scholarly journals Ionospheric Scintillation Prediction on S4 and ROTI Parameters Using Artificial Neural Network and Genetic Algorithm

2021 ◽  
Vol 13 (11) ◽  
pp. 2092
Author(s):  
Alireza Atabati ◽  
Mahdi Alizadeh ◽  
Harald Schuh ◽  
Lung-Chih Tsai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Signal To Noise Ratio ◽  
Ionospheric Scintillation ◽  
Winter Solstice ◽  
Ionospheric Scintillations ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Gnss Observations

Irregularities in electron density usually correlate with ionospheric plasma perturbations. These variations causing radio signal fluctuations, in response, generate ionospheric scintillations that frequently occur in low-latitude regions. In this research, the combination of an artificial neural network (ANN) with the genetic algorithm (GA) was implemented to predict ionospheric scintillations. The GA method was considered for obtaining the ANN model’s initial weights. This procedure was applied to GNSS observations at GUAM (13.58°E, 144.86°N, 201.922H) station for the daily prediction of ionospheric amplitude scintillations via predicting the signal-to-noise ratio (S4) or via prediction of the rate of TEC index (ROTI). Thirty-day modeling was carried out for three months in January, March, and July, representing different seasons of the winter solstice, equinox, and summer solstice during three different years, 2015, 2017, and 2020, with different solar activities. The models, along with ionospheric physical data, were used for the daily prediction of ionospheric scintillations for the consequent day after the modeling. The prediction results were evaluated using S4 derived from GNSS observations at GUAM station. The designed model has the ability to predict daily ionospheric scintillations with an accuracy of about 81% for the S4 and about 80% for the ROTI.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN)

2014 ◽  
Vol 902 ◽  
pp. 431-436 ◽  
Author(s):  
A. Shahpanah ◽  
S. Poursafary ◽  
S. Shariatmadari ◽  
A. Gholamkhasi ◽  
S.M. Zahraee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Waiting Time ◽  
Container Terminal ◽  
Hybrid Approach ◽  
Simulation Software ◽  
Optimum Number ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

A queuing network model related to arrival, departure and berthing process of ships at port container terminal is presented in this paper. The important datas collected from PTP port container terminal located at Malaysia. Based on the case study the model was built with using Arena 13.5 simulation software. Especially this study proposes a hybrid approach consisting of Genetic algorithm (GA), Artificial Neural Network (ANN) to find the the optimum number of equipments at berthing area of port container terminal. The input data that used in ANN obtained from Arena results. The main goal of this study is reduced waiting time of each ship at port container terminal, and Based on the result the optimum waiting time 50 will be achieved.

scholarly journals Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3415-3421
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Virtual Machines ◽  
Fitness Function ◽  
Medium Size ◽  
Cloud Environment ◽  
Edos Attacks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Economic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.

scholarly journals Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

2013 ◽  
Vol 3 (4) ◽  
pp. 243-250 ◽  
Author(s):  
Samira Arabgol ◽  
Hoo Sang Ko
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Negative Impact ◽  
Healthcare Waste ◽  
Mean Squared Errors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

scholarly journals Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Montri Inthachot ◽  
Veera Boonjing ◽  
Sarun Intakosum
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stock Price ◽  
Future Trend ◽  
Past Time ◽  
Hybrid Intelligence ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand’s SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid’s prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

Optimization of Culture Medium for Maximal Production of Spinosad Using an Artificial Neural Network - Genetic Algorithm Modeling

2015 ◽  
Vol 25 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Zhou Lan ◽  
Chen Zhao ◽  
Weiqun Guo ◽  
Xiong Guan ◽  
Xiaolin Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Saccharopolyspora Spinosa ◽  
Medium Components ◽  
Key Factor ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Rsm Model

<b><i>Background:</i></b> Spinosyns, products of secondary metabolic pathway of <i>Saccharopolyspora spinosa</i>, show high insecticidal activity, but difficulty in enhancing the spinosad yield affects wide application. The fermentation process is a key factor in this case. <b><i>Methods:</i></b> The response surface methodology (RMS) and artificial neural network (ANN) modeling were applied to optimize medium components for spinosad production using <i>S. spinosa </i>strain CGMCC4.1365. Experiments were performed using a rotatable central composite design, and the data obtained were used to construct an ANN model and an RSM model. Using a genetic algorithm (GA), the input space of the ANN model was optimized to obtain optimal values of medium component concentrations. <b><i>Results:</i></b> The regression coefficients (R<sup>2</sup>) for the ANN and RSM models were 0.9866 and 0.9458, respectively, indicating that the fitness of the ANN model was higher. The maximal spinosad yield (401.26 mg/l) was obtained using ANN/GA-optimized concentrations. <b><i>Conclusion:</i></b> The hybrid ANN/GA approach provides a viable alternative to the conventional RSM approach for the modeling and optimization of fermentation processes.

scholarly journals IMPLEMENTATION OF GENETIC ALGORITHM BASED ARTIFICIAL NEURAL NETWORK TO IDENTIFY VEGETABLES WITH PHYSIOLOGICAL DISEASES

2013 ◽  
pp. 72-77
Author(s):  
Bibhu Prasad ◽  
Ashima Sindhu Mohanty ◽  
Ami Kumar Parida
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Vision System ◽  
Computer Vision System ◽  
Accuracy Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Neural Network Technology

We synthetically applied computer vision, genetic algorithm and artificial neural network technology to automatically identify the vegetables (tomatoes) that had physiological diseases. Initially tomatoes’ images were captured through a computer vision system. Then to identify cavernous tomatoes, we analyzed the roundness and detected deformed tomatoes by applying the variation of vegetable’s diameter. Later, we used a Genetic Algorithm (GA) based artificial neural network (ANN). Experiments show that the above methods can accurately identify vegetables’ shapes and meet requests of classification; the accuracy rate for the identification for vegetables with physiological diseases was up to 100%. [Nature and Science. 2005; 3(2):52-58].

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