Data Science is a multidisciplinary field that involves extracting knowledge and insights from data using scientific methods, processes, algorithms, and tools. It combines aspects of statistics, mathematics, computer science, and domain expertise to analyze and interpret data, uncover patterns, make predictions, and solve complex problems. Data scientists utilize techniques such as data mining, machine learning, statistical modeling, and data visualization to extract valuable information and support data-driven decision-making in various industries and domains.
A team of four University of Nebraska–Lincoln students partnered with national retailer Buckle to create an AI-powered online shopping tool that replicates the personalized in-store stylist experience. Their solution enables natural language searches, generating millions of inventory tags to help guests find outfits more intuitively.
Researchers at Pacific Northwest National Laboratory have developed a method to measure how well AI models, specifically neural network potentials, are trained to predict material properties. The approach quantifies uncertainty to enhance model trust and identifies areas where additional training is required.
Researchers from NOVA IMS have developed Counterfactual SMOTE, an advanced oversampling method that improves minority class detection in imbalanced healthcare datasets. By generating boundary-focused, noise-free synthetic samples, it significantly enhances AI model accuracy, especially for rare but critical outcomes.
Researchers at Waseda University used machine learning to optimize the molecular design and experimental conditions of photo-actuated organic crystals, dramatically increasing their mechanical output. Their approach achieved up to 3.7 times greater blocking force and was 73 times more efficient than traditional methods.
Researchers at the University of Maryland School of Medicine emphasize that combining artificial intelligence with traditional mathematical modeling yields the most reliable outcomes in predictive cancer medicine. They also advocate for ethical, open, and reproducible data sharing to advance precision healthcare and maintain patient privacy.
Researchers developed an automated, transparent method using dynamic topic modeling to extract investable themes from financial reports, creating thematic indices without human input. The method captures evolving industrial trends more precisely than traditional classification systems.
Researchers at Florida Atlantic University have developed a real-time ASL alphabet recognition system combining YOLOv11 and MediaPipe to accurately interpret sign language using only a webcam. The system achieves 98.2% accuracy under diverse lighting and backgrounds, offering a scalable and accessible communication tool for the deaf community.
A large-scale analysis by Mount Sinai researchers reveals that generative AI models can alter medical recommendations based solely on a patient’s socioeconomic and demographic background. The study calls for early AI assurance protocols to prevent bias and ensure equitable healthcare delivery.
AI-powered Internet of Medical Things (IoMT) devices enable early disease detection, real-time monitoring, and personalised treatment, transforming patient care and healthcare systems. A new global study outlines a comprehensive roadmap for integrating IoMT into modern healthcare, highlighting its life-saving potential and practical challenges.
Washington State University researchers developed an AI tool that predicts animal species and geographic hotspots likely to host orthopoxviruses, such as smallpox and mpox. The model integrates viral genetics with host traits, enabling early identification of potential zoonotic threats.
Texas A&M researchers have created a new AI-driven method called Symbolic Modeling that outperforms traditional asset pricing models by uncovering hidden market dynamics. The model improves prediction accuracy while offering a unified approach to handling diverse financial datasets.
Researchers at Mount Sinai have developed an AI model, PFTSleep, that analyzes entire nights of sleep with unprecedented accuracy. Trained on over 1 million hours of data, it classifies sleep stages more effectively than traditional methods, paving the way for advanced clinical applications.
AI has the potential to revolutionize health care by streamlining workflows, enhancing diagnostics, and improving patient outcomes, but barriers such as data privacy, high costs, and regulatory challenges slow its adoption. This comprehensive review offers a roadmap to overcome these hurdles and unlock AI's full potential in medicine.
Researchers from the Chinese Academy of Sciences emphasize the urgent need to align AI data systems with the complex, multi-level nature of scientific data to improve AI performance and reliability. They call for global standards and frameworks to structure data properly and foster AI's healthy development.
Researchers have developed ProtoSnap, an AI-driven approach that precisely identifies and reproduces cuneiform characters, making ancient script interpretation more accurate and accessible.
Researchers from KIIT and Chandragupt Institute of Management explore how machine learning transforms big data challenges into opportunities, enabling industries to harness vast data resources effectively.
Researchers have developed DeepGuard, an AI-powered tool that accurately detects fake images and traces their origins, enhancing security and preventing misinformation.
Researchers at Jefferson Lab developed an AI-based system, DIDACT, to optimize high-performance computing clusters by continually learning and selecting the best-performing model daily, reducing system downtime and costs.
Scientists at NYU have developed an AI model that learns how humans generate goals by studying how they create games, potentially advancing AI's understanding of human intentions.
LLM4SD is a cutting-edge AI tool that can analyze scientific literature, generate hypotheses, and predict molecular properties with high accuracy, making scientific discovery faster and more transparent. The open-source system also provides explainable insights, unlike traditional black-box validation tools.
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