Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization

2021 ◽  
Vol 30 (3) ◽  
pp. 2663-2685
Author(s):  
Xuan-Nam Bui ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
Dinh-An Nguyen ◽  
Hoang-Bac Bui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cuckoo Search ◽  
Ground Vibrations ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Artificial Neural

scholarly journals Enhanced Cuckoo Search Optimization and Hybrid Firefly Artificial Neural Network Algorithm for Cyberbullying Detection on Twitter Dataset

2021 ◽  
pp. 455-470
Author(s):  
Sherly T. T. ◽  
Dr. B. Rosiline Jeetha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cuckoo Search ◽  
Feature Subset Selection ◽  
Experimental Result ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Artificial Neural ◽  
Cyberbullying Detection ◽  
The Given

With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. Cyberbullying detection is generally in social networks like Twitter is one of the focussed research area. Cyberbullying is serious and widespread issues affecting increasingly more Internet users. Text mining tools are detecting cyber bullying and deal with several issues. However the existing system has issue with time consumption and inaccurate Cyberbullying detection results for the given Twitter dataset. To avoid the above mentioned issues, in this work, Enhanced Cuckoo Search optimization (ECSO) and Hybrid Firefly Artificial Neural Network (HFANN) algorithm is proposed. The proposed system contains three main phases are such as preprocessing, feature subset selection and classification. The preprocessing is done by using k-means algorithm for reducing the noise data from the given Twitter dataset. It handles the missing features and redundancy features through k-means centroid values and min max normalization respectively. It is used to increase the classification accuracy more effectively. The pre-processed features are taken into feature selection process for obtaining more informative features from the Twitter dataset. It is performed by using ECSO algorithm and the objective function is used to compute the relevant and important feature based on the best fitness values. Then the HFANN algorithm is applied for classification through training and testing model. It classifies the features more accurately using best fireflies rather than the previous algorithms. The experimental result proves that the proposed ECSO+HFANN algorithm provides better classification performance in terms of lower time complexity, higher precision, recall, f-measure and accuracy than the existing algorithms.

Prediction of blast-induced ground vibration using empirical models and artificial neural network (Bakhtiari Dam access tunnel, as a case study)

2019 ◽  
Vol 26 (7-8) ◽  
pp. 520-531 ◽  
Author(s):  
Ali M Rajabi ◽  
Alireza Vafaee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Ground Vibration ◽  
Ground Vibrations ◽  
Blasting Operation ◽  
Access Road ◽  
Artificial Neural

Blasting operation is among the most common methods of rock excavation in the civil engineering and mining operations. Ground vibration is the most unfavorable effect of blasting operation such that failure to accurately control this problem causes damage to adjacent structures. In this regard, geotechnical engineers face the challenge of accurately predicting blast-induced ground vibrations. Geographical location of Bakhtiari Dam (located in the southwest of Iran) is needed to construct an access road to its nearest city through the rough topography. To establish the access road in the plan, blasting operation methods have been used. In this study, blast-induced ground vibrations in the study area are evaluated using five common functional forms of the empirical model and their corrected regression coefficient for the area. Then, the ground vibrations generated in the study area were predicted by designing an artificial neural network model. For this purpose, the maximum charge per delay, the distance between the blast point and monitoring stations, and the ground vibration values were surveyed for 80 blast events, and their necessary parameters were determined. A total of 64 datasets were used to obtain the coefficients of the empirical models and to create the artificial neural network model. In addition, 16 datasets were used to estimate the performance and accuracy of each model. To measure the accuracy of the constructed models, some statistical parameters were also used. The results show that in the study area, the artificial neural network model presents the most accurate and appropriate model for predicting blast-induced ground vibrations. The neural network proposed in this research is suggested for areas with geological features resembling those of the present study.

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