Applying FCM to Predict the Behaviour of Loyal Customers in the Mobile Telecommunications Industry

Using empirical data from the Kuwaiti mobile telecommunications sector, this study models a fuzzy cognitive map (FCM) to investigate the reciprocal effects of customer loyalty and its antecedents in an emerging market context. This study investigates the effect of perceived service quality, perceived service value and brand equity on customer loyalty and the simultaneous analysis of the reverse causality of these variables. Data pertaining to 350 subscribers were analysed. According to the results, the model reaches the equilibrium when brand equity and customer loyalty are increased and reach an optimal level. Based on these findings, the authors provide implications for managers in the mobile telecom industry

A Line Flow Granular Computing Approach for Economic Dispatch with Line Constraints

© 2017 IEEE. Line flow calculation plays a critically important role to guarantee the stable operation of power system in economic dispatch (ED) problems with line constraints. This paper presents a line flow granular computing approach for power flow calculation to assist the investigation on ED with line constraints, where the hierarchy method is adopted to divide the power network into multiple layers to reduce computational complexity. Each layer contains granules for granular computing, and the layer network is reduced by Ward equivalent retaining the PV nodes and boundary nodes of tie lines to decrease the data dimension. Then, Newton-Raphson method is applied further to calculate the power line flows within the layer. This approach is tested on IEEE 39-bus and 118-bus systems. The testing results show that the granular computing approach can solve the line flow problem in 9.2 s for the IEEE 118-bus system, while the conventional AC method needs 44.56 s. The maximum relative error of the granular computing approach in line flow tests is only 0.43%, which is quite small and acceptable. Therefore, the case studies demonstrate that the proposed granular computing approach is correct, effective, and can ensure the accuracy and efficiency of power line flow calculation

Aggregation of classifiers: a justifiable information granularity approach.

In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace

Modelo de clasificación para la deserción estudiantil en las universidades públicas del Perú

Las tecnologías de información y comunicación cumplen un rol relevante en los diferentes campos del conocimiento, actualmente existe mayor capacidad para identificar patrones y anomalías en los datos de una organización utilizando la inteligencia artificial; el estudio tuvo como objetivo desarrollar un modelo de clasificación para la deserción estudiantil aplicando aprendizaje automático con el método autoML del framework H2O.ai, se ha tenido en cuenta la dimensionalidad de las características socioeconómicas y académicas. La metodología empleada fue de tipo predictivo y diseño no experimental, observacional y prospectivo; para ello, se aplicó un cuestionario de 20 ítems a 237 estudiantes de la Escuela de Posgrado matriculados en los programas de maestrías en educación. La investigación tuvo como resultado un modelo de aprendizaje automático supervisado, máquina de refuerzo de gradiente, para clasificar la deserción estudiantil, logrando así identificar los principales factores asociados que influyen en la deserción, obteniendo un coeficiente Gini del 92.20%, AUC del 96.10% y un LogLoss del 24.24% representando un modelo con desempeño eficiente. Se concluye que el modelo es apropiado por sus métricas de rendimiento, ofreciendo ventajas como trabajar con datos desequilibrados, validación cruzada y realizar predicciones en tiempo real