Symbolic data are usually composed of some categorical variables used to represent discrete entities in many real-world applications. Mining symbolic is more difficult than numerical due the lack inherent geometric properties this type data. In paper, we use two kinds kernel learning methods create a estimation model and nonlinear classification algorithm for By using smoothing method, construc...

Abstract Driven by digital technologies, manufacturers aim to tap into data-driven business models, in which value is generated from data as a complement physical products. However, this transformation can be complex, different archetypes of models require substantially and technical capabilities. While there are manifold contributions research on capability development, an integrated aligned p...

Image classification and retrieval systems have gained more attention because of easier access to high-tech medical imaging. However, the lack of availability of large-scaled balanced labelled data in medicine is still a challenge. Simplicity, practicality, efficiency, and effectiveness are the main targets in medical domain. To achieve these goals, Radon transformation, which is a well-known t...

background: according to quality transformation of services during the time, the quality requirements and standards also change and recognition of these requirements and transformation process can assist in recognizing the quality dimensions of services. the aim of this research was to explain the transformation process, requirements and quality dimensions of services in hospitals.   methods: t...