A comprehensive survey on sentiment analysis: Approaches, challenges and trends
Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
Machine learning and data mining in manufacturing
A Dogan, D Birant - Expert Systems with Applications, 2021 - Elsevier
Manufacturing organizations need to use different kinds of techniques and tools in order to
fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining …
fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining …
Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a
pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented …
pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented …
Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time
S Ayvaz, K Alpay - Expert Systems with Applications, 2021 - Elsevier
In this study, a data driven predictive maintenance system was developed for production
lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the …
lines in manufacturing. By utilizing the data generated from IoT sensors in real-time, the …
Towards understanding ensemble, knowledge distillation and self-distillation in deep learning
Z Allen-Zhu, Y Li - arXiv preprint arXiv:2012.09816, 2020 - arxiv.org
We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …
how the superior performance of ensemble can be distilled into a single model using …
Objects are different: Flexible monocular 3d object detection
The precise localization of 3D objects from a single image without depth information is a
highly challenging problem. Most existing methods adopt the same approach for all objects …
highly challenging problem. Most existing methods adopt the same approach for all objects …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT
B Jia, X Zhang, J Liu, Y Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With rapid growth in data volume generated from different industrial devices in IoT, the
protection for sensitive and private data in data sharing has become crucial. At present …
protection for sensitive and private data in data sharing has become crucial. At present …
[HTML][HTML] Boosting methods for multi-class imbalanced data classification: an experimental review
Since canonical machine learning algorithms assume that the dataset has equal number of
samples in each class, binary classification became a very challenging task to discriminate …
samples in each class, binary classification became a very challenging task to discriminate …