PROGRAM SUMMARY No. of lines in distributed program, including test data, etc.: 481 No. of bytes in distributed program, including test data, etc.: 14540.8 Distribution format: .py, .csv Programming language: Python Computer: Any workstation or laptop computer running TensorFlow, Google Colab, Anaconda, Jupyter, pandas, NumPy, Microsoft Azure and Alteryx. Operating system: Windows and Mac OS, Linux.

Nature of problem: Navier-Stokes equations are solved numerically in ANSYS Fluent using Reynolds stress model for turbulence. The simulated values of friction factor are validated with theoretical and experimental data obtained from literature. Artificial neural networks are then used for a prediction-based augmentation of friction factor. The capabilities of the neural networks is discussed, in regard to computational cost and domain limitations.

Solution method: The simulation data is obtained through Reynolds stress modelling of fluid flow through pipe. This data is augmented using the artificial neural network model that predicts within and without data domain.

Restrictions: The code used in this research is limited to smooth pipe bends, in which friction factor is analysed using a steady state incompressible fluid flow.

Runtime: The artificial neural network produces results within a span of 20 seconds for three-dimensional geometry, using the allocated free computational resources of Google Colaboratory cloud-based computing system.

Data and code for reproducing figures in published work.

High Power Laser Science and Engineering

https://doi.org/10.1017/hpl.2022.47

Code used various python packages including tensorflow.

Conda environment was created with (on 6th Jan 2022)
conda create --name tf tensorflow notebook tensorflow-probability pandas tqdm scikit-learn matplotlib seaborn protobuf opencv scipy scikit-image scikit-optimize Pillow PyAbel libclang flatbuffers gast --channel conda-forge

This dataset contains high-quality images of six distinct bird species, curated for use in image classification, computer vision, and biodiversity research tasks. Each bird species included in this dataset is well-represented, making it ideal for training and evaluating deep learning models.

📂 Dataset Highlights: * Total Images: 811 * Classes: 6 unique bird species * Balanced Labels: Nearly equal distribution across classes * Use Cases: Image classification, model benchmarking, transfer learning, educational projects, biodiversity analysis

🧠 Potential Applications: * Training deep learning models like CNNs for bird species recognition * Fine-tuning pre-trained models using a small and balanced dataset * Educational projects in ornithology and computer vision * Biodiversity and wildlife conservation tech solutions

🛠️ Suggested Tools: * Python (Pandas, NumPy, Matplotlib) * TensorFlow / PyTorch for model development * OpenCV for image preprocessing * Streamlit for creating interactive demos

Board Game Ratings by Country

Global User Ratings of Board Games

By Michael Petrey [source]

About this dataset

This dataset, originating from the beloved board game community site BoardGameGeek and subsequently expanded by Jesse van Elteren to create a more detailed canvas of data, is now further enriched here with additional geographic location information. Broadening the original framework beyond gaming metrics alone enables researchers and enthusiasts to explore international trends, regional preferences, and cultural influences that may permeate the rich tapestry of games.

1. userID: This indicates a unique identifier assigned to each user within the BoardGameGeek online community. It helps track individual users' behavior as they rate various games.

2. gameID: This specifies a unique identifier aligned with each board game listed on their platform. It serves as an index that allows us to distinguish between different games receiving ratings.

3. rating: Reflects the score out of 10 given by a user for a specific game in their review posted on BoardGameGeek's platform allowing us to understand just how well-received or popular any particular game is among its audience.

4. country: A newly added field denoting which country the respective reviewer resides in - be it USA, UK, Australia or elsewhere - enriches this dataset with crucial geographic detail initially absent can now enable examinations of demographic patterns and trends based around location.

By adding this layer of geolocational context for users who contribute reviews and rates games on Boardgamegeek.com (BGG), this dataset opens up new avenues exploring not only which games are rated high but also where these ratings coming from globally; creating opportunities for deeper study into localised impacts within global gaming communities.

This versatile compendium forms an essential database for those interested in analyzing trends in board gaming as it provides both comprehensive detail-oriented insights about individual games based on user approval ratings while simultaneously enabling larger-scale contemplation regarding how localized norms potentially influence review scores across diverse geographical regions worldwide relating back directly towards central theme - an appreciation of board games

How to use the dataset

• Understand the Dataset: The first step is to understand what data is there and what it represents. This dataset includes board game ratings from users along with their country information. Each row represents a unique rating by a user for a particular game from a specific country.

• Load the Data: Using Python libraries like pandas, you can conveniently load this dataset for computational analysis. You would use pd.read_csv('file_path') function.

• Data Exploration: Start digging into this data by checking its distribution, outliers and missing values etc using plots like histograms or boxplots as well as statistical methods . These all tools are present in seaborn, matplotlib and pandas libraries in python.

• Statistical Analysis: You could then compute average ratings per country or rank countries according to their mean rating, thus comparing how different countries score games on an average level.

• Identify Top Rated Games: Identify the board games with highest overall user ratings regardless of geography providing insights about global preferences about certain boardgames that would serve valuable for manufacturers and retailers globally alike.

• Countrywise Phenomenon: Analyze game popularity within specific countries - are some games more popular in certain places? Does popularity correlate strongly with high ratings?

7a.**Machine Learning Modelling:** Based on user reviews make machine learning models for predicting which type of games will be liked/disliked by people belonging to different geographical locations

7b.Or ML models can predict future trends based on historical data or provide interesting pattern recognition capabilities that could result in potential business strategies .

8b.Make recommendations based on users previous reviews also termed as collaborative filtering

8a.Or use popular recommendation algorithms such as cosine similarity measures to recommend new games they might enjoy.

• While using any form of modelling don't forget to split the dataset into training and testing set before developing and validating your model.

• cuda, tensorflow or pytorch libraries can be used for applying deep learning techniques.

In sum, this data se...

PyLibAPIs.7z : contains public API data (mongodb dump) for these frameworks:

TensorFlow

Keras

scikit-learn

Pandas

Flask

Django

Label.xlsx: cintains issues and their labels

This repository contains three folders which contain either the data or the source code for the three main chapters (Chapter 3, 4, and 5) in the thesis. Those folders are 1) Dataset (Chapter 3): This file contains phonocardigrams signals (/PhysioNet2016) used in Chapter 3 and 4 as the upstream pretraining data. This is a public dataset. /SourceCode includes all the statistical analysis and visualization scripts for Chapter 3. Yaseen_dataset and PASCAL contain phonocardigrams signals with pathological features, Yaseen_dataset serves as the downstream finetuning dataset in Chapter 3, while PASCAL datasets serves as the secondary testing dataset in Chapter 3. 2) Dataset (Chapter 4): /SourceCode includes all the statistical analysis and visualization scripts for Chapter 4. 3) Dataset (Chapter 5): PAD-UFES-20_processed contains dermatology images processed from the PAD-UFES-20 dataset, which is a public dataset. The dataset is used in the Chapter 5. And /SourceCode includes all the statistical analysis and visualization scripts for Chapter 5.Several packges are mendatory to run the source code, including:Python > 3.6 (3.11 preferred), TensorFlow > 2.16, Keras > 3.3, NumPy > 1.26, Pandas > 2.2, SciPy > 1.13

Data product and code for: Ehmen et al.: Spatiotemporal Distribution of Dissolved Inorganic Carbon in the Global Ocean Interior - Reconstructed through Machine Learning

Note that due to the data limit on Zenodo only a compressed version of the ensemble mean is uploaded here (compressed_DIC_mean_15fold_ensemble_aveRMSE7.46_0.15TTcasts_1990-2023.nc). Individual ensemble members can be generated through the weight and scaler files found in weights_and_scalers_DIC_paper.zip and the code "ResNet_DIC_loading_past_prediction_2024-12-28.py" (see description below).

This dataset provides grayscale pixel values for brain tumor MRI images, stored in a CSV format for simplified access and ease of use. The goal is to create a "MNIST-like" dataset for brain tumors, where each row in the CSV file represents the pixel values of a single image in its original resolution. This format makes it convenient for researchers and developers to quickly load and analyze MRI data for brain tumor detection, classification, and segmentation tasks without needing to handle large image files directly.

Motivation and Use Cases

Brain tumor classification and segmentation are critical tasks in medical imaging, and datasets like these are valuable for developing and testing machine learning and deep learning models. While there are several publicly available brain tumor image datasets, they often consist of large image files that can be challenging to process. This CSV-based dataset addresses that by providing a compact and accessible format. Potential use cases include: - Tumor Classification: Identifying different types of brain tumors, such as glioma, meningioma, and pituitary tumors, or distinguishing between tumor and non-tumor images. - Tumor Segmentation: Applying pixel-level classification and segmentation techniques for tumor boundary detection. - Educational and Rapid Prototyping: Ideal for educational purposes or quick experimentation without requiring large image processing capabilities.

Data Structure

This dataset is structured as a single CSV file where each row represents an image, and each column represents a grayscale pixel value. The pixel values are stored as integers ranging from 0 (black) to 255 (white).

CSV File Contents

• Pixel Values: Each row contains the pixel values of a single grayscale image, flattened into a 1-dimensional array. The original image dimensions vary, and rows in the CSV will correspondingly vary in length.
• Simplified Access: By using a CSV format, this dataset avoids the need for specialized image processing libraries and can be easily loaded into data analysis and machine learning frameworks like Pandas, Scikit-Learn, and TensorFlow.

How to Use This Dataset

1. Loading the Data: The CSV can be loaded using standard data analysis libraries, making it compatible with Python, R, and other platforms.
2. Data Preprocessing: Users may normalize pixel values (e.g., between 0 and 1) for deep learning applications.
3. Splitting Data: While this dataset does not predefine training and testing splits, users can separate rows into training, validation, and test sets.
4. Reshaping for Models: If needed, each row can be reshaped to the original dimensions (retrieved from the subfolder structure) to view or process as an image.

Technical Details

• Image Format: Grayscale MRI images, with pixel values ranging from 0 to 255.
• Resolution: Original resolution, no resizing applied.
• Size: Each row’s length varies according to the original dimensions of each MRI image.
• Data Type: CSV file with integer pixel values.

Acknowledgments

This dataset is intended for research and educational purposes only. Users are encouraged to cite and credit the original data sources if using this dataset in any publications or projects. This is a derived CSV version aimed to simplify access and usability for machine learning and data science applications.

Emotion Prediction with Quantum5 Neural Network AI Machine Learning - By Emirhan BULUT

V1

I have created an artificial intelligence software that can make an emotion prediction based on the text you have written using the Semi Supervised Learning method and the RC algorithm. I used very simple codes and it was a software that focused on solving the problem. I aim to create the 2nd version of the software using RNN (Recurrent Neural Network). I hope I was able to create an example for you to use in your thesis and projects.

V2

I decided to apply a technique that I had developed in the emotion dataset that I had used Semi-Supervised learning in Machine Learning methods before. This technique is produced according to Quantum5 laws. I developed a smart artificial intelligence software that can predict emotion with Quantum5 neuronal networks. I share this software with all humanity as open source on Kaggle. It is my first open source project in NLP system with Quantum technology. Developing the NLP system with Quantum technology is very exciting!

Happy learning!

Emirhan BULUT

Head of AI and AI Inventor

Emirhan BULUT. (2022). Emotion Prediction with Quantum5 Neural Network AI [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/2129637

The coding language used:

Python 3.9.8

Libraries Used:

Keras

Tensorflow

NumPy

Pandas

Scikit-learn (SKLEARN)

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Quantum%205.png" alt="Emotion Prediction with Quantum5 Neural Network on AI - Emirhan BULUT">

https://raw.githubusercontent.com/emirhanai/Emotion-Prediction-with-Semi-Supervised-Learning-of-Machine-Learning-Software-with-RC-Algorithm---By/main/Emotion%20Prediction%20with%20Semi%20Supervised%20Learning%20of%20Machine%20Learning%20Software%20with%20RC%20Algorithm%20-%20By%20Emirhan%20BULUT.png" alt="Emotion Prediction with Semi Supervised Learning of Machine Learning Software with RC Algorithm - Emirhan BULUT">

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan@isap.solutions

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

🌍 Air Quality Index (AQI) Prediction using Neural Networks

This notebook focuses on predicting Air Quality Index (AQI) values by estimating Carbon Monoxide (CO) concentration using a Neural Network Regression Model trained on environmental pollutant data.

The model follows the EPA (Environmental Protection Agency) standard formula for converting CO concentration (in ppm) to AQI levels.

⚙️ Workflow Overview

1. Data Preprocessing

   • Cleaned and normalized the dataset
   • Removed date/time and irrelevant columns
   • Scaled input and output features using MinMaxScaler

2. Model Building (Neural Network)

   • Built a deep regression model using TensorFlow/Keras
   • Activation: ReLU
   • Optimizer: Adam
   • Loss: Mean Squared Error (MSE)

3. Prediction Phase

   • Model predicts CO concentration based on given input features
   • Predictions are inverse-transformed to get real-world ppm values

4. AQI Calculation (EPA Standard)

   • AQI computed using the official EPA breakpoint formula
   • Converts CO ppm into an AQI score ranging from 0–500

5. Visualization

   • Distribution of pollutants
   • Correlation heatmap
   • Comparison of Predicted CO vs AQI Levels
   • AQI Category visualization

🧠 Why This Project?

Air pollution is one of the most pressing global issues today.
By combining machine learning with environmental science, this notebook helps predict pollution levels and interpret air quality using AI-driven insights.

📊 Tech Stack

• Python
• TensorFlow / Keras
• NumPy, Pandas, Matplotlib, Seaborn
• Scikit-learn

🏁 Results

✅ Accurate CO prediction using neural network regression
✅ Dynamic AQI computation based on EPA standards
✅ Clear and intuitive visualizations

🚀 "AI can’t clean the air — but it can help us understand how bad it really is."

https://github.githubassets.com/images/modules/site/home/footer-illustration.svg" alt="GitHub">

Image credits: https://github.com

Introduction

This is a dataset that contains all commit messages and its related metadata from 32 popular GitHub repositories. These repositories are:

• tensorflow/tensorflow
• pytorch/pytorch
• torvalds/linux
• python/cpython
• rust-lang/rust
• microsoft/TypeScript
• microsoft/vscode
• golang/go
• numpy/numpy
• scikit-learn/scikit-learn
• openbsd/src
• freebsd/freebsd-src
• pandas-dev/pandas
• scipy/scipy
• tidyverse/ggplot2
• kubernetes/kubernetes
• postgres/postgres
• nodejs/node
• facebook/react
• angular/angular
• matplotlib/matplotlib
• apache/httpd
• nginx/nginx
• opencv/opencv
• ipython/ipython
• rstudio/rstudio
• jupyterlab/jupyterlab
• gcc-mirror/gcc
• apple/swift
• denoland/deno
• apache/spark
• llvm/llvm-project

Credits

Image credits: Unsplash - yancymin