Data-driven Framework Research Articles

Early Quality Prediction of Complex Double-Walled Hollow Turbine Blades Based on Improved Whale Optimization Algorithm

Abstract The precision in forming complex double-walled hollow turbine blades significantly influences their cooling efficiency, making the selection of appropriate casting process parameters critical for achieving fine-casting blade formation. However, the high cost associated with real blade casting necessitates strategies to enhance product formation rates and mitigate cost losses stemming from the overshoot phenomenon. We propose a machine learning (ML) data-driven framework leveraging an enhanced whale optimization algorithm (WOA) to estimate product formation under diverse process conditions to address this challenge. Complex double-walled hollow turbine blades serve as a representative case within our proposed framework. We constructed a database using simulation data, employed feature engineering to identify crucial features and streamline inputs, and utilized a whale optimization algorithm-back-propagation neural network (WOA-BP) as the foundational ML model. To enhance WOA-BP’s performance, we introduce an optimization algorithm, the improved chaos whale optimization-back-propagation (ICWOA-BP), incorporating cubic chaotic mapping adaptation. Experimental evaluation of ICWOA-BP demonstrated an average mean absolute error of 0.001995 mm, reflecting a 36.21% reduction in prediction error compared to conventional models, as well as two well-known optimization algorithms (particle swarm optimization (PSO), quantum-based avian navigation optimizer algorithm (QANA)). Consequently, ICWOA-BP emerges as an effective tool for early prediction of dimensional quality in complex double-walled hollow turbine blades.

Turbulence statistics estimation across a step change in roughness via interpretable network-based modelling

Abstract This study proposes a data-driven methodology to complement existing time-series measurement tools for turbulent flows. Specifically, a cluster-based transition network model is employed for the estimation of velocity time traces and their corresponding statistics. The method is tested on a laboratory-modelled turbulent boundary layer over a step change in surface roughness, where velocity time series are recorded for training and validation purposes via Laser Doppler Anemometry. Results show that our approach can estimate velocity and momentum flux statistics within experimental uncertainty over a rough surface through an unsupervised approach, and across the step change in roughness through a semi-supervised variant. The friction velocity across the domain is also estimated with 10\% relative error compared to the measured value. The proposed methodology is interpretable robust against the main methodological parameters. A reliable data-driven framework is hence provided that can be integrated within existing laboratory setups to supplement or partially replace measurement systems, as well as to reduce wind tunnel running times.

Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns.

A generalized incremental harmonic balance method by combining a data-driven framework for initial value selection of strongly nonlinear dynamic systems

A generalized incremental harmonic balance method by combining a data-driven framework for initial value selection of strongly nonlinear dynamic systems

Solving Viscous Burgers’ Equation: Hybrid Approach Combining Boundary Layer Theory and Physics-Informed Neural Networks

In this paper, we develop a hybrid approach to solve the viscous Burgers’ equation by combining classical boundary layer theory with modern Physics-Informed Neural Networks (PINNs). The boundary layer theory provides an approximate analytical solution to the equation, particularly in regimes where viscosity dominates. PINNs, on the other hand, offer a data-driven framework that can address complex boundary and initial conditions more flexibly. We demonstrate that PINNs capture the key dynamics of the Burgers’ equation, such as shock wave formation and the smoothing effects of viscosity, and show how the combination of these methods provides a powerful tool for solving nonlinear partial differential equations.

RadioGAT: A Joint Model-Based and Data-Driven Framework for Multi-Band Radiomap Reconstruction via Graph Attention Networks

RadioGAT: A Joint Model-Based and Data-Driven Framework for Multi-Band Radiomap Reconstruction via Graph Attention Networks

AI in customer feedback integration: A data-driven framework for enhancing business strategy

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources, including surveys, reviews, social media, and customer support interactions. The proposed framework facilitates real-time feedback analysis, enabling businesses to identify trends, customer preferences, and potential pain points more efficiently. By integrating AI with existing customer relationship management (CRM) systems, businesses can automate the categorization and prioritization of feedback, allowing for timely responses and more effective problem-solving. Furthermore, predictive analytics tools within the framework can forecast customer needs, allowing businesses to tailor products and services to meet evolving expectations. This framework also supports continuous improvement by enabling businesses to track the impact of changes implemented based on customer feedback. Additionally, AI’s ability to personalize the customer experience by recognizing patterns and individual preferences plays a crucial role in increasing customer satisfaction and loyalty. The data-driven insights generated through AI integration can guide businesses in refining their marketing, product development, and customer service strategies, leading to improved operational efficiency and better alignment with customer expectations. In conclusion, the integration of AI into customer feedback mechanisms represents a significant advancement for data-driven business strategy development. This framework not only enhances feedback accuracy and speed but also empowers businesses to deliver more personalized and customer-centric solutions.

Using the SPY Index for Linear Regression Analysis of AAPL Stock Returns

This study investigates the predictive relationship between Apple’s (AAPL) stock returns and the SPY index, representing the S&P 500. Utilizing a linear regression model, we aim to quantify the extent to which changes in the SPY index can explain variations in AAPL returns. Our findings show that the model parameters—an intercept (Beta0) of 0.001 and a slope (Beta1) of 1.067—indicate a strong positive correlation between these variables. The model’s predictive accuracy is validated using Root Mean Square Error (RMSE), with an in-sample RMSE of 0.000123 and an out-of-sample RMSE of 0.000234. We also introduce a hedging strategy to mitigate risks associated with market volatility. This research contributes to the field by providing a robust, data-driven framework for predicting stock returns and managing risk.

A Knowledge-Based Planning model for IMRT in breast and lung cancer

The advent of Knowledge-Based Planning (KBP) models has introduced a transformative approach to Intensity-Modulated Radiation Therapy (IMRT) treatment planning in breast cancer and lung cancer cases. This paper explores the application of KBP models to these specific cancer types, highlighting their potential to enhance treatment accuracy, efficiency, and patient outcomes. By leveraging historical treatment data and machine learning techniques, KBP-IMRT offers a data-driven framework for optimizing dose distributions, minimizing radiation exposure to healthy tissues, and improving overall treatment plan quality. Through a comprehensive review of the literature and clinical case studies, this paper underscores the advantages of KBP-IMRT, such as streamlined planning processes and improved plan consistency, while acknowledging the challenges associated with model development and implementation. As the field of radiotherapy continues to evolve, KBP models hold the promise of shaping the future of personalized and precise cancer treatment strategies.

Are migrant workers at greater risk of workplace deaths? A systematic review and meta-analysis

Abstract Background Globally, 170 million individuals migrate for work. Yet, evidence on their mortality risk remains unclear. Our aim was to synthesize global evidence on migrant worker mortality risk compared to local workers and to identify the multiple intersecting social determinants of mortality. Methods We conducted a systematic review of peer-reviewed literature which reported on mortality outcome of migrant workers. Studies published between 1 Jan 2000 to 17 Jan 2023 in English were searched in MEDLINE, Embase, PsycINFO, and Ovid Global Health. Meta-analysis using random effects model was used to calculate pooled estimates and narrative synthesis was used to develop a data-driven framework on intersectional social determinants. Results Out of 11,495 identified records, 44 were included, of which 11 were pooled in meta-analyses. The combined migrant worker death count was 44,338, with data from 16 countries, including migrants from agriculture, construction, and service industries. Compared to local workers, migrant workers had a higher risk of fatal occupational injury (pooled RR = 1.71, 95%CI: 1.22-2.38, I² = 99.4%), and a lower risk of all-cause mortality (pooled RR = 0.94, 95%CI: 0.88-0.99, I² = 90.7%). Key social determinants associated with increased mortality risk were either migration-related (e.g. lower language proficiency, undocumented status) or labour-related (e.g. precarious employment, labour migration policies) interacting over migrant workers’ life course. Conclusions As the largest evidence synthesis to date, these findings indicate that migrant workers have higher fatal occupational injury rates than local workers, indicating the need for tailored protections for migrant workers. These data confirm that the all-cause mortality advantage observed in migrant populations applies to the working population. Future interventions should be designed to address both migration- and labour-related determinants of migrant worker health. Key messages • Migrant workers have a higher risk of workplace fatality despite being generally healthier than local workers, explained by structural determinants such as precarious employment. • Future interventions must address migration- and labour-related social determinants of health at structural levels, such as extending labour protection laws to migrant workers.

Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns

Understanding the landscape determinants that escalate Lyme disease (LD) risk through various times and regions is vital for appraising disease susceptibility and shaping precise intervention and prevention strategies. This research introduces a novel data-driven framework to identify potential indicators from an extensive array of potential variables. We then deployed an advanced spatiotemporal weighted regression (STWR) model to investigate how landscape fragmentation metrics correlate with the spatiotemporal variability of LD incidence rate in Connecticut towns. We proposed a data-driven filtering framework to select five variables from a large data pool. The analysis unveils that LD incidence rates exhibit heightened sensitivity to proportional or exponential shifts in landscape fragmentation; logarithmic and squared transformations of landscape metrics shed light on lesser effects and venue for potential parabolic relationships. Observations also disclose significant spatial trends, showing elevated LD incidence rates in locales with vast, uninterrupted deciduous forests, alongside contributions from wetland ecosystem-related variables to the rise in disease occurrence. Compared with Geographically Weighted Regression (GWR), the STWR model proved more potent and reliable with higher R2 and lower estimated standard errors (SE). The STWR model is highly flexible in terms of spatiotemporal variations in data. The STWR results further reversely indicate the changes made by the Center for Disease and Prevention (CDC) in the case classification of LD in 2008. The integration of data-driven and model-driven approaches in this study delivers a robust framework that combines empirical pattern detection with theoretical insight, enhancing the robustness and predictive power of ecological studies.

A data-driven framework for the definition of heat in CV research for better comprehension of climate changes on human health

Abstract Background Recent literature has identified numerous combinations of meteorological indicators, methods and comparison thresholds to define heat in order to analyse its impact on adverse cardiovascular (CV) events. In fact, despite increasing attention to the issue due to ongoing climate change, there is no standardised definition of heat nor guidelines for its establishment, and in studies often little attention is given to the selected definition, despite its impact on the results. Purpose To fill this gap, our aim was to propose a data-driven framework for selecting the temperature threshold for defining heat in the context of studies on adverse CV outcomes, combining the most common approaches identified in the literature. Methods The following five-step framework is proposed: 1) Selection of temperature lags based on the contour plot obtained by the distributed lag non-linear model (DLNM); 2) Estimation of the relative risk (RR) of having a CV emergency in heat versus non-heat days by Poisson regression considering the 90th-99th percentile (%ile) of the temperature distribution for each lag; 3) Selection of the final lag based on the curvatures of the 10 included %iles; 4) Calculation of the % change in RR from %ile i to %ile i+1; 5) Selection of the heat threshold, defined as the %ile from which all the next %iles are associated with a higher % change, or, if not existing, the %ile preceding the one with the highest % change. We tested this framework, for each year, on retrospective CV emergencies in May-September 2017-2022 in Milan, Italy, using the apparent temperature data, accounting for air temperature, relative humidity and wind speed. Results The contour plot of the DLNM, controlled for air pollution, seasonality and long-term trend, showed that lags 0 and 1 were characterised by an increased RR for a CV emergency. The RR estimated for each %ile for each considered lag (i.e., heat on day i, heat on day i or on day i-1, and heat on day i and on day i-1), produced a similar curvature, so first lag option was arbitrarily selected. For Milan, the 95th %ile was the point from which all the subsequent %iles were associated to progressively higher % changes, resulting in a RR of having a CV emergency compared to non-heat days equal to 1.113 (95% CI: 1.087-1.139). The mean daily apparent temperature values associated to such %ile, varying from 27.7 °C to 29.9 °C depending on the year, should then be considered as the heat threshold in the examined context. Conclusion We proposed a flexible data-driven framework for the definition of heat that could easily be extended to similar scenarios. Although the framework still requires some arbitrary choices to be made, and the need of validation on other real-world data, it could provide a valuable guideline for relevant research, and could contribute to the standardization of heat definition for CV health research aiming at a better comprehension of climate changes on human health.

Employee’s Security and Health Augmentation in the Hotel Sector

The vibrant and quick-paced environment that defines the hotel sector is defined by a strong emphasis on employee welfare. This study examines the various aspects of worker health and safety in the context of the hospitality sector with the goal of identifying problems, prevalent behaviors, and appropriate remedial measures. The study uses a mixed-methods approach, combining quantitative analysis of safety event data with qualitative interviews with important industry players. In-depth interviews with hotel managers, staff members, and safety specialists are conducted as part of the qualitative component to elucidate the myriad difficulties faced by hotel staff in upholding their health and safety. The effects of irregular work schedules and sleep patterns, workplace stress, and ergonomic considerations all emerge as major themes. The study also examines how well the current training and safety practices work, looking for flaws and potential improvement areas. The study examines historical safety event data from a wide range of hotels on the quantitative front. The goal of this analysis is to spot trends, prevalent accident causes, and geographic regions with a higher prevalence of safety concerns. The results offer a data-driven framework for creating focused interventions and preventive actions. The research also examines how technology can support initiatives to increase employee safety in hotels. We investigate the possibilities for real-time monitoring systems, wearable technology, and enhanced training simulations to improve safety awareness and reactivity. A series of suggestions for enhancing employee health and safety in the hotel business are provided as part of the study's conclusion. The incorporation of technical solutions, specialized training programs, and changes to policy are all included in these suggestions. The hotel business may cultivate a safer and healthier work environment by resolving the issues mentioned, enhancing overall employee well-being and assisting in the industry's long-term success.

A data-driven framework for occupancy optimisation strategies towards energy demand reduction in non-domestic buildings

Proactive strategies for data-driven operational schedules based on monitored occupancy patters can enable energy demand reduction and optimal resource utilisation. A replicable framework that enables strategic closure of specific thermal zones is introduced and design and operational considerations are discussed. The potential of the framework is illustrated through a case study building, where up to 6% annual energy savings were estimated, highlighting the effectiveness of zone closures. Further findings indicated that total energy savings from the simultaneous closure of multiple zones were marginally larger compared to savings from closing off zones individually, depending on zone capacity and use. Therefore, balanced considerations should take place prior to selecting which zones to close off, also taking into account user acceptability, capacity of systems and controls as well as the internal layout of the building. The research enhances the understanding of the relationship between occupancy and energy demand, while offering recommendations for more energy efficient and sustainable building design and operations that require minimal capital cost. Practical Application This paper provides design and operational considerations based on a robust and replicable framework for energy savings by optimising operational building schedules based on monitored occupancy patterns. This allows a proactive implementation of data-driven strategies that maximise resource utilisation, such as closure of specific zones during periods of low occupancy, without requiring any physical intervention. The findings on a case study building demonstrate that annual energy savings can be achieved, underscoring the potential for substantial cost reductions and improved energy efficiency. Applications can also extend to optimising energy demand for energy flexibility.

A Review on Advancement in Analytical Quality by Design (AQbD)

Analytical Quality by Design (AQbD) represents a transformative methodology in pharmaceutical development, anchored in a systematic, risk-based, and data-driven framework. This approach optimizes analytical methods, fostering heightened product quality, efficient regulatory compliance, and informed decision-making. The industry's increasing acceptance of AQbD principles signifies a paradigm shift towards enhanced efficiency, sustainability, and global harmonization. This review comprehensively explores AQbD principles, regulatory perspectives, and its applications, particularly in analytical method development, including high-performance liquid chromatography (HPLC) and high-performance thin-layer chromatography (HPTLC). Emphasis is placed on the symbiotic relationship between AQbD and analytical method validation (AMV), elucidating their collective role in ensuring reliable and accurate analytical results. Integrating AQbD in method transfer, automation, and control strategies underscores its pivotal role in achieving robust, efficient, and compliant analytical processes. The review delves into lifecycle management and continuous improvement, coupled with AQbD principles, ensuring sustained method reliability throughout the pharmaceutical product lifecycle. AQbD's significant contribution to pharmaceutical lifecycle management, optimization, and change control is explored, emphasizing its systematic, data-driven, and risk-based approach to method development, validation, and ongoing enhancement. This review illuminates AQbD's transformative impact on pharmaceutical analysis, aligning with industry trends toward quality, efficiency, and regulatory compliance.

A systemic approach for assessing infrastructure component importance in hazard-prone communities

Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.

Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient

The piezoelectricity of piezoceramics considerably depends on the doped compositions. However, the conventional trial-and-error approach to composition design is time-consuming and inefficient when searching for high-performance piezoceramics with various dopants. In this study, we introduce a data-driven framework to accelerate the design and discovery of lead-free (K,Na)NbO3 (KNN) materials with enhanced piezoelectric performance. The proposed framework integrates machine learning with feature engineering, surrogate-based optimisation, and experimentation in an active learning loop. Using feature engineering techniques, we demonstrated that the piezoelectric coefficient d33 of KNN materials can be predicted based on a set of elemental and atomic properties. We evaluated six machine learning models and found that support vector regression with a radial basis function kernel achieved high accuracy in predicting the d33 values of KNN ceramics. After three iterations of experimentation and optimisation, we identified an optimal composition with a relatively high d33 of ∼353 pC/N from thousands of possible compositions using a pure exploitation strategy. This study demonstrates an efficient and systematic approach to the composition design of KNN-based piezoceramics, which can be applied to investigate the diverse functional properties of other materials.

Rational Design of HER2-Targeted Combination Therapies to Reverse Drug Resistance in Fibroblast-Protected HER2+ Breast Cancer Cells.

Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells. Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts. Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity. Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells. The online version contains supplementary material available at 10.1007/s12195-024-00823-0.

Multisource geoscience data-driven framework for subsidence risk assessment in urban area

Land subsidence, especially in developed cities, poses significant risks to human life, social property, and urban sustainability. Taking Liwan District in southern China as an example, this study proposed an acceptable framework for regional land subsidence risk assessment while complying with current national assessment system. With integrating the multi-source geospatial data from remote sensing and various geology surveys into ArcGIS, the subsidence risk assessment was carried out based on the subsidence susceptibility mapping, hazard and vulnerability surveying by using a series of data-driven methods. The results showed that, (i) although not all surface deformations detected by InSAR technology were caused by subsidence, they were instrumental in updating subsidence records; (ii) with the help of spatial correlation analysis using weight evidence as well as multi-source data fusion in high spatial resolution, the Random Forest-based classification models effectively identified the land use types and accurately mapped the land subsidence susceptibility; (iii) the hazard and vulnerability surveying based on a series of newly developed combined weight methods, improved the reliability of risk assessment; (iv) the extremely high- and high-risk areas from the zoning of the land subsidence, provided target areas for further management and prevention of land subsidence. This comprehensive and quantitative assessment framework highlights the need for continued monitoring in subsidence-prone regions, helping to propose strategies for risk mitigation and adaptive planning in urban areas.

A Data-driven Spectral Model of Main-sequence Stars in Gaia DR3

Precise spectroscopic classification of planet hosts is an important tool of exoplanet research at both the population and individual system level. In the era of large-scale surveys, data-driven methods offer an efficient approach to spectroscopic classification that leverages the fact that a subset of stars in any given survey has stellar properties that are known with high fidelity. Here, we use The Cannon, a data-driven framework for modeling stellar spectra, to train a generative model of spectra from the Gaia Data Release 3 Radial Velocity Spectrometer (RVS). Our model derives stellar labels with precisions of 72 K in T eff, 0.09 dex in logg, 0.06 dex in [Fe/H], 0.05 dex in [α/Fe], and 1.9 km s−1 in v broad for main-sequence stars observed by Gaia DR3 by transferring GALAH labels, and is publicly available at https://github.com/isabelangelo/gaiaspec. We validate our model performance on planet hosts with available Gaia RVS spectra at SNR>50 by showing that our model is able to recover stellar parameters at ≥20% improved accuracy over the existing Gaia stellar parameter catalogs, measured by the agreement with high-fidelity labels from the Spectroscopic Observations of Cool Stars survey. We also provide metrics to test for stellar activity, binarity, and reliability of our model outputs and provide instructions for interpreting these metrics. Finally, we publish updated stellar labels and metrics that flag suspected binaries and active stars for Kepler Input Catalog objects with published Gaia RVS spectra.