#data-preprocessing News & Analysis

3 articles tagged with #data-preprocessing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Jun 236/10

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Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing

Researchers demonstrate that preprocessing raw IoT sensor data into structured textual formats significantly improves the accuracy of edge-deployed language models for environmental monitoring, narrowing the performance gap with cloud-based systems while maintaining low latency. Testing on indoor and outdoor air-quality datasets shows local model accuracy improving from 50.9% to 81.7% indoors and 63.7% to 89.3% outdoors through progressive prompt enrichment, achieving inference speeds near 0.22 seconds.

AINeutralarXiv – CS AI · May 285/10

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Improving Requirements Classification with SMOTE-Tomek Preprocessing

Researchers applied SMOTE-Tomek preprocessing to address class imbalance in requirements engineering classification, achieving 76.16% accuracy with logistic regression compared to a 58.31% baseline. The technique combines synthetic minority oversampling with Tomek link removal and stratified K-fold validation on the PROMISE dataset of 969 categorized requirements.

AINeutralarXiv – CS AI · Apr 206/10

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Consistency Analysis of Sentiment Predictions using Syntactic & Semantic Context Assessment Summarization (SSAS)

Researchers introduce SSAS, a framework that improves LLM consistency for sentiment analysis by applying hierarchical classification and iterative summarization to enforce bounded attention on raw text. Testing on three standard datasets shows the method reduces analytical variance by up to 30%, addressing the fundamental challenge of using non-deterministic LLMs for enterprise-grade analytics.

🧠 Gemini