Learning Vector Quantization and Radial Basis Function Performance Comparison Based Intrusion Detection System

2021 ◽  
pp. 561-572
Author(s):  
Joël T. Hounsou ◽  
Pamela Bélise Ciza Niyomukiza ◽  
Thierry Nsabimana ◽  
Ghislain Vlavonou ◽  
Fulvio Frati ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Radial Basis Function ◽  
Vector Quantization ◽  
Basis Function ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Vector Quantization ◽  
Performance Comparison ◽  
Radial Basis

scholarly journals Perbandingan Algoritma LVQ dan RBFNN Untuk Identifikasi Glaukoma dan Diabetes Retinopati Pada Citra Fundus

2020 ◽  
Vol 1 (1) ◽  
pp. 68-77
Author(s):  
Kevin Oktavius ◽  
Siska Devella
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Vector Quantization ◽  
Basis Function ◽  
Confusion Matrix ◽  
Learning Vector Quantization ◽  
Radial Basis

Penyakit mata merupakan salah satu masalah kesehatan utama pada semua orang terutama pada kaum lansia, penyakit mata yang paling umum menyerang lansia diantaranya adalah glaukoma dan retinopati diabetes. Penyakit glaukoma dan diabetes retinopati dapat diketahui melalui citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma Learning Vector Quantization dengan Radial Basis Function Neural Network untuk klasifikasi penyakit glaukoma dan diabetes retinopati (accuracy, precision, recall) berdasarkan citra fundus resolusi tinggi. Dataset yang digunakan berjumlah 45 citra fundus yang terdiri dari 15 citra fundus terjangkit glaukoma, 15 citra fundus terjangkit diabetes retinopati dan 15 citra fundus mata normal. Pada perhitungan dengan confusion matrix hasil tertinggi didapatkan pada algoritma radial basis function neural network dengan spread=20 dan MN=10 menghasilkan rata-rata accuracy sebesar 81,06%, precision sebesar 80,83% dan recall sebesar 73,33% jika dibandingkan dengan algoritma learning vector quantization dengan lvqnet=50 dan epoch=45 menghasilkan rata-rata accuracy sebesar 80,85%, precision sebesar 73,33% dan recall sebesar 77,14%.

Intelligent Radial Basis Function Neural Network for Intrusion Detection in Battle Field

2018 ◽  
pp. 151-173
Author(s):  
Kirupa Ganapathy
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Radial Basis Function ◽  
Learning Process ◽  
Basis Function ◽  
Detection System ◽  
The Neural Network ◽  
Training Samples ◽  
Radial Basis

Defense at boundary is nowadays well equipped with perimeter protection, cameras, fence sensors, radars etc. However, in battlefield there is more feasibility of entering of a non-native human and unknowing stamping of the explosives placed in the various paths by the native soldiers. There exists no alert system in the battlefield for the soldiers to identify the intruder or the explosives in the field. Therefore, there is a need for an automated intelligent intrusion detection system for battlefield monitoring. This chapter proposes an intelligent radial basis function neural network (RBFNN) technique for intrusion detection and explosive identification. The proposed intelligent RBFNN implements some intellectual components in the algorithm to make the neural network think before learning the training samples. Involvement of intellectual components makes the learning process simple, effective and efficient. The proposed technique helps to reduce false alarm and encourages timely detection thereby providing extensive support for the native soldiers and save the life of the mankind.

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