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Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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通信ネットワークにおける知識抽出のためのハイブリッド・マシン・ラーニング技術と,高度な意思決定に基づく複雑なq-Rungオーソパア・フージー・フレームワーク

Hushuang Zeng1, Yonghua Zhang2

  • 1Digital and Intelligent Operations Center, Guangxi Power Grid Co., Ltd, Nanning, 530022, China.

Scientific reports
|August 21, 2025
PubMed
まとめ

ハイブリッド・マシン・ラーニングと 複雑な Q-ラング・オルトペア・フージー・セットは 通信ネットワークの知能を高めます これらの方法は,データ駆動型アプリケーションをサポートするレジリエントで効率的なネットワークのためのデータ分析を改善します.

キーワード:
そして 意思決定支援システム複合 q-rung 整形ペア 模糊値スマート・マシン・ラーニング・アプリケーションWASPAS法について

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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