A Comparative Study on Parkinson's Disease Diagnosis Using Neural Networks and Artificial Immune System

Er, Orhan; Çetin, Onursal; Bascil, M. Serdar; Temurtaş, Feyzullah

doi:10.1166/jmihi.2016.1606

Cited by 23 publications

(15 citation statements)

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“…Some of the areas of its applications include the area of physical science and engineering [? ], medicine [20], business [21], education [22], and almost all areas of our daily activities.…”

Section: B Adaptive Linear Neurons (Adaline)mentioning

confidence: 99%

A Multi-Criteria Recommendation Framework using Adaptive Linear Neuron

Hassan¹,

Hamada²,

Yusuf³

2020

IJACSA

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Recent developments in the field of recommender systems have led to a renewed interest in employing some of the sophisticated machine learning algorithms to combine multiple characteristics of items during the process of making recommendations. Considerable number of research papers have been published on multi-criteria recommendation techniques. Most of these studies have focused only on using some basic statistical methods or simply by extending the similarity computation of the traditional heuristic-based techniques to model the system. Researchers have not treated the uncertainty that exists about the relationship between multi-criteria modelling approaches and effectiveness of some of the complex and powerful machine learning techniques; in fact, no previous study has investigated the role of artificial neural networks to design and develop the system using aggregation function approach. This paper seeks to remedy these challenges by analysing the performance of multi-criteria recommender systems, modelled by integrating an adaptive linear neuron that was trained using delta rule, and asymmetric singular value decomposition algorithms. The proposed model was implemented, trained and tested using a multi-criteria dataset for recommending movies to users based on action, story, direction, and visual effects of movies. Taken together, the empirical results of the study suggested that there is a strong association between artificial neural networks and the modelling approaches of multicriteria recommendation technique.

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Section: B Adaptive Linear Neurons (Adaline)mentioning

confidence: 99%

A Multi-Criteria Recommendation Framework using Adaptive Linear Neuron

Hassan¹,

Hamada²,

Yusuf³

2020

IJACSA

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“…Orhan et. al (11) compared some techniques such as artificial immune system, feed forward, learning vector quantization and probabilistic neural network algorithms for detection of PD. 10-fold cross validation method was employed for disease detection.…”

Section: Revised Manuscript Received On July 22 2019mentioning

confidence: 99%

Performance Analysis of various Neural Network functions for Parkinson’s disease Classification using EEG and EMG

Paul*,

Saikia,

Hussain

et al. 2019

IJITEE

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Artificial neural network (ANN) is a significant tool for classification of various types of disease using either Biosignals/images or may be any kind of physical parameters. Establishment of appropriate combination of learning, transfer function and training function is a very tedious task. Here, we compared the performance of different training parameters in feed forward neural network for differentiating of Parkinson’s disease using human brain (Electroencephalogram) and muscle signals (Electromyogram) features as the input vector. 3 different types of training algorithm with six training functions is used. They are Gradient Descent algorithms (traingd, traingdm), Conjugate Gradient algorithms (trainscg, traincgp) and Quasi-Newton algorithms (trainbfg, trainlm). Proposed work compared the mentioned algorithm in terms of mean square error, classification rate (%),R-value and the elapsed time. Study showed that trainlm (Levenberg-Marquardt) best fits for larger data set. It showed the highest accuracy rate of 100% with 0 mismatch classification with a best validation mean square error of 0.0040254 in 3 epochs with a elapsed time of 1.12 seconds. The R-value found was 0.9998 which is in nearly equals to 1. Hence, Levenberg-Marquardt can be used as a training function for the classification of any brain disorder

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“…In addition, [18] were used probabilistic neural network, feed forward, artificial immune system and learning vector quantization and study these methods then got the comparative of the results.…”

Section: Literature Reviewmentioning

confidence: 99%