Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation

The selection of a suitable document representation approach plays a crucial role in the performance of a document clustering task. Being able to pick out representative words within a document can lead to substantial improvements in document clustering. In the case of web documents, the HTML markup that defines the layout of the content provides additional structural information that can be further exploited to identify representative words. In this paper we introduce a fuzzy term weighing approach that makes the most of the HTML structure for document clustering. We set forth and build on the hypothesis that a good representation can take advantage of how humans skim through documents to extract the most representative words. The authors of web pages make use of HTML tags to convey the most important message of a web page through page elements that attract the readers’ attention, such as page titles or emphasized elements. We define a set of criteria to exploit the information provided by these page elements, and introduce a fuzzy combination of these criteria that we evaluate within the context of a web page clustering task. Our proposed approach, called Abstract Fuzzy Combination of Criteria (AFCC), can adapt to datasets whose features are distributed differently, achieving good results compared to other similar fuzzy logic based approaches and TF-IDF across different datasets

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Development and Implementation of a Reliable Decision Fusion and Pattern Recognition System for Object Detection and Condition Monitoring

A monitoring task of production system (bucket-wheel excavator) is investigated for the development and realization of a multisensor-based monitoring system. The objective of the monitoring system is to obtain in real time reliable decisions on the presence of target objects (large stones) in the transported material during the production process to avoid disturbances or failures of the transportation process. Due to the complexity of the considered production system, different physical effects are used for the development of the multisensor-based monitoring system. The measured signals are acquired using different sensors (five acceleration sensors, two load cells, and a laser scanner). Due to the inevitable and varying time shift between the stimulations of the individual sensors, each signal is individually subjected to preprocessing, feature extraction, and classification process. The proposed monitoring system consists of three modules: acceleration, laser scanner, and decision fusion modules. For the acceleration module which uses acceleration signals of five different acceleration sensors, two detection approaches are developed. The first approach (STFT-SVM) is based on Short-Time Fourier Transform (STFT) as feature extraction tool, Support Vector Machine (SVM) for the classification, and a novel decision fusion process to fuse the individual decisions. The second approach (CWT-SVM) is based Continuous Wavelet Transform (CWT) as feature extraction tool, Support Vector Machine (SVM) for the classification, and a rule-based decision fusion process to fuse the individual decisions. Both approaches are trained, validated, and tested using real industrial data. The developed approaches show strong improvements in detection and false alarm rates. Due to the implementation complexity and the high number of false alarms of the STFT-SVM approach in comparison to the CWT-SVM approach, the CWT-SVM-based approach is chosen for the development of the overall monitoring system. The Laser scanner module which processes the laser scanner signal consists of prefiltering, filtering, validation, and classification process. The module is validated, and successfully tested on real industrial data. The decision fusion module fuses the decisions of both detection modules in order to obtain a final reliable decision. Three fusion techniques are investigated, which are OR-logic, Bayesian Combination Rule (BCR), and the new developed decision fusion technique Basic Belief Fusion (BBF). Due to the characteristics of the considered application, the OR-Logic is chosen to perform the fusion task. For the online realization, the weightometer module is added to avoid false alarms which could be caused by acceleration module. Additionally modifications and simplification processes are performed in order to overcome the hardware limitations The proposed monitoring approach is developed for online and real time implementation, and it achieves high detection rate, with minimum false alarms rate, thus the production process disturbance is minimized

Spatio-temporal forecasting of network data

In the digital age, data are collected in unprecedented volumes on a plethora of networks. These data provide opportunities to develop our understanding of network processes by allowing data to drive method, revealing new and often unexpected insights. To date, there has been extensive research into the structure and function of complex networks, but there is scope for improvement in modelling the spatio-temporal evolution of network processes in order to forecast future conditions. This thesis focusses on forecasting using data collected on road networks. Road traffic congestion is a serious and persistent problem in most major cities around the world, and it is the task of researchers and traffic engineers to make use of voluminous traffic data to help alleviate congestion. Recently, spatio-temporal models have been applied to traffic data, showing improvements over time series methods. Although progress has been made, challenges remain. Firstly, most existing methods perform well under typical conditions, but less well under atypical conditions. Secondly, existing spatio-temporal models have been applied to traffic data with high spatial resolution, and there has been little research into how to incorporate spatial information on spatially sparse sensor networks, where the dependency relationships between locations are uncertain. Thirdly, traffic data is characterised by high missing rates, and existing methods are generally poorly equipped to deal with this in a real time setting. In this thesis, a local online kernel ridge regression model is developed that addresses these three issues, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network. The model parameters can vary spatially and temporally, allowing it to better model the time varying characteristics of traffic data, and to deal with abnormal traffic situations. Methods are defined for linking the spatially sparse sensor network to the physical road network, providing an improved representation of the spatial relationship between sensor locations. The incorporation of the spatio-temporal neighbourhood enables the model to forecast effectively under missing data. The proposed model outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p