The statistic shows artificial intelligence frameworks ranked by power score in 2018. TensorFlow has the highest score and ranks as the number *** AI deep learning framework with a score of *****.

The Fake Image Machine Learning and Deep Learning Detection market has emerged as a critical frontier in safeguarding the integrity of digital content. As the rise of deepfake technology and manipulated images proliferates across social media platforms and news outlets, industries including media, security, and adve

Introduction

Machine Learning Statistics: Machine learning (ML) is a niche research area that has transformed into the heart of modern technology and drives innovations across many industries. It can learn from data, make decisions, and improve over time. Thus, it is a crucial part of applications, from personalized recommendations on streaming platforms to self-driving cars.

Many businesses have embraced machine learning statistics in various sectors, and more organizations are investing in this technology to enhance their operations. The rate at which ML is adopted is mind-boggling, with the market expected to be worth USD 209.91 billion by 2029, representing a compound annual growth rate (CAGR) of 38.8% from 2022. ML adoption is at an unprecedented pace due to its importance in enabling artificial intelligence and greater digital transformation processes.

Thus, as business entities and government agencies increasingly use machine learning for competitive advantage and efficiency, knowing essential statistics about this technology provides useful insights into its current impacts and prospects.

The United States is by far the largest producer of notable machine learning programs in 2024, with **, ahead of China's **. It is notable that France, Germany, and UK, despite accounting for smaller economic size and population, now outproduce China on machine learning programs together, producing some ** models versus China's **.

The Deep Learning Frameworks market has witnessed significant growth and evolution, establishing itself as a critical component in the landscape of artificial intelligence and machine learning. These frameworks provide developers with essential tools for building and training deep neural networks, enabling the autom

A supplement is provided for the paper: Dataset and the conde for reprduction of the results.

Gibbs randomness-compression proposition: An efficient deep learning
doi: [10.48550/arXiv.2505.23869](https://arxiv.org/abs/2505.23869)

This repository contains all the necessary files to ensure the full reproducibility of the experiments conducted in the Reproducible Nexus Experiment project. The structure is organized to facilitate access to input data, intermediate processing, and final results, ensuring transparency and replicability.

1. /inputs/

Contains the original input files used in the experiment.

• Shapefiles by state:

• *_setores_censitarios/*.shp

• Indicators by state:

• IDHM_NEXUS_*.csv

2. /processing/

Includes intermediate files generated during processing, which serve as inputs for the next steps.

• Additional geolocation information:

• loc_dict_brazil_2010_income.pkl

• Datasets processados:

• clusters_data_9000.csv

• Original TFRecords:

• tfrecords_raw/brazil_2010_*.gz

• Processed TFRecords:

• tfrecords_processed/brazil_2010_*.gz

• Fold division:

• dhs_incountry_co.pkl

• Features generated for the models:

• dhs_co_income.npz

• Modeling files:

• features.npz

• params.json

• results.csv

3. /outputs/

Contains the final results of the experiment, ready to be analyzed or used in reports.

• Predictions and performance:

• /logs/income/alldata.csv

• /logs/income/incountry_predsnew.csv

• /logs/income/performance.csv

• Predictions of combined models:

• /logs/income/resnet_ms_concat/test_preds.npz

Although it is a powerful paradigm for the processing of scientific evidence into facts and truths, and for the construction of phenomenological models that account for randomness, the framework of classical statistics can often be restrictive and inflexible. Parallel to the development of statistical methods, computer scientists have developed their own paradigm of machine learning, which focuses on a more computational perspective of the processing of data into facts and predictions. Since its conception, the theory of statistical learning was introduced and has been able to unify the flexibility of machine learning methods with the theoretical rigour of statistical theory. Thus, machine learning methods, when applied in the right way, can be used to generate statistical inference in the same way as traditional techniques. We shall introduce a number of machine learning algorithms and their applications and describe how they can be used for statistical inference.

In 2021, with ** percent, improving customer experience represents the top artificial intelligence and machine learning use cases. The deployment of machine learning and artificial intelligence can advance a variety of business processes.

The dataset contains the following features: Year, Industry Type, Contribution to GDP, Growth by GDP, Employment Types, and Total Employment of Kenya. This dataset was extracted from Statistical reports published by Kenya National Bureau of Statistics reports from 2011 to 2023. Researchers utilised advanced statistical techniques, machine and deep learning algorithms to predict the current extent of working poverty in Kenya, and assist policy makers in making informed decisions for future policy formulations.

The statistic shows the artificial intelligence frameworks ranked by the share of unique mentions in publications of the arXiv repository from January 2012 to February 2018. TensorFlow was mentioned in 5.9 percent of all publications in arXiv during that time period.

Work in progress...

NeuriPhy - Neuroimaging Dataset for Physics-Informed Learning

This dataset was developed in the context of my master's thesis titled "Physics-Guided Deep Learning for Sparse Data-Driven Brain Shift Registration", which investigates the integration of physics-based biomechanical modeling into deep learning frameworks for the task of brain shift registration. The core objective of this project is to improve the accuracy and reliability of intraoperative brain shift prediction by enabling deep neural networks to interpolate sparse intraoperative data under biomechanical constraints. Such capabilities are critical for enhancing image-guided neurosurgery systems, especially when full intraoperative imaging is unavailable or impractical.

The dataset integrates and extends data from two publicly available sources: ReMIND and UPENN-GBM. A total of 207 patient cases (45 cases from ReMIND and 162 cases from UPENN-GBM), each represented as a separate folder with all relevant data grouped per case, are included in this dataset. It contains preoperative imaging (unstripped), synthetic ground truth displacement fields, anatomical segmentations, and keypoints, structured to support machine learning and registration tasks.

For details on the image acquisition and other topics related to the original datasets, see their original links above.

Contents

• Imaging Data:
  • T1ce: Preoperative contrast-enhanced T1-weighted MRI scans.
  • T2: Preoperative T2-weighted MRI scans, including mostly T2-SPACE, but also native T2 and T2-BLADE acquisitions depending on the case.
  • All MRI scans are in NIfTI format and have been resampled to the same isotropic resolution (1x1x1 mm). Intra-patient rigid coregistration was performed as part of preprocessing with the "General Registration (BRAINS)" extension of 3D Slicer.

• Synthetic Displacement Fields:
  • Biomechanically simulated ground truth displacement fields were generated using a meshless approach and by solving differential equations of nonlinear elasticity using explicit methods, as described in 1, 2, 3, 4.
  • For each patient, 1 to 5 simulations were successfully performed, each with a different gravity vector orientation according to a plausible surgical entry point, creating variability in the deformations obtained. Overall, the dataset contains 394 simulations that aimed to predict the intraoperative state after tumor-resection-induced brain shift.
  • Includes the initial and displaced (final) coordinates of several points in the brain volume that were used to generate the displacement field using a multi-level BSpline interpolation algorithm.
  • These displacement fields were mainly intended for use as supervision in deep learning-based registration methods.

• Keypoints:
  • Sparse 3D keypoints and their descriptors were generated using the 3D SIFT-Rank algorithm on the T1ce images (or T2 if T1ce was unavailable).
  • Keypoints are provided for each case in both voxel space and world coordinates (RAS?), being suitable for sparse registration or landmark-based evaluation.

• Segmentations:
  • Brain segmentations were automatically generated using SynthSeg, a deep learning model capable of robust whole-brain segmentation with scans of any contrast and resolution.
  • Tumor segmentations are included from the original datasets.
  • All segmentations are provided in the NRRD format.

Data Structure

Each patient folder contains the following subfolders:

images/: Preoperative MRI scans (T1ce, T2) in NIfTI format.

segmentations/: Brain and tumor segmentations in NRRD format.

simulations/: Biomechanically simulated displacement fields with initial and final point coordinates (LPS) in .npz and .txt formats, respectively.

keypoints/: 3D SIFT-Rank keypoints and their descriptors in both voxel space and world coordinates (RAS?) as .key files.

The folder naming and organization are consistent across patients for ease of use and scripting.

Source Datasets

ReMIND: is a multimodal imaging dataset of 114 brain tumor patients that underwent image-guided surgical resection at Brigham and Women’s Hospital, containing preoperative MRI, intraoperative MRI, and 3D intraoperative ultrasound data. It includes over 300 imaging series and 350 expert-annotated segmentations such as tumors, resection cavities, cerebrum, and ventricles. Demographic and clinico-pathological information (e.g., tumor type, grade, eloquence) is also provided.

UPENN-GBM: comprises multi-parametric MRI scans from de novo glioblastoma (GBM) patients treated at the University of Pennsylvania Health System. It includes co-registered and skull-stripped T1-weighted, T1-weighted contrast-enhanced, T2-weighted, and FLAIR images. The dataset features high-quality tumor and brain segmentation labels, initially produced by automated methods and subsequently corrected and approved by board-certified neuroradiologists. Alongside imaging data, the collection provides comprehensive clinical metadata including patient demographics, genomic profiles, survival outcomes, and tumor progression indicators.

Use Cases

This dataset is tailored for researchers and developers working on:

• Deformable image registration
• Physics-informed machine learning
• Intraoperative brain shift modeling
• Sparse data interpolation and deep learning
• Multi-modal image alignment in neuroimaging

It is especially well-suited for evaluating learning-based registration methods that incorporate physical priors or aim to generalize under sparse supervision.

Newsle led the global machine learning industry in 2021 with a market share of ***** percent, followed by TensorFlow and Torch. The source indicates that machine learning software is utilized for the application of artificial intelligence (AI) that allows systems the ability to automatically or "artificially" learn and improve functions based on experience without being specifically programmed to do so.

The Visual Deep Learning market is rapidly evolving, driven by advancements in artificial intelligence and machine learning technologies. As organizations increasingly seek to harness the power of visual data, the deployment of deep learning models in computer vision applications has emerged as a cornerstone of inno

This statistic shows the importance of big data analysis and machine learning technologies worldwide as of 2019. Tensorflow was seen as the most important big data analytics and machine learning technology, with 59 percent of respondents stating that it was important to critial for their organization.

On May 21st, 2021, we held the webinar "Covid-19 and AI: unexpected challenges and lessons". This short note presents its highlights.

This dataset contains the weights of a convolutional neural network (CNN) trained to recognize the presence of solar panels on aerial photos. In particular, it contains the saved state of a ResNet50 CNN that has been trained on a dataset containing annotated high-resolution aerial images of two regions in the south of the Netherlands. Many photos in this dataset have been annotated multiple times, and the annotations are not always unanimous. The dataset of aerial images together with annotations can be downloaded from here.

The model for detecting whether solar panels are present in aerial photos has been developed under the DeepSolaris and DeepGeoStat projects. Corresponding Pytorch code can be found here. The code also demonstrates how to load the saved state into a ResNet50 model, and use it for detecting solar panels on aerial photos.

This research was conducted under:

• ESS action 'Merging Geostatistics and Geospatial Information in Member States' (grant agreement no.: 08143.2017.001-2017.408),
• ESS topic B5674-2020-GEOS (project 101033951 2020-NL-GEOS-DEEP-GEO-STAT),
• a research program of Statistics Netherlands (https://www.cbs.nl)

The basic architecture of a deep transfer algorithm with separation of concerns. The algorithm is feed some data D, has some bank of prior knowledge in D, and relies on two components: a standard machine learning algorithm to analyze the data, and an agent to build an informative prior and/or set the hyperparameters. In this particular case, supervised learning is done with Gaussian processes, but it could also use Support Vector Machines (setting the parameters) or any other supervised learning algorithm. We are looking for the best model given our data and our bank of prior data-sets. In this case, the agent's role is to establish the prior, i.e.: to create a bias toward more likely functions, and choose the hyper-parameters for Gaussian inference. Modelling with Gaussian processes requires a few free parameters (the hyperparameters) and the agent learn to select them. To search efficiently, the agent (reinforcement learning) will use natural language processing, read the labels in the data-sets, i.e.: x1 = humidity, x2 = Linux distribution, and learn to exploit this information to establish the best informative prior. Unlike other deep transfer algorithms like TAMAR, this approach can deal with an arbitrarily high number of sources and has no fixed method of performing transfer: it learns to do it. Reinforcement learning relies on rewards, in this case the reward will be established by the errors of the model during cross-validation and generalization, and how well it performs against a non-informative prior (if available). It should be possible to also tests agents against each other (i.e.: each with a different supervised learning algorithm). An important tool used to exploit the information in the label will be semantic clustering (unsupervised learning), which should clusters similar variables together and help the agent learn how to perform effective transfer.

The paper investigated the use of deep learning for deriving the number of active sweat glands and the sweat rate per gland from a(n in-silico) discrete sweat sensing device. The study was completely in silico. This dataset includes the trained neural networks that were evaluated for this study (.keras, version in READ ME), the synthetic datasets that were used for training and testing (.parquet) and the results of the tests (.xlsx). The latter contains more results than presented in the paper (including the precision and recall).

GWAS Summary Statistics for Abdomen Pancreas Aging