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

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

Implementasi Rough Neural Network dalam Identifikasi Kepuasan Konsumen Mediasi Bisnis

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.

scholarly journals Data Mining using Artificial Neural Network POS_NEG Rules

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.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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