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 arithmeti...
- Autores:
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/20764
- Acceso en línea:
- http://hdl.handle.net/20.500.12010/20764
http://expeditio.utadeo.edu.co
- Palabra clave:
- Analítica de datos
Análisis de sistemas -- Tesis y disertaciones académicas
Teoría de sistemas -- Tesis y disertaciones académicas
Diseño de sistemas -- Tesis y disertaciones académicas
- Rights
- License
- Abierto (Texto Completo)
| Summary: | 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. |
|---|