Information and communication was the industry with the most usage of artificial intelligence (AI) such as machine learning for data analysis in Denmark in 2023 with 29 enterprises. Construction made up the least share with none.

In 2021, with 57 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 statistic shows artificial intelligence frameworks ranked by power score in 2018. TensorFlow has the highest score and ranks as the number one AI deep learning framework with a score of 96.77.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

Access the summary of the Artificial intelligence and Advanced Machine Learning market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

Machine Learning Statistics 2023

• To make the most of massive data sets, 8% of firms utilize machine learning, deep learning, data analysis, and natural language processing.
• Security is a top priority for any business.
• A.I. accounts for around 25% of I.T. By 2026, the hardware market is anticipated to be worth $87.68 billion.
• COVID has resulted in a 12% decrease in A.I. chip-making enterprises.
• By 2025, the American deep-learning market is anticipated to be worth $80 million.
• 91.5% of the world's largest corporations invest in artificial intelligence.
• Senior executives personally oversee 75% of artificial intelligence projects.
• Tesla's self-driving vehicles traveled more than 188 billion miles in the fourth quarter of 2019.
• When used multiple times over a six-month period, voice assistance usage increased by 5%.

This repository contains all the input and output data (including maps) related to Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm.

In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%.

This data set contains the source data for figures 2 and 4, which are the main Results figures. Data are given in both csv and MAT file formats. The MATLAB scripts for generating the figures are also provided.

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The machine learning market is fragmented. The market vendors are increasingly focusing on quality, pricing, and innovation to strengthen their position in the machine learning market. The machine learning market research report offers comprehensive vendor information and analysis that help in getting a clear picture of the competitive landscape of the market.

Some of the key vendors operating in the global machine learning market are:

Alibaba Group Holding Ltd.Alphabet Inc.Amazon.com Inc.Cisco Systems Inc.Hewlett Packard Enterprise Development LPInternational Business Machines Corp.Microsoft Corp.Salesforce.com Inc.SAP SESAS Institute Inc.

Introduction

Artificial Intelligence in Military Statistics: The integration of Artificial Intelligence (AI) into military operations marks a transformative shift in defense, leveraging machine learning, robotics, natural language processing, and computer vision to enhance decision-making, efficiency, and tactical advantages.

These technologies underpin a wide array of applications, from autonomous drones and cybersecurity defenses to predictive logistics and advanced training simulations. Fundamentally altering the landscape of military strategies and operations.

While offering significant benefits in operational precision and risk reduction, the deployment of AI in the military sphere also raises critical ethical and legal questions. Particularly concerning autonomous weaponry and the delegation of critical decisions to machines.

This evolution demands careful navigation of ethical frameworks, regulatory measures, and strategic considerations. Underscoring the pivotal role of AI in shaping future defense mechanisms and international security dynamics.

Newsle led the global machine learning industry in 2021 with a market share of 88.71 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 Automated Machine Learning Market Report is Segmented by Solution (Standalone or On-Premises and Cloud), Automation Type (Data Processing, Feature Engineering, Modeling, and Visualization), End User (BFSI, Retail and E-Commerce, Healthcare, and Manufacturing), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

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Factors such as increasing adoption of cloud-based offerings will further boost the manufacturing volume of machine learning during the forecast period 2020-2024. The annual manufacturing volume data will aid vendors in gauging the demand for the product during the next five years.

The machine learning market report also provides several other key information including:

CAGR of the market during the forecast period 2020-2024
Detailed information on factors that will drive machine learning market growth during the next five years
Precise estimation of the machine learning market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the machine learning market industry across APAC, Europe, MEA, North America, and South America
A thorough analysis of the market’s competitive landscape and detailed information on vendors
Comprehensive details of factors that will challenge the growth of machine learning market vendors

three of these models are available:

This dataset is composed by data from 77 college students (55% woman) enrolled in the 2nd and 3rd year of a private Medical School from the state of Minas Gerais, Brasil. They answered to 12 psychological or educational tests: 1) Inductive Reasoning Developmental Test (TDRI), 2) Metacognitive Control Test (TCM), 3) TDRI' Self-Appraisal scale (SA_TDRI), 4) TCM' Self-Appraisal scale (SA_TCM), 5) Brazilian High School Exam (ENEM), 6) Processing Speed Test (SP), 7) Perceptual Discrimination Test (DIS), 8) Perceptual Control Test (PC), 9) Conceptual Control Test (CC), 10) Short-term Memory Test (STM), 11) Working Memory Test (WM), and the 12) Brazilian Learning Approaches Scale (DeepAp).

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 Machine Learning (ML) Platforms market, a vital segment of the broader artificial intelligence landscape, is experiencing a significant transformation that shapes industries worldwide. ML platforms enable businesses to harness the power of data by creating algorithms capable of recognizing patterns and making pr

The Tiny Machine Learning (TinyML) market is rapidly transforming the landscape of artificial intelligence by enabling machine learning capabilities on resource-constrained devices. With its rise, TinyML leverages the capabilities of lightweight models to perform inference tasks directly on edge devices, minimizing

The Deep Learning market has emerged as a pivotal force in the realm of artificial intelligence, offering powerful solutions that impact a multitude of industries, including healthcare, finance, automotive, and technology. Defined as a subset of machine learning that employs neural networks to analyze vast amounts o

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The Machine Learning Market is predicted to witness an Accelerate growth rate. The Incremental growth and the Accelerate growth momentum will encourage established as well as new vendors to make investments and strengthen their position in the Machine Learning Market.

The Machine Learning Market prediction has been derived by triangulating data from multiple sources and approaches. While arriving at the market size, we have considered data points, such as the size of the parent market and the revenues of key market participants, such as Alibaba Group Holding Ltd., Alphabet Inc., Amazon.com Inc., Cisco Systems Inc., Hewlett Packard Enterprise Development LP, International Business Machines Corp., Microsoft Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc..