10,184 research outputs found

    A systematic review of data quality issues in knowledge discovery tasks

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    Hay un gran crecimiento en el volumen de datos porque las organizacionesĀ capturan permanentemente la cantidad colectiva de datos para lograrĀ un mejor proceso de toma de decisiones. El desafĆ­o mas fundamentalĀ es la exploraciĆ³n de los grandes volĆŗmenes de datos y la extracciĆ³n deĀ conocimiento Ćŗtil para futuras acciones por medio de tareas para el descubrimientoĀ del conocimiento; sin embargo, muchos datos presentan malaĀ calidad. Presentamos una revisiĆ³n sistemĆ”tica de los asuntos de calidadĀ de datos en las Ć”reas del descubrimiento de conocimiento y un estudio deĀ caso aplicado a la enfermedad agrĆ­cola conocida como la roya del cafĆ©.Large volume of data is growing because the organizations are continuouslyĀ capturing the collective amount of data for better decision-makingĀ process. The most fundamental challenge is to explore the large volumesĀ of data and extract useful knowledge for future actions through knowledgeĀ discovery tasks, nevertheless many data has poor quality. We presentedĀ a systematic review of the data quality issues in knowledge discoveryĀ tasks and a case study applied to agricultural disease named coffee rust

    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

    One-Class Classification: Taxonomy of Study and Review of Techniques

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    One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Designing algorithms to aid discovery by chemical robots

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    Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery
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