Fuzzification of training data class membership binary values for neural network algorithms

Tajti, Tibor

doi:10.33039/ami.2020.10.001

Cited by 5 publications

(4 citation statements)

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“…To further advance the field and enhance model performance, future research could explore the following developments: employing segmentation techniques to refine the input data, exploring the potential benefits of using fuzzification techniques for refining binary class membership values during model training, investigating alternative ensemble methods that may provide additional benefits, experimenting with different datasets to assess the robustness of the models, and incorporating various machine learning models, such as recurrent neural networks, to address the specific challenges of the task [4,11,12]. While the results presented in this study are promising, it is important to conduct additional research and analysis to fully comprehend the behavior and potential of these models, as this understanding can ultimately lead to more accurate and reliable methods.…”

Section: Discussionmentioning

confidence: 99%

CAPTCHA recognition using machine learning algorithms with various techniques

Kovács,

Tajti

2023

AMI

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In this paper, we present research results on the recognition of text-based CAPTCHA tests using advanced machine learning algorithms and techniques. Text-based CAPTCHAs serve as a crucial security measure to prevent automated access to various web services, but their effectiveness depends on their resistance to sophisticated recognition techniques. To this end, we focus on evaluating and enhancing the performance of recognition models using a Convolutional Neural Network (CNN) as the base model. We propose an integrated approach, which incorporates a systematic parameter optimization strategy using Grid Search Cross-Validation (Grid Search CV) and the Ensemble Voting Method to improve the performance of the recognition model. The use of Grid Search CV enables us to fine-tune the hyperparameters of the CNN model, leading to an optimal configuration. Further, we investigate the effectiveness of the Ensemble Voting Method to aggregate the predictions from multiple CNN models, each with a set of the optimal parameters obtained from the Grid Search CV. The methods' performance was evaluated through multiple learning sessions, assessing their effectiveness in recognizing text-based CAPTCHAs under various scenarios.

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Section: Discussionmentioning

confidence: 99%

CAPTCHA recognition using machine learning algorithms with various techniques

Kovács,

Tajti

2023

AMI

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“…The scientific contribution of this work is based on a set of original and improved methods and models for estimating the effort and cost of developing software projects. Through the approach presented, improved methods based on existing models will be using: different data sets, clustering methods 25 and fuzzification methods 26,27 for ANN architecture constructed based on Taguchi's orthogonal vector plan and different activation functions. 28,29 In addition, various metrics were calculated, and criteria set such as mean absolute error (MAE), magnitude relative error (MRE), mean magnitude relative error (MMRE), 30 prediction on three criteria (PRED) 31,32 correlation (Pearson's, Spearman's, and R 2 ).…”

Section: Related Workmentioning

confidence: 99%

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

Ranković

Ivanović

et al. 2022

Concurrency and Computation

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Rapid and accurate assessment of software project development using artificial intelligence tools can be essential for success in the software industry. This article has two objectives: to reduce the magnitude relative error (MRE) value in estimating the effort and cost of software development using the proposed artificial neural network architecture based on the Taguchi method and examine the influence of input variables on the change in relative error value. Clustering and fuzzification methods further mitigate the heterogeneous structure of the different project values of the datasets used.Taguchi method contributes to the reduction of the number of iterations by 99%, which achieves a significant reduction in estimation and value of MRE. By monitoring additional criteria, such as prediction, correlation, and comparing two activation functions, such as sigmoid and radial basis function, the proposed model's correctness, reliability, and stability are confirmed. Significantly better results are expected using the sigmoid activation function and a decrease in the value of the mean (MRE).

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“…It is also possible to fuzzify the binary class membership values. These matters are discussed in detail in the context of neural network algorithms in [34], where it is convincingly asserted that machine learning can "think" between false and true.…”

Section: Classification Vs Regressionmentioning

confidence: 99%

A Framework for Crowd Management during COVID-19 with Artificial Intelligence

Almutairi¹,

Yamin

Halikias³

et al. 2021

Sustainability

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COVID-19 requires crowded events to enforce restrictions, aimed to contain the spread of the virus. However, we have seen numerous events not observing these restrictions, thus becoming super spreader events. In order to contain the spread of a human to human communicable disease, a number of restrictions, including wearing face masks, maintaining social distancing, and adhering to regular cleaning and sanitization, are critical. These restrictions are absolutely essential for crowded events. Some crowded events can take place spontaneously, such as a political rally or a protest march or a funeral procession. Controlling spontaneous crowded events, like a protest march, political rally, celebration after a sporting event, or concert, can be quite difficult, especially during a crisis like the COVID-19 pandemic. In this article, we review some well-known crowded events that have taken place during the ongoing pandemic. Guided by our review, we provide a framework using machine learning to effectively organize crowded events during the ongoing and for future crises. We also provide details of metrics for the validation of some components in the proposed framework, and an extensive algorithm. Finally, we offer explanations of its various functions of the algorithm. The proposed framework can also be adapted in other crises.

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Fuzzification of training data class membership binary values for neural network algorithms

Cited by 5 publications

References 0 publications

CAPTCHA recognition using machine learning algorithms with various techniques

CAPTCHA recognition using machine learning algorithms with various techniques

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

A Framework for Crowd Management during COVID-19 with Artificial Intelligence

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