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Multi-marker-LD based genetic algorithm for tag SNP selection
Published 2014“…The performance of the three algorithms are compared with those of a recognized tag SNP selection algorithm using three different real data sets from the HapMap project. …”
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63
Convergence and steady-state analysis of the normalized least mean fourth algorithm
Published 2007“…Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. …”
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Convergence and steady-state analysis of the normalized least mean fourth algorithm
Published 2007“…Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. …”
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Scatter search metaheuristic for homology based protein structure prediction. (c2015)
Published 2015“…The 3D models predicted by our algorithm show improved root mean standard deviations (RMSD) with respect to the native structures.…”
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masterThesis -
66
Predicting COVID-19 cases using bidirectional LSTM on multivariate time series
Published 2022“…Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. …”
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Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers
Published 2024“…In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. …”
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AI-based remaining useful life prediction and modelling of seawater desalination membranes
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doctoralThesis -
69
Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier
Published 2021“…Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. …”
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Just-in-time defect prediction for mobile applications: using shallow or deep learning?
Published 2023“…In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. …”
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ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
Published 2022“…In this paper, we propose data-driven machine learning (ML) schemes to efficiently solve these problems in wireless LAN (WLAN) networks. …”
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Developing a UAE-Based Disputes Prediction Model using Machine Learning
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doctoralThesis -
73
The Relation Between Respiratory & Acute Coronary Syndrome Using Data Mining Techniques
Published 2018“…In healthcare world, data science is one of the most important sciences that helps in predicting diseases, and despite the availability of medical data from laboratory tests, most medical institutions in middle east region still do not benefit from these data in diseases analysis and prediction. …”
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Artificial intelligence-based methods for fusion of electronic health records and imaging data
Published 2022“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”
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Neural network-based failure rate prediction for De Havilland Dash-8 tires
Published 2006“…An artificial neural network (ANN) model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the twolayered feed-forward back-propagation algorithm as a learning rule is developed. …”
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An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study
Published 2024“…The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.…”
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Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012)
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masterThesis -
79
Predicting the Heats of Fusion of Ionic Liquids via Group Contribution Modeling and Machine Learning
Published 2022Get full text
doctoralThesis -
80
Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
Published 2023“…Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases.…”