scholarly journals Benchmarking the Data Mining Algorithms with Adaptive Neuro-Fuzzy Inference System in GSM Churn Management

10.5772/6451 ◽  
2009 ◽  
Author(s):  
Adem Karahoca ◽  
Dilek Karahoca ◽  
Nizamettin Ayd
Keyword(s):  
Data Mining ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Data Mining Algorithms ◽  
Neuro Fuzzy ◽  
Mining Algorithms ◽  
Churn Management

scholarly journals PEMETAAN BENTUK DAN PENCEGAHAN PENIPUAN E-COMMERCE

2020 ◽  
pp. 851
Author(s):  
Ni Komang Arista Dewi ◽  
Luh Putu Mahyuni
Keyword(s):  
Data Mining ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Digital Publishing ◽  
Privacy Concern ◽  
Inference System ◽  
Neuro Fuzzy

Seiring berkembangnya transaksi jual beli, penipuan elektronik juga turut meningkat sehingga mengakibatkan banyak konsumen yang telah mengalami kerugian akibat penipuan yang terjadi. Tujuan dari penelitian ini adalah untuk mengetahui jenis penipuan yang dapat terjadi dalam perdagangan elektronik dan pencegahan yang dapat dilakukan. Dalam penelitian ini disajikan review dengan metode pendekatan interpretif atas artikel terkait, dengan proses pemetaan pada artikel yang dikumpulkan melalui situs Google Cendekia, Elsevier, Springer, Taylor & Francis, dan MDPI (Multidisciplinary Digital Publishing Institute). Dari sumber tersebut, 105 artikel berhasil dikumpulkan, setelah proses seleksi artikel berdasarkan 10 tahun terahir dan kesesuaian pembahasan akhirnya diperoleh 55 artikel. Hasil penelitian ini adalah ditemukan berbagai jenis penipuan pada keempat kategori e-coomerce serta penipuan pada sistem pebayaran dan penipuan pada e-commerce yang menyangkut pelanggan. Metode modern pendeteksi penipuan juga disajikan dalam penelitian ini, seperti data mining, jaringan bayesan, algoritma, mesin pendukung vector, pemrograman genetik, pohon pengambilan keputusan, Adaptive Neuro-Fuzzy Inference System, Situs Web Bantuan Perlindungan (PAW), dan Model Privacy Antecedent-Privacy Concern-Outcomes (APCO). Dengan penjabaran pada hasil penelitian ini, konsumen diharapkan untuk lebih berhati-hati saat melakukan transaksi di situs e-commerce agar terhindar dari berbagai tindak penipuan.

scholarly journals An Intelligent Data Mining Model for Diabetic Patients Data Using Hybrid Adaptive Neuro- Fuzzy Inference System

2019 ◽  
Vol 8 (6) ◽  
pp. 1498-1503
Keyword(s):  
Data Mining ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Health Clinic ◽  
Diabetic Patients ◽  
Inference System ◽  
Clustering Model ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Mining Model

Proper diagnosis of diabetic based on the patient’s medical analysis results is an important factor. Data mining helps in analyzing such data includes complex meaningful terms to diagnosis and supports the patients to take remedy action based on the accurate results. The proposed model is a data mining model for analyzing diabetic patient’s data using sugeno type adaptive neuro fuzzy inference system with principle component analysis as a hybrid system. The experimental model validated through 200 different data obtained from health clinic with 25 different attributes. The proposed model classifies the data with accuracy of 94.6% where as conventional rough set and k means clustering model produces less classification accuracy of 74.5% and 77.6%.

scholarly journals Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

2014 ◽  
Vol XL-2/W3 ◽  
pp. 19-23
Author(s):  
P. Akhavan ◽  
M. Karimi ◽  
P. Pahlavani
Keyword(s):  
Data Mining ◽  
Cutaneous Leishmaniasis ◽  
Fuzzy Inference System ◽  
Prevalence Rate ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

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