scholarly journals Reconstruction of High Resolution Medical Image Using General Regression Neural Network (GRNN)

2020 ◽  
Vol 10 (2) ◽  
pp. 137
Author(s):  
Yudha Satya Perkasa ◽  
Khoerun Nisa Syaja'ah ◽  
Lyana Ismadelani ◽  
Rena Denya Agustina
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Input Image ◽  
Image Resolution ◽  
General Regression Neural Network ◽  
Diagnostic Process ◽  
X Rays ◽  
Input Layer ◽  
Image Position ◽  
General Regression

Low image resolution has deficiencies in the diagnostic process, this will affect the quality of the image in describing an object in certain tissues or organs, especially in the process of examining patients by doctors or physicians based on the results of imaging medical devices such as CT-scans, MRIs and X-rays. Therefore, this study had developed a General Regression Neural Network (GRNN) type artificial neural network system to reconstruct a medical image so that the image has a significant resolution for the analysis process. The GRNN input layer uses grayscale intensity values with variations in the image position coordinates to produce an optimal resolution. There are four layers in this method, the first is input layer, the second is hidden layer, the third is summation, and the last layer is output. We examined the two parameters with different interval values of 0.2 and of 0.5. The result shows that the interval value of 0.2 is the optimal value to produce an output image that is identical to the input image. This is also supported by the results of the intensity curve of the RGB pattern matched between target and output.

scholarly journals Prediksi Harga Rumah Menggunakan General Regression Neural Network

2021 ◽  
Vol 8 (1) ◽  
pp. 59-66
Author(s):  
Evi Febrion Rahayuningtyas ◽  
Feny Novia Rahayu ◽  
Yufis Azhar
Keyword(s):  
Neural Network ◽  
General Regression Neural Network ◽  
Output Layer ◽  
Input Layer ◽  
Non Linear ◽  
Pattern Layer ◽  
General Regression

Rumah merupakan salah satu kebutuhan manusia selain sandang dan pangan. Hirarki Kebutuhan Maslow menyebutkan bahwa rumah adalah salah satu Physiological Needs. Dewasa ini, kebutuhan akan rumah sering dijadikan sebagai sumber investasi bagi pengusaha properti yang tentunya menghasilkan investasi yang tidak dapat diremehkan. Keuntungan dalam jual beli rumah dijadikan alasan bagi seseorang dalam melakukan transaksi jual beli rumah itu sendiri. Sehingga munculah harga yang tidak terprediksi pada harga rumah yang ada. Penelitian ini menggunakan General Regression Neural Network sebagai pemodelannya. GRNN menggunakan dasar regresi non linear dimana strukturnya terdiri dari 4 layer, yaitu Input Layer, Pattern Layer, Summation Layer dan Output Layer.  Variabel dependen yang digunakan berjumlah 6. Terdiri dari tanggal transaksi, jarak rumah dari stasiun MRT, jumlah toko atau minimarket yang dekat dengan rumah, umur rumah, dan letak geografis berupa longitude dan latitude. Variabel independen berupa harga rumah dalam satuan luas Ping yang selanjutnya dikonversi menjadi 10.000 New Dollar Taiwan/Ping.  Hasil yang didapatkan berupa data actual dan data predicted yang divisualisasikan dengan line plot. Dilakukan juga uji akurasi dan uji kinerja pemodelan menggunakan 3 jenis evaluasi dengan hasil dari ketiga jenis evaluasi tersebut adalah 58,72 untuk skor MSE, skor 7,66 untuk RMSE dan 5,99 untuk skor MAE.

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mina Salehi ◽  
Siamak Farhadi ◽  
Ahmad Moieni ◽  
Naser Safaie ◽  
Mohsen Hesami
Keyword(s):  
Neural Network ◽  
Fruit Fly ◽  
Corylus Avellana ◽  
General Regression Neural Network ◽  
Fruit Fly Optimization Algorithm ◽  
Harvesting Time ◽  
Fruit Fly Optimization ◽  
General Regression ◽  
Concentration Levels

Abstract Background Paclitaxel is a well-known chemotherapeutic agent widely applied as a therapy for various types of cancers. In vitro culture of Corylus avellana has been named as a promising and low-cost strategy for paclitaxel production. Fungal elicitors have been reported as an impressive strategy for improving paclitaxel biosynthesis in cell suspension culture (CSC) of C. avellana. The objectives of this research were to forecast and optimize growth and paclitaxel biosynthesis based on four input variables including cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day and CSC harvesting time in C. avellana cell culture, as a case study, using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) via data mining approach for the first time. Results GRNN-FOA models (0.88–0.97) showed the superior prediction performances as compared to regression models (0.57–0.86). Comparative analysis of multilayer perceptron-genetic algorithm (MLP-GA) and GRNN-FOA showed very slight difference between two models for dry weight (DW), intracellular and extracellular paclitaxel in testing subset, the unseen data. However, MLP-GA was slightly more accurate as compared to GRNN-FOA for total paclitaxel and extracellular paclitaxel portion in testing subset. The slight difference was observed in maximum growth and paclitaxel biosynthesis optimized by FOA and GA. The optimization analysis using FOA on developed GRNN-FOA models showed that optimal CE [4.29% (v/v)] and CF [5.38% (v/v)] concentration levels, elicitor adding day (17) and harvesting time (88 h and 19 min) can lead to highest paclitaxel biosynthesis (372.89 µg l−1). Conclusions Great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion support excellent performance of developed GRNN-FOA models. Overall, GRNN-FOA as new mathematical tool may pave the way for forecasting and optimizing secondary metabolite production in plant in vitro culture.

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