Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure: A case study in Sweden

Shahri, Abbas Abbaszadeh; Shan, Chunling; Zäll, Emma; Larsson, Stefan

doi:10.1016/j.jrmge.2021.07.006

Cited by 39 publications

(3 citation statements)

References 54 publications

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“…Each hidden layer has one or more neurons. Each neuron in a layer receives inputs from several other neurons of the previous layer, performs some arithmetic operations on them and passes the sum to one or more neurons of the next layer [ 46 , 47 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…A dropout layer is introduced after each max-pool layer with a dropout ratio of 0.3, which generalizes the model [ 31 ]. The ‘Nadam’ optimizer (Adam optimizer with Nesterov momentum) [ 45 , 46 ] was used with a learning rate of 0.0001.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The final output dense layer contains a single node and utilizes the ‘Sigmoid’ activation function. The ‘Nadam’ optimizer [ 45 , 46 ] is used with a learning rate of 0.0001.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

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et al. 2022

Sensors

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Dementia affects the patient’s memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

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Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Kumar

Vekkot

Lalitha

et al. 2022

Sensors

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Fault-Fold System

Liao

2024

Applied Seismic Geostructures

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A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis

Shahri

Shan

Larsson

2023

Engineering with Computers

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There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, and thus interpretable 3D subsurface views from such integrated heterogeneous data requires developing a new methodology for convenient post-modeling analyses. To this end, in the current paper a hybrid ensemble-based automated deep learning approach for 3D modeling of subsurface geological bedrock using multisource data is proposed. The uncertainty then was quantified using a novel ensemble randomly automated deactivating process implanted on the jointed weight database. The applicability of the automated process in capturing the optimum topology is then validated by creating 3D subsurface geo-model using laser-scanned bedrock-level data from Sweden. In comparison with intelligent quantile regression and traditional geostatistical interpolation algorithms, the proposed hybrid approach showed higher accuracy for visualizing and post-analyzing the 3D subsurface model. Due to the use of integrated multi-source data, the approach presented here and the subsequently created 3D model can be a representative reconcile for geoengineering applications.

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Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure: A case study in Sweden

Cited by 39 publications

References 54 publications

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Fault-Fold System

A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis

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