Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches

Striuk, Oleksandr; Kondratenko, Yuriy

doi:10.47839/ijc.20.3.2278

Cited by 14 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning is a method of machine learning based on the structure of artificial neural networks. Such a structure "can be trained using supervised and unsupervised learning algorithms" (Striuk & Kondratenko, 2021).…”

Section: Theoretical Fundamentals Of the Researchmentioning

confidence: 99%

Digital technologies for conducting dictations in Ukrainian

Piatykop,

Pronina,

Kotykhova

2023

UESIT

View full text Add to dashboard Cite

At present, modern information technologies are developing very rapidly. A lot of different software tools are being created to improve and simplify people's lives. This is especially true in the context of distance learning. In the context of online learning, the traditional form of conducting dictations needs to be transformed. Therefore, it is possible to propose automation of the process of conducting and checking dictations using software. The paper analyzes scientific research and publications of the current state of language synthesis technologies and text similarity testing. With the help of C# and the language synthesis libraries of Microsoft, Google, Amazon, software has been developed that allows the user to listen and type text, and then automatically check it with the initial sample. The conducted testing for different types of errors showed the possibility and expediency of development the system for conducting and checking dictations. Follow-up activities will focus on improving the effectiveness of the program. The use of such programs will help improve the organization of conducting and checking dictations during distance learning and self-training of students.

show abstract

Section: Theoretical Fundamentals Of the Researchmentioning

confidence: 99%

Digital technologies for conducting dictations in Ukrainian

Piatykop,

Pronina,

Kotykhova

2023

UESIT

View full text Add to dashboard Cite

show abstract

“…BFill() is used to backfill the dataset's missing values. NaN values in the pandas dataframe will be retroactively filled in [17,23].…”

Section: Data Cleaning and Preprocessingmentioning

confidence: 99%

Breast tumor prediction and feature importance score finding using machine learning algorithms

Kabir,

Ahmmed,

Siddique

et al. 2023

reks

View full text Add to dashboard Cite

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process. The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes.

show abstract

“…Overall, GANs have become a crucial tool in the deep learning toolkit, facilitating the creation of high-quality synthetic data for numerous applications [3].…”

Section: A Background Of Gansmentioning

confidence: 99%

“…ENERATIVE Adversarial Networks (GANs) are a type of deep learning algorithm first proposed by Ian Goodfellow in 2014 [1]. The primary motivation behind GANs is to create high-quality synthetic data for various applications, such as image, photo, and video generation [2,3]. Further advancements in GANs development eventually demonstrated huge progress in such significant scientific and applied domains as anomaly detection [4], cybersecurity [5], medicine and drug discovery, forensics, material science, and astronomy research [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

Optimization Strategy for Generative Adversarial Networks Design

Striuk,

Kondratenko

2023

IJC

View full text Add to dashboard Cite

Generative Adversarial Networks (GANs) are a powerful class of deep learning models that can generate realistic synthetic data. However, designing and optimizing GANs can be a difficult task due to various technical challenges. The article provides a comprehensive analysis of solution methods for GAN performance optimization. The research covers a range of GAN design components, including loss functions, activation functions, batch normalization, weight clipping, gradient penalty, stability problems, performance evaluation, mini-batch discrimination, and other aspects. The article reviews various techniques used to address these challenges and highlights the advancements in the field. The article offers an up-to-date overview of the state-of-the-art methods for structuring, designing, and optimizing GANs, which will be valuable for researchers and practitioners. The implementation of the optimization strategy for the design of standard and deep convolutional GANs (handwritten digits and fingerprints) developed by the authors is discussed in detail, the obtained results confirm the effectiveness of the proposed optimization approach.

show abstract

Generative Adversarial Neural Networks and Deep Learning: Successful Cases and Advanced Approaches

Cited by 14 publications

References 31 publications

Digital technologies for conducting dictations in Ukrainian

Digital technologies for conducting dictations in Ukrainian

Breast tumor prediction and feature importance score finding using machine learning algorithms

Optimization Strategy for Generative Adversarial Networks Design

Contact Info

Product

Resources

About