Ensemble of Artificial Neural Networks and K-Nearest Neighbor for classification of granite images

Mesin pendeteksi uang kertas menjadi salah satu objek yang diperhatikan untuk diteliti dan dikembangkan. Mesin pendeteksi uang kertas Indonesia yang ditemukan seperti di stasiun kereta api di suatu kota, terdapat kegagalan dalam mengenali nilai uang kertas tertentu. Tujuan dari penelitian ini adalah membangun model dari pengenalan nilai uang kertas menggunakan K-Nearest Neighbor (KNN) yang merupakan metode yang paling sederhana dan paling penting dalam pengenalan pola, hal ini ditunjukkan pada akurasi yang diperoleh lebih tinggi dibandingkan metode lainnya seperti Artificial Neural Networks (ANN) dan Feedforward Neural Network (FNN). Model yang diusulkan menggunakan ekstraksi fitur, terdapat beberapa fitur yang digunakan untuk pengenalan uang kertas seperti yang pernah dilakukan menggunakan ekstraksi fitur tekstur. Penelitian ini menggunakan ekstraksi fitur warna. Warna memberikan informasi yang berarti dan nilai-nilai yang penting dalam proses mendeskripsikan suatu objek. Warna yang digunakan adalah Red, Green, Blue (RGB). Hasil disajikan pada dataset 40 gambar uang kertas yang terdiri dari pecahan 2000 rupiah keluaran lama, 2000 rupiah keluaran baru, 5000 rupiah keluaran lama, dan 5000 rupiah keluaran baru. Pendekatan yang diusulkan terlihat kinerja yang cukup baik dengan menggunakan metode KNN. Dari 16 data uji menunjukkan 15 objek uang kertas berhasil dideteksi dengan benar. Akurasi yang dihasilkan sebesar 93,7% dengan nilai K=5.

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Enhanced user authentication through typing biometrics with artificial neural networks and k-nearest neighbor algorithm

Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256) ◽

10.1109/acssc.2001.987628 ◽

2001 ◽

Author(s):

Fadhli Wong Mohd Hasan Wong ◽

A.S.M. Supian ◽

A.F. Ismail ◽

Lai Weng Kin ◽

Ong Cheng Soon

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Nearest Neighbor ◽

User Authentication ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm ◽

Artificial Neural

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Classification of ground-truth fire debris samples using artificial neural networks

Forensic Chemistry ◽

10.1016/j.forc.2021.100313 ◽

2021 ◽

Vol 23 ◽

pp. 100313

Author(s):

Nicholas A. Thurn ◽

Taylor Wood ◽

Mary R. Williams ◽

Michael E. Sigman

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Ground Truth ◽

Fire Debris ◽

Artificial Neural

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Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations – ERRATUM

Data-Centric Engineering ◽

10.1017/dce.2021.4 ◽

2021 ◽

Vol 2 ◽

Author(s):

Giuseppe D’Alessio ◽

Alberto Cuoci ◽

Alessandro Parente

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Artificial Neural Networks ◽

High Dimensional ◽

Artificial Neural ◽

Adaptive Chemistry

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Classification of knitted fabric defect detection using Artificial Neural Networks

2019 International Conference on Advances in Computing and Communication Engineering (ICACCE) ◽

10.1109/icacce46606.2019.9079951 ◽

2019 ◽

Author(s):

Subrata Das ◽

Amitabh Wahi ◽

S. Sundaramurthy ◽

N Thulasiram ◽

S Keerthika

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Defect Detection ◽

Knitted Fabric ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Artificial Neural

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Chemical profiling and classification of illicit heroin by principal component analysis, calculation of inter sample correlation and artificial neural networks

Talanta ◽

10.1016/j.talanta.2005.03.041 ◽

2005 ◽

Vol 67 (2) ◽

pp. 360-367 ◽

Cited By ~ 31

Author(s):

P ESSEIVA ◽

F ANGLADA ◽

L DUJOURDY ◽

F TARONI ◽

P MARGOT ◽

...

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Artificial Neural Networks ◽

Principal Component ◽

Component Analysis ◽

Chemical Profiling ◽

Analysis Calculation ◽

Sample Correlation ◽

Artificial Neural

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Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

Applied Spectroscopy ◽

10.1366/0003702914335238 ◽

1991 ◽

Vol 45 (10) ◽

pp. 1706-1716 ◽

Cited By ~ 17

Author(s):

Mark Glick ◽

Gary M. Hieftje

Keyword(s):

Neural Network ◽

Neural Networks ◽

Glow Discharge ◽

Multivariate Calibration ◽

Emission Spectra ◽

K Nearest Neighbor ◽

The Neural Network ◽

Calibration Sets ◽

Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

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