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This paper presents a novel data-driven approach to mitigating employee attrition using machine learning and data engineering techniques. The proposed framework integrates data from various human resources systems and leverages advanced feature engineering to capture a comprehensive set of factors influencing attrition. The study outlines a robust modeling approach that addresses challenges such as imbalanced datasets, categorical data handling, and model interpretation. The methodology includes careful consideration of training and testing strategies, baseline model establishment, and the development of calibrated predictive models. The research emphasizes the importance of model interpretation using techniques like SHAP values to provide actionable insights for organizations. Key design choices in algorithm selection, hyperparameter tuning, and probability calibration are discussed. This approach enables organizations to proactively identify attrition risks and develop targeted retention strategies, ultimately redu

Medicaid is a federal-state program that provides healthcare to over 80 million low-income Americans, including pregnant women, children, and individuals with disabilities. Up against a host of problems, including rising healthcare costs, disparity in access, and the management of chronic conditions among at-risk groups, Medicaid is one of the biggest healthcare payers in the U.S. Just as Medicare does, the use of Artificial Intelligence (AI) offers a major opportunity to change the delivery of care and operational efficiency in Medicaid [1] [16]. While there has been extensive conversation about AI in Medicare, the unique population and requirements of Medicaid require customized AI applications [1]. Chronic disease management, improving admin tasks, and a reduction in costs are amongst the ways AI tools can help, especially by focusing on social determinants of health (SDOH) that are important for Medicaid populations. The study will assess the ability of AI-enabled systems to reinforce Medicaid in handling its particular challenges while facilitating fair and quality care for its entire population of beneficiaries [8] [9].

This paper presents a theoretical framework for an AI-driven data quality monitoring system designed to address the challenges of maintaining data quality in high-volume environments. We examine the limitations of traditional methods in managing the scale, velocity, and variety of big data and propose a conceptual approach leveraging advanced machine learning techniques. Our framework outlines a system architecture that incorporates anomaly detection, classification, and predictive analytics for real-time, scalable data quality management. Key components include an intelligent data ingestion layer, adaptive preprocessing mechanisms, context-aware feature extraction, and AI-based quality assessment modules. A continuous learning paradigm is central to our framework, ensuring adaptability to evolving data patterns and quality requirements. We also address implications for scalability, privacy, and integration within existing data ecosystems. While practical results are not provided, it lays a robust theoretical foundation for future research and implementations, advancing data quality management and encouraging the exploration of AI-driven solutions in dynamic environments.

Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained language models to classify or detect trash talk or toxic in-game messages The study employs and evaluates the performance of pre-trained BERT and GPT language models in detecting toxicity within in-game chats. Using publicly available APIs, in-game chat data from DOTA 2 game matches were collected, processed, reviewed, and labeled as non-toxic, mild (toxicity), and toxic. The study was able to collect around two thousand in-game chats to train and test BERT (Base-uncased), BERT (Large-uncased), and GPT-3 models. Based on the three models' state-of-the-art performance, this study concludes pre-trained language models' promising potential for addressing online hate speech and in-game insulting trash talk.

Fox News correspondent David Spunt has the latest on questions over whether the former president can hold office again on Special Report. A liberal reporter added fuel to online fire that a conservative news outlet was duped by a former President Trump impersonator, or even artificial intelligence – resulting in an embarrassing editor's note. Last week, Trump called into right-wing channel Real America's Voice for an interview that resulted in online speculation that the outlet had spoken with an impostor. Audio was shaky, and speculation erupted that Trump either had a cold, poor service or something more malicious, such as someone impersonating the 45th president, or modern technology generating the interview with old clips of Trump. Zachary Petrizzo, a politics reporter for the left-wing Daily Beast, took things a step further and reported that Real America's Voice owner Robert Sigg told him that the company would investigate whether the call was some sort of prank.

If you want to spend a little less on a deluxe robot vacuum, today is the day. Amazon is selling the iRobot Roomba S9 a self-emptying station and the iRobot Braava Jet m6 bundle for $999. When we reviewed the S9 back in 2021, we gave it 4.5 out of 5 stars and an Editors' Choice Award. "The S9 is worth every penny," we said. It's also our pick as the most sophisticated robot vacuum you can get in our very own 2022 roundup. We also reviewed the iRobot Braava Jet m6 and gave it an equally powerful 4.5 out of 5 stars plus an Editors' Choice Award.

Artificial intelligence company Torch. … AI alone plans to add 70 jobs in Leawood this year, and CEO Brian Weaver said it could add 400 local jobs in …

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Editor's note: This article is based on a roundtable discussion sponsored by Optum. The full report of the roundtable discussion, Strategy: The Key Factor in the Future of Healthcare Innovation, is available as a free download. Innovation is paving the way for hospitals and healthcare systems to move into the future and truly change healthcare delivery. While solutions featuring artificial intelligence, robotic process automation, and natural language processing are fueling advances, healthcare innovation is more than a technology play. Effective transformation requires formulating new strategies for payment, reimagining models of care, applying real-time data, and addressing social determinants of health.