بدائل البحث:
tool implementing » model implementing (توسيع البحث), trial implementing (توسيع البحث), from implementing (توسيع البحث)
python model » action model (توسيع البحث), motion model (توسيع البحث)
tool implementing » model implementing (توسيع البحث), trial implementing (توسيع البحث), from implementing (توسيع البحث)
python model » action model (توسيع البحث), motion model (توسيع البحث)
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181
Building footprtints from 1970s Hexagon spy satellite images for four global urban growth hotspots
منشور في 2025"…The data represent the final results, that means, after merging models with different chip sizes and post-processing (see manuscript). …"
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182
Tracking when the number of individuals in the video frame changes.
منشور في 2025"…The removal of unnecessary keypoint data is achieved through a Python code that allows specified ranges of tracking data obtained from DeepLabCut to be rewritten as NaN (no data) (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003002#pbio.3003002.s019" target="_blank">S1 Protocol</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003002#pbio.3003002.s010" target="_blank">S10C Fig</a>). …"
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183
Indirect Reciprocity and the Evolution of Prejudicial Groups
منشور في 2024"…This is conducted through an agent based model over a population of agents that interact through a `donation game' in which resources are donated to third parties at a cost without receiving a direct benefit. …"
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184
Summary of Tourism Dataset.
منشور في 2025"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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185
Segment-wise Spending Analysis.
منشور في 2025"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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186
Hyperparameter Parameter Setting.
منشور في 2025"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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187
Marketing Campaign Analysis.
منشور في 2025"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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188
Visitor Segmentation Validation Accuracy.
منشور في 2025"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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189
Integration of VAE and RNN Architecture.
منشور في 2025"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"
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190
Satellite monitoring of Greenland wintertime buried lake drainage
منشور في 2025"…Buried_lake_drainage_code</p><p dir="ltr">This folder contains two Python Jupyter Notebooks for detecting wintertime buried lake drainages (BLDs). …"
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191
CpG Signature Profiling and Heatmap Visualization of SARS-CoV Genomes: Tracing the Genomic Divergence From SARS-CoV (2003) to SARS-CoV-2 (2019)
منشور في 2025"…</p><p dir="ltr">Heatmap Images :</p><p dir="ltr">Heatmaps for CpG counts and O/E ratios comparing Wuhan-Hu-1 with its closest and most distant relatives.</p><p dir="ltr">Python Script :</p><p dir="ltr">Full Python code used for data processing, distance calculation, and heatmap generation.…"
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192
Core data
منشور في 2025"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"
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193
Thermally Activated Resonant Tunnelling in GaAs/AlGaAs Triple Barrier Heterostructures
منشور في 2024"…Measurements were automated using bespoke written python code.<br><br>Results are published in the article at http://iopscience.iop.org/0268-1242/30/10/105035 <br>…"
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194
<b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b>
منشور في 2025"…</p><p dir="ltr"> 'boxplot.py': This script is executed in Visual Studio Code, using Python 3.10.4 as the runtime environment.…"
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195
(A) Sampling locations and ranges of <i>I. feisthamelii</i> (purple) and <i>I. podalirius</i> (teal) butterflies.
منشور في 2025"…(B) Sampling locations of butterflies from the <i>Iphiclides</i> HZ. The dashed line represents the approximate HZ center, based on samples collected by Lafranchis et al. …"
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196
Datasets from the Programmatic Analysis of Fuel Treatments: from the landscape to the national level Joint Fire Science Project (14-5-01-1)
منشور في 2025"…Included for each study site are individual rasters representing the fire affected resources for that study site. …"
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197
Folder with all data and algorithms
منشور في 2025"…<p dir="ltr">Spatially Offset Raman Spectroscopy (SORS) has emerged as a potential tool for non-invasive biomedical diagnostics, enabling molecularly specific probing of subsurface tissues. …"
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198
End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting
منشور في 2025"…</p><h3>policy/</h3><p dir="ltr">This folder contains pickled trajectories, in the form of a Python list.</p><p dir="ltr">The list's elements are TrajWithRew dataclass objects from the Imitation Python library (https://imitation.readthedocs.io/en/latest/)</p><p dir="ltr">TrajWithRew contains 4 main fields</p><ul><li> obs - the (unnormalised) observations, in the form of a [WINDOW_LENGTH * NUM_CHANNELS] array</li><li> acts - the actions in the form of a [WINDOW_LENGTH - 1 * NUM_ACTS] array</li><li> infos - the info values at each timestep, as a [WINDOW_LENGTH - 1] array of dicts</li><li> terminals - boolean indicating if that trajectory segment is a terminal segment</li><li> rews - the rewards as a [WINDOW_LENGTH - 1] array</li></ul><p dir="ltr">Each TrajWithRew represents not a full episodic trajectory, as is usually the case with Imitiation - rather they represent segments of a full episodic trajectory, of length WINDOW_LENGTH. …"
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199
Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data
منشور في 2024"…</p><ol><li><b>MaskRCNN-Segmented Objects</b>:</li></ol><p dir="ltr"> - `.jpg` files representing segmented objects detected by the MaskRCNN model.…"
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200
Supplementary Material for: The prediction of hematoma growth in acute intracerebral hemorrhage: from 2-dimensional shape to 3-dimensional morphology
منشور في 2025"…We subsequently constructed the 3-dimensional morphology models, including the probability of hematoma morphology (PHM) and the probability of comprehensive model (PCM), to predict HG. …"