Choosing the best embryogenesis medium in carrot by data mining technology

Abstract Plant cell, tissue and organ culture (PCTOC) is extensively used to propagate faster and more plants, to produce virus-free plants, and secondary metabolites production as well. This requires the optimization of PCTOC conditions for each plant and final aim. Optimizing the micropropagation is time-consuming and costly, because it is different from the plant and even for each variety. In addition requires the optimization of the concentration and type of hormone and the type of explants for each variety in the stages of callogenesis, embryogenesis, shooting and rooting. Hence, today researchers have used Data Mining with using an artificial neural network (ANN) to predict the best conditions for tissue culture and saved time and money considerably. In this research, radial basis function (RBF) model was used to predict the best conditions for carrot tissue culture and the results showed that the highest and the least sensitivity were related to variety and percentage of Agar in liter, respectively. The results prediction of the RBF model showed that the percentage of embryogenesis was 62.5%, but the percentage of embryogenesis in laboratory obtain 75%. The results showed that the RBF model is a good model to predict the results.

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Implementasi Rough Neural Network dalam Identifikasi Kepuasan Konsumen Mediasi Bisnis

Jurnal Kajian Ilmiah ◽

10.31599/jki.v18i2.274 ◽

2018 ◽

Vol 18 (2) ◽

pp. 184

Author(s):

Ikrimah Afifah Trivanni

Keyword(s):

Neural Network ◽

Data Mining ◽

New York ◽

Artificial Neural Network ◽

Rough Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

Data mining menjadi topik hangat yang sangat bermanfaat di era saat ini. Sistem Artificial Neural Network (ANN) dan rough set yang merupakan metode data mining dapat digabungkan yang selanjutnya disebut sebagai metode Rough Neural Network (RNN). Siste, roughset dalam RNN berfungsi untuk mereduksi atribut untuk optimalisasi informasi sedangkan ANN berfungsi untuk membentuk jaringan dari kumpulan data reduksi tersebut. Metode ini dapat digunakan di berbagai bidang misalnya bisnis yakni dalam mengidentifikasi kepuasan konsumen. Perlindungan hak maupun kewajiban dalam bisnis adalah hal penting di negara maju, contohnya New York yang telah membentuk Departement of Consumen Affairs (DCA). Ribuan mediasi tercatat telah dilakukan oleh DCA New York sehingga pendekatan struktur terhadap kepuasan konsumen merupakan hal penting dalam meninjau apakah layanan mediasi yang dilakukan telah baik. Oleh karena itu, tujuan penelitian ini adalah mengimplementasikan metode RNN pada suatu dataset komplain konsumen terhadap pelayanan mediasi DCA New York. Hasil penelitian pada proses awal, rough set menunjukkan bahwa atribut yang efektif untuk menghasilkan kepuasan konsumen yang optimal adalah atribut Business State, Complaint Result, Duration of Mediation, dan Complaint Type. Eror yang dihasilkan pada jaringan tiruan kepuasan konsumen (Satisfaction) sebesar 345,828 dengan langkah yang dilalui untuk mencapai model yang mungkin adalah sebanyak 65137 langkah. Model RNN menunjukkan selisih eror yang kecil antara data latih dan data tes, artinya model RNN konsisten dalam memprediksi kepuasan konsumen untuk kedepannya.

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Recommendation system with Automated Web Usage data mining by using K-Nearest Neighbour(KNN) classification and Artificial Neural Network (ANN) algorithm

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2017.8022 ◽

2017 ◽

Vol V (VIII) ◽

pp. 160-168

Author(s):

Er. Jyoti

Keyword(s):

Neural Network ◽

Data Mining ◽

Artificial Neural Network ◽

Recommendation System ◽

Nearest Neighbour ◽

Web Usage ◽

Knn Classification ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Usage Data

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A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-spatial Attribute Data Mining: A Case Study Experience

Artificial Neural Network Modelling - Studies in Computational Intelligence ◽

10.1007/978-3-319-28495-8_21 ◽

2016 ◽

pp. 443-472 ◽

Cited By ~ 1

Author(s):

Subana Shanmuganathan

Keyword(s):

Neural Network ◽

Data Mining ◽

Artificial Neural Network ◽

Attribute Data ◽

Study Experience ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hybrid Artificial Neural Network

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Data Mining using Artificial Neural Network POS_NEG Rules

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v11i6.3046 ◽

2013 ◽

Vol 11 (6) ◽

pp. 2709-2714

Author(s):

Pushkar Shinde ◽

Dr. Varsha Patil

Keyword(s):

Neural Network ◽

Data Mining ◽

Sensitivity Analysis ◽

Artificial Neural Network ◽

Backpropagation Algorithm ◽

The Neural Network ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann ◽

Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

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