Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction
本文通过结合XGBoost与半监督学习方法，构建了一个包含770个样本的多阶段肝细胞癌(HCC)基因生物标志物数据集，以提高HCC分类精度。
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Potential of Artificial Intelligence Algorithms for Identification of Relevant Diagnostic and Prognostic Biomarkers of Early-Stage Liver Cancer
研究使用深度学习和可解释AI，通过转录组生物标志物数据集识别早期肝癌的诊断和预后生物标志物，解决数据不平衡问题并提高模型准确性。
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Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
本文通过实验对比分析了FedML、Flower、Substra和OpenFL四种联邦学习框架在不同客户端数量下的可扩展性和性能，以指导其有效部署。
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An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations
This study addresses the challenge of ineffective response to noisy energy consumption alerts in office building equipment monitoring by non-expert personnel. The authors propose an end-to-end agent pipeline that integrates hybrid SSA-LSTM time-series forecasting, attention-enhanced LSTM-VAE for variational anomaly detection, and a three-stage LangChain agent framework (Context/Diagnosis/Report). By incorporating RAG with a dynamic retrieval mechanism, the system reduces context sources from six to three–six while maintaining performance and improving inference efficiency. A novel reflective memory layer is introduced to establish a human-in-the-loop feedback cycle. Notably, the approach achieves 100% pass rates across all 16 anomaly scenarios on a local 7B large language model, with the best LLM backend scoring 90.4/100, significantly enhancing alert interpretability and maintenance prioritization capabilities.
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Semi-Supervised Learning-Based Genetic Biomarkers Dataset for Multiple-Stage Hepatocellular Carcinoma Prediction
本文通过结合XGBoost与半监督学习方法，构建了一个包含770个样本的多阶段肝细胞癌(HCC)基因生物标志物数据集，以提高HCC分类精度。
0 citationsRead paper
Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM
本文针对文化遗产建筑风格分类难题，提出基于CLIP嵌入和SVM的多模态框架，结合图像与文本特征，实现阿联酋住宅建筑风格高精度分类。
0 citationsRead paper
Potential of Artificial Intelligence Algorithms for Identification of Relevant Diagnostic and Prognostic Biomarkers of Early-Stage Liver Cancer
研究使用深度学习和可解释AI，通过转录组生物标志物数据集识别早期肝癌的诊断和预后生物标志物，解决数据不平衡问题并提高模型准确性。
0 citationsRead paper
Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis
本文通过实验对比分析了FedML、Flower、Substra和OpenFL四种联邦学习框架在不同客户端数量下的可扩展性和性能，以指导其有效部署。
0 citationsRead paper
An Agentic AI Pipeline for Appliance-Level Energy Anomaly Detection and LLM-Driven Recommendations
This study addresses the challenge of ineffective response to noisy energy consumption alerts in office building equipment monitoring by non-expert personnel. The authors propose an end-to-end agent pipeline that integrates hybrid SSA-LSTM time-series forecasting, attention-enhanced LSTM-VAE for variational anomaly detection, and a three-stage LangChain agent framework (Context/Diagnosis/Report). By incorporating RAG with a dynamic retrieval mechanism, the system reduces context sources from six to three–six while maintaining performance and improving inference efficiency. A novel reflective memory layer is introduced to establish a human-in-the-loop feedback cycle. Notably, the approach achieves 100% pass rates across all 16 anomaly scenarios on a local 7B large language model, with the best LLM backend scoring 90.4/100, significantly enhancing alert interpretability and maintenance prioritization capabilities.
0 citationsRead paper