Using Artificial Intelligent to Model Predict the Biological Resilience With an Emphasis on Population of cyanobacteria in Jajrood River in The Eastern Tehran, Iran

2022 ◽  
Author(s):  
Naghmeh Jafarzadeh ◽  
S. Ahmad Mirbagheri ◽  
Taher Rajaee ◽  
Afshin Danehkar ◽  
Maryam Robati
Keyword(s):  
Artificial Intelligent

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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.

"Consumer Perception and Adoption Intention of Artificial Intelligent Speaker: Non-Users Perspective"

2019 ◽  
Vol 30 (2) ◽  
pp. 193-213 ◽  
Author(s):  
Jin-Myong Lee ◽  
Minji Jung ◽  
Jurae Lee ◽  
Ye-eun Kim ◽  
Chiyeon An
Keyword(s):  
Consumer Perception ◽  
Artificial Intelligent ◽  
Adoption Intention

scholarly journals Novel artificial intelligent/neural network system for staging of nonalcoholic steatohepatitis

2021 ◽  
Author(s):  
Takeshi Okanoue ◽  
Toshihide Shima ◽  
Yasuhide Mitsumoto ◽  
Atsushi Umemura ◽  
Kanji Yamaguchi ◽  
...  
Keyword(s):  
Neural Network ◽  
Nonalcoholic Steatohepatitis ◽  
Network System ◽  
Artificial Intelligent ◽  
Neural Network System
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