scholarly journals Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

Sensors ◽  
2015 ◽  
Vol 15 (5) ◽  
pp. 11665-11684 ◽  
Author(s):  
Shaharil Saad ◽  
Allan Andrew ◽  
Ali Shakaff ◽  
Abdul Saad ◽  
Azman Kamarudin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Indoor Air Quality Assessment Based on Genetic Artificial Neural Network

2013 ◽  
Vol 726-731 ◽  
pp. 1147-1150 ◽  
Author(s):  
Zhe Hua Du
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Quality Assessment ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Air Quality Assessment ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Artificial Neural

A new method for indoor air quality assessment with training artificial neural network prediction model was presented. Genetic algorithm was used to train artificial neural network prediction model in this method. The train sample for artificial neural network learning model was grading standard. According to the weight and threshold after network study, this method could be used to make assessment on indoor air quality. The example of indoor air quality assessment demonstrated that this method improved prediction precision. This assessment method is more accurate than the traditional assessment methods. Moreover, it has the ability of global optimization, self-learning, self-adaption. This algorithm overcomes the shortcomings of traditional BP artificial neural network algorithm.

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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