Deep Bayesian network architecture for Big Data mining

Njah, Hasna; Jamoussi, Salma; Mahdi, Walid

doi:10.1002/cpe.4418

Cited by 17 publications

(5 citation statements)

References 61 publications

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“…In other words, they map raw data to categories by recognising patterns that, for example, would suggest a "cat" or "elephant" label for an input image. A feedforward network is trained on labelled images until it can accurately categorise them with the fewest errors [11,20,[22][23][24][25]. Using the pretrained set of parameters, the network categorises data it has never seen before (or weights, generally called as a model).…”

Section: Lstmmentioning

confidence: 99%

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[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

Revathy

Aurchana²,

Arieth

et al. 2022

Journal of Nanomaterials

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The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The importance of agricultural crops in India has been understated in terms of production during the last two decades, owing to global warming and other factors. Policymakers and farmers would benefit from imagining crop yields well before harvest to help them make appropriate marketing and storage decisions. Crop yields will benefit from such estimates as well. Several systems for predicting and modelling agricultural yields have been developed in the past, with varying degrees of effectiveness, due to the fact that they do not objectively account for meteorological aspects and seasonal climate variations. The method makes use of climate data from Thanjavur, India’s soils. To choose the optimal crop for a particular set of input and climate conditions, the system leverages real-time input of location-specific soil attributes. The model is been tested with various machine learning techniques such as NB, KNN, SVM, and decision tree. Among all these methods, the SVM gives the good results. The accuracy of the model was determined using LSTM, SVM, and Tabu search optimization. TSO with SVM has an 89% accuracy value.

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Section: Lstmmentioning

confidence: 99%

“…This research focuses on predicting future yields using weather data. Accurate weather-to-crop-yield models 2 Journal of Nanomaterials are critical not just for anticipating agricultural consequences [11,12].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

Revathy

Aurchana²,

Arieth

et al. 2022

Journal of Nanomaterials

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“…72 In order to handle the exhaustive and empirical neural network parameterization process, a new deep Bayesian network architecture was proposed by adopting the principle of multilayer Bayesian network in order to make use of the edges’ significance, the causality, and the uncertainty of the Bayesian network for improving the meaningfulness of the hidden layers and the latent variable’s connections. 73 …”

Section: Discussionmentioning

confidence: 99%

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling

Luo

Tseng

Cui³

et al. 2019

BJR|Open

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Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.

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“…HBNs are usually strictly hierarchical. This means that, similar to the architecture of deep neural networks, they restrict all nodes to have parents only in higher layers [ 30 , 31 ]. Nevertheless, group Bayesian networks can be seen as a special case of loose HBNs.…”

Section: Introductionmentioning

confidence: 99%

From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

et al. 2021

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In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network.

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Deep Bayesian network architecture for Big Data mining

Cited by 17 publications

References 61 publications

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

[Retracted] HANA: A Performance‐Based Machine Learning and Neural Network Approach for Climate Resilient Agriculture

Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling

From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

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