Knowledge discovery: data and text mining

Data mining and text mining refer to techniques, models, algorithms, and processes for knowledge discovery and extraction. Basic de nitions are given together with the description of a standard data mining process. Common models and algorithms are presented. Attention is given to text clustering, how to convert unstructured text to structured data (vectors), and how to compute their importance and position within clusters

William James's interpretation of personality and value

Thesis (M.A.)--Boston UniversityThis thesis has two basic purposes from which it derives two central methods of investigating and evaluating James's interpretation of personality and value. The first purpose is to extract all those strands in James's thinking that are pertinent to his view of the whole man. The second purpose is to evaluate this doctrine, first as it functioned within its native climate of opinion, and secondly as it contributes to the present or modern climate of opinion

The effect of weed density, root senescence, and egg density on western corn rootworm larval establishment, survivorship, and damage potential

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on October 31, 2007).Includes bibliographical references.Thesis (M.S.) University of Missouri-Columbia 2007.Dissertations, Academic -- University of Missouri--Columbia -- Entomology.Western corn rootworm, Diabrotica virgifera virgifera Leconte beetle emergence from and root damage to transgenic (Bt) maize (Zea mays L.) plants producing the Cry3Bb1 protein and isoline plants in the presence of varying Setaria spp. densities, egg densities, and Setaria spp. control times was evaluated in two separate field experiments. The nutritional value of senescing maize and Setaria faberi R.A.W. Herrm roots to neonate and second instar western corn rootworm larvae was evaluated in three separate greenhouse experiments. Greenhouse evaluations include glyphosate sprayed S. faberi, glyphosate sprayed maize, and maize severed below the growing point. Larval recovery, weight change, and adults emergence was monitored for each experiment. Results of the field experiment showed significantly greater root damage to the isoline hybrids than the transgenic hybrid. In one of sixteen comparisons with the transgenic hybrid, it produced significantly more beetles in the weedy plot than the weed-free plot. This is the first time this has been documented in a field situation. Results of the greenhouse experiments showed that once S. faberi was sprayed with glyphosate, it became nutritiously deficient to both neonate and second instar larvae within the first five days. Severed maize became nutritiously deficient between 5 and 10 d after the plant was killed. Glyphosate sprayed maize become nutritiously deficient between 10 and 15 d after the plants were sprayed. A minimal number of adults were recovered from any greenhouse treatment, except living maize or S. faberi

La superálgebra de Jordan A{t}

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Unsupervised learning models-based CRM anomaly detection using GPU

Deep learning models have improved several business intelligence tools like Customer relationship Management(CRM) systems. However, those models have increased the need for advanced computational capacity and infrastructure. Modern accelerators are starting to have floating-point precision arithmetic problems generated by highly streamlined systems, powered by the need to process an ever-increasing volume of data and increasingly complex models to attend to the necessity to identify customer data that allow consolidating products or services. We focus on CRM anomalies detection using GPU(Graphics Processor Unit) because they are a relevant source of money drain for organizations and directly affect the relationship between clients and suppliers. Our results present the combination of deep learning models with a computational structure that could access by organizations, but with a combination that reduces the number of features that achieve answers to CRM system.#AnáliticaDeDatosLos modelos de aprendizaje profundo han mejorado varias herramientas de inteligencia empresarial, como los sistemas de gestión de relaciones con el cliente (CRM). Sin embargo, esos modelos han aumentado la necesidad de infraestructura y capacidad computacional avanzada. Los aceleradores modernos están comenzando a tener problemas aritméticos de precisión de punto flotante generados por sistemas altamente optimizados, impulsados ​​por la necesidad de procesar un volumen cada vez mayor de datos y modelos cada vez más complejos para atender la necesidad de identificar datos de clientes que permitan consolidar productos o servicios. . Nos enfocamos en la detección de anomalías de CRM utilizando GPU (Graphics Processor Unit) porque son una fuente relevante de drenaje de dinero para las organizaciones y afectan directamente la relación entre clientes y proveedores. Nuestros resultados presentan la combinación de modelos de aprendizaje profundo con una estructura computacional a la que podrían acceder las organizaciones, pero con una combinación que reduce la cantidad de funcionalidades que logran respuestas al sistema CRM