Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning for Covid-19 Forecasting

2021 ◽  
Vol 19 (8) ◽  
pp. 169-181
Author(s):  
P. Renukadevi ◽  
Dr.A. Rajiv Kannan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Optimization Algorithm ◽  
Technological Development ◽  
Predictive Performance ◽  
Linear Factor ◽  
Corona Virus ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Recently the COVID’19 is extensively increasing around the world with many challenges for researchers. Rigorous respiratory disease corona virus 2 show aggression to many parts of COVID’19 affected patients, together with brain and lungs. The changeableness of Corona virus with likely to infect Central Nervous System emphasize the necessity for technological development to identify, handle, and take care of brain damages in COVID’19 patients. An exact short-term predicting the quantity of newly infected and cured cases is vital for resource optimization to stop or reduce the growth of infection. The previous system designed a Linear Decreasing Inertia Weight based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach for COVID-19 forecasting. However, the ensemble learning is required to improve the prediction outcome via integrating many approaches. This approach allows the production of better predictive performance compared to a single model. For solving this problem, the proposed system designed an Improved Linear Factor based Grasshopper Optimization Algorithm with Ensemble Learning (ILFGOA with EL) for covid-19 forecasting. Initially, the COVID-19 forecasting dataset is taken as an input. With the help of min-max approach, data normalization is done. Then the optimal features are selected by using Improved Linear Factor based Grasshopper Optimization Algorithm (ILFGOA) algorithm to improve the prediction accuracy. Based on the selected features, Ensemble Learning (EL) which includes Hyperparameter based Convolutional Neural Network (HCNN) is utilized to identify infected and demise cases across india for a period of time. The outcome of analysis shows that the introduced method attains better execution against previous system with regard to error rate, accuracy, precision, recall and f-measure.

Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm

2021 ◽  
pp. 004051752110592
Author(s):  
Zhiyu Zhou ◽  
Wenxiong Deng ◽  
Yaming Wang ◽  
Zefei Zhu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Random Vector ◽  
Optimization Algorithm ◽  
Activation Function ◽  
Support Vector ◽  
Functional Link ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

To improve accuracy in clothing image recognition, this paper proposes a clothing classification method based on a parallel convolutional neural network (PCNN) combined with an optimized random vector functional link (RVFL). The method uses the PCNN model to extract features of clothing images. Then, the structure-intensive and dual-channel convolutional neural network (i.e., the PCNN) is used to solve the problems of traditional convolutional neural networks (e.g., limited data and prone to overfitting). Each convolutional layer is followed by a batch normalization layer, and the leaky rectified linear unit activation function and max-pooling layers are used to improve the performance of the feature extraction. Then, dropout layers and fully connected layers are used to reduce the amount of calculation. The last layer uses the RVFL as optimized by the grasshopper optimization algorithm to replace the SoftMax layer and classify the features, further improving the stability and accuracy of classification. In this study, two aspects of the classification (feature extraction and feature classification) are improved, effectively improving the accuracy. The experimental results show that on the Fashion-Mnist dataset, the accuracy of the algorithm in this study reaches 92.93%. This value is 1.36%, 2.05%, 0.65%, and 3.76% higher than that of the local binary pattern (LBP)-support vector machine (SVM), histogram of oriented gradients (HOG)-SVM, LBP-HOG-SVM, and AlexNet-sparse representation-based classifier algorithms, respectively, effectively demonstrating the classification performance of the algorithm.

scholarly journals The improved grasshopper optimization algorithm and its applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peng Qin ◽  
Hongping Hu ◽  
Zhengmin Yang
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Optimization Problems ◽  
Stock Exchange ◽  
Gravity Force ◽  
Shanxi Province ◽  
Grasshopper Optimization Algorithm ◽  
Predicted Model ◽  
Grasshopper Optimization

AbstractGrasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

scholarly journals Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Ankita Tyagi ◽  
Ritika Mehra
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Heart Diseases ◽  
Classification Model ◽  
Ecg Signal ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Selection Of

AbstractAutomatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

scholarly journals A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training

2020 ◽  
Vol 25 (1) ◽  
pp. 118
Author(s):  
Samer Alsammarraie ◽  
Nazar K. Hussein
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Back Propagation ◽  
Global Optimum ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Searching Ability

The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.   http://dx.doi.org/10.25130/tjps.25.2020.018  

Sign in / Sign up

Export Citation Format

Share Document