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.

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 Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
R. Manjula Devi ◽  
S. Kuppuswami ◽  
R. C. Suganthe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Model ◽  
Training Data ◽  
Experimental Result ◽  
Training Algorithms ◽  
Data Set ◽  
Training Time ◽  
Input Sample ◽  
Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

scholarly journals Autoencoder Artificial Neural Network Public Key Cryptography in Unsecure Public channel Communication

2019 ◽  
Vol 8 (11) ◽  
pp. 4023-4032
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Communication ◽  
Public Key Cryptography ◽  
Experimental Result ◽  
Main Concern ◽  
New Approach ◽  
Digital World ◽  
Cryptographic Algorithm ◽  
Artificial Neural

As we all known that cryptography is a procedure to hide data so that it can’t be access or modified by any unauthorized entity. At the present digital world security is a main concern. To maintain this security there are many cryptographic algorithm exist. But the world technology grew each and every day so we have to find some new algorithms to maintain the security at higher level. In the proposed and implemented work used artificial neural network to increase the security during data communication in digital world. Autoencoder Neural Network is a new approach in the era of digital world so that used here in cryptographic algorithm to increase the strength of the security. There are three basic aims of cryptography availability, privacy and integrity easily achieved by this new approach. This work examine that the attacker can’t get access the data however he/she exist in the same network or not. Neural Network’s uncertainty property make this possible. This approach also examined on different data size and key size. Proposed work used the autoencoder for encryption and decryption. The final experimental result show our purposed algorithm efficient and accurate and also show how this approach perform better. Proposed and implemented algorithm can be easily used for secure data communication with more efficiently

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