EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit حسب Gonzalo Colmenarejo (650249)

"…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …"

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission حسب Monika Salesh (15306281)

الموضوعات:

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission حسب Monika Salesh (15306281)

الموضوعات:

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission حسب Monika Salesh (15306281)

الموضوعات:

Multifunctional Lead-Free Halide Perovskite-Based Nanogenerator for Enhanced Energy Harvesting and Information-Encrypted Transmission حسب Monika Salesh (15306281)

الموضوعات:

SCADET algorithm parameter configuration (Python implementation). حسب Xiaolong Zhang (470454)

Simple implementation examples of agent AI on free energy calculation and phase-field simulation حسب Toshiyuki Koyama (22828581)

"…</p> <p>Using Gibbs energy calculations and diffusion simulations as examples, we demonstrated the implementation method and usefulness of simple agent AI, where sample python codes are distributed as supplemental materials.…"

Illustration of the text transformation process before and after synonym replacement: The process begins with a sample initial text segment (Sample text before synonym replacement’... حسب Enock Niyonkuru (21246879)

الموضوعات:

Schematic of the approach: This schematic illustrates the entire workflow of the project. حسب Enock Niyonkuru (21246879)

الموضوعات:

Text Transformation Pipeline: An example of the multi-stage text transformation pipeline applied to a sample abstract (PMID: 30609739). حسب Enock Niyonkuru (21246879)

الموضوعات:

Comparative analysis of WordNet replacement impact on data distribution. حسب Enock Niyonkuru (21246879)

الموضوعات:

Comparison of window size for embedding with Wordnet synonym replacement (WN) and without (PM). While Table 1 compared the effects of different values of using a window size of 10,... حسب Enock Niyonkuru (21246879)

الموضوعات:

Non-biomedical word replacement algorithm: This algorithm outlines the process for replacing non-biomedical words in a corpus using WordNet. حسب Enock Niyonkuru (21246879)

الموضوعات:

Comparison of mean interconcept distance for embedding with WordNet synonym replacement (WN) and without (PM). The initial number of unique concepts in the total corpus was 3,018,9... حسب Enock Niyonkuru (21246879)

الموضوعات:

Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... حسب Enock Niyonkuru (21246879)

الموضوعات:

Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence" حسب Yifei Gu (9507104)

"…The detailed implementation, source code and demo dataset are included in dedicated folders for each chapter.…"

Data&Codes.zip حسب Zer0 Star (20545655)

"…</p><p dir="ltr">To facilitate the widespread use of the proposed framework, we have implemented it as the <b><i>ESLocalIndi</i></b> open-source package in Python, making it easily accessible to geographers. …"

CSMILES: A Compact, Human-Readable SMILES Extension for Conformations حسب James W. Furness (22319738)

"…A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. …"

CSMILES: A Compact, Human-Readable SMILES Extension for Conformations حسب James W. Furness (22319738)

"…A two-way conversion from three-dimensional (3D) structure to CSMILES has been implemented, and the article is accompanied by a Python code which effectuates such conversions. …"

Code حسب Baoqiang Chen (21099509)

"…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). …"