scholarly journals Online Healthcare Medium for Disease-Treatment using Modified ANN based Classification and Ranking

2020 ◽  
Vol 9 (3) ◽  
pp. 4029-4036
Keyword(s):  
Neural Network ◽  
Information Technologies ◽  
Online Review ◽  
Current Investigation ◽  
Weight Optimization ◽  
Disease Treatment ◽  
User Query ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann

The fundamental purpose of the healthcare information medium in social networks is centered on ascertaining the opinions of several people regarding specific user queries. In the backdrop of ever-increasing accessibility and attractiveness of the opinion-rich resources as evidenced by the online review sites and personal blogs, the emerging opportunities and challenges dynamically make use information technologies to go in for and to comprehend the outlook of the vast majority of users. However, it is unfortunate that the time-honored finds its waterloo in locating the impending issue of deploying internet with a view to identify and generate appropriate conclusions regarding the specified ailments. The current investigation effectively carries out the function of processing the user query with the able assistance of the MedHelp website and subsequently forwards the pertinent traits to the sentiwordnet for performing the sentimental examination. It is followed by the creation of the score in accordance with the positivity and negativity of the content in the website. In this regard, the Artificial Neural Network (ANN) is ably guided with the aim of creating rank for the websites. And the weight optimization for ANN is elegantly executed by the efficient Grasshopper Optimization Algorithm (GOA). The technique is performed on the powerful platform of JAVA and the consequent outcomes assessed exhibits incredible decrease in the error rate.

scholarly journals Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi
Keyword(s):  
Compressive Strength ◽  
Optimization Algorithms ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Tree Model ◽  
Grasshopper Optimization Algorithm ◽  
Compressive Strength Of Concrete ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann ◽  
Industry Needs

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

scholarly journals Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10826
Author(s):  
Hamed Izadgoshasb ◽  
Amirreza Kandiri ◽  
Pshtiwan Shakor ◽  
Vittoria Laghi ◽  
Giada Gasparini
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Cement Mortar ◽  
Iteration Process ◽  
Machine Learning Method ◽  
Structural Members ◽  
Grasshopper Optimization Algorithm ◽  
3D Printed ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann

Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.

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.

Binary Grasshopper Optimization Based Feature Selection For Intrusion Detection System Using Feed Forward Neural Network Classifier

2020 ◽  
Vol 13 ◽  
Author(s):  
M. Jeyakarthic ◽  
A. Thirumalairaj
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Superior Performance ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Background: Due to the advanced improvement in internet and network technologies, significant number of intrusions and attacks takes place. An intrusion detection system (IDS) is employed to prevent distinct attacks. Several machine learning approaches has been presented for the classification of IDS. But, IDS suffer from the curse of dimensionality that results to increased complexity and decreased resource exploitation. Consequently, it becomes necessary that significant features of data must be investigated by the use of IDS for reducing the dimensionality. Aim: In this article, a new feature selection (FS) based classification system is presented which carries out the FS and classification processes. Methods: Here, the binary variants of the Grasshopper Optimization Algorithm called BGOA is applied as a FS model. The significant features are integrated using an effective model to extract the useful ones and discard the useless features. The chosen features are given to the feed forward neural network (FFNN) model to train and test the KDD99 dataset. Results: The validation of the presented model takes place using a benchmark KDD Cup 1999 dataset. By the inclusion of FS process, the classifier results gets increased by attaining FPR of 0.43, FNR of 0.45, sensitivity of 99.55, specificity of 99.57, accuracy of 99.56, Fscore of 99.59 and kappa value of 99.11. Conclusion: The experimental outcome ensured the superior performance of the presented model compared to diverse models under several aspects and is found to be an appropriate tool for detecting intrusions.

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.

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