The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

International Journal of Artificial Intelligence Impact Factor 2024-2025 - 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.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

Objectives: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses. Study design: After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals’ JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI. Results: Of the 3999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n=64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs 3.5, P<0.001), and screened more abstracts per author (302.2 vs 140.3, P=0.009) and per included study (189.0 vs 365.8, P<0.001) while inspecting less full texts per author (5.3 vs 14.0, P=0.005). No differences were found in citation counts (0.5 vs 0.6, P=0.600), inspected full texts per included study (3.8 vs 3.4, P=0.481), completion times (74.0 vs 123.0, P=0.205) or AMSTAR-2 (7.5 vs 6.3, P=0.119). Conclusion: AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews. Methods Dataset produced from bibliographic references to COVID-19 systematic reviews obtained from the COVID-19 Living Overview of Evidence database. We obtained accessibility information and download links from the Unpaywall database, and indexed the resulting downloaded files with the OpenSemanticSearch search engine.

International journal of machine learning and computing Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Machine Learning and Computing - IJMLC is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of machine learning and computing. The focus is to publish papers on state-of-the-art machine learning and computing. Submitted papers will be reviewed by technical committees of the Journal and Association. The audience includes researchers, managers and operators for machine learning and computing as well as designers and developers. All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing. IJMLC is an open access journal which focus on publishing original and peer reviewed research papers on all aspects of machine learning and computing. And the topics include but not limited to: Adaptive systems Business intelligence Biometrics Bioinformatics Data and web mining Intelligent agent Financial engineering Inductive learning Geo-informatics Pattern Recognition Logistics Intelligent control Media computing Neural net and support vector machine Hybrid and nonlinear system Fuzzy set theory, fuzzy control and system Knowledge management Information retrieval Intelligent and knowledge based system Rough and fuzzy rough set Networking and information security Evolutionary computation Ensemble method Information fusion Visual information processing Computational life science Abstract & indexing Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.

Product Review Analytics AI Market Outlook

As per our latest research, the global Product Review Analytics AI market size was valued at USD 1.42 billion in 2024, with a robust year-on-year growth rate, and is projected to reach USD 7.65 billion by 2033 at a CAGR of 20.6% over the forecast period. The rapid expansion of e-commerce platforms, the surge in online customer engagement, and the increasing need for actionable insights from user-generated content are key growth factors propelling the market. The integration of artificial intelligence into product review analytics is fundamentally reshaping how businesses interpret customer sentiment, optimize product offerings, and enhance customer experiences globally.

The primary growth driver for the Product Review Analytics AI market is the exponential rise in online consumer activity, especially in the e-commerce and retail sectors. As consumers increasingly rely on digital platforms for shopping, the volume of product reviews and feedback has escalated dramatically. Businesses are leveraging AI-powered analytics to sift through massive volumes of unstructured data, extracting meaningful patterns and sentiments that inform product development and marketing strategies. This shift towards data-driven decision-making is further accelerated by advancements in natural language processing and machine learning, which enable more accurate sentiment analysis, trend identification, and predictive modeling. The ability to automate the extraction of actionable insights from vast review datasets has become a critical competitive advantage for enterprises aiming to enhance customer satisfaction and loyalty.

Another significant growth factor is the broadening application of Product Review Analytics AI across diverse industry verticals beyond traditional retail and e-commerce. Sectors such as hospitality, automotive, and consumer electronics are increasingly adopting AI-driven review analytics to understand customer preferences, identify pain points, and benchmark against competitors. These industries are recognizing the value of real-time feedback analysis in optimizing product features, improving service delivery, and refining marketing messages. Additionally, the proliferation of omnichannel retailing and the convergence of physical and digital touchpoints are driving organizations to invest in sophisticated AI solutions that can aggregate and analyze reviews from multiple sources, including social media, online marketplaces, and brand websites. This holistic approach to customer feedback management is fueling demand for advanced analytics platforms capable of delivering granular, actionable insights.

The rising importance of brand reputation management and regulatory compliance is also contributing to the growth of the Product Review Analytics AI market. In an era where a single negative review can significantly impact sales and brand image, organizations are prioritizing the monitoring and analysis of customer feedback to proactively address issues and mitigate risks. AI-enabled review analytics platforms offer automated alerting, sentiment tracking, and anomaly detection features that empower businesses to respond swiftly to emerging trends and potential crises. Furthermore, regulatory frameworks related to consumer data protection and transparency are prompting companies to adopt compliant analytics solutions that ensure ethical handling of user-generated content. These dynamics are fostering a culture of continuous improvement and accountability, further accelerating market adoption.

Regionally, North America currently dominates the Product Review Analytics AI market, driven by the presence of leading technology providers, high digital adoption rates, and the concentration of e-commerce giants. However, Asia Pacific is expected to exhibit the fastest growth over the forecast period, propelled by the rapid expansion of online retail, increasing smartphone penetration, and growing investment in AI technologies across emerging economies such as China and India. Europe also represents a significant market, with strong regulatory frameworks and a mature retail landscape fostering adoption. The Middle East & Africa and Latin America are gradually catching up, supported by digital transformation initiatives and rising consumer awareness. This regional diversification underscores the global relevance and growth potential of Product Review Analytics AI solutions.

Component Analysis

The Prod

BackgroundInpatient violence in clinical and forensic settings is still an ongoing challenge to organizations and practitioners. Existing risk assessment instruments show only moderate benefits in clinical practice, are time consuming, and seem to scarcely generalize across different populations. In the last years, machine learning (ML) models have been applied in the study of risk factors for aggressive episodes. The objective of this systematic review is to investigate the potential of ML for identifying risk of violence in clinical and forensic populations.MethodsFollowing Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a systematic review on the use of ML techniques in predicting risk of violence of psychiatric patients in clinical and forensic settings was performed. A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Risk of bias and applicability assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST).ResultsWe identified 182 potentially eligible studies from 2,259 records, and 8 papers were included in this systematic review. A wide variability in the experimental settings and characteristics of the enrolled samples emerged across studies, which probably represented the major cause for the absence of shared common predictors of violence found by the models learned. Nonetheless, a general trend toward a better performance of ML methods compared to structured violence risk assessment instruments in predicting risk of violent episodes emerged, with three out of eight studies with an AUC above 0.80. However, because of the varied experimental protocols, and heterogeneity in study populations, caution is needed when trying to quantitatively compare (e.g., in terms of AUC) and derive general conclusions from these approaches. Another limitation is represented by the overall quality of the included studies that suffer from objective limitations, difficult to overcome, such as the common use of retrospective data.ConclusionDespite these limitations, ML models represent a promising approach in shedding light on predictive factors of violent episodes in clinical and forensic settings. Further research and more investments are required, preferably in large and prospective groups, to boost the application of ML models in clinical practice.Systematic review registration[www.crd.york.ac.uk/prospero/], identifier [CRD42022310410].

AI-Enhanced Product Review Moderation Market Outlook

According to our latest research, the AI-Enhanced Product Review Moderation market size reached USD 1.92 billion globally in 2024, with a robust year-on-year growth driven by the increasing adoption of artificial intelligence in digital commerce. The market is expected to expand at a CAGR of 17.2% from 2025 to 2033, reaching a projected value of USD 8.45 billion by the end of the forecast period. This exceptional growth is primarily attributed to the exponential rise in online transactions and the critical need for brands and platforms to maintain the integrity, authenticity, and trustworthiness of user-generated content. As per our latest research, the integration of advanced AI technologies for automated content moderation is transforming customer experience and operational efficiency across diverse industry verticals.

The growth trajectory of the AI-Enhanced Product Review Moderation market is underpinned by several compelling factors. Firstly, the surge in e-commerce and digital retail has precipitated an unprecedented volume of user-generated product reviews, making manual moderation both resource-intensive and inefficient. AI-driven moderation tools leverage natural language processing (NLP), sentiment analysis, and machine learning algorithms to automatically detect spam, inappropriate content, and fake reviews in real time. This not only streamlines the review management process but also ensures a safer and more reliable shopping environment for consumers. As a result, businesses are increasingly investing in AI-enhanced moderation solutions to safeguard their brand reputation, comply with evolving regulatory requirements, and foster consumer trust.

Another significant driver accelerating market growth is the rapid evolution of AI technologies, especially in the context of deep learning and contextual analysis. Modern AI moderation platforms are now capable of understanding nuanced language, cultural references, and even subtle forms of manipulation or bias in product reviews. This technological sophistication allows for more accurate and context-aware moderation, minimizing the risk of false positives or negatives that can undermine user experience. Furthermore, AI-powered solutions offer scalability and adaptability, enabling businesses to handle fluctuating review volumes during peak seasons or promotional events without compromising on moderation quality. This operational agility is a key differentiator in todayÂ’s highly competitive digital marketplace.

Moreover, the rising emphasis on regulatory compliance and data privacy is catalyzing the adoption of AI-enhanced review moderation tools. Governments and industry bodies across the globe are introducing stricter norms to combat deceptive practices, misinformation, and harmful content in online reviews. AI-based moderation platforms offer robust audit trails, transparency, and customizable filters that help organizations adhere to these regulations efficiently. Additionally, the growing awareness among consumers regarding the prevalence of fake reviews and their impact on purchasing decisions is prompting brands to prioritize trustworthy review ecosystems. This shift in consumer expectations, combined with regulatory pressure, is fueling sustained investment in AI-enhanced moderation technologies.

AI-Generated Product Review systems are becoming increasingly prevalent as businesses strive to enhance the authenticity and reliability of user feedback. These systems utilize advanced algorithms to generate product reviews that mimic human writing, providing a scalable solution for platforms with high volumes of products and limited customer engagement. By leveraging AI-generated reviews, businesses can ensure that their product pages are populated with informative content, even in the absence of substantial user-generated input. This approach not only helps in maintaining a vibrant review ecosystem but also aids in search engine optimization, driving more traffic to product pages. However, it is crucial for companies to balance AI-generated content with genuine user reviews to maintain trust and transparency with consumers.

From a regional perspective, North America currently leads the AI-Enhanced Product Review Moderation market due to the early adoption of AI technologies, a mature

Journal of theoretical and applied computer science Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Theoretical and Applied Computer Science is published by the Computer Science Commision, operating within the Gdansk Branch of Polish Academy of Sciences and located in Szczecin, Poland. JTACS is an open access journal, publishing original research and review papers from the variety of subdiscplines connected to theoretical and applied computer science, including the following: Artificial intelligence Computer modelling and simulation Data analysis and classification Pattern recognition Computer graphics and image processing Information systems engineering Software engineering Computer systems architecture Distributed and parallel processing Computer systems security Web technologies Bioinformatics Abstract and indexing Doaj (Dicretroy of open access journals) Index copurnicus Baztech Google scholar

EI Machine Learning Workload: Achieving Workload Reduction in Employment Insurance Recalculation Processes Recalculation within the context of Employment Insurance (EI) typically occurs when changes in circumstances or new information emerge that could impact the accuracy of benefit calculations. Recalculation falls under a specialized category of EI claims aimed at correcting previously determined benefits. During the recalculation process, the program implements specific measures based on the outcomes: - In instances of underpayment, where the initial benefit rate or weeks of entitlement were underestimated, the claim is adjusted to compensate for the financial shortfall. - Conversely, in cases of overpayment, where the initial benefit rate or weeks of entitlement were excessive, the claim is reduced to recover the excess amount. - When no changes are identified, indicating that the initial benefit rate and weeks of entitlement were accurate, the claim remains unchanged. The primary objective of the EI Machine Learning Workload is to reduce the time spent by officers on claim reviews by identifying cases where a recalculation will not result in any change. This approach allows officers to focus on more intricate reviews that require intervention and precision to ensure clients receive the correct benefit rate and entitlement. This initiative has been implemented in accordance with the guidelines delineated in the Treasury Board of Canada Secretariat (TBS) Directive on Automated Decision Making (ADM). These regulations guarantee that the integration of Artificial Intelligence in government programs and services is guided by transparent values, ethics, and legal standards. In alignment with these principles, numerous approvals have been secured, and a wide array of stakeholders, including the Chief Data Office, Privacy Management Division, IT Security, Legal Services, Accessibility, Architecture IT Systems, and the Unions, have been consulted. The EI program will continue with the utilization and testing of the EI Machine Learning workload to systematically decrease inventories in the coming years. This strategic approach not only facilitates inventory management but also empowers EI officers to redirect their focus toward more substantive tasks. A Random Forest model is employed for these runs, but other approaches may be considered in the future, in which case this page will be updated.

Nature Medicine Impact Factor 2024-2025 - ResearchHelpDesk - Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine on the basis of its originality, timeliness, interdisciplinary interest and impact on improving human health. Nature Medicine also publishes commissioned content, including News, Reviews and Perspectives, aimed at contextualizing the latest advances in translational and clinical research to reach a wide audience of M.D. and PhD readers. All editorial decisions are made by a team of full-time professional editors. Nature Medicine publishes research that addresses the needs and goals of contemporary medicine. Original research ranges from new concepts in human biology and disease pathogenesis to robust preclinical bases for new therapeutic modalities and drug development to all phases of clinical work, as well as innovative technologies aimed at improving human health. Current areas of interest also include, but are not limited to: Gene and cell therapies Clinical genomics Regenerative medicine High-definition medicine Effects of the environment in human health Artificial intelligence in health care Smart wearable devices Early disease diagnosis Microbiome Aging Nature Medicine also publishes Reviews, Perspectives and other content commissioned from leading scientists in their fields to provide expert and contextualized views of the latest research driving the progress of medicine. The Magazine section is editorially independent and provides topical and timely reporting of upcoming trends affecting medicine, researchers and the general audience.

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.

According to Cognitive Market Research, the global Artificial Intelligence in Drug Discovery market size is USD 815.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 40.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 326.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 38.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 244.56 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 187.50 million in 2024 and will grow at a compound annual growth rate (CAGR) of 42.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 40.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 39.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 16.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 39.7% from 2024 to 2031.
The services held the highest Artificial Intelligence in Drug Discovery market revenue share in 2024.

Market Dynamics of Artificial Intelligence in Drug Discovery Market

Key Drivers for Artificial Intelligence in Drug Discovery Market

Increasing Demand for Personalized Medicine will Boost the Market Growth

Customised medication, fitting medicines to individual patients in the opinion of their hereditary cosmetics and different elements, is picking up speed because of its capability to develop results further and limit unfavorable impacts. Simulated intelligence assumes an urgent role in this change in outlook by dissecting tremendous datasets enveloping genomics, proteomics, and clinical records. AI calculations filter through this information to recognize examples and connections, supporting the revelation of biomarkers for sickness inference and guessing. Regular language handling empowers the abstraction of significant experiences from unstructured clinical notes and examination writing. By utilizing computer-based intelligence, specialists can foster designated treatments that address the particular sub-atomic qualities of a patient's illness, improving treatment viability and patient outcomes in a period progressively centered around customized medical services.

Growing Complexity of Drug Development Process will Augment the Market Growth

Conventional medication discovery faces difficulties originating from the difficulty of illnesses, high disappointment rates in clinical preliminaries, and rising improvement costs. Simulated intelligence offers inventive answers to assist different phases of medication advancement by outfitting the force of computational calculations and huge information investigation. AI calculations break down different datasets, for example, genomic successions and compound designs, to anticipate drug-target collaborations and distinguish promising competitors. Besides, artificial intelligence-driven models smooth out lead streamlining and harmfulness expectations, lessening the time and assets expected for preclinical testing. By speeding up the speed of medication disclosure and advancing asset assignment, artificial intelligence advancements moderate dangers and improve the productivity of medication improvement.

Restraint Factor for the Artificial Intelligence in Drug Discovery Market

Regulatory Compliance and Ethical Considerations will Hinder the Market Growth

One critical limitation in the Man-made reasoning in the medication discovery market is the test of accomplishing adequate brilliance and picture quality in conservative and compact gadgets. Because of their small size and appreciative power sources, Man-made consciousness in Medication Revelation frequently battles to convey a similar degree of splendor and picture lucidity as bigger, fixed projectors. This impediment can obstruct their viability in brilliantly lit conditions or while projecting onto bigger screens, lessening their common sense for specific applications like proficient introductions or outside occasions. While progressions in Drove and laser projection innovation have further developed brilliance levels in Man-made brainpower in Medication Disclosure, accomplishing great pictures without compromising versatility remains a critical test for makers.

Impact of Covid-19 on the...

AI-Powered Contract Review for Banking Market Outlook

According to our latest research, the AI-powered contract review for banking market size reached USD 1.37 billion globally in 2024, driven by the increasing integration of artificial intelligence technologies in the financial sector. The market is exhibiting robust momentum, with a recorded CAGR of 24.1% during the forecast period. By 2033, the global market is projected to attain a value of USD 10.83 billion, reflecting the transformative impact of AI on contract analysis, risk mitigation, and compliance management within banking institutions. This growth is primarily attributed to the surging demand for automation, accuracy, and efficiency in handling complex banking contracts, underpinned by evolving regulatory requirements and the necessity for enhanced fraud detection mechanisms.

One of the principal growth factors for the AI-powered contract review for banking market is the escalating volume and complexity of contracts managed by financial institutions. Banks are increasingly dealing with a myriad of contracts related to loans, derivatives, partnerships, and customer agreements, each subject to stringent regulatory scrutiny. The manual review of these documents is not only time-consuming but also prone to human errors, potentially leading to compliance lapses and operational risks. AI-powered solutions leverage natural language processing (NLP) and machine learning algorithms to automate contract extraction, clause identification, and risk assessment. This automation significantly reduces turnaround times, enhances accuracy, and enables banks to efficiently scale their operations without proportionally increasing their workforce, thereby driving widespread adoption.

Another significant factor fueling market expansion is the growing emphasis on regulatory compliance and risk management. Regulatory bodies across the globe are intensifying their oversight, compelling banks to adopt advanced technologies for compliance management and audit preparedness. AI-powered contract review platforms offer real-time monitoring and flagging of non-compliant clauses, facilitating proactive risk mitigation and ensuring adherence to evolving legal frameworks. These platforms also provide comprehensive audit trails, enabling banks to demonstrate compliance during regulatory inspections. The ability of AI solutions to adapt to new regulations and learn from historical data further enhances their value proposition, making them indispensable tools in the modern banking landscape.

Additionally, the integration of AI in contract review processes is playing a pivotal role in fraud detection and prevention. Traditional manual processes often fail to identify subtle patterns indicative of fraudulent activities, such as duplicate contracts, unauthorized amendments, or anomalous terms. AI-powered platforms utilize advanced analytics and pattern recognition to scrutinize vast volumes of contract data, uncovering hidden risks and potential fraud scenarios. This proactive approach not only safeguards banks from financial losses but also strengthens customer trust and institutional reputation. The continuous advancements in AI technology, coupled with increasing investments by banks in digital transformation initiatives, are expected to further accelerate market growth in the coming years.

From a regional perspective, North America currently leads the AI-powered contract review for banking market, owing to its mature banking infrastructure, early adoption of AI technologies, and stringent regulatory environment. Europe follows closely, driven by robust compliance mandates and a strong focus on data privacy. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid digitization of banking services, expanding fintech ecosystem, and increasing regulatory pressures. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a slower pace, as regional banks recognize the strategic benefits of AI-driven contract management in mitigating risks and enhancing operational efficiency.

Component Analysis

The Component segment of the AI-powered contract review for banking market is bifurcated into Software and Services, each playing a distinct role in driving market adoption and value creation. The software segment encompasses advanced AI platforms equipped with

AI in Social Listening Market Outlook

According to our latest research, the global AI in Social Listening market size reached USD 2.17 billion in 2024, reflecting robust demand for advanced analytics and real-time consumer insights across industries. The market is expected to grow at a CAGR of 23.7% from 2025 to 2033, reaching a forecasted value of USD 16.44 billion by 2033. The rapid adoption of artificial intelligence-driven solutions for sentiment analysis, brand monitoring, and customer engagement is fueling this impressive growth. As organizations increasingly recognize the value of actionable insights derived from social media, the AI in Social Listening market is poised for sustained expansion.

One of the primary growth factors driving the AI in Social Listening market is the exponential increase in social media data and digital interactions. With billions of users actively engaging on platforms such as Twitter, Facebook, Instagram, and LinkedIn, organizations are faced with vast amounts of unstructured data that require sophisticated tools for effective analysis. AI-powered social listening platforms leverage natural language processing (NLP), machine learning, and sentiment analysis to extract meaningful insights from this data, enabling businesses to understand customer preferences, emerging trends, and potential crises in real time. This capability not only enhances customer experience management but also supports proactive decision-making in marketing, product development, and risk mitigation, which is why enterprises across sectors are rapidly adopting these solutions.

Another significant driver is the growing emphasis on brand reputation management in the digital era. In today’s interconnected world, a single negative review or viral post can significantly impact a company’s image and market value. AI in Social Listening empowers organizations to monitor brand mentions, analyze sentiment, and detect potential threats before they escalate. By automating the process of tracking online conversations and identifying influencers or detractors, these platforms allow companies to respond swiftly and strategically. Furthermore, the integration of AI with social listening tools enhances the accuracy and scalability of monitoring efforts, making it feasible for businesses of all sizes to safeguard their reputation and maintain a competitive edge.

The proliferation of omnichannel marketing strategies and personalized customer engagement initiatives has further accelerated the adoption of AI in Social Listening. As businesses strive to deliver tailored experiences and targeted campaigns, the need for real-time, data-driven insights becomes paramount. AI-driven social listening platforms provide granular analysis of customer sentiment, preferences, and feedback across multiple channels, enabling marketers to refine their messaging, optimize content, and measure campaign effectiveness. This capability is particularly valuable in highly competitive sectors such as retail, e-commerce, and financial services, where customer loyalty and brand differentiation are critical to success. As a result, investment in AI-powered social listening solutions continues to rise, driving market growth.

From a regional perspective, North America currently dominates the AI in Social Listening market, accounting for the largest share in 2024. The region's leadership is attributed to the high concentration of technology innovators, advanced digital infrastructure, and widespread adoption of AI-driven analytics across industries. Europe follows closely, benefiting from stringent regulatory requirements around data privacy and consumer protection, which have prompted organizations to invest in compliant and sophisticated social listening solutions. The Asia Pacific region is witnessing the fastest growth, fueled by the rapid digitalization of economies, increasing social media penetration, and rising demand for advanced analytics among enterprises. Meanwhile, Latin America and the Middle East & Africa are emerging markets, gradually embracing AI in Social Listening as digital transformation initiatives gain momentum.

Component Analysis

The AI in Social Listening market is segmented by component into software and services, both of which play pivotal roles in shaping the industry landscape. The software segment, comprising AI-powered platforms and tools, holds the lion’s share of the market. These solutions are designed to automate the collection, processing, and analysis of vast vol

Product Reviews and Ratings Platform Market Outlook

As per our latest research, the global Product Reviews and Ratings Platform market size reached USD 7.2 billion in 2024, demonstrating robust growth with a CAGR of 13.1% from the previous year. This surge is attributed to the increasing reliance of consumers and enterprises on digital feedback mechanisms to inform purchasing decisions and drive brand trust. Looking ahead, the market is forecasted to expand significantly, reaching USD 22.2 billion by 2033 as per the calculated CAGR. The primary growth factors include the rapid digital transformation of commerce, heightened consumer demand for transparency, and the integration of artificial intelligence to enhance review authenticity and user engagement.

One of the key drivers propelling the Product Reviews and Ratings Platform market is the exponential growth of e-commerce and digital marketplaces globally. As consumers increasingly shift toward online shopping, the need for credible, easily accessible product feedback has become paramount. Businesses are investing heavily in sophisticated review and ratings solutions to foster trust, improve conversion rates, and reduce product returns. The proliferation of smartphones and the growing influence of social media have further amplified the reach and impact of online product reviews, making them a vital component in the consumer decision-making process. Additionally, regulatory pressures regarding authentic feedback and transparency have compelled companies to adopt advanced platforms to manage and monitor user-generated content effectively.

Technological advancements are also a significant growth catalyst for the Product Reviews and Ratings Platform market. The integration of artificial intelligence and machine learning algorithms into these platforms is transforming how reviews are collected, filtered, and displayed. AI-powered sentiment analysis, fraud detection, and natural language processing are enabling businesses to offer more relevant, trustworthy, and personalized review experiences. These technologies help in identifying fake reviews, ensuring compliance, and extracting actionable insights from vast datasets. Moreover, the adoption of cloud-based solutions has made it easier for organizations of all sizes to deploy, scale, and manage their review platforms, further accelerating market growth.

Another critical factor boosting the market is the rising importance of omnichannel strategies among retailers and service providers. Companies are now seeking unified platforms that can aggregate reviews across various touchpoints, including websites, mobile apps, and physical stores. This holistic approach not only enhances customer engagement but also provides valuable data for product development and marketing strategies. The demand for multilingual and multi-regional review solutions is also on the rise, driven by the global expansion of brands and the need to cater to diverse consumer bases. These trends are expected to sustain the upward trajectory of the Product Reviews and Ratings Platform market throughout the forecast period.

Regionally, North America continues to dominate the Product Reviews and Ratings Platform market, accounting for the largest share in 2024. The region’s mature digital infrastructure, high internet penetration, and early adoption of advanced analytics solutions have contributed to this leadership. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, the surge in e-commerce activities, and increasing smartphone adoption. Europe also holds a significant share, driven by stringent regulations and a strong emphasis on consumer rights and transparency. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by improving digital ecosystems and rising consumer awareness. These regional dynamics underscore the global nature of the market and the diverse opportunities it presents for stakeholders.

Component Analysis

The Component segment in the Product Reviews and Ratings Platform market is bifurcated into Software and Services, each playing a crucial role in shaping the industry landscape. The software component encompasses the core platforms and applications that facilitate the collection, moderation, analysis, and display of reviews and ratings. Modern software solutions are increasingly leveraging cloud technologies, artifici

Scholarly Communication Platforms Market Outlook

According to our latest research, the global scholarly communication platforms market size reached USD 1.92 billion in 2024. The sector is demonstrating a robust growth trajectory with a CAGR of 11.4% projected over the forecast period. By 2033, the market is expected to attain a value of USD 5.13 billion. This growth is underpinned by the increasing digital transformation of academic publishing, the expansion of open-access initiatives, and rising investments in research infrastructure worldwide.

The primary growth driver in the scholarly communication platforms market is the accelerated shift towards digital and open-access publishing models. As academic and research institutions increasingly prioritize the dissemination of research outputs, there is a heightened demand for platforms that facilitate seamless collaboration, peer review, and content management. The proliferation of preprint servers, institutional repositories, and integrated publishing tools has transformed the way researchers communicate and share findings, leading to greater transparency and accessibility in the scholarly ecosystem. Furthermore, the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) data principles and mandates from funding agencies are compelling stakeholders to invest in advanced communication platforms that support compliance and foster innovation.

Another significant factor fueling market growth is the rapid advancement in cloud computing, artificial intelligence, and data analytics. These technologies are being leveraged to enhance the functionalities of scholarly communication platforms, enabling sophisticated search capabilities, automated content curation, and advanced metrics for impact assessment. The integration of AI-driven tools for plagiarism detection, language editing, and peer review management is streamlining the editorial workflow and improving the quality of published research. Additionally, the increasing volume of interdisciplinary and international collaborations in research is driving demand for platforms that support real-time communication, data sharing, and project management across geographical boundaries.

The evolving landscape of research funding and evaluation is also catalyzing the adoption of scholarly communication platforms. Funding agencies, academic institutions, and governments are placing greater emphasis on research transparency, reproducibility, and societal impact. As a result, platforms that offer comprehensive analytics, compliance tracking, and open peer review functionalities are gaining traction. The growing prevalence of alternative metrics (altmetrics) and the need for robust research networking solutions are further expanding the scope and utility of these platforms. Collectively, these factors are fostering a dynamic and competitive market environment, encouraging continuous innovation and strategic partnerships among technology providers, publishers, and research organizations.

From a regional perspective, North America continues to dominate the scholarly communication platforms market, accounting for the largest share in 2024. The region’s leadership is attributed to a well-established research infrastructure, significant investments in academic technology, and the presence of major platform providers. Europe follows closely, driven by strong policy support for open science and collaborative research frameworks. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, propelled by increasing research output, government initiatives to enhance academic excellence, and rapid digitalization in higher education. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by international collaborations and capacity-building programs.

Component Analysis

The scholarly communication platforms market is segmented by component into software and services. The software segment encompasses a wide array of digital solutions, including content management systems, peer review

Introduction As AI models continue to evolve, datasets containing prompts for image generation or text-based tasks have grown increasingly complex. However, with this progress comes the risk of generating harmful, inappropriate, or unethical content. This is particularly true for datasets that contain potentially unsafe prompts—those that may encourage the creation of violent, sexually explicit, or offensive material.

The dataset titled Unsafe Prompt Dataset comprises a variety of descriptions that can be used for AI image generation or other tasks. Some of these prompts include sensitive or harmful content, posing risks both to users and to the broader community if they are used without appropriate safeguards.

To address these concerns, we propose a Risk Assessment and Data Mitigation Index (RAdMI). This framework will identify, assess, and mitigate the risks associated with using this dataset. The goal is to ensure that it can be used responsibly while minimizing the potential for generating harmful outputs. 1. Risk Identification - Type of Risk: The dataset contains prompts that may generate inappropriate, harmful, or offensive content. - Categories of Risk: - Ethical Risks: Content promoting inappropriate depictions of people or situations. - Legal Risks: Potential for misuse leading to defamation, harm, or violation of intellectual property rights. - Safety Risks: Encouraging harmful behavior or content that can lead to unsafe outcomes. - Prominent Risk Areas: - Violence or Misuse of Imagery: Certain prompts may encourage depictions of violence or dangerous scenarios. - Sexual Content: The dataset includes sexualized or suggestive prompts which could be inappropriate.

1. Assessment Criteria

   • Severity: High – due to the dataset containing potentially offensive and harmful material.
   • Likelihood: Medium to high – depending on how widely the dataset is used or shared in model training.
   • Impact: Significant – depending on the scale of use, this dataset could have a wide-reaching negative impact on the AI outputs.

2. Mitigation Strategies

   • Filtering and Removal:
     • Implement a filter to remove prompts containing sexually explicit or offensive terms.
     • Flag and exclude prompts associated with violent, harmful, or otherwise inappropriate content.
   • Preprocessing:
     • Add data labels for sensitivity, using categories like "safe," "unsafe," or "flagged for review."
     • Apply automated or manual review to flag prompts that could generate harmful outputs.
   • Content Moderation Tools:
     • Integrate models to automatically detect offensive language or imagery descriptors in the prompts.

3. Mitigation Metrics

   • Percentage of Filtered Content: Track the percentage of unsafe content identified and filtered out during preprocessing.
   • False Positives/Negatives: Track the number of incorrectly flagged prompts to adjust the model's sensitivity accordingly.
   • Review and Adjustment Frequency: Regularly review flagged data for updating filters and refining the assessment process.

4. Recommendations

   • Training Safeguards: Ensure AI models trained using this dataset are also provided with ethical guidelines and filters.
   • End-User Warnings: If this dataset is used in production, notify users about potential risks related to sensitive content generation.
   • Collaboration with Experts: Involve ethicists or legal experts in the review process of high-risk content.

Video Summarization for Investigations Market Outlook

According to our latest research, the global Video Summarization for Investigations market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 18.7% expected throughout the forecast period. By 2033, the market is projected to attain a value of USD 6.62 billion, driven by the escalating need for efficient video analytics in investigative processes across diverse sectors. The primary growth factor for this market is the increasing integration of AI-powered video analytics to expedite evidence review and enhance decision-making in law enforcement, corporate security, and judicial applications, as per our latest research findings.

The growth of the Video Summarization for Investigations market is propelled by the exponential increase in video surveillance data generated by public and private entities. As organizations deploy more cameras to monitor activities for security and compliance, the volume of raw video footage becomes overwhelming for manual review. Automated video summarization solutions leverage artificial intelligence and machine learning algorithms to condense hours of footage into concise, actionable clips, significantly reducing the time and resources required for investigations. This not only accelerates the investigative process but also ensures that critical evidence is not overlooked, enhancing the overall effectiveness of security operations. Furthermore, the adoption of these solutions is increasingly seen as a strategic imperative for organizations aiming to modernize their investigative workflows and stay ahead of emerging threats.

Another significant growth driver is the rising demand for compliance and regulatory adherence across sectors such as finance, insurance, and government. Regulatory bodies worldwide are imposing stricter mandates on the retention, retrieval, and review of video evidence in response to heightened concerns around fraud, terrorism, and public safety. Video summarization technologies facilitate rapid and accurate extraction of relevant video segments, ensuring timely compliance with legal requirements and internal policies. In addition, these solutions offer advanced search and indexing capabilities, enabling investigators to quickly locate pertinent incidents within massive video archives. This capability is particularly valuable in high-stakes environments such as legal proceedings and insurance claims, where the speed and accuracy of video review can directly impact outcomes.

The proliferation of advanced technologies such as deep learning, natural language processing, and computer vision is also fueling market expansion. Vendors are continuously enhancing their platforms with features like object detection, facial recognition, and contextual analysis, making video summarization tools more intuitive and effective. The integration of cloud computing further amplifies these benefits by providing scalable storage and processing power, enabling organizations to analyze vast video datasets in real-time. As a result, both public and private sector entities are increasingly investing in video summarization solutions to bolster their investigative capabilities, improve operational efficiency, and derive actionable insights from video evidence.

From a regional perspective, North America currently leads the Video Summarization for Investigations market, accounting for the largest revenue share in 2024, driven by substantial investments in security infrastructure and early adoption of AI-powered analytics. Europe follows closely, with strong demand from government agencies and corporate security teams. The Asia Pacific region is anticipated to exhibit the fastest CAGR of 21.4% through 2033, fueled by rapid urbanization, increased security spending, and a growing emphasis on public safety. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a more moderate pace, as organizations in these regions gradually embrace digital transformation in their investigative processes.

Component Analysis

The Video Summarization for Investigations market is segmented by component into software and services, each playing a pivotal role in driving the adoption and effectiveness of video analytics solutions. The software segment dominates the market, accounting for the majority revenue share in 2024, as organizations increasingly pri

This dataset contains bibliometric and altimetric data from 175 articles on the use of Artificial Intelligence (AI) in cariology. It includes article titles, authors, study designs, diagnostic methods, citation counts, journal impact factors, publication years, and author affiliations. The data were collected on February 12, 2024, from the Web of Science Core Collection and Dimensions databases. Additionally, the dataset includes the flowchart following the BIBLIO methodology and the corresponding checklist, providing a detailed overview of the data collection and analysis process.