An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

A Message Length Verification of Modern Messaging Systems

Nowadays, there has been a rapid increase in the variety and popularity of messaging systems and social networks. It is imperative to consider the effect and impact of the number of words feature on the verification process for modern messaging systems such as Twitter, Facebook, SMS and Email. Given the volume of text is often a restricted factor (due to the nature of messaging systems), key to this investigation is a better understanding of what length of message is required to improve performance. A large historical dataset containing 50 participants, the four datasets containing a large number of messaging system samples (4539 samples for Facebook, 13,616 for Twitter, 6538 for Email and 106,359 for Text message), the best performance was for Text messages, with an EER of 7.6% if the number of words was more than nine;followed by Email with an EER of 14.9% if the number of words was between 25 to 60;then, Twitter tweets, with an EER of 22.5% if the number of words was less than ten. Finally, the Facebook platform with an EER of 31.9% if the number of words was over 11.