Representation of Data

Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network

Effect of Historical Buildings Representation in Cyberspace in Creating Tourists’ Destination Image (Qualitative Study of Traditional Accommodations in Kashan)

Introduction: Understanding the representation components of the historical buildings in cyberspace and their impact on the mental image of the tourists is a significant fact in tourism recognition and management. A part of this subject is the impact of place representation on the destination image of the tourist. In this research, the destination is traditional accommodations that attract tour...

The Role of Architectural Representation on the Formation of the Mental Image of Tourist Destination (Case Study: Traditional Dwellings in the Data Theory Method)

The aim of this study was to explain the role of place representation in shaping the mental image of tourists from traditional residences. Representing the architecture of traditional residences in cyberspace as one of the most important sources of effective information in shaping the image of tourist destinations is of great importance. Today, traditional resorts as one of the most important t...

Algorithms for Segmenting Time Series

As with most computer science problems, representation of the data is the key to ecient and eective solutions. Piecewise linear representation has been used for the representation of the data. This representation has been used by various researchers to support clustering, classication, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain...

Neuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...