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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.LG English(EN) ·

    SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

    Researchers have developed an open-source platform called SepsisAI Orchestrator to streamline the deployment of AI models for early sepsis detection in clinical settings. The platform addresses challenges like data heterogeneity and the gap between research prototypes and hospital environments. It integrates data preprocessing, a LightGBM classifier served via APIs, and a clinical dashboard, all orchestrated using Docker and Kubernetes. Performance testing revealed a specific optimal replica count for host CPUs to minimize latency and avoid request failures, a finding not previously quantified for clinical AI inference. AI

    IMPACT Provides a scalable infrastructure solution to bridge the gap between AI model development and real-world clinical application for sepsis detection.

  2. RESEARCH · arXiv cs.CL English(EN) · · [2 sources]

    From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification

    This paper explores sentiment classification using various machine learning models, including traditional methods like Naive Bayes and SVM, alongside transformer-based models such as RoBERTa and DistilBERT. The study evaluated these models on the IMDb dataset for categorizing movie reviews into positive and negative sentiments. RoBERTa achieved the highest accuracy at 93.02%, and an ensemble approach combining multiple models further enhanced classification performance. AI

    IMPACT This research highlights RoBERTa's effectiveness in sentiment analysis and demonstrates the benefits of model ensembling for improved accuracy.