://doi.org/10.1016/j.asoc.2014.08.041},
  journal = {Applied Soft Computing},
  bibtexkey = {KERAMATI2014994},
  author = {A. Keramati, R. Jafari-Marandi, M. Aliannejadi, I. Ahmadian, M. Mozaffari, U. Abbasi},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition},
  abstract = {Student attrition â€“ the departure from an institution of higher learning prior to the achievement of a degree or earning due educational credentials â€“ is an administratively important, scientifically interesting and yet practically challenging problem for decision makers and researchers. This study aims to find the prominent variables and their conditional dependencies/interrelations that affect student attrition in college settings. Specifically, using a large and feature-rich dataset, proposed methodology successfully captures the probabilistic interactions between attrition (the dependent variable) and related factors (the independent variables) to reveal the underlying, potentially complex/non-linear relationships. The proposed methodology successfully predicts the individual students' attrition risk through a Bayesian Belief Network-driven probabilistic model. The findings suggest that the proposed probabilistic graphical/network method is capable of predicting student attrition with 84% in AUC â€“ Area Under the Receiver Operating Characteristics Curve. Using a 2-by-2 investigational design framework, this body of research also compares the impact and contribution of data balancing and feature selection to the resultant prediction models. The results show that (1) the imbalanced dataset produces similar predictive results in detecting the at-risk students, and (2) the feature selection, which is the process of identifying and eliminating unnecessary/unimportant predictors, results in simpler, more understandable, interpretable, and actionable results without compromising on the accuracy of the prediction task.},
  doi = {https://doi.org/10.1016/j.ejor.2019.03.037},
  journal = {European Journal of Operational Research},
  bibtexkey = {DELEN2020575},
  author = {Dursun Delen, Kazim Topuz, Enes Eryarsoy},
  source = {SCIENCE}
}

@{,
  category = {INCOLLECTION},
  year = {2018},
  title = {Chapter 14 - Customer Response Modeling},
  abstract = {Most organizations, whether for profit or nonprofit purposes, exist to develop and promote some things or ideas related to their organization. One of the major activities of these organizations is to appeal to people outside their organizations to join them, support them, or purchase their goods or services. Traditional means of doing this included offering goods and services in storefronts, by advertisements in appropriate venues and by contacting a broad spectrum of people by phone or mail. These methods are rather passive. The philosophy was to build it, show it, and advertise or promote it, and customers would come. Since the early 1990s, many businesses have taken a more active approach by using various technological approaches to identify specific prospective customers and going after their business, rather than waiting for them to respond to the passive appeals. The key issue in this process is identifying which prospects are most likely to respond to the appeals. The activity of identifying prospects and quantifying their likelihood to respond is one of earliest applications of data mining technology to business.},
  doi = {https://doi.org/10.1016/B978-0-12-416632-5.00014-1},
  bibtexkey = {NISBET2018279},
  author = {Robert Nisbet, Gary Miner, Ken Yale},
  source = {SCIENCE}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints},
  abstract = {In many real world applications classification models are required to be in line with domain knowledge and to respect monotone relations between predictor variables and the target class, in order to be acceptable for implementation. This paper presents a novel heuristic approach, called RULEM, to induce monotone ordinal rule based classification models. The proposed approach can be applied in combination with any rule- or tree-based classification technique, since monotonicity is guaranteed in a post-processing step. RULEM checks whether a rule set or decision tree violates the imposed monotonicity constraints and existing violations are resolved by inducing a set of additional rules which enforce monotone classification. The approach is able to handle non-monotonic noise, and can be applied to both partially and totally monotone problems with an ordinal target variable. Two novel justifiability measures are introduced which are based on RULEM and allow to calculate the extent to which a classification model is in line with domain knowledge expressed in the form of monotonicity constraints. An extensive benchmarking experiment and subsequent statistical analysis of the results on 14 public data sets indicates that RULEM preserves the predictive power of a rule induction technique while guaranteeing monotone classification. On the other hand, the post-processed rule sets are found to be significantly larger which is due to the induction of additional rules. E.g., when combined with Ripper a median performance difference was observed in terms of PCC equal to zero and an average difference equal to âˆ’0.66%, with on average 5 rules added to the rule sets. The average and minimum justifiability of the original rule sets equal respectively 92.66% and 34.44% in terms of the RULEMF justifiability index, and 91.28% and 40.1% in terms of RULEMS, indicating the effective need for monotonizing the rule sets.},
  doi = {https://doi.org/10.1016/j.asoc.2017.

repository: pesobreiro/SLRDropout

repo_name: pesobreiro/SLRDropout

extension: bib