A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers

2007 ◽  
Vol 30 (10) ◽  
pp. 2201-2212 ◽  
Author(s):  
Adel Nadjaran Toosi ◽  
Mohsen Kahani
Keyword(s):  
Intrusion Detection ◽  
Soft Computing ◽  
New Approach ◽  
Computing Model ◽  
Neuro Fuzzy ◽  
Fuzzy Classifiers

Fuzzy Classification Methods Based Diagnosis of Parkinson’s disease from Speech Test Cases

2019 ◽  
Vol 12 (2) ◽  
pp. 100-120
Author(s):  
Niousha Karimi Dastjerd ◽  
Onur Can Sert ◽  
Tansel Ozyer ◽  
Reda Alhajj
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Learning Approaches ◽  
Healthy Individuals ◽  
Data Set ◽  
Sound Recordings ◽  
Acceptable Accuracy ◽  
Computing Model ◽  
Neuro Fuzzy ◽  
Fuzzy Classifiers

Background: Together with the Alzheimer’s disease, Parkinson’s disease is considTogether with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease.ered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease. This paper contributes an attractive prediction framework based on some machine learning approaches. Several fuzzy classifiers have been employed in the process to distinguish people with Parkinsonism from healthy individuals. The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” available from the UCI repository. The results reported in this paper are better than the results reported by Sakar et al., where the same dataset was used, but with different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al. Objectives: This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals. Methods: Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed. Results: The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” of 40 subjects available on the UCI repository. Conclusion: The results achieved show that FURIA, MLP- Bagging - SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al.

An adapting soft computing model for intrusion detection system

2021 ◽  
Author(s):  
Husam Ibrahiem Husain Alsaadi ◽  
Rafah M. ALmuttari ◽  
Osman Nuri Ucan ◽  
Oguz Bayat
Keyword(s):  
Intrusion Detection ◽  
Soft Computing ◽  
Intrusion Detection System ◽  
Detection System ◽  
Computing Model

scholarly journals Soft computing model for students’ evaluation in educational institute

2021 ◽  
Vol 1913 (1) ◽  
pp. 012137
Author(s):  
O K Chaudhari ◽  
Rajshri Gupta ◽  
T A Thakre
Keyword(s):  
Soft Computing ◽  
Computing Model ◽  
Educational Institute

Soft computing model for predicting the wear resistance of friction stir processed aluminum alloy AA5083

2021 ◽  
Author(s):  
K. Santhosh Kumar ◽  
R. Vaira Vignesh ◽  
G. Ajith Kumar ◽  
A. Abiram ◽  
R. Padmanaban
Keyword(s):  
Wear Resistance ◽  
Aluminum Alloy ◽  
Soft Computing ◽  
Friction Stir ◽  
Computing Model

Modelling of municipal solid waste gasification using an optimised ensemble soft computing model

Fuel ◽  
2021 ◽  
Vol 289 ◽  
pp. 119903
Author(s):  
Navid Kardani ◽  
Annan Zhou ◽  
Majidreza Nazem ◽  
Xiaoshan Lin
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Soft Computing ◽  
Computing Model ◽  
Waste Gasification
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