Application of backpropagation artificial neural network to predict human development index of Maluku Province

Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.

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Klasifikasi Tanaman Obat Berdasarkan Ekstraksi Fitur Morfologi Daun Menggunakan Jaringan Syaraf Tiruan

Jurnal Informatika ◽

10.31311/ji.v5i2.3770 ◽

2018 ◽

Vol 5 (2) ◽

pp. 169-174

Author(s):

Kana Saputra S ◽

Mochammad Iswan Perangin-Angin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Feature Extraction ◽

Medicinal Plants ◽

Medicinal Plant ◽

Leaf Morphology ◽

Recognition Rate ◽

Learning Rate ◽

Artificial Neural ◽

Backpropagation Artificial Neural Network

Abstrak Indonesia telah lama mengenal dan menggunakan tanaman yang berkhasiat sebagai obat. Dari banyaknya tanaman obat yang ada di dunia, 80% tanaman obat tumbuh di hutan tropika yang berada di Indonesia. Sekitar 28.000 spesies tanaman tumbuh dan 1.000 spesies diantaranya telah digunakan sebagai tanaman obat. Dengan banyaknya spesies tanaman obat dan tingkat kemiripan yang tinggi dapat menyebabkan kesalahan dalam proses identifikasi jenis tanaman obat. Sehingga dibutuhkan bantuan komputer untuk mengenali jenis tanaman obat tersebut. Tujuan dari penelitian ini adalah untuk mengidentifikasi jenis tanaman obat menggunakan jaringan syaraf tiruan backpropagation berdasarkan ekstraksi fitur morfologi daun. Hasilnya menujukkan bahwa perubahan nilai learning rate mempengaruhi hasil identifikasi jenis tanaman obat berdasarkan fitur morfologi daun. Hasil perhitungan rata-rata nilai recognition rate sebesar 90% untuk data training dan 75,56% untuk data testing terjadi saat learning rate 0,01. Nilai learning rate terbaik untuk identifikasi jenis tanaman obat adalah 0,01 dengan jumlah rata-rata epoch sebesar 11,67 dan MSE sebesar 0,13. Ini menunjukkan bahwa metode ekstraksi fitur morfologi daun dan algoritma jaringan syaraf tiruan backpropagation sangat baik digunakan untuk mengidentifkasi jenis tanaman obat. Kata Kunci: Ekstraksi Fitur, Jaringan Syaraf Tiruan Backpropagation, Morfologi Daun, Tanaman Obat Abstract Indonesia has known and used a nutritious plant as a medicine. most of the medicinal plants in the world that is 80% of medicinal plants grown in tropical forests in Indonesia. the plant grows about 28,000 species and 1,000 species of which have been used as medicinal plants. Many species of medicinal plants with a high degree of similarity can cause errors in the process of identifying medicinal plants. Because the problem was needed computer assistance to recognize the types of medicinal plants. This research proposed to identify species of medicinal plants using backpropagation artificial neural network based on leaf morphological feature extraction. The results showed that changes in the value of learning rate influence the identification of medicinal plant species based on leaf morphology features. The calculation average of recognition rate is 90% for training data and 75.56% for data testing occurs at learning rate 0.01. The best learning rate for plant species identification is 0.01 with epoch average is 11.67 and MSE is 0.13. The results of this research concluded that the leaf morphology feature extraction method and backpropagation artificial neural network algorithm are very well used to identify the types of medicinal plants. Keywords: Backpropagation Artificial Neural Network, Feature Extraction, Leaf Morphology, Medicinal Plant

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Upper Stomach Disorder Detection System using Backpropagation Artificial Neural Network

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2020/62882020 ◽

2020 ◽

Vol 8 (8) ◽

pp. 4426-4432

Author(s):

Fitria Hidayanti

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Detection System ◽

Artificial Neural ◽

Stomach Disorder ◽

Backpropagation Artificial Neural Network

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Forecast of Wind Speed with a Backpropagation Artificial Neural Network in the Isthmus of Tehuantepec Region in the State of Oaxaca, Mexico

Acta Universitaria ◽

10.15174/au.2012.335 ◽

2012 ◽

Vol 22 ◽

pp. 7-14 ◽

Cited By ~ 1

Author(s):

Ernesto Cortés Pérez ◽

Airel Nuñez Rodríguez ◽

Rosa Edith Moreno De La Torre ◽

Orlando Lastres Danguillecourt ◽

José Rafael Dorrego Portela

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Speed ◽

Training Method ◽

University Campus ◽

Coefficient Corrélation ◽

Isthmus Of Tehuantepec ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Backpropagation Artificial Neural Network

This paper presents the preliminary results of setting up an artificial neural network (ANN) of the feed forward type with the backpropagation training method for forecast wind speed in the region in the Isthmus of Tehuantepec, Oaxaca, Mexico. The database used covers the years from June 2008 - November 2011, and was supplied by a meteorological station located at the Isthmus University campus Tehuantepec. The experiments were done using the following variables: wind speed, pressure, temperature and date. At the same time were done seven tests combining these variables, comparing their mean square error (MSE) and coefficient correlation r, with data the predicting and experimental. In this research, is proposed a ANN of two hidden layers, for a forecast of 48 hours.

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