scholarly journals An Ann Simulation of Single Server with Infinite Capacity Queuing System

2019 ◽  
Vol 8 (12) ◽  
pp. 4067-4071 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mathematical Models ◽  
Analytical Method ◽  
Arrival Rate ◽  
Queuing System ◽  
Single Server ◽  
Queuing Models ◽  
Artificial Neural Network Ann ◽  
Number Of Customers

Successful formulation of queuing models depends on arrival rate, nature of waiting in queues, type of service and customer leaving the system depends on type of arrival, nature of service, number of servers deputed, type of queues, number of customers approaching for service in the system and delay. Kendall notations are popularly used for designating the queuing models like M/M/C/E/D. Various mathematical models have been developed to solve the queuing problem analytically. However solving queuing models with power of computers is the new area of research and this work intends to develop single server infinite capacity queuing system using Artificial Neural Network(ANN). The results of simulation are compared with that of analytical method.

scholarly journals Prediksi Tingkat Kedatangan Wisatawan Asing Menggunakan Metode Backpropagation (Studi Kasus: Kepulauan Mentawai)

2020 ◽  
pp. 98-103
Author(s):  
Salman Alfarisi Salimu ◽  
Yuhandri Yunus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Foreign Exchange ◽  
National Economy ◽  
Arrival Rate ◽  
Tourism Industry ◽  
Training Data ◽  
Optimal Prediction ◽  
Mentawai Islands ◽  
Artificial Neural Network Ann

The tourism industry is always growing and plays an important role in the national economy, both as the second largest contributor to foreign exchange and as a large labor absorber. This study aims to optimize production using the Artificial Neural Network (ANN) method. The technique used is Backpropagation. The data processed is data on the number of foreign tourists from 2017 to 2019 in the Mentawai Islands. The results of the momentum obtained are 2-5-1 on the division of data into 2, namely training data for 2017 and 2018 and test data for 2019. The optimal prediction result is 0.99847, so this research is very helpful in predicting the arrival rate of foreign tourists in Mentawai Islands.

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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