scholarly journals BAMCAFE: A Bayesian machine learning advanced forecast ensemble method for complex turbulent systems with partial observations

2021 ◽  
Vol 31 (11) ◽  
pp. 113114
Author(s):  
Nan Chen ◽  
Yingda Li
Keyword(s):  
Machine Learning ◽  
Ensemble Method ◽  
Partial Observations ◽  
Bayesian Machine Learning

scholarly journals An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1285
Author(s):  
Mohammed Al-Sarem ◽  
Faisal Saeed ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  
Badiea Abdulkarem Mohammed ◽  
Tawfik Al-Hadhrami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ensemble Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ensemble Model ◽  
Security Attacks ◽  
Data Set ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning

2021 ◽  
Vol 139 ◽  
pp. 105002
Author(s):  
Rohitash Chandra ◽  
Sally Cripps ◽  
Nathaniel Butterworth ◽  
R. Dietmar Muller
Keyword(s):  
Machine Learning ◽  
Precipitation Reconstruction ◽  
Bayesian Machine Learning

Bayesian Machine Learning: EEG\/MEG signal processing measurements

2016 ◽  
Vol 33 (1) ◽  
pp. 14-36 ◽  
Author(s):  
Wei Wu ◽  
Srikantan Nagarajan ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Bayesian Machine Learning

Optimization of Joint Equalization of High-Speed Signals using Bayesian Machine Learning

2021 ◽  
Author(s):  
N. Dikhaminjia ◽  
G. Tsintsadze ◽  
Z. Kiguradze ◽  
J. He ◽  
M. Tsiklauri ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Bayesian Machine Learning

scholarly journals دراسة وتحليل مرضى Covid-19 باستخدام طرق التعلم الآلي

2021 ◽  
Vol 10 (1) ◽  
pp. 37-47
Author(s):  
ياسر الجناحي ياسر الجناحي
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Ensemble Method ◽  
Boosted Tree

. أنظمة التعلم الآلي (Machine Learning) في الرعاية الصحية تستخدم للتعرف على الأمراض وتشخيصها باستخدام بيانات المريض. وقد أدى استخدام أنظمة التعلم الآلي في التكنولوجيا إلى إصلاح وتحسين الرعاية الصحية، من خلال الكشف التلقائي عن الأمراض وتشخيصها، والتي بدورها تحسن صحة المريض وتنقذ الأرواح. لذلك، في هذه الدراسة، تم استخدام خوارزميات التعلم الآلي للتنبؤ بوفاة المرضى وتعافيهم. وباستخدام عدة خوارزميات سيتم توقع وفاة أو تعافي المرضى. وقد أعطت خوارزميات الـ Naïve Bayes و Bagged Trees أفضل معدلات أداء بنسبة 79? و 77? على التوالي. ومع ذلك، من حيث الدقة، أظهرت خوارزميات تصنيف الشجرة المتوسطة (MediumTree)(ensemble method Boosted Tree) والشجرة المجموعة المعززة دقة 89?. وأخيرًا أظهرت هذه الدراسة أن استخدام تقنية التعلم الآلي يمكن أن تنبه مقدمي الرعاية الصحية لتقديم علاج أسرع لمرضى فيروس كورونا عالي الخطورة (COVID-19) مما يساعد في إنقاذ الأرواح وتحسن جودة خدمة الرعاية الصحية.

scholarly journals Beyond Accuracy: Investigating the Potential Clinical Utility of Predicting Functional Dependency and Severe Disability or Death in Successfully Reperfused Patients using Machine Learning

2020 ◽  
Author(s):  
Raphael Meier ◽  
Meret Burri ◽  
Samuel Fischer ◽  
Richard McKinley ◽  
Simon Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Clinical Utility ◽  
Prediction Models ◽  
A Priori ◽  
Secondary Analysis ◽  
Severe Disability ◽  
Ensemble Method ◽  
Net Benefit ◽  
Primary Analysis

AbstractObjectivesMachine learning (ML) has been demonstrated to improve the prediction of functional outcome in patients with acute ischemic stroke. However, its value in a specific clinical use case has not been investigated. Aim of this study was to assess the clinical utility of ML models with respect to predicting functional impairment and severe disability or death considering its potential value as a decision-support tool in an acute stroke workflow.Materials and MethodsPatients (n=1317) from a retrospective, non-randomized observational registry treated with Mechanical Thrombectomy (MT) were included. The final dataset of patients who underwent successful recanalization (TICI ≥ 2b) (n=932) was split in order to develop ML-based prediction models using data of (n=745, 80%) patients. Subsequently, the models were tested on the remaining patient data (n=187, 20%). For comparison, baseline algorithms using majority class prediction, SPAN-100 score, PRE score, and Stroke-TPI score were implemented. The ML methods included eight different algorithms (e.g. Support Vector Machines and Random forests), stacked ensemble method and tabular neural networks. Prediction of modified Rankin Scale (mRS) 3–6 (primary analysis) and mRS 5–6 (secondary analysis) at 3 months was performed using 25 baseline variables available at patient admission. ML models were assessed with respect to their ability for discrimination, calibration and clinical utility (decision curve analysis).ResultsAnalyzed patients (n=932) showed a median age of 74.7 (IQR 62.7–82.4) years with (n=461, 49.5%) being female. ML methods performed better than clinical scores with stacked ensemble method providing the best overall performance including an F1-score of 0.75 ± 0.01, an ROC-AUC of 0.81 ± 0.00, AP score of 0.81 ± 0.01, MCC of 0.48 ± 0.02, and ECE of 0.06 ± 0.01 for prediction of mRS 3–6, and an F1-score of 0.57 ± 0.02, an ROC-AUC of 0.79 ± 0.01, AP score of 0.54 ± 0.02, MCC of 0.39 ± 0.03, and ECE of 0.19 ± 0.01 for prediction of mRS 5–6. Decision curve analyses suggested highest mean net benefit of 0.09 ± 0.02 at a-priori defined threshold (0.8) for the stacked ensemble method in primary analysis (mRS 3–6). Across all methods, higher mean net benefits were achieved for optimized probability thresholds but with considerably reduced certainty (threshold probabilities 0.24–0.47). For the secondary analysis (mRS 5–6), none of the ML models achieved a positive net benefit for the a-priori threshold probability 0.8.ConclusionsThe clinical utility of ML prediction models in a decision-support scenario aimed at yielding a high certainty for prediction of functional dependency (mRS 3–6) is marginal and not evident for the prediction of severe disability or death (mRS 5–6). Hence, using those models for patient exclusion cannot be recommended and future research should evaluate utility gains after incorporating more advanced imaging parameters.

scholarly journals Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features

2021 ◽  
Vol 18 (19) ◽  
pp. 10048
Author(s):  
Meng Ji ◽  
Wenxiu Xie ◽  
Riliu Huang ◽  
Xiaobo Qian
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Information ◽  
Self Care ◽  
Health Resources ◽  
Health Issues ◽  
Mental Health Information ◽  
Learning Classifier ◽  
Mental Health Issues ◽  
Bayesian Machine Learning

Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.

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