GeoReVi: A knowledge discovery and data management tool for subsurface characterization

Linsel, Adrian; Bär, Kristian; Haas, J.C.; Hornung, Jens; Greb, Matthias D.; Hinderer, Matthias

doi:10.1016/j.softx.2020.100597

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the stage of data collection, the Knowledge Discovery in Database (KDD ) method is employed, which is useful for extracting data from the database in order to identify patterns of patients infected with covid-19 by examining downloaded data patterns [14] - [16]. The stages of KDD method implementation are outlined as follows:…”

Section: A Data Preparationmentioning

confidence: 99%

Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm

Sugiono,

Hadi,

Zaeni

et al. 2023

Ilk. J. Ilm.

View full text Add to dashboard Cite

The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions

show abstract

Section: A Data Preparationmentioning

confidence: 99%

Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm

Sugiono,

Hadi,

Zaeni

et al. 2023

Ilk. J. Ilm.

View full text Add to dashboard Cite

show abstract

Recent developments in geographic information systems across different application domains: a review

Sowmiya Narayanan,

Manimaran

2023

Knowl Inf Syst

View full text Add to dashboard Cite

Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

Shahbazi

Byun

2021

IFS

View full text Add to dashboard Cite

Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline.

show abstract

GeoReVi: A knowledge discovery and data management tool for subsurface characterization

Cited by 3 publications

References 13 publications

Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm

Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm

Recent developments in geographic information systems across different application domains: a review

Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches

Contact Info

Product

Resources

About