Deep learning-based intelligent fault diagnosis methods toward rotating machinery
S Tang, S Yuan, Y Zhu - Ieee Access, 2019 - ieeexplore.ieee.org
Fault diagnosis of rotating machinery plays a significant role in the industrial production and
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …
Ensembles of deep lstm learners for activity recognition using wearables
Recently, deep learning (DL) methods have been introduced very successfully into human
activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the …
activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the …
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation
development through its ability to analyze and continuously monitor large datasets. Fused …
development through its ability to analyze and continuously monitor large datasets. Fused …
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease
detection by using computer-aided diagnosis from fundus image has emerged as a new …
detection by using computer-aided diagnosis from fundus image has emerged as a new …
An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain
ZH Kilimci, AO Akyuz, M Uysal, S Akyokus… - …, 2019 - Wiley Online Library
Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks,
reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical …
reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical …
A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data
X Yuan, L Xie, M Abouelenien - Pattern Recognition, 2018 - Elsevier
In medical diagnosis, eg bowel cancer detection, a large number of examples of normal
cases exists with a much smaller number of positive cases. Such data imbalance usually …
cases exists with a much smaller number of positive cases. Such data imbalance usually …
Class-imbalanced deep learning via a class-balanced ensemble
Class imbalance is a prevalent phenomenon in various real-world applications and it
presents significant challenges to model learning, including deep learning. In this work, we …
presents significant challenges to model learning, including deep learning. In this work, we …
Parallel computing method of deep belief networks and its application to traffic flow prediction
L Zhao, Y Zhou, H Lu, H Fujita - Knowledge-Based Systems, 2019 - Elsevier
Deep belief networks (DBNs) with outstanding advantages of learning input data features
have attained particular attention and are applied widely in image processing, speech …
have attained particular attention and are applied widely in image processing, speech …
Ensemble deep learning and machine learning: applications, opportunities, challenges, and future directions
The convergence of ensemble deep learning and machine learning has become a critical
strategy for tackling intricate challenges across diverse fields such as healthcare, finance …
strategy for tackling intricate challenges across diverse fields such as healthcare, finance …
Machine learning-based application for predicting risk of type 2 diabetes mellitus (t2dm) in saudi arabia: A retrospective cross-sectional study
Earlier detection of individuals at the highest risk of developing diabetes is crucial to avoid
the disease's prevalence and progression. Therefore, we aim to build a data-driven …
the disease's prevalence and progression. Therefore, we aim to build a data-driven …