scholarly journals Arabic Documents Classification by a Radial Basis Hybridization

2021 ◽  
Vol 15 ◽  
pp. 140-147
Author(s):  
Taher Zaki ◽  
Driss Mammass ◽  
Abdellatif Ennaji ◽  
Stéphane Nicolas
Keyword(s):  
Kernel Function ◽  
Hybrid System ◽  
Semantic Indexing ◽  
New Approach ◽  
Statistical Measures ◽  
Radial Basis ◽  
Classical Models

In this paper, we propose a hybrid system for contextual and semantic indexing of Arabic documents, bringing an improvement to classical models based on n-grams and the Okapi model. This new approach takes into account the concept of the semantic vicinity of terms. We proceed in fact by the calculation of similarity between words using an hybridization of NGRAMs-OKAPI statistical measures and a kernel function in order to identify relevant descriptors. Terminological resources such as graphs and semantic dictionaries are integrated into the system to improve the indexing and the classification processes.

scholarly journals Latent likelihood ratio tests for assessing spatial kernels in epidemic models

2020 ◽  
Vol 81 (3) ◽  
pp. 853-873
Author(s):  
David Thong ◽  
George Streftaris ◽  
Gavin J. Gibson
Keyword(s):  
Likelihood Ratio ◽  
Kernel Function ◽  
Control Strategies ◽  
Spatial Separation ◽  
Likelihood Ratio Tests ◽  
New Approach ◽  
Competing Models ◽  
Spatio Temporal ◽  
Fully Bayesian ◽  
The Relationship

Abstract One of the most important issues in the critical assessment of spatio-temporal stochastic models for epidemics is the selection of the transmission kernel used to represent the relationship between infectious challenge and spatial separation of infected and susceptible hosts. As the design of control strategies is often based on an assessment of the distance over which transmission can realistically occur and estimation of this distance is very sensitive to the choice of kernel function, it is important that models used to inform control strategies can be scrutinised in the light of observation in order to elicit possible evidence against the selected kernel function. While a range of approaches to model criticism is in existence, the field remains one in which the need for further research is recognised. In this paper, building on earlier contributions by the authors, we introduce a new approach to assessing the validity of spatial kernels—the latent likelihood ratio tests—which use likelihood-based discrepancy variables that can be used to compare the fit of competing models, and compare the capacity of this approach to detect model mis-specification with that of tests based on the use of infection-link residuals. We demonstrate that the new approach can be used to formulate tests with greater power than infection-link residuals to detect kernel mis-specification particularly when the degree of mis-specification is modest. This new tests avoid the use of a fully Bayesian approach which may introduce undesirable complications related to computational complexity and prior sensitivity.

scholarly journals iDiSC: A New Approach to IoT-Data-Intensive Service Components Deployment in Edge-Cloud-Hybrid System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 59172-59184 ◽  
Author(s):  
Xiaowei Chen ◽  
Songtao Tang ◽  
Zhihui Lu ◽  
Jie Wu ◽  
Yucong Duan ◽  
...  
Keyword(s):  
Hybrid System ◽  
New Approach ◽  
Data Intensive ◽  
Service Components

System of Semantic Integration of Non-Structuralized Documents in Natural Language in the Domain of Metallurgy

2013 ◽  
Vol 58 (3) ◽  
pp. 927-930 ◽  
Author(s):  
S. Kluska-Nawarecka ◽  
K. Regulski ◽  
M. Krzyżak ◽  
G. Leśniak ◽  
M. Gurda
Keyword(s):  
Information Search ◽  
Latent Semantic Indexing ◽  
Semantic Integration ◽  
Semantic Indexing ◽  
Text Documents ◽  
New Approach ◽  
Automatic Clustering ◽  
System Administrator ◽  
Mining Methods ◽  
Ontological Model

Abstract This paper presents assumptions for a system of automatic cataloging and semantic text documents searching. As an example, a document repository for metals processing technology was used. The system by using ontological model provides the user with a new approach to the exploration of database resources - easier and more intuitive information search. In the current document storage systems, searching is often based only on keywords and descriptions created manually by the system administrator. The use of text mining methods, especially latent semantic indexing, allows automatic clustering of documents with respect to their content. The result of this clustering is integrated with the ontological model, making navigation through documents resources intuitive and does not require the manual creation of directories. Such an approach seems to be particularly useful in a situation where we are dealing with large repositories of unstructured documents from such sources as the Internet. This situation is very typical for cases of searching information and knowledge in the area of metallurgy, for example with regard to innovation and non-traditional suppliers of materials and equipment.

Effects of Surface Wettability on Heterogeneous Boiling

2009 ◽  
Author(s):  
Hai Trieu Phan ◽  
Nadia Caney ◽  
Philippe Marty ◽  
Ste´phane Colasson ◽  
Je´roˆme Gavillet
Keyword(s):  
Heat Transfer ◽  
Boiling Heat Transfer ◽  
Nucleate Boiling ◽  
Contact Angles ◽  
Surface Wettability ◽  
Water Contact ◽  
New Approach ◽  
Major Subject ◽  
Nucleate Boiling Heat Transfer ◽  
Classical Models

Although boiling process has been a major subject of research for several decades, its physics still remain unclear and require further investigation. This study aims at highlighting the effects of the surface wettability on pool boiling heat transfer. Nanocoating techniques were used to vary the water contact angle from 20 to 110° by modifying nanoscale surface topography and chemistry. The experimental results obtained disagree with the predictions of the classical models. A new approach of nucleation mechanism is established to clarify the nexus between the surface wettability and the nucleate boiling heat transfer. In this approach, we introduce the concept of macro- and micro-contact angles to explain the observed phenomenon.

scholarly journals Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Glori Stephani Saragih ◽  
Sri Hartini ◽  
Zuherman Rustam
Keyword(s):  
Radial Basis Function ◽  
Kernel Function ◽  
Basis Function ◽  
Cross Validation ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Radial Basis ◽  
Rbf Kernel ◽  
Rbf Kernel Function ◽  
Fold Cross Validation

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.</span></span>

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