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341
Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO
Published 2022“…Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. …”
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342
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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343
Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier
Published 2021“…Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. …”
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344
Design and implementation of a deep learning-empowered m-Health application
Published 2023“…In the first phase, the corresponding image is extracted and sent to a web service. Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. …”
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345
Generic metadata representation framework for social-based event detection, description, and linkage
Published 2020“…SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects’ descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM’s dimensions. …”
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346
Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test
Published 2019“…Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. …”
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347
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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348
A Navigation System for Indoor/Outdoor Environments with an Unmanned Ground Vehicle (UGV)
Published 2015Get full text
doctoralThesis -
349
Predicting insulin dosage for diabetic patients to reach optimal glucose levels. (c2012)
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masterThesis -
350
Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers
Published 2023“…To develop the DT, a dense deep neural network (DNN) machine learning approach is used. The DT is trained offline using a set of experimental data and updated online to get the maximum possible accuracy. …”
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351
Performance Analysis of Artificial Neural Networks in Forecasting Financial Time Series
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doctoralThesis -
352
Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques
Published 2024“…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
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353
Machine learning approach for the classification of corn seed using hybrid features
Published 2020“…The nine optimized features have been acquired by employing the correlation-based feature selection (CFS) technique with the Best First search algorithm. To build the classification models, Random forest (RF), BayesNet (BN), LogitBoost (LB), and Multilayer Perceptron (MLP) were employed using optimized multi-feature using (10-fold) cross-validation approach. …”
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354
Clustering Tweets to Discover Trending Topics about دبي (Dubai)
Published 2018“…One of these social networks is Twitter - a microblogging hub and rich environment of data. Scanning tweets online is a hard task and searching effortlessly to find intended topic from huge amount of data is also time consuming. …”
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355
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356
Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic
Published 2020“…The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. …”
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357
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358
Real-Time Social Robot’s Responses to Undesired Interactions Between Children and their Surroundings
Published 2022“…Additionally, we evaluate the performance of the best developed model with children. Machine learning algorithms experiments showed that XGBoost achieved the best performance across all metrics (e.g., accuracy of 90%) and provided fast predictions (i.e., 0.004 s) for the test samples. …”
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359
CEAP
Published 2016“…We propose as well a propagation algorithm that disseminates only the final decisions (instead of the whole dataset) among clusters with the aim of reducing the overhead of either exchanging results between each set of vehicles or repeating the detection steps for the already detected malicious vehicles. …”
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360
Teachers' Perceptions of the Role of Artificial Intelligence in Facilitating Inclusive Practices for Students with Special Educational Needs and Disabilities: A Case Study in a Pri...
Published 2025“…Findings referred these barriers to limited teacher training, technological accessibility, and data privacy concerns, as well as ethical biases in AI algorithms. …”
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