A Novel Approach Based to Neural Network and Flower Pollination Algorithm to Predict Number of COVID-19 Cases

2021 ◽  
Author(s):  
Ceren BAŞTEMUR KAYA ◽  
Ebubekir KAYA
Keyword(s):  
Neural Network ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Novel Approach

Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm

2018 ◽  
Vol 29 (1) ◽  
pp. 787-798 ◽  
Author(s):  
Pijush Dutta ◽  
Asok Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Liquid Flow ◽  
Liquid Flow Rate ◽  
Pipe Diameter ◽  
Flower Pollination Algorithm ◽  
Sensor Output ◽  
Flower Pollination ◽  
Artificial Neural

Abstract Controlling liquid flow is one of the most important parameters in the process control industry. It is challenging to optimize the liquid flow rate for its highly nonlinear nature. This paper proposes a model of liquid flow processes using an artificial neural network (NN) and optimizes it using a flower pollination algorithm (FPA) to avoid local minima and improve the accuracy and convergence speed. In the first phase, the NN model was trained by the dataset obtained from the experiments, which were carried out. In these conditions, the liquid flow rate was measured at different sensor output voltages, pipe diameter and liquid conductivity. The model response was cross-verified with the experimental results and found to be satisfactory. In the second phase of work, the optimized conditions of sensor output voltages, pipe diameter and liquid conductivity were found to give the minimum flow rate of the process using FPA. After cross-validation and testing subdatasets, the accuracy was nearly 94.17% and 99.25%, respectively.

scholarly journals Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Sudip Mandal ◽  
Abhinandan Khan ◽  
Goutam Saha ◽  
Rajat K. Pal
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Real World ◽  
Large Scale ◽  
Cuckoo Search ◽  
Genetic Networks ◽  
Computational Time ◽  
Flower Pollination Algorithm ◽  
Small Scale ◽  
Flower Pollination

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

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