Hyperspectral image compression using 3D discrete cosine transform and support vector machine learning

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.

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Computational Complexity Reduction of the Support Vector Machine Classifiers for Image Analysis Tasks Through the Use of the Discrete Cosine Transform

2006 IEEE International Conference on Automation, Quality and Testing, Robotics ◽

10.1109/aqtr.2006.254658 ◽

2006 ◽

Author(s):

M. Gordan ◽

A. Georgakis ◽

O. Tsatos ◽

G. Oltean ◽

L. Miclea

Keyword(s):

Support Vector Machine ◽

Image Analysis ◽

Computational Complexity ◽

Discrete Cosine Transform ◽

Complexity Reduction ◽

Support Vector ◽

Cosine Transform

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Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression

The Visual Computer ◽

10.1007/s00371-019-01753-z ◽

2019 ◽

Vol 36 (7) ◽

pp. 1473-1490 ◽

Cited By ~ 3

Author(s):

Nadia Zikiou ◽

Mourad Lahdir ◽

David Helbert

Keyword(s):

Image Compression ◽

Support Vector Regression ◽

Hyperspectral Image ◽

Support Vector ◽

Hyperspectral Image Compression

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Forecast of driving load of hybrid electric vehicles by using discrete cosine transform and Support Vector Machine

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) ◽

10.1109/ijcnn.2008.4634106 ◽

2008 ◽

Cited By ~ 2

Author(s):

Jian Yang ◽

Xi Huang ◽

Ying Tan ◽

Xingui He

Keyword(s):

Support Vector Machine ◽

Discrete Cosine Transform ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Support Vector ◽

Cosine Transform ◽

Hybrid Electric

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Face Recognition Based on Discrete Cosine Transform and Support Vector Machine

2005 International Conference on Neural Networks and Brain ◽

10.1109/icnnb.2005.1614838 ◽

2006 ◽

Cited By ~ 1

Author(s):

Lihong Zhao ◽

Yu Cai ◽

Jinghong Li ◽

Xinhe Xu

Keyword(s):

Support Vector Machine ◽

Face Recognition ◽

Discrete Cosine Transform ◽

Support Vector ◽

Cosine Transform

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Discrete Cosine Transform-based Microscope Focal Adjustment with RBC Classification Using Support Vector Machine

2021 11th International Conference on Biomedical Engineering and Technology ◽

10.1145/3460238.3460240 ◽

2021 ◽

Author(s):

Lester N. Manuel ◽

Analyn N. Yumang ◽

Joseph Keenu L. Maravilla ◽

Joseph Bryan G. Ibarra ◽

Ericson D. Dimaunahan

Keyword(s):

Support Vector Machine ◽

Discrete Cosine Transform ◽

Support Vector ◽

Cosine Transform

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IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

Jurnal Ilmiah Teknologi dan Rekayasa ◽

10.35760/tr.2020.25i1.2571 ◽

2020 ◽

Vol 25 (1) ◽

pp. 24-38

Author(s):

Eka Patriya

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Support Vector Regression ◽

Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

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