SSA Breast Missing Data Patterns (Synthetic)

  Dataset summary

This module provides a synthetic missing-data sandbox for oncology care in African healthcare contexts, focusing on:

Realistic loss-to-follow-up (LTFU) and retention patterns over 0–24 months. Incomplete diagnostic and laboratory test results (ordered vs completed vs available in records). Non-random missingness driven by facility type, distance, socioeconomic status (SES), and insurance.

The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/ssa-breast-missing-data-patterns.

Yearly citation counts for the publication titled "Missing data and prediction: the pattern submodel".

Incomplete covariate vectors are known to be problematic for estimation and inferences on model parameters, but their impact on prediction performance is less understood. We develop an imputation-free method that builds on a random partition model admitting variable-dimension covariates. Cluster-specific response models further incorporate covariates via linear predictors, facilitating estimation of smooth prediction surfaces with relatively few clusters. We exploit marginalization techniques of Gaussian kernels to analytically project response distributions according to any pattern of missing covariates, yielding a local regression with internally consistent uncertainty propagation that utilizes only one set of coefficients per cluster. Aggressive shrinkage of these coefficients regulates uncertainty due to missing covariates. The method allows in- and out-of-sample prediction for any missingness pattern, even if the pattern in a new subject’s incomplete covariate vector was not seen in the training data. We develop an MCMC algorithm for posterior sampling that improves a computationally expensive update for latent cluster allocation. Finally, we demonstrate the model’s effectiveness for nonlinear point and density prediction under various circumstances by comparing with other recent methods for regression of variable dimensions on synthetic and real data. Supplemental materials are available online.

Provide a pattern of missing content and improvement methods for the establishment of electronic data files by the guarantor institution.

Analysed dataset of 29 network meta-analyses and selected empirical prior distributions for between-trial variance. (TXT 39 kb)

♦Fisher’s exact test showed that the differences in the missed diagnosis rates were significant among the three patterns (p = 0.014);▲The amputation rate in pattern II was much higher than the other two patterns (Fisher’s exact test: pattern I vs. II, p = 0.028; pattern II vs. pattern III, p = 0.039, respectively).Missed diagnosis rates and amputation rates of different patterns.

The proposed dataset aims to facilitate research in automatic dark pattern detection on e-commerce websites. Unlike previous approaches that relied on manually extracted features, this dataset focuses solely on text data automatically extracted from web pages. The inspiration for this dataset comes from previous work by Mathur et al. in 2019, which contained 1,818 dark pattern texts from shopping sites. To create a balanced dataset, non-dark pattern texts were added to this existing dataset.

A. Dark Pattern Texts in E-commerce Sites: The initial dataset of dark patterns, manually curated by Mathur et al., contained 1,818 dark pattern texts from 1,254 shopping sites. From this dataset, texts with missing or duplicate data were excluded, resulting in 1,178 dark pattern texts.

B. Non-Dark Pattern Texts in E-commerce Sites: Negative samples, or non-dark pattern texts, were collected from the same e-commerce websites where the dark patterns were sourced. This involved the following steps:

1. Collecting web pages: Web pages from e-commerce sites were gathered using headless Chrome. If a website was unreachable or encountered errors, it was ignored. JavaScript execution was employed to ensure comprehensive content retrieval, as most websites rely on JavaScript for page rendering.

2. Extracting texts: After collecting web pages, the Puppeteer library was used to scrape content, including screenshots and text. Unlike Mathur et al.'s approach, which focused on text within UI components, this method targeted text from the entire web page.

By combining these steps, the dataset comprises both dark pattern and non-dark pattern texts, enabling research into automatic dark pattern detection without the need for manually extracted features.

• This dataset is designed for learning how to identify missing data in Python.
• It focuses on techniques to detect null, NaN, and incomplete values.
• It includes examples of visualizing missing data patterns using Python libraries.
• Useful for beginners practicing data preprocessing and data cleaning.
• Helps users understand missing data handling methods for machine learning workflows.
• Supports practical exploration of datasets before model training.

For each test, and each study, there are scores missing, although all test co-occur at least once.

Missing Person Detection Market Outlook

As per our latest research, the global Missing Person Detection market size reached USD 2.14 billion in 2024, with a robust year-on-year growth pattern. The market is set to expand at a CAGR of 14.8% during the forecast period, aiming to achieve a value of USD 6.12 billion by 2033. This growth is primarily driven by the rapid adoption of advanced AI-powered identification technologies, increased government initiatives to improve public safety, and the integration of real-time surveillance systems across multiple sectors.

One of the primary growth factors propelling the Missing Person Detection market is the significant advancement in artificial intelligence and machine learning. These technologies have revolutionized the way missing persons are located by enabling real-time analysis of vast amounts of surveillance footage, social media data, and biometric records. Law enforcement agencies and search and rescue teams are increasingly leveraging AI-powered facial recognition and video analytics to accelerate identification processes, minimize false positives, and enhance the accuracy of detection. The integration of these technologies into existing surveillance infrastructure has not only improved operational efficiency but also reduced the time required to locate missing individuals, which is critical in time-sensitive rescue missions. Moreover, the continuous evolution of these technologies is expected to further drive market growth by providing more sophisticated and reliable detection solutions.

Another significant driver for the Missing Person Detection market is the growing collaboration between government agencies, private organizations, and non-governmental organizations (NGOs). Governments worldwide are investing heavily in upgrading their public safety infrastructure, including the deployment of advanced detection systems at airports, train stations, and public events. Additionally, private organizations specializing in security solutions are partnering with law enforcement to provide cutting-edge technologies and services. NGOs focused on human trafficking and child protection are also adopting these systems to enhance their search and rescue capabilities. This multi-stakeholder approach has fostered a robust ecosystem that supports the development and deployment of innovative detection solutions, thereby accelerating market expansion.

The increasing prevalence of urbanization and large-scale public gatherings has heightened the demand for effective missing person detection solutions. With urban areas becoming more densely populated, the risk of individuals going missing in crowded environments has escalated, necessitating the deployment of advanced surveillance and detection systems. Major sporting events, concerts, and festivals often require real-time monitoring to ensure public safety and quickly respond to missing person incidents. The integration of GPS and geolocation technologies with AI-driven analytics enables authorities to track individuals' movements and respond swiftly to emergencies. This trend is expected to continue as urbanization intensifies and public safety remains a top priority for governments and event organizers alike.

The concept of Person of Interest Tracking has become increasingly relevant in the context of missing person detection. This approach involves the use of advanced surveillance and data analysis techniques to monitor individuals who may be connected to missing person cases. By leveraging AI-driven analytics and real-time data processing, authorities can track the movements and activities of persons of interest, providing valuable insights that can aid in the swift resolution of cases. This method not only enhances the efficiency of search operations but also helps in identifying patterns and connections that may not be immediately apparent. As technology continues to evolve, Person of Interest Tracking is expected to play a crucial role in the broader strategy of missing person detection, offering a proactive tool for law enforcement agencies worldwide.

From a regional perspective, North America currently dominates the Missing Person Detection market due to its early adoption of advanced surveillance technologies, strong government initiatives, and a well-established public safety infrastructure. Europe follows closely, driven by stringent regulations on public safety and

Fossil-based estimates of diversity and evolutionary dynamics mainly rely on the study of morphological variation. Unfortunately, organism remains are often altered by post-mortem taphonomic processes such as weathering or distortion. Such a loss of information often prevents quantitative multivariate description and statistically controlled comparisons of extinct species based on morphometric data. A common way to deal with missing data involves imputation methods that directly fill the missing cases with model estimates. Over the last several years, several empirically determined thresholds for the maximum acceptable proportion of missing values have been proposed in the literature, whereas other studies showed that this limit actually depends on several properties of the study dataset and of the selected imputation method, and is by no way generalizable. We evaluate the relative performances of seven multiple imputation techniques through a simulation-based analysis under three distinct patterns of missing data distribution. Overall, Fully Conditional Specification and Expectation-Maximization algorithms provide the best compromises between imputation accuracy and coverage probability. Multiple imputation (MI) techniques appear remarkably robust to the violation of basic assumptions such as the occurrence of taxonomically or anatomically biased patterns of missing data distribution, making differences in simulation results between the three patterns of missing data distribution much smaller than differences between the individual MI techniques. Based on these results, rather than proposing a new (set of) threshold value(s), we develop an approach combining the use of multiple imputations with procrustean superimposition of principal component analysis results, in order to directly visualize the effect of individual missing data imputation on an ordinated space. We provide an R function for users to implement the proposed procedure.

In this paper we modify the expectation maximization algorithm in order to estimate the parameters of the dynamic factor model on a dataset with an arbitrary pattern of missing data. We also extend the model to the case with a serially correlated idiosyncratic component. The framework allows us to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant, for example, for young economies for which many indicators have been compiled only recently. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting of the euro area gross domestic product.

The sample included in this dataset represents children who participated in a cross-sectional study, a smaller cohort of which was followed up as part of a longitudinal study reported elsewhere (Bull et al., 2021). In the original study, 347 children were recruited. As data was found to be likely missing completely at random (χ2 = 29.445, df = 24, p = .204, Little, 1998), listwise deletion was used, and 23 observations were deleted from the original dataset. This dataset includes three hundred and twenty-four participants that composed the final sample of this study (162 boys, Mage = 6.2 years, SDage = 0.3 years). Children in this sample were in their second year of kindergarten (i.e., the year before starting primary school) in Singapore. The dataset includes children's sociodemographic information (i.e., age and sex) and performance on different mathematical skills. Children were assessed on a computer-based 0-100 number line task and on the Mathematical Reasoning and Numerical Operations subtests from the Wechsler Individual Achievement Test II (WIAT II). The initial variables recorded on the dataset were children's estimates on each of the target numbers included on the 0-100 number line task, and their accuracy for both subtests of the WIAT II. Several more variables were created based on these original ones. The variables included in the dataset are: Age = Child’s age (in months) Sex = Boy/Girl (parent reported; boy=1, girl=2) Maths_reason = Mathematical reasoning (Math Reasoning subtest from the Wechsler Individual Achievement Test II) Num_Ops = Numerical Operations (Numerical Operations subtest from the Weschler Individual Achievement Test II) Mathematical_achievement = Mathematical achievement (Composite score created by adding the raw scores from the Numerical Operations and Mathematical Reasoning subtests from the Weschler Individual Achievement Test II) P3 to P96 = Placement of the estimate on the 0-100 number line for each respective target number (i.e., P3 corresponds to the placement of the estimate provided when the target number was 3) NLE100PAE = 0-100 number line (Percent absolute error) NP100_Corr = Correlation of individual estimates to target numbers (Spearman’s correlation; p > .05= 0, p < .05 = 1) NP100LinAICc = AICc value obtained for the linear model (9999 = model cannot be fitted) NP100LogAICc = AICc value obtained for the logarithmic model (9999 = model cannot be fitted) NP100PowerAICc = AICc value obtained for the unbounded power model (9999 = model cannot be fitted) NP1001cycleAICc = AICc value obtained for the one-cycle power model (9999 = model cannot be fitted) NP1002cycleAICc = AICc value obtained for the two-cycle power model (9999 = model cannot be fitted) Best_fit_NP100_repshift = Best fitting model based on the representational shift account (0 = model cannot be fitted, 1 = linear, 2 = logarithmic) AICc_bestmodel_repshift = AICc value of the best fitting model based on the representational shift account AICc_diff_repshift = AICc difference (ΔAICc) between both models (i.e, linear and logarithmic) based on the representational shift account AICc_diff_cat_repshift = categorical value created based on AICc_diff_repshift (0 = model cannot be fitted, 1= best fitting model does not have strong support (ΔAIcc < 2), 2 = best fitting model has strong support (ΔAIcc > 2)) Best_fit_NP100_propjudg = Best fitting model based on the proportional judgment account (0 = model cannot be fitted, 3 = unbounded power model, 4 = one-cycle power model, 5 = two-cycle power model) AICc_bestmodel_propjudg = AICc value of the best fitting model based on the proportional judgment account AICc_diff_propjudg_unb = AICc difference (ΔAIcc) between the best fitting model based on the proportional judgment account and the unbounded power model AICc_diff_propjudg_1cyc = AICc difference (ΔAIcc) between the best fitting model based on the proportional judgment account and the one-cycle power model AICc_diff_propjudg_2cyc = AICc difference (ΔAIcc) between the best fitting model based on the proportional judgment account and the two-cycle power model AICc_diff_cat_propjudg = categorical value created based on AICc differences between the best fitting model and the following one based on the proportional judgment account (0 = model cannot be fitted, 1= best fitting model does not have strong support (ΔAIcc < 2), 2 = best fitting model has strong support (ΔAIcc > 2)) Best_fit_NP100_between = Best fitting model when comparing all models to each other (0= model cannot be fitted, 1 = linear, 2 = logarithmic, 3 = unbounded power model, 4 = one-cycle power model, 5 = two-cycle power model) AICc_bestmodel_between = AICc value of the best fitting model from comparing all models to each other AICc_diff_linear_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the linear model AICc_diff_log_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all model to each other and the logarithmic model AICc_diff_power_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the unbounded power model AICc_diff_1cycle_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the one-cycle power model AICc_diff_2cycle_NP100 =AICc difference (ΔAIcc) between the best fitting model based on comparing all models to each other and the two-cycle power model AICc_diff_cat_between = categorical value created based on AICc differences between the best fitting model and the following one based on the comparison of all models to each other (0 = model cannot be fitted, 1= best fitting model does not have strong support (ΔAIcc < 2), 2 = best fitting model has strong support (ΔAIcc > 2))

Additional file 2: R code for all 1,024 models available at https://riskcalc.org/ExtendedPBCG/ .

1.The analysis of morphological diversity frequently relies on the use of multivariate methods for characterizing biological shape. However, many of these methods are intolerant of missing data, which can limit the use of rare taxa and hinder the study of broad patterns of ecological diversity and morphological evolution. This study applied a mutli-dataset approach to compare variation in missing data estimation and its effect on geometric morphometric analysis across taxonomically-variable groups, landmark position and sample sizes. 2.Missing morphometric landmark data was simulated from five real, complete datasets, including modern fish, primates and extinct theropod dinosaurs. Missing landmarks were then estimated using several standard approaches and a geometric-morphometric-specific method. The accuracy of missing data estimation was determined for each estimation method, landmark position, and morphological dataset. Procrustes superimposition was used to compare the eigenvectors and principal component scores of a geometric morphometric analysis of the original landmark data, to datasets with A) missing values estimated, or B) simulated incomplete specimens excluded, for varying levels of specimens incompleteness and sample sizes. 3.Standard estimation techniques were more reliable estimators and had lower impacts on morphometric analysis compared to a geometric-morphometric-specific estimator. For most datasets and estimation techniques, estimating missing data produced a better fit to the structure of the original data than exclusion of incomplete specimens, and this was maintained even at considerably reduced sample sizes. The impact of missing data on geometric morphometric analysis was disproportionately affected by the most fragmentary specimens. 4.Missing data estimation was influenced by variability of specific anatomical features, and may be improved by a better understanding of shape variation present in a dataset. Our results suggest that the inclusion of incomplete specimens through the use of effective missing data estimators better reflects the patterns of shape variation within a dataset than using only complete specimens, however the effectiveness of missing data estimation can be maximized by excluding only the most incomplete specimens. It is advised that missing data estimators be evaluated for each dataset and landmark independently, as the effectiveness of estimators can vary strongly and unpredictably between different taxa and structures.

Overview: The Grayscale Maze Patterns Dataset is an extensive collection of 1,250,000 PNG images, each depicting a unique maze. The images are in a 91x91 pixel grid format where each pixel represents an element of the maze in greyscale: walls and paths. This comprehensive dataset is designed for use in a variety of applications including, but not limited to, algorithm development, machine learning, and educational purposes related to computational problem-solving and image analysis.

Objective: To provide a rich dataset for the development, training, and benchmarking of algorithms in the fields of pathfinding, image recognition, and machine learning.

Size: Number of Images: 1,250,000 Dimensions of Each Image: 91x91 pixels Image Format: PNG (Portable Network Graphics), lossless compression

Pixel Values: 1. Wall pixels: Value (0) (Black) 2. Path pixels: Value (255) (White)

Pixel Value (0-255): The grayscale value indicating the type of maze element.

Data Quality: The dataset has been generated and curated to ensure high fidelity and uniformity across all images. The greyscale format and the PNG image type ensure that the quality of the image data is preserved without loss.

Use Cases: Creation of educational tools for demonstrating computational thinking and problem-solving. Research in AI for recognizing and interpreting complex patterns and structures in images. Benchmarking of pathfinding algorithms in simple to complex maze structures.

It is well-known that many species with small diaspores can disperse far during extended temporal scales (many years). However, studies on short temporal scales usually only cover short distances (in, e.g., bryophytes up to 15 m). By using a novel experimental design, studying the realized dispersal, we extend this range by almost two orders of magnitude. We recorded establishment of the fast-growing moss Discelium nudum on introduced suitable substrates, placed around a translocated, sporulating mother colony. Around 2,000 pots with acidic clay were placed at different distances between 5 m and 600 m, in four directions, on a raised bog, with increased pot numbers with distance. The experiment was set up in April–May and the realized dispersal (number of colonized pots) was recorded in September. Close to the mother colony (up to 10 m), the mean colonization rates (ratio of colonized pots) exceeded 50%. At distances between 10 and 50 m colonization dropped sharply, but beyond 50 m the mean colonization rates stabilized and hardly changed (1–3%). The estimated density of spores causing establishments at the further distances (2–6 spores/m2) was realistic when compared to the estimated spore output from the central colonies. Our study supports calculations from earlier studies, limited to short distances, that a majority of the spores disperse beyond the nearest vicinity of a source. The even colonization pattern at further distances raises interesting questions about under what conditions spores are transported and deposited. However, it is clear that regular establishment is likely at the km-scale for this and many other species with similar spore output and dispersal mechanism.

Thirty-six years of aboveground net primary productivity (ANPP) data collected across a topographic sequence in the semiarid shortgrass steppe of North America to examine patterns and drivers of spatiotemporal variability in ANPP. ANPP data were collected from the 6,500 ha USDA-Central Plains Experimental Range (CPER), which is part of the Long-Term Agroecosystem Research (LTAR; 2012-present; https://ltar.ars.usda.gov/) network, a former Long-Term Ecological Research station (LTER, 1983-2012), and located in the shortgrass steppe of north-central Colorado, USA. Additional information and referenced materials about many of the long-term studies initiated on the CPER can be found: https://dx.doi.org/10.25675/10217/81141. The topography at the CPER is characterized by gently rolling hills, and the topographic positions for data collection were focused along a catena in one of the most common ecological sites on the CPER, Loamy Plains (ID: R067BY002CO; NRCS, 2020). The plant community included four herbaceous plant functional types (PFTs): 1) perennial, warm-season, C4 grasses (primarily Bouteloua gracilis [Willd. ex Kunth] Lag ex Griffiths and B. dactyloides [Nutt.] J.T. Columbus), 2) perennial, cool-season, C3 grasses (primarily Pascopyrum smithii [Rydb] A. Love and Hesperostipa comata [Trin. & Rupr.] Barkworth ssp. comata), 3) cool-season, annual grass (Vulpia octoflora [Walter] Rydb.), and 4) forbs (primarily Sphaeralcea coccinea [Nutt.] Rydb.). Shrubs, subshrubs, and cactus were present but do not represent a large component of total ANPP and were not included in this study. Daily precipitation data were obtained from a long-term (1979-2018) precipitation gauge associated with the National Atmospheric Deposition program (Site ID: NTN-CO22; http://nadp.slh.wisc.edu/), located on site. Missing precipitation data were gap-filled using CPER headquarters data (1939-2018), or from the Soil Climate Analysis Network (SCAN) rain gauge (1997-2018, Site Number: 2017; https://wcc.sc.egov.usda.gov/), depending on proximity and temporal overlap. Following gap-filling, precipitation data were omitted if >10% of the time series was missing for each focal time period (e.g. fall or spring). Resources in this dataset:Resource Title: Gap filled precipitation data from the Central Plains Experimental Range, Nunn, Colorado from 1980-2018. File Name: CPER-PPT_gapfilled_1980-2018.csvResource Title: Data Dictionary for Gap filled precipitation data from the Central Plains Experimental Range, Nunn, Colorado from 1980-2018. File Name: CPER-PPT_DataDictionary.csvResource Title: Long-Term aboveground net primary production for functional group types on the Central Plains Experimental Range, Nunn, Colorado from 1983-2018. File Name: CPER-LTNPP_bypft_1983-2018.csvResource Title: Data Dictionary for Long-Term aboveground net primary production for functional group types on the Central Plains Experimental Range, Nunn, Colorado from 1983-2018. File Name: CPER-LTNPP_bypft_1983-2018_DataDictionary.csvResource Title: Dictionary of species within each functional group type in the LTNPP data collected on the Central Plains Experimental Range, Nunn, Colorado from 1983-2018. File Name: CPER-LTNPP_bypft_1983-2018_SppInFG_DataDictionary.csv

Additional file 2. The file gives an example of a Phyton program to calculate the three missing data indicators.

Lost Person Behavior Analytics Market Outlook

According to our latest research, the Lost Person Behavior Analytics market size reached USD 1.46 billion in 2024 globally, demonstrating robust momentum with a recorded CAGR of 14.2% during the past few years. The market is projected to expand to USD 4.11 billion by 2033, driven by the increasing adoption of advanced analytics, artificial intelligence, and real-time data integration in search and rescue operations. One of the primary growth factors for this market is the rising demand for intelligent solutions that can significantly reduce the time and resources required to locate missing individuals in diverse and often challenging environments.

The surge in adoption of digital transformation initiatives across government agencies and law enforcement bodies is significantly propelling the growth of the Lost Person Behavior Analytics market. The integration of sophisticated behavioral analytics tools, geospatial intelligence, and predictive modeling has enabled organizations to enhance their operational efficiency and success rates in locating missing persons. Additionally, the proliferation of mobile devices, IoT sensors, and social media data streams provides a wealth of actionable data, which, when analyzed using advanced algorithms, can offer critical insights into lost person behavior patterns. This technological convergence is fostering a paradigm shift in how search and rescue missions are strategized and executed, thereby fueling market expansion.

Another vital growth driver is the increasing frequency and severity of natural disasters, humanitarian crises, and civil emergencies worldwide. As climate change intensifies and urban populations swell, the likelihood of individuals going missing during disasters has escalated. This has prompted governments and non-profit organizations to invest heavily in lost person behavior analytics solutions to bolster their preparedness and response capabilities. The ability to predict likely movement patterns and probable locations of missing individuals based on behavioral analytics has proven invaluable in minimizing casualties and optimizing resource deployment during such critical events.

Furthermore, the growing emphasis on public safety, regulatory mandates, and community awareness campaigns are catalyzing market growth. Law enforcement agencies and commercial security providers are increasingly recognizing the value of predictive analytics in not only reactive search operations but also in proactive risk mitigation. The market is also witnessing heightened collaboration between public and private sectors, with technology vendors, research institutions, and first responder organizations pooling resources to develop more robust, scalable, and interoperable analytics platforms. This collaborative ecosystem is accelerating innovation and driving the adoption of lost person behavior analytics solutions across a wider spectrum of applications.

Regionally, North America remains the dominant market, fueled by significant government investments, advanced technological infrastructure, and a strong presence of leading analytics vendors. Europe follows closely, with a keen focus on integrating behavioral analytics into cross-border law enforcement and disaster management frameworks. The Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, increasing disaster vulnerability, and expanding public safety initiatives. Latin America and the Middle East & Africa are also witnessing growing adoption, albeit at a more gradual pace, as awareness and infrastructure continue to develop. Overall, the regional outlook for the lost person behavior analytics market is highly positive, with substantial growth opportunities across both developed and emerging economies.

Component Analysis

The Component segment of the Lost Person Behavior Analytics market is categorized into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment, encompassing advanced analytics platforms, machine learning engines, and geospatial mapping tools, dominates the market in terms of revenue generation. These solutions are designed to process vast amounts of structured and unstructured data from disparate sources, delivering actionable insights that enhance the effectiveness of search and rescue operations. The continual evolution of software capabilities, such as real-time data visualization, pattern recognition, and predictive modeling, is a key driver for this segment’s sustained growth. As organiz