scholarly journals IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 70375-70386 ◽  
Author(s):  
Mohammed Khalaf ◽  
Haya Alaskar ◽  
Abir Jaafar Hussain ◽  
Thar Baker ◽  
Zakaria Maamar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Ensemble Machine Learning ◽  
Severity Prediction ◽  
Machine Learning Models

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

scholarly journals Medium-term load forecasting in isolated power systems based on ensemble machine learning models

2022 ◽  
Vol 8 ◽  
pp. 612-618
Author(s):  
Pavel Matrenin ◽  
Murodbek Safaraliev ◽  
Stepan Dmitriev ◽  
Sergey Kokin ◽  
Anvari Ghulomzoda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Load Forecasting ◽  
Learning Models ◽  
Medium Term ◽  
Ensemble Machine Learning ◽  
Machine Learning Models

scholarly journals A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Baha Ihnaini ◽  
M. A. Khan ◽  
Tahir Abbas Khan ◽  
Sagheer Abbas ◽  
Mohammad Sh. Daoud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Recommendation System ◽  
Disease Diagnosis ◽  
Diabetic Patients ◽  
Learning Models ◽  
Smart Healthcare ◽  
Ensemble Machine Learning ◽  
Life Threatening ◽  
Machine Learning Models

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.

scholarly journals Interpreting tree ensemble machine learning models with endoR

2022 ◽  
Author(s):  
Albane Ruaud ◽  
Niklas A Pfister ◽  
Ruth E Ley ◽  
Nicholas D Youngblut
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
R Package ◽  
Metagenomic Data ◽  
Learning Models ◽  
Model Interpretation ◽  
Ensemble Machine Learning ◽  
Fermenting Bacteria ◽  
Microbiome Data ◽  
Machine Learning Models

Background: Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa or genomic content may be associated. Results: We developed endoR, a method to interpret a fitted tree ensemble model. First, endoR simplifies the fitted model into a decision ensemble from which it then extracts information on the importance of individual features and their pairwise interactions and also visualizes these data as an interpretable network. Both the network and importance scores derived from endoR provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed the performance of endoR on both simulated and real metagenomic data. We found endoR to infer true associations with more or comparable accuracy than other commonly used approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to gain insights into components of the microbiome that predict the presence of human gut methanogens, as these hydrogen-consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Conclusion: Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems. An implementation of endoR is available as an open-source R-package on GitHub (https://github.com/leylabmpi/endoR).

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