Unsupervised Learning Based On Artificial Neural Network: A Review

2018 ◽  
Author(s):  
Happiness Ugochi Dike ◽  
Yimin Zhou ◽  
Kranthi Kumar Deveerasetty ◽  
Qingtian Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Unsupervised Learning ◽  
Artificial Neural

scholarly journals KLASIFIKASI SUARA MANUSIA KE DALAM SOPRAN, MEZZO SOPRAN, ALTO, TENOR, BARITON, BASS DENGAN SELF ORGANIZING MAP

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Andreas Saputra ◽  
Sri Suwarno ◽  
Lukas Chrisantyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Unsupervised Learning ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
Self Organizing

Self Organizing Map adalah metode jaringan syaraf tiruan (Artificial Neural Network) yang biasa digunakan untuk melakukan proses klasifikasi dengan sifat unsupervised learning atau pelatihan tak terbimbing. Cluster yang akan digunakan akan ditentukan secara manual hanya saja dalam prosesnya data yang masuk akan dikelompokan secara otomatis tanpa adanya intevensi dari sistem. Penelitian ini menerapkan Self Organizing Map untuk melakukan klasifikasi data berupa rekaman suara dengan format file WAV karena merupakan format audio yang belum terkompresi ke dalam sopran, mezzo sopran, alto, tenor, baritone, dan bass. Dalam pengambilan data untuk input melalui proses preemphasis, frame, blocking, dan windowing sebelum dirubah menjadi sinyal diskrit dengan Fast Fourier Transform. Data berupa rata-rata magnitude menjadi input dalam sistem klasifikasi Self Organizing Map. Dalam penelitian ini hasil yang didapat belum sesuai dengan harapan karena data tidak mengelompok dengan baik.Self Organizing Map adalah metode jaringan syaraf tiruan (Artificial Neural Network) yang biasa digunakan untuk melakukan proses klasifikasi dengan sifat unsupervised learning atau pelatihan tak terbimbing. Cluster yang akan digunakan akan ditentukan secara manual hanya saja dalam prosesnya data yang masuk akan dikelompokan secara otomatis tanpa adanya intevensi dari sistem. Penelitian ini menerapkan Self Organizing Map untuk melakukan klasifikasi data berupa rekaman suara dengan format file WAV karena merupakan format audio yang belum terkompresi ke dalam sopran, mezzo sopran, alto, tenor, baritone, dan bass. Dalam pengambilan data untuk input melalui proses preemphasis, frame, blocking, dan windowing sebelum dirubah menjadi sinyal diskrit dengan Fast Fourier Transform. Data berupa rata-rata magnitude menjadi input dalam sistem klasifikasi Self Organizing Map. Dalam penelitian ini hasil yang didapat belum sesuai dengan harapan karena data tidak mengelompok dengan baik.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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