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Synthetic Data Generation Market Size 2024-2028

The synthetic data generation market size is forecast to increase by USD 2.88 billion at a CAGR of 60.02% between 2023 and 2028.

The global synthetic data generation market is expanding steadily, driven by the growing need for privacy-compliant data solutions and advancements in AI technology. Key factors include the increasing demand for data to train machine learning models, particularly in industries like healthcare services and finance where privacy regulations are strict and the use of predictive analytics is critical, and the use of generative AI and machine learning algorithms, which create high-quality synthetic datasets that mimic real-world data without compromising security.
This report provides a detailed analysis of the global synthetic data generation market, covering market size, growth forecasts, and key segments such as agent-based modeling and data synthesis. It offers practical insights for business strategy, technology adoption, and compliance planning. A significant trend highlighted is the rise of synthetic data in AI training, enabling faster and more ethical development of models. One major challenge addressed is the difficulty in ensuring data quality, as poorly generated synthetic data can lead to inaccurate outcomes.
For businesses aiming to stay competitive in a data-driven global landscape, this report delivers essential data and strategies to leverage synthetic data trends and address quality challenges, ensuring they remain leaders in innovation while meeting regulatory demands

What will be the Size of the Market During the Forecast Period?

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Synthetic data generation offers a more time-efficient solution compared to traditional methods of data collection and labeling, making it an attractive option for businesses looking to accelerate their AI and machine learning projects. The market represents a promising opportunity for organizations seeking to overcome the challenges of data scarcity and privacy concerns while maintaining data diversity and improving the efficiency of their artificial intelligence and machine learning initiatives. By leveraging this technology, technology decision-makers can drive innovation and gain a competitive edge in their respective industries.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Healthcare and life sciences
  Retail and e-commerce
  Transportation and logistics
  IT and telecommunication
  BFSI and others


Type

  Agent-based modelling
  Direct modelling


Data

  Tabular Data
  Text Data
  Image & Video Data
  Others


Offering Band

  Fully Synthetic Data
  Partially Synthetic Data
  Hybrid Synthetic Data


Application

  Data Protection
  Data Sharing
  Predictive Analytics
  Natural Language Processing
  Computer Vision Algorithms
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    Japan
    India


  Middle East and Africa



  South America

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period. In the thriving healthcare and life sciences sector, synthetic data generation is gaining significant traction as a cost-effective and time-efficient alternative to utilizing real-world data. This market segment's rapid expansion is driven by the increasing demand for data-driven insights and the importance of safeguarding sensitive information. One noteworthy application of synthetic data generation is in the realm of computer vision, specifically with geospatial imagery and medical imaging.

For instance, in healthcare, synthetic data can be generated to replicate medical imaging, such as MRI scans and X-rays, for research and machine learning model development without compromising patient privacy. Similarly, in the field of physical security, synthetic data can be employed to enhance autonomous vehicle simulation, ensuring optimal performance and safety without the need for real-world data. By generating artificial datasets, organizations can diversify their data sources and improve the overall quality and accuracy of their machine learning models.

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The healthcare and life sciences segment was valued at USD 12.60 million in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the m

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

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Artificial Intelligence Text Generator Market Size 2024-2028

The artificial intelligence (AI) text generator market size is forecast to increase by USD 908.2 million at a CAGR of 21.22% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One of these trends is the increasing popularity of AI generators in various sectors, including education for e-learning applications. Another trend is the growing importance of speech-to-text technology, which is becoming increasingly essential for improving productivity and accessibility. However, data privacy and security concerns remain a challenge for the market, as generators process and store vast amounts of sensitive information. It is crucial for market participants to address these concerns through strong data security measures and transparent data handling practices to ensure customer trust and compliance with regulations. Overall, the AI generator market is poised for continued growth as it offers significant benefits in terms of efficiency, accuracy, and accessibility.

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The market is experiencing significant growth as businesses and organizations seek to automate content creation across various industries. Driven by technological advancements in machine learning (ML) and natural language processing, AI generators are increasingly being adopted for downstream applications in sectors such as education, manufacturing, and e-commerce. 
Moreover, these systems enable the creation of personalized content for global audiences in multiple languages, providing a competitive edge for businesses in an interconnected Internet economy. However, responsible AI practices are crucial to mitigate risks associated with biased content, misinformation, misuse, and potential misrepresentation.

How is this Artificial Intelligence (AI) Text Generator Industry segmented and which is the largest segment?

The artificial intelligence (AI) text generator industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Service


Application

  Text to text
  Speech to text
  Image/video to text


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) text generators have gained significant traction in various industries due to their efficiency and cost-effectiveness in content creation. These solutions utilize machine learning algorithms, such as Deep Neural Networks, to analyze and learn from vast datasets of human-written text. By predicting the most probable word or sequence of words based on patterns and relationships identified In the training data, AIgenerators produce personalized content for multiple languages and global audiences. The application spans across industries, including education, manufacturing, e-commerce, and entertainment & media. In the education industry, AI generators assist in creating personalized learning materials.

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The solution segment was valued at USD 184.50 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market holds the largest share in the market, driven by the region's technological advancements and increasing adoption of AI in various industries. AI text generators are increasingly utilized for content creation, customer service, virtual assistants, and chatbots, catering to the growing demand for high-quality, personalized content in sectors such as e-commerce and digital marketing. Moreover, the presence of tech giants like Google, Microsoft, and Amazon in North America, who are investing significantly in AI and machine learning, further fuels market growth. AI generators employ Machine Learning algorithms, Deep Neural Networks, and Natural Language Processing to generate content in multiple languages for global audiences.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and c

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Market Analysis of Text-to-Video Generator The global text-to-video generator market is estimated to reach a value of $XX million by 2033, expanding at a CAGR of XX% during the forecast period. Key drivers for this growth include the increasing demand for engaging and visually appealing content, the proliferation of social media platforms, and advances in artificial intelligence (AI) and machine learning (ML). AI-powered text-to-video generators enable users to create high-quality videos from text in a cost-effective and time-saving manner. The market is segmented based on application (personal use, commercial use), types (on-cloud, on-premise), and region. Key players include Lumen5, Vidnami, Wave.video, Animaker, Biteable, and OpenAI. North America, Europe, and the Asia Pacific are major regional markets. The growing adoption of cloud-based text-to-video generators and the increasing availability of video-based content on various platforms are expected to fuel market growth. However, concerns regarding data privacy and security may pose challenges to market expansion. Market Overview The global text-to-video generator market is projected to reach $5.5 billion by 2028, growing at a CAGR of 20.2% from 2022. The increasing demand for engaging and visually appealing content, advancements in artificial intelligence (AI) and natural language processing (NLP), and growing adoption across industries are driving this growth.

SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment.SDNist is available via pip install: pip install sdnist==1.2.8 for Python >=3.6 or on the USNIST/Github. The sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.

This file is supplementary material for the manuscript Racial Bias in AI-Generated Images, which has been submitted to a peer-reviewed journal. This dataset/paper examined the image-to-image generation accuracy (i.e., the original race and gender of a person’s image were replicated in the new AI-generated image) of a Chinese AI-powered image generator. We examined the image-to-image generation models transforming the racial and gender categories of the original photos of White, Black and East Asian people (N =1260) in three different racial photo contexts: a single person, two people of the same race, and two people of different races.

Data for generating a pie chart on the distribution of feedback categories of AI Baby Generator.

The global AI Comic Generator market is projected to reach a valuation of million by 2033, exhibiting a CAGR of 20.6% during the forecast period (2025-2033). The market growth is primarily driven by the increasing demand for AI-generated content in various industries, including personal, school, advertising, film and television. Additionally, the growing popularity of AI-powered tools and the increasing adoption of cloud-based services are further contributing to the market expansion. Key trends influencing the market include the advancements in natural language processing (NLP) and machine learning (ML) algorithms, which enable AI-powered comic generators to produce more sophisticated and engaging content. Moreover, the emergence of subscription-based models and the availability of free and paid versions are creating a wider range of options for users. The market is highly competitive, with established players such as ComicsMaker.ai, AI Comic Factory, and Fotor, along with emerging startups like Neural Canvas and Mage.space, vying for market share. The regional landscape is dominated by North America, Europe, and Asia Pacific, with significant growth potential in emerging markets like South America, the Middle East & Africa, and Asia Pacific.

This data set provides heat detector temperatures in a single-story ranch structure with a living room, kitchen, dining room, and three bedrooms. 20000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various fire conditions and door opening conditions. The fire is described based on t-squared law. The peak heat release rate and time to peak range from approximately 1667 kW to 4620 kW and from 210 s to 1540 s, respectively. A detailed description of this work can be found in Ref. [2].[1] Reneke, P.A., Peacock, R.D., Gilbert, S.W. and Cleary, T.G., 2021. CFAST Consolidated Fire and Smoke Transport (Version 7) Volume 5: CFAST Fire Data Generator (CData). NIST Technical Note 1889v5. Gaithersburg, MD.[2] Fu, E.Y., Tam, W.C., Wang, J., Peacock, R., Reneke, P., Ngai, G., Leong, H.V. and Cleary, T., 2021, May. Predicting Flashover Occurrence using Surrogate Temperature Data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14785-14794).

The AI image generator market is experiencing explosive growth, projected to be valued at $910.4 million in 2025. While the CAGR is not provided, considering the rapid advancements and increasing adoption of AI in creative fields, a conservative estimate of 30% CAGR from 2025 to 2033 seems reasonable. This translates to substantial market expansion, driven by factors such as the accessibility of user-friendly platforms, the decreasing cost of AI processing, and the rising demand for unique and personalized visual content across various sectors, including marketing, gaming, and e-commerce. The market segmentation reveals a strong presence of both Android and iOS applications catering to both personal and enterprise needs. Leading companies like Midjourney, Stable Diffusion, and DALL-E 2 are continually innovating, introducing new features and improving image quality, fostering market competitiveness and pushing boundaries of creative AI applications. The diverse range of applications and the continuous improvement in AI algorithms are key factors shaping market trends. Increased ease of use and accessibility are democratizing image generation, enabling individuals and small businesses to create professional-quality visuals without extensive design expertise. However, challenges remain, including concerns about copyright infringement, potential biases embedded in training data, and the need for effective ethical guidelines surrounding AI-generated content. Future growth will depend on addressing these concerns and ensuring responsible innovation. The regional breakdown suggests a strong market presence across North America, Europe, and Asia-Pacific, with potential for significant expansion in emerging markets as access to technology and internet penetration increase. The forecast period (2025-2033) presents ample opportunity for further market expansion and innovation within the AI image generation sector.

This project contains instructions and codes to reconstruct a dataset for the development and evaluation of forensic tools for detecting machine-generated text in social media.

-We are not releasing full twitter data to comply with Twitter terms of service. -We are also not releasing generators and machine generated data for ethical reasons.

The AI Text-to-Art Generator market is anticipated to reach a value of 1,477 million by 2033, exhibiting a CAGR of 10.2% from 2025 to 2033. The market's growth is driven by the increasing adoption of AI technologies for artistic creation, personalized art generation, and the surging popularity of digital art. Moreover, the growing use of AI text-to-art generators in various industries, such as art, education, fashion, and entertainment, is expected to further propel market growth. Key trends in the market include the emergence of advanced AI algorithms for more realistic and creative art generation, the integration of AI text-to-art generators with other software applications, and the growing availability of user-friendly and accessible platforms. However, challenges such as the need for high-quality training data, potential copyright issues, and ethical concerns related to AI-generated art may pose some restraints on market expansion. The market is dominated by players like Deep Dream Generator, DeepAI, Google Colaboratory (Colab), Hotpot, NeuroGen, Nexus, NightCafe Creator, OpenAI, Starryai, Text2Art, and Wombo Dream AI, among others. "Let the words paint a masterpiece, where imagination knows no bounds."

Data for generating a bar chart on feedback counts by category for NSFW Generator AI.

The global AI Video Generators market is projected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX%. The market growth is attributed to the increasing demand for personalized and engaging video content, advancements in machine learning and natural language processing, and the rising popularity of social media platforms. Key market drivers include the growing adoption of AI-powered video editing software, increasing demand for video content for marketing and advertising, and the rising popularity of video streaming services. Key market trends include the emergence of generative text-to-video apps, the integration of AI into existing video editing tools, and the development of AI-powered video analytics solutions. Key market restraints include data privacy and security concerns, the limited availability of high-quality training data, and the high cost of AI video generation. The market is segmented by application (personal user, enterprise, others) and type (video editors with AI editing tools, generative text-to-video apps). Key companies in the market include Pictory, Synthesia, HeyGen, Deepbrain AI, Synthesys, InVideo, Veed.io, Elai.io, NeuralFrames, Colossyan, FlexClip, Wave Video.

This dataset webpage contains datasets of exisiting and proposed models:tubular.ziptubular2.ziptubular3.zip B Model of Fault And Short-Circuit Analysis of Synchronous Generator presented in my 3rd Probably last Speaker Presentation in Conference - 2025*. I have clubbed this paper also in the same manuscript as otherwise it will only remain as pre-print with rejection on vague reasons. As this webpage was created earlier and not an open-access, I have posted the same data in 'Data: B-Non-Linear Theory' which is an open-access webpage due to two reasons - (i) I keep getting emails to email them data, and I don't want to re-post to other website (unless necessary) (ii) My Presentation in Conference is open-access conference proceeding where I am submitting this research. Thus, all the data can be freely downloaded from 'Data: B-Non-Linear Theory'.To see Abstract of the Complete Presentation/Talk , pls click above title and refer - Data: B-Non-Linear Theory Abstract (Related to this dataset) -

Market Analysis: AI Perfume Generator The global AI Perfume Generator market is poised to expand significantly, driven by advancements in artificial intelligence (AI) and the growing demand for personalized fragrance experiences. The market size, valued at XXX million in 2025, is projected to reach XX million by 2033, exhibiting a remarkable CAGR of XX% during the forecast period. This growth is attributed to the increasing adoption of AI in the beauty and cosmetic industry, as well as the emergence of new technologies that enable the creation of highly customized perfumes tailored to individual preferences. Key trends include the integration of AI algorithms with sensors that analyze personal data to suggest optimal scents, as well as the use of AI-powered platforms for faster and more efficient product development. Competitive Landscape and Regional Dynamics The AI Perfume Generator market is characterized by the presence of established players such as Innosol, Scentronix, and EveryHuman, as well as emerging startups like Scentalytics and NINU. These companies are investing heavily in research and development to enhance their offerings and gain a competitive advantage. Regionally, North America and Europe are expected to remain प्रमुख markets due to the high adoption of AI technology and the presence of a large consumer base for personalized fragrances. However, Asia Pacific is also witnessing notable growth, with China and India emerging as potential hotspots for market expansion. Strategic partnerships and collaborations between companies are likely to shape the future of the industry, driving innovation and creating new opportunities for growth.

The AI Contract Generator market is rapidly evolving, driven by the increasing need for efficiency and accuracy in contract management across various industries. These sophisticated tools leverage artificial intelligence to automate the creation, analysis, and management of contracts, significantly reducing the time

The AI video generation platform market is experiencing rapid growth, driven by increasing demand for automated video content creation across various sectors. The market, valued at approximately $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This robust growth is fueled by several factors, including the rising adoption of AI-powered tools by businesses to streamline their marketing and communication efforts, the escalating need for personalized video content, and the expanding availability of user-friendly platforms that require minimal technical expertise. The market is segmented by video type (text, graphic, and music videos) and application (personal and enterprise), with the enterprise segment expected to dominate due to higher investment capacity and greater need for efficient content production. Key players are continuously innovating to offer enhanced features, including improved AI-driven scriptwriting, realistic avatars, and seamless integration with existing workflows, further accelerating market expansion. The significant growth potential is further amplified by advancements in natural language processing (NLP) and computer vision, allowing for more sophisticated video generation capabilities. However, challenges remain, such as the need for high-quality data to train AI models, concerns about the ethical implications of AI-generated deepfakes, and the ongoing need for human oversight to ensure quality and accuracy. Nevertheless, ongoing technological advancements and the increasing affordability of AI video generation platforms are poised to overcome these hurdles, propelling sustained market expansion throughout the forecast period. The geographic distribution of the market is diverse, with North America and Europe currently holding the largest market shares, but rapid growth is anticipated in Asia-Pacific regions driven by increasing internet penetration and digital adoption.

Data for generating a bar chart on feedback counts by category for AI Baby Generator.