A Survey on Various Techniques in Internet of Things (IoT) Implementation: A Comparative Study

As per the current trends in computing research socialization and Personalization in Internet of Things (IOT) environment are quite trending and they are being widely used. The main aim of research work is to provide socialized and personalized services along with creating awareness of predicting the service. Here various kind of methods are discussed which can be used for predicting user intention in large variety of IOT based applications such as smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. By common consent it is found that the prediction is made usually for finding techniques that can be accessed by the mobile user, predicting the next page that is most likely to be used by web user, predicting favorite and most likely TV program that can be viewed by user, getting a list of browsing usage and need of user and also predicting user navigational patterns, predicting future climate conditions, predicting the health and welfare of user, predicting user intention so that implicit could be made and human-like interactions could be possible by accepting implicit commands, predicting the exact amount of traffic at a particular location, predicting curricular performance of student in schools & colleges, having prediction of frequency of natural calamities and their occurrences such as floods, earthquakes over a long period of time & also the required time in which precautionary measures could be adopted, predicting & detecting the frauds in which false user try to make transaction in the name of genuine user, predicting the steps and work done by the user to improve the business, predicting & detecting the intruder acting in the network, by the help of context history predicting the mood transition information of the user, etc. Here in this topic of discussion, different techniques such as Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms are used for prediction

B2C E-Commerce Customer Churn Management: Churn Detection using Support Vector Machine and Personalized Retention using Hybrid Recommendations

E-Commerce industry, especially the players in Business-to-Consumer (B2C) sector is witnessing immense competition for survival - by means of trying to penetrate to the customer base of their peers and at the same time not letting their existing customers to churn. Avoiding customer attrition is critical for these firms as the cost of acquiring new customers are going high with more and more players entering into the market with huge capital investments and new penetration strategies. Identifying potential parting away customers and preventing the churn with quick retention actions is the best solution in this scenario. It is also important to understand that what the customer is trying to achieve by opting for a move out so that personalized win back strategies can be applied. E-Commerce industry always possess huge amount of customer data which include information on searches performed, transactions carried out, periodicity of purchases, reviews contributed, feedback shared, etc. for every customers they possess. Data mining and machine learning can help in analyzing this huge volume of data, understanding the customer behavior and detecting possible attrition candidates. This paper proposes a framework based on support vector machine to predict E-Commerce customer churn and a hybrid recommendation strategy to suggest personalized retention actions

Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

Proceso de descubrimiento de conocimiento para predecir el abandono de tratamiento en una entidad de salud pública

El presente proyecto académico de fin de carrera tiene como objetivo mostrar el proceso automatizado de cada etapa del proceso de descubrimiento con el fin de predecir el abandono en los tratamientos de cáncer de una entidad de salud pública con una precisión eficiente basándose en características o factores determinados en la etapa de análisis junto con los miembros de la institución. La información resultante servirá de apoyo para que los administradores de la entidad de salud puedan plantear las políticas y estrategias personalizadas de retención de pacientes. Como se mencionó anteriormente, se tomaron en cuenta todas las etapas del proceso de descubrimiento de conocimiento - análisis, extracción, pre-procesamiento, estimación del modelo e interpretación - para que la información resultante pueda ser confiable y oportuna para la toma de decisiones. Adicionalmente, como parte de la etapa de extracción de datos, se encontró la necesidad de diseñar un DataMart que organice y facilite el análisis de información, no solo para el proyecto actual, sino para otras necesidades que puedan surgir en el futuro. Cada etapa tuvo apoyo de herramientas de software y metodologías que han sido ampliamente usadas con éxito en este tipo de proyectos. Se escogieron herramientas gratuitas que tendrían mayor apoyo a los requerimientos del proyecto como la automatización de los procesos, diseño del DataMart y el proceso general de Minería de Datos. En conclusión, el proyecto culminó con éxito cumpliendo los estipulado en cada uno de los resultados esperados, por lo cual, se puede determinar que el proceso automatizado podrá ser útil para determinar que pacientes abandonan su tratamiento y brindar la información oportuna a los encargados de tomar las decisiones.Tesi

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium