بدائل البحث:
modelling algorithm » scheduling algorithm (توسيع البحث)
method algorithm » mould algorithm (توسيع البحث)
data present » net present (توسيع البحث), past present (توسيع البحث)
modelling algorithm » scheduling algorithm (توسيع البحث)
method algorithm » mould algorithm (توسيع البحث)
data present » net present (توسيع البحث), past present (توسيع البحث)
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Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study
منشور في 2021"…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …"
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Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
منشور في 2021"…The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set.…"
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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory
منشور في 2025الموضوعات: -
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Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
منشور في 2023"…This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. …"
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111
Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain
منشور في 2017"…The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. …"
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112
Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems
منشور في 2022"…Furthermore, this paper demonstrates the advantages of the developed ADL algorithm approach and DSM prediction of the DT using vector autoregressive model for anomaly detection in utility gas turbines with data from an operational power plant.…"
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113
A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
منشور في 2021"…In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. …"
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114
Design of adaptive arrays based on element position perturbations
منشور في 1993"…The main advantage of using this technique over the other commonly used methods is that the amplitudes and phases of the array elements can be used mainly to steer the main beam towards the desired signal. …"
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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
منشور في 2021"…However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. …"
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116
Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning
منشور في 2023الموضوعات: -
117
Sentiment visualization of correlation of loneliness mapped through social intelligence analysis
منشور في 2024"…Social media platforms have become a valuable source of data to study this phenomenon.</p><h3>Objectives</h3><p dir="ltr">This paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. …"
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118
A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method
منشور في 2022"…Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …"
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The use of multi-task learning in cybersecurity applications: a systematic literature review
منشور في 2024"…This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field.…"