IEEE Transactions on Pattern Analysis and Machine Intelligence CiteScore 2024-2025 - ResearchHelpDesk - The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

Journal of machine learning research Publication fee - ResearchHelpDesk - The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.

In 2023, a total of 7185 research publications on artificial intelligence (AI) were recorded in Singapore, indicating a significant increase in the number of research publications on this topic from the year before. In comparison, the number of publications on AI in the country reached 3,659 in 2014.

International Journal of Artificial Intelligence Acceptance Rate - ResearchHelpDesk - The main aim of the International Journal of Artificial Intelligence™ (ISSN 0974-0635) is to publish refereed, well-written original research articles, and studies that describe the latest research and developments in the area of Artificial Intelligence. This is a broad-based journal covering all branches of Artificial Intelligence and its application in the following topics: Technology & Computing; Fuzzy Logic; Neural Networks; Reasoning and Evolution; Automatic Control; Mechatronics; Robotics; Parallel Processing; Programming Languages; Software & Hardware Architectures; CAD Design & Testing; Web Intelligence Applications; Computer Vision and Speech Understanding; Multimedia & Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and AI Planning Strategies and Tools, Computational Theories of Learning; Signal, Image & Speech Processing; Intelligent System Architectures; Knowledge Representation; Bioinformatics; Natural Language Processing; Mathematics & Physics. The International Journal of Artificial Intelligence (IJAI) is a peer-reviewed online journal and is published in Spring and Autumn i.e. two times in a year. The International Journal of Artificial Intelligence (ISSN 0974-0635) was reviewed, abstracted and indexed in the past by the INSPEC The IET, SCOPUS (Elsevier Bibliographic Databases), Zentralblatt MATH (io-port.net) of European Mathematical Society, Indian Science Abstracts, getCITED, SCImago Journal & Country Rank, Newjour, JournalSeek, Math-jobs.com’s Journal Index, Academic keys, Ulrich's Periodicals Directory, IndexCopernicus, and International Statistical Institute (ISI, Netherlands)Journal Index. The IJAI is already in request process to get reviewed, abstracted and indexed by the Clarivate Analytics Web of Science (Also known as Thomson ISI Web of Knowledge SCI), Mathematical Reviews and MathSciNet of American Mathematical Society, and by other agencies.

Artificial intelligence (AI) holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security, the law, transportation, even technology itself. On February 11, 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American AI Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative call for Federal agencies to prioritize AI research and development (R&emp;D) investments, enhance access to high-quality cyberinfrastructure and data, ensure that the Nation leads in the development of technical standards for AI, and provide education and training opportunities to prepare the American workforce for the new era of AI. In support of the American AI Initiative, this National AI R&emp;D Strategic Plan: 2019 Update defines the priority areas for Federal investments in AI R&emp;D. This 2019 update builds upon the first National AI R&emp;D Strategic Plan released in 2016, accounting for new research, technical innovations, and other considerations that have emerged over the past three years. This update has been developed by leading AI researchers and research administrators from across the Federal Government, with input from the broader civil society, including from many of America’s leading academic research institutions, nonprofit organizations, and private sector technology companies. Feedback from these key stakeholders affirmed the continued relevance of each part of the 2016 Strategic Plan while also calling for greater attention to making AI trustworthy, to partnering with the private sector, and other imperatives.

The East Asia & Pacific region is responsible for the majority of Artificial Intelligence (AI) journal publications, holding a share of 26.7 percent of the world total in 2020. AI journal publications provides a signal for the impact of artificial intelligence research and development throughout the world.

Global Total Number of Scientific Publications in Artificial Intelligence Share by Country (Units (Publications)), 2023 Discover more data with ReportLinker!

Please cite the following paper when using this dataset:N. Thakur and C.Y. Han, “An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection,” Journal of COVID, 2022, Volume 5, Issue 3, pp. 1026-1049AbstractThis dataset is one of the salient contributions of the above-mentioned paper. It presents a total of 522,886 Tweet IDs of the same number of Tweets about the SARS-CoV-2 Omicron Variant posted on Twitter since the first detected case of this variant on November 24, 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.Data DescriptionThe Tweet IDs are presented in 7 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. The data collection followed a keyword-based approach and tweets comprising the "omicron" keyword were filtered, collected, and added to this dataset. Filename: TweetIDs_November.txt (No. of Tweet IDs: 16471, Date Range of the Tweet IDs: November 24, 2021 to November 30, 2021)Filename: TweetIDs_December.txt (No. of Tweet IDs: 99288, Date Range of the Tweet IDs: December 1, 2021 to December 31, 2021)Filename: TweetIDs_January.txt (No. of Tweet IDs: 92860, Date Range of the Tweet IDs: January 1, 2022 to January 31, 2022)Filename: TweetIDs_February.txt (No. of Tweet IDs: 89080, Date Range of the Tweet IDs: February 1, 2022 to February 28, 2022)Filename: TweetIDs_March.txt (No. of Tweet IDs: 97844, Date Range of the Tweet IDs: March 1, 2022 to March 31, 2022)Filename: TweetIDs_April.txt (No. of Tweet IDs: 91587, Date Range of the Tweet IDs: April 1, 2022 to April 20, 2022)Filename: TweetIDs_May.txt (No. of Tweet IDs: 35756, Date Range of the Tweet IDs: May 1, 2022 to May 12, 2022) Here, the last date for May is May 12 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets. The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.

This document includes relevant text from the 2016 and 2019 national AI R&D strategic plans, along with updates prepared in 2023 based on Administration and interagency evaluation of the National AI R&D Strategic Plan: 2019 Update as well as community responses to a Request for Information on updating the Plan. The 2019 strategies were broadly determined to be valid going forward. The 2023 update adds a new Strategy 9, which establishes a principled and coordinated approach to international collaboration in AI research.

In 2022, Nanyang Technological University published 1,660 research on Artificial Intelligence (AI), making it the institution with the most AI publications in Singapore in that year. The National University of Singapore followed with 1,392 publications in the same research field.

Forecast: Number of Scientific Publications in Artificial Intelligence in Thailand 2024 - 2028 Discover more data with ReportLinker!

The advent of distributed computing has revolutionized data processing, storage, and computation, enabling scalable and decentralized architectures. However, the increasing complexity of distributed systems—spanning cloud, edge, fog, serverless, and quantum computing environments—presents significant challenges related to resource management, latency optimization, fault tolerance, and security. This paper investigates the integration of artificial intelligence (AI) into these paradigms to enhance their adaptability, scalability, and autonomic capabilities.

We propose a framework wherein AI-driven mechanisms, including machine learning algorithms, deep reinforcement learning models, and neural networks, facilitate self-optimization, dynamic orchestration, and predictive analytics within distributed ecosystems. By examining AI’s role in augmenting decision-making processes, automating resource allocation, and enabling self-healing systems, this research highlights its transformative potential in addressing the limitations of conventional distributed computing infrastructures.

The key contributions of this study include: (1) a systematic review of AI methodologies applied to cloud and edge computing for real-time performance enhancements, (2) a novel exploration of AI-quantum computing convergence to optimize hybrid processing models, and (3) the development of an architectural framework for autonomic and self-managing distributed systems, ensuring resilience and fault-tolerance.

Findings indicate that AI integration significantly improves operational efficiency, reduces energy consumption, and strengthens security protocols within distributed networks. The proposed AI-enhanced frameworks demonstrate high adaptability in dynamic environments, paving the way for next-generation computing systems capable of autonomous decision-making and intelligent task execution.

This study’s implications extend to critical domains, including industrial automation, healthcare informatics, and smart city infrastructures, where AI-powered distributed systems are poised to drive innovation. Future research will explore the ethical dimensions of AI deployment, sustainable computing practices, and the refinement of algorithms for emerging distributed computing paradigms.

The statistic shows the top ten countries with the highest number of artificial intelligence (AI) research institutions with publications as of 2017. The United States accounted for 43 percent of AI research institutions with publications worldwide.

In 2023, around 4,141 researches on Artificial Intelligence (AI) were published in Vietnam, indicating a slight decrease in the number of research publications on this topic from the year before. In the last decade, the number of publications on AI in the country had been growing year after year.

The global market size in the 'Machine Learning' segment of the artificial intelligence market was forecast to continuously increase between 2024 and 2030 by in total 424.1 billion U.S. dollars (+534.87 percent). After the seventh consecutive increasing year, the market size is estimated to reach 503.41 billion U.S. dollars and therefore a new peak in 2030. Find further information concerning the market size in the 'Machine Learning' segment of the artificial intelligence market in Spain and the market size change in the 'Computer Vision' segment of the artificial intelligence market in the United States. The Statista Market Insights cover a broad range of additional markets.

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Market Dynamics of the Artificial Intelligence in the Retail market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Impact of COVID–19 on the Artificial Intelligence in the Retail market

The COVID-19 pandemic significantly influenced artificial intelligence in the retail market, accelerating the adoption of A.I. technologies across the industry. With lockdowns, social distancing measures, and a surge in online shopping, retailers turned to A.I. to navigate the challenges posed by the pandemic. AI-powered solutions played a crucial role in optimizing supply chain management, predicting shifts in consumer behavior, and enhancing e-commerce experiences. Retailers lever...

This document contains eight ethics scenarios about Artificial Intelligence (AI) relevant to Information and knowledge management and library professionals.The document consists of ethics scenarios each followed by a set of notes which are prompts to discussion. The document ends with a set of summative questions, and a very select reading list.

By being made available in CC/BY/SA licence it is made possible for users to edit them to suit a particular sector or organisational context and to update them as new concerns emerge.

It is part of an on-going project to refine understanding of the ethical issues for information professionals, which will be published in the future.

Please cite the following paper when using this dataset:N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007AbstractThe exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today’s living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 Tweets about exoskeletons that were posted in a 5-year period from 21 May 2017 to 21 May 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

The data is from qualitative case study research into the implementation of the Rapid Recruitment project at Walmart, US, in 2020. One of the key elements of the rapid recruitment project was the use of a machine learning algorithm in the hiring system for hourly paid store-level associates (employees). The research involved semi-structured interviews with fourteen respondents with different roles and responsibilities in relation to the hiring process including: seven head office staff responsible for developing and implementing the system, five store-level managers and HR staff who used the system and two recently recruited employees. Interviews lasted 30 to 90 minutes and were conducted via video conferencing during the Covid-19 pandemic from September to December 2020. Interviews were supplemented with bi-weekly meetings with a business sponsor at the organisation and follow-up information gathered by email. Interviews were recorded and transcribed by the researchers. The interviews explored: recent changes to the hiring system, aims and objectives of the changes, the of motivations behind the changes, the development and implementation process, user adoption and perceptions of the new system and its effectiveness. The research found that the Rapid Recruitment project had largely been successful. Most users were using the new system as intended, the system had sped up the hiring process, enabled the organisation to hire greater numbers of staff during the increased demand due to the pandemic and the organisation reported that it had improved hiring outcomes (90-day turnover rates). However, not all users were confident in the new system or trusted the technology used, which in some cases meant that they were not using the system in the way intended, potentially undermining some of the objectives of the changes. Interview data could not be deposited to the archive because it was protected by a non-disclosure agreement (NDA) but research documents and metadata is deposited.

The Digital Futures at Work Research Centre (Dig.IT) will establish itself as an essential resource for those wanting to understand how new digital technologies are profoundly reshaping the world of work. Digitalisation is a topical feature of contemporary debate. For evangelists, technology offers new opportunities for those seeking work and increased flexibility and autonomy for those in work. More pessimistic visions, in contrast, see a future where jobs are either destroyed by robots or degraded through increasingly precarious contracts and computerised monitoring. Take Uber as an example: the company claims it is creating opportunities for self-employed entrepreneurs; while workers' groups increasingly challenge such claims through legal means to improve their rights at work.

While such positive and pessimistic scenarios abound of an increasingly fragmented, digitalised and flexible transformation of work across the globe, theoretical understanding of contemporary developments remains underdeveloped and systematic empirical analyses are lacking. We know, for example, that employers and governments are struggling to cope with and understand the pace and consequences of digital change, while individuals face new uncertainties over how to become and stay 'connected' in turbulent labour markets. Yet, we have no real understanding of what it means to be a 'connected worker' in an increasing 'connected' economy. Drawing resources from different academic fields of study, Dig.IT will provide an empirically innovative and international broad body of knowledge that will offer authoritative insights into the impact of digitalisation on the future of work.

The Dig.IT centre will be jointly led by the Universities of Sussex and Leeds, supported by leading experts from Aberdeen, Cambridge, Manchester and Monash Universities. Its core research programme will cover four broad-ranging research themes. Theme one will set the conceptual and quantitative base for the centre's activities. Theme two involves a large-scale survey of Employers' Digital Practices at Work. Theme three involves qualitative research on employers' and employees' experiences of digitalisation at work across 4 sectors (Creative industries, Business Services, Consumer Services, Public Services). Theme 4 examines how the disconnected attempt to reconnect, through Public Employment Services, the growth of new types of self-employment, platform work and workers' responses to building new forms of voice and representation in an international context. Specific projects include:

1. The Impact of Digitalisation on Work and Employment -Conceptualising digital futures, historically, regionally and internationally -Comparative regulation of digital employment

2. Mapping regional and international trends of digital technology and work

3. Employers' Digital Practices at Work Survey

4. Employers' and employees' experiences of digital work across sectors -Changing management processes...