Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

Predicting Purchase Proneness of Anonymous User in Mobile Commerce

In recent years, mobile commerce is developing rapidly because of the popularity of mobile devices. However, for the difficulty of the mobile device input, the users of the e-commerce websites usually don’t log on the website when they are browsing, which resulting in a situation that a large number of website visitors are anonymous users. In order to increase sales revenue and expand market share, an effective prediction of anonymous users’ purchases proneness is very helpful in providing targeted marketing strategy for website to induce anonymous users to purchase. In the past, customer segmentation was mainly analyzed and modeled by customers’ historical data. But the history data of anonymous users can’t be obtained on mobile commerce sites. This method is difficult to put into management practice. In order to solve this problem, this paper proposes a method based on random forest of using user clickstream data to forecast purchase proneness in real time. This method includes two stages: the model training part and the user purchasing proneness prediction part. In the model training part, a classifier based on random forest algorithm is trained. In the users\u27 predicting part, the classifier is used to predict the user\u27s purchase proneness in real time. The method proposed can be effectively applied in the real-time prediction of anonymous users\u27 purchasing proneness, and the results of prediction will help enterprises implement the marketing measures in real time

Distributed Load Testing by Modeling and Simulating User Behavior

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system or its usage. Lack of adaptation can prevent the integration of load testing into system quality assurance, leading to an incomplete evaluation of system quality. The goal of this research is to improve the state of software engineering by addressing open challenges in load testing of human-machine systems with a novel process that a) models and classifies user behavior from streaming and aggregated log data, b) adapts to changes in system and user behavior, and c) generates distributed workload by realistically simulating user behavior. This research contributes a Learning, Online, Distributed Engine for Simulation and Testing based on the Operational Norms of Entities within a system (LODESTONE): a novel process to distributed load testing by modeling and simulating user behavior. We specify LODESTONE within the context of a human-machine system to illustrate distributed adaptation and execution in load testing processes. LODESTONE uses log data to generate and update user behavior models, cluster them into similar behavior profiles, and instantiate distributed workload on software systems. We analyze user behavioral data having differing characteristics to replicate human-machine interactions in a modern microservice environment. We discuss tools, algorithms, software design, and implementation in two different computational environments: client-server and cloud-based microservices. We illustrate the advantages of LODESTONE through a qualitative comparison of key feature parameters and experimentation based on shared data and models. LODESTONE continuously adapts to changes in the system to be tested which allows for the integration of load testing into the quality assurance process for cloud-based microservices

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Analítica visual de datos para representación de la interacción en una red social privada y con restricciones de privacidad

TFM Máster Universitario en Sistemas Inteligentes - Universidad de Salamanca En este trabajo se realiza una propuesta para estudiar los datos que se van a generar en la red social privada y anónima del proyecto WYRED, con el n de extraer conocimiento sobre cómo interaccionan sus usuarios, tanto entre ellos, como con la propia plataforma. Para ello se parte de la creación de un sistema que generará un conjunto de datos de prueba, lo más parecido posible al original, y de una revisión sistemática de la literatura que ha permitido conocer las principales visualizaciones y el contexto en el que se aplican. Con esta información y teniendo en cuenta el impacto de la privacidad a la hora de tratar los datos del proyecto, se ha propuesto una arquitectura exible y completa para el desarrollo de las visualizaciones interactivas que van a permitir visualizar los datos anteriormente generados. Finalmente, se presentan varios casos de uso donde se demuestra la idoneidad de la analítica visual para realizar análisis de los datos del proyecto y extraer conocimiento, de manera sencilla

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available