Название: Big Data and Data Science Engineering: Volume 7
Автор: Roger Lee
Издательство: Springer
Год: 2025
Страниц: 188
Язык: английский
Формат: pdf (true), epub
Размер: 46.3 MB

The main purpose of this book is to seek peer-reviewed original research papers on the foundations and new developments in Big Data, Cloud Computing and Data Science Engineering. The focus will also be on publishing in a timely manner, the results of applying new and emerging technologies originating from research in Big Data, Cloud Computing and Data Science Engineering. The finding of this book can be applied to a variety of areas, and applications can range across many fields.

The exponential growth of user-generated content on social networks has necessitated the development of accurate methods for extracting sentiments from text. Sentiment Analysis involves understanding people’s opinions, feelings, and attitudes towards various entities, which is crucial for applications such as improving human–computer interaction, educational tools, and conversational agents. This paper presents Deep Learning methods that were implemented for sentiment and also has as a special focus the examination of the transparency and interpretability of these Deep Learning models through the use of LIME. By integrating LIME, we aim to illustrate the decision-making processes of complex Deep Learning models, providing insights into which features contribute to specific predictions. This approach not only enhances the reliability and fairness of sentiment analysis systems but also builds greater trust in Machine learning procedures.

Image Outpainting is a technique where the outside of an image is filled in a continuous manner, taking into account the context of the image. The range of applications is extensive, and numerous approaches using different generative models are researched. In recent years, using Generative Adversarial Networks (GAN) has garnered attention due to considerations of generation quality and computational cost. However, conventional methods using GAN often generate low-quality complemented images that are blurry and have low predictive accuracy due to Spectral Bias. This paper presents a novel Image Outpainting algorithm based on WaveFill using wavelet transform and Neural Neighbor Style Transfer (NNST).






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