MODELING VOLUME CHANGE PROPERTIES OF HYDRATED-LIME ACTIVATED RICE HUSK ASH (HARHA) MODIFIED SOFT SOIL FOR CONSTRUCTION PURPOSES BY ARTIFICIAL NEURAL NETWORK (ANN)

2020 ◽  
pp. 88-110 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rice Husk ◽  
Volume Change ◽  
Rice Husk Ash ◽  
Soft Soil ◽  
Hydrated Lime ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals PREDIKSI KUAT TEKAN PERVIOUS PAVING DENGAN CAMPURAN ABU SEKAM DENGAN MENGGUNAKAN PEMODELAN ARTIFICIAL NEURAL NETWORK (ANN)

2019 ◽  
Vol 1 (1) ◽  
pp. 46-52
Author(s):  
Erna Suryani ◽  
Wahyu Naris Wari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Rice Husk ◽  
Empirical Equation ◽  
Rice Husk Ash ◽  
Asphalt Pavement ◽  
Construction Material ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Pervious Paving (Paving Berpori) adalah material konstruksi yang terbuat dari semen, air, agregat dan bahan campuran lainnya. Paving berpori dapat diapilkasikan pada trotoar, area bermain dan jalan perumahan. Dengan menggunakan paving berpori air akan langsung meresap, sehingga akan mencegah adanya genangan air pada lapis permukaan paving. Metode penelitian yang digunakan adalah menggunakan AAPA (Australian Asphalt Pavement Association) dimana dilakukan sistem Trial Eror. Campuran yang digunakan adalah 1:4, dengan menggunakan gradasi terbuka. Kuat tekan yang di rencanakan yaitu 18,00 MPa, masuk dalam kategori mutu B untuk tempat parkir mobil, pejalan kaki dan taman kota. Campuran paving menggunakan abu sekam padi sebagai reduksi semen dengan persentase 0%, 10%, 20% dan 30%. Pelaksanaan pekerjaan dimulai dari pengambilan bahan baku, pengujian material, perencanaan komposisi dan pembuatan benda uji dengan ukuran P = 21 cm, L = 11,5 cm dan T = 6 cm. Uji kuat tekan dilakukan untuk mengetahui pengaruh abu sekam sebagai bahan reduksi semen pada Paving Berpori. Nilai kuat tekan yang didapatkan akan menjadi input pada program Matlab untuk mendapatkan pemodelan Persamaan Empiris dengan ARTIFICIAL NEURAL NETWORK (ANN) sehingga didapatkan nilai kuat tekan dari berbagai komposisi penambahan bahan abu sekam. Dari hasil penelitian didapatkan persentase tertinggi dicapai pada tambahan abu sekam 30%.Kata kunci : Abu sekam, Artificial Neural Network (ANN), Pervious Paving, Kuat tekan, Persamaan EmpirisPervious Paving is a construction material made from cement, water, aggregate and other materials. Pervious paving can be applied to right on sidewalks, play ground and residential roads. By using Pervious Paving, the water will absorb quickly, so it will prevent the puddles on the surface layer. AAPA (Australian Asphalt Pavement Association) is the reserach methode which we used with Trial and Eror. The mixture of ingredients is 1: 4 with the open gradation. The compressive strength designed is 18 MPa, which is in category B for parking car, pedestrian and city park. Paving mixture consisted of rice husk ash as cement reduction with a percentage of 0%, 10%, 20% and 30%. The work starting from the taking of raw materials, material testing, composition planning and the making of specimens with sizes P = 21 cm, L = 11.5 cm and T = 6 cm. The compressive strength test was conducted to determine the effect of husk ash addition. The compressive strength will be input to the Matlab program to obtain the Empirical Equation modelling with ARTIFICIAL NEURAL NETWORK (ANN). Based on the results of the study, the highest percentage was achieved in the mixture with an addition of 30% rice husk ash.Keywords: Rice husk ash, Artificial Neural Network (ANN), Pervious Paving, Compressive strength, Empirical Equation

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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