scholarly journals Forest Fire Evolution Prediction Using a Hybrid Intelligent System

2010 ◽  
pp. 64-71
Author(s):  
Aitor Mata ◽  
Bruno Baruque ◽  
Belén Pérez-Lancho ◽  
Emilio Corchado ◽  
Juan M. Corchado
Keyword(s):  
Forest Fire ◽  
Intelligent System ◽  
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.

Forecasting Rainfall Based On Hybrid Intelligent System

2016 ◽  
Author(s):  
Hatem Abdel-Kader ◽  
Ibrahim Selim ◽  
Amira Ahmed ◽  
Mona Mohamed
Keyword(s):  
Intelligent System ◽  
Hybrid Intelligent System

Radiological Images in the Construction of Hybrid Intelligent System

2020 ◽  
pp. 43-50
Author(s):  
B. A. Kobrinskii ◽  
◽  
D. D. Dolotova ◽  
V. V. Donitova ◽  
A. V. Gavrilov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cerebral Ischemia ◽  
Computed Tomography Angiography ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Radiological Feature ◽  
Chronic Cerebral Ischemia ◽  
Computed Tomography Images ◽  
Linguistic Rules ◽  
Hybrid Intelligent System

So far, the concept of image row or tuples in the development of intelligent systems has been discussed in relation to the role of phenotypic (external) manifestations of diseases in diagnostics. This study introduces the idea of neuroimaging tuples as a tool to make a prognosis of the course of chronic cerebral ischemia. The phenomenon of leukoaraiosis is analyzed as a radiological feature of chronic brain ischemia and a predictor of stroke. Image tuples are formed from the results of computed tomography, computed tomography angiography, magnetic resonance imaging, of 85 patients with chronic cerebral ischemia. Native computed tomography images were processed with adaptive filtering methods. Computed tomography angiography results were processed through a vesselness filter that allows development of 3D reconstructions of vasculature in leukoaraiosis areas. The problem of fuzzy images, the principles of comparative analysis of images and the possibility of using confidence factors in the image tuples are discussed in the article. A scheme of a hybrid intelligent system that combines traditional logic-linguistic rules and images based on primary information and reconstruction of the original DICOM images in the knowledge base was developed. The sphere of the application is stroke risk prediction using an intelligent system.

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