بدائل البحث:
acid optimization » based optimization (توسيع البحث), lead optimization (توسيع البحث), art optimization (توسيع البحث)
over optimization » other optimization (توسيع البحث), convex optimization (توسيع البحث), model optimization (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
values over » values per (توسيع البحث)
based acid » based bci (توسيع البحث), based ai (توسيع البحث), based agi (توسيع البحث)
acid optimization » based optimization (توسيع البحث), lead optimization (توسيع البحث), art optimization (توسيع البحث)
over optimization » other optimization (توسيع البحث), convex optimization (توسيع البحث), model optimization (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
values over » values per (توسيع البحث)
based acid » based bci (توسيع البحث), based ai (توسيع البحث), based agi (توسيع البحث)
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1
Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results
منشور في 2025الموضوعات: -
2
Datasets and their properties.
منشور في 2023"…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"
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3
Parameter settings.
منشور في 2023"…In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. …"
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4
Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish)
منشور في 2024"…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …"
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5
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Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
منشور في 2020"…We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.…"
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7
Sample image for illustration.
منشور في 2024"…Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. …"
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8
Comparison analysis of computation time.
منشور في 2024"…Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. …"
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9
Process flow diagram of CBFD.
منشور في 2024"…Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. …"
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10
Precision recall curve.
منشور في 2024"…Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. …"
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11
Quadratic polynomial in 2D image plane.
منشور في 2024"…Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. …"
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12
Seed mix selection model
منشور في 2022"…For each data set, we initialized a starting population of plant species equal to the desired number of plant species in the mix. The genetic algorithm then operated over 1000 iterations, applying crossover and mutation processes to optimize bee richness. …"
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13
DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
منشور في 2024"…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …"
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14
Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx
منشور في 2024"…The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached RCal2 = 0.86 and RVal2 = 0.84, with a Kappa value of 0.53. …"
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15
Table 1_Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.docx
منشور في 2024"…</p>Methods<p>We retrieved over 8,000 NPC patient samples with associated clinical information from the Surveillance, Epidemiology, and End Results (SEER) database. …"
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16
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
منشور في 2025"…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…"