Early Detection of Gearbox Damage, Development of ANN Forecast Maintenance Tool

Laguna, Rodrigo; Geremias, Nicolas; Mauiri, Diego; Alfonso, Julián; Chiruzzo, Luis; Ezzatti, Pablo; Gutiérrez, Alejandro Israel Barranco

doi:10.1109/tdla47668.2020.9326244

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DeepInsight method pioneered a strategy by converting non-image data to image form and then processing it to CNN for classification for various kinds of datasets. It has been widely used in various fields such as in cancer research [10][11][12], viral infections [13], sparse data [14], power energy [15], business and manufacturing [16], time-series data [17][18][19], traffic cash analysis [20], human activity recognition [21], feature representation [22], intrusion detection [23], spine surgery [24] and HVAC fault diagnosis [25]. Moreover, DeepInsight was a component in the Kaggle.com competition hosted by MIT and Harvard University that secured rank1 on the leaderboard [26].…”

Section: Introductionmentioning

confidence: 99%

DeepInsight-3D for precision oncology: an improved anti-cancer drug response prediction from high-dimensional multi-omics data with convolutional neural networks

Sharma

Lysenko

Boroevich

et al. 2022

Preprint

View full text Add to dashboard Cite

Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcomes. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. In this study, we propose a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multiomics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while explicitly encoding the connection between them. DeepInsight-3D was able to outperform two other state-of-the-art methods proposed for this task. These advances can facilitate the development of better personalized treatment strategies for different cancers in the future.

show abstract

Section: Introductionmentioning

confidence: 99%

DeepInsight-3D for precision oncology: an improved anti-cancer drug response prediction from high-dimensional multi-omics data with convolutional neural networks

Sharma

Lysenko

Boroevich

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This process establishes spatial relationships between features, enhancing the feature extraction capabilities. Recent advancements have extended the utility of DeepInsight beyond CNN architectures and classification tasks 7,8,[13][14][15][16][17][18] .…”

mentioning

confidence: 99%

“…With the advent of techniques like DeepInsight 6 , the repertoire of deep learning techniques for tabular data analysis has expanded, offering the promise of automated feature learning and improved predictive accuracy 6,7,[13][14][15][16][17][18][19][20]20,21 . DeepInsight has consistently demonstrated success in uncovering hidden patterns for various kinds of data 13,14,18 and was used as part of the winning model in the Kaggle.com competition hosted by MIT and Harvard University 22 . Nevertheless, there remains room for further improvements, as currently DeepInsight primarily operates on a single representation of the data.…”

mentioning

confidence: 99%

Enhanced analysis of tabular data through Multi-representation DeepInsight

Sharma,

López,

Jia

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields.

show abstract