scholarly journals Enabling Customer Reward with a Hybrid Intelligent System (Case Study: JandJ Shopping Mall)

2017 ◽  
Vol 168 (8) ◽  
pp. 15-22
Author(s):  
Okon E. ◽  
Enyindah P. ◽  
Enefiok A.
Keyword(s):  
Intelligent System ◽  
Shopping Mall ◽  
Hybrid Intelligent System

Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran

2017 ◽  
Vol 25 ◽  
pp. 1-17 ◽  
Author(s):  
Eghbal Rahimikia ◽  
Shapour Mohammadi ◽  
Teymur Rahmani ◽  
Mehdi Ghazanfari
Keyword(s):  
Tax Evasion ◽  
Intelligent System ◽  
Corporate Tax ◽  
System A ◽  
Hybrid Intelligent System

A class of hybrid intelligent system for fault diagnosis in electric power systems

1998 ◽  
Vol 23 (1-3) ◽  
pp. 207-224 ◽  
Author(s):  
Patricia R.S Jota ◽  
Syed M Islam ◽  
Tony Wu ◽  
Gerard Ledwich
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Electric Power ◽  
Intelligent System ◽  
Electric Power Systems ◽  
Hybrid Intelligent System

Sparse Matrix Approach in Neural Networks for Effective Medical Data Sets Classifications

2020 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Sagir Masanawa ◽  
Hamza Abubakar
Keyword(s):  
Intelligent System ◽  
Sparse Matrix ◽  
Data Classification ◽  
Medical Data ◽  
Data Sets ◽  
Matrix Approach ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Intelligent System ◽  
Medical Data Classification

In this paper, a hybrid intelligent system that consists of the sparse matrix approach incorporated in neural network learning model as a decision support tool for medical data classification is presented. The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners to accelerate diagnosis and treatment processes. The sparse matrix approach incorporated in neural network learning algorithm for scalability, minimize higher memory storage capacity usage, enhancing implementation time and speed up the analysis of the medical data classification problem. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. The proposed intelligent classification system maximizes the intelligently classification of medical data and minimizes the number of trends inaccurately identified. To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Hepatitis, SPECT Heart and Cleveland Heart from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity. The results were analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system was effective in undertaking medical data classification tasks.

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