Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and Clonal Selection Theory Inspired Algorithms

2016 ◽  
Vol 30 (09) ◽  
pp. 1660008 ◽  
Author(s):  
Ayodele Lasisi ◽  
Rozaida Ghazali ◽  
Mustafa Mat Deris ◽  
Tutut Herawan ◽  
Fola Lasisi
Keyword(s):  
Feature Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Recognition System ◽  
Clonal Selection Algorithm ◽  
Immune Recognition ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Detection Rates ◽  
Clonal Selection Theory

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (CLONALG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with CLONALG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the CLONALG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-CLONALG, FRFS-AIRS, VQRS-CLONALG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

scholarly journals Breast Cancer Classification using Nature-inspired Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1024-1027
Keyword(s):  
Breast Cancer ◽  
Clonal Selection ◽  
Recognition System ◽  
Clonal Selection Algorithm ◽  
Immune Recognition ◽  
Self Organizing Map ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Hierarchical Learning ◽  
Nature Inspired Algorithm

Breast Cancer is one among the dangerous ailments that roots up of deaths among women worldwide. Lots of risk factors have been identified through research though the exact reasons of breast cancer are not yet fully understood The Artificial Immune Recognition System classifier helps to classify the type of breast cancer on Wisconsin dataset which provides accurate prediction of the classes of breast cancer. i.e, Benign and Malignant. This system focuses on supervised classification with the help of clonal selection algorithm, Hierarchical Learning Vector Quantization, Multipass Self Organizing Map. The goal of this system can implement the algorithm to classify the cancer accurately and to compare the error rate, f-measure with previous classification algorithms.

scholarly journals APPLICATION OF CLONAL SELECTION ALGORITHM FOR PID CONTROLLER SYNTHESIS OF MIMO SYSTEMS IN OIL AND GAS INDUSTRY

2019 ◽  
Vol 9 (3) ◽  
pp. 50-53
Author(s):  
Olga Shiryayeva ◽  
Timur Samigulin
Keyword(s):  
Oil And Gas ◽  
Mimo Systems ◽  
Clonal Selection ◽  
Oil And Gas Industry ◽  
Controller Synthesis ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Gas Industry ◽  
Smart Technologies

This paper presents the results of the Smart technologies application to the synthesis of MIMO-systems in oil and gas industry. In particular, there is considered a multidimensional multiply connected system for gas distillation process control through a distillation column with regulators configured on the basis of Smart-technologies – clonal selection algorithm (CLONALG) of an artificial immune system (AIS).

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