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Applied Science & Technology Full Text (H.W. Wilson)

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Applied Science & Technology Full Text (H.W. Wilson)

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Modeling solubility of CO 2 -N 2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state.

• Authors : Nakhaei-Kohani R; Department of Chemical & Petroleum Engineering, Shiraz University, Shiraz, Iran.; Taslimi-Renani E

Subjects: Artificial Intelligence* ; Carbon Dioxide*; Gases

• Source: Scientific reports [Sci Rep] 2022 Mar 07; Vol. 12 (1), pp. 3625. Date of Electronic Publication: 2022 Mar 07.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models.

• Authors : Bhagat SK; Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.; Paramasivan M

Subjects: Artificial Intelligence* ; Lead*; Adsorption

• Source: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2021 Jun; Vol. 28 (24), pp. 31670-31688. Date of Electronic Publication: 2021 Feb 20.Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499

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Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games.

• Authors : Polk, Spencer; Oommen, B. John

Subjects: GAME theory; BOARD games; ARTIFICIAL intelligence

• Source: Applied Intelligence; Aug2018, Vol. 48 Issue 8, p1893-1911, 19p

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Challenging state-of-the-art move ordering with Adaptive Data Structures.

• Authors : Polk, Spencer; Oommen, B. John

Subjects: ARTIFICIAL intelligence; DECISION making; HEURISTIC programming

• Source: Applied Intelligence; May2018, Vol. 48 Issue 5, p1128-1147, 20p

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Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review.

• Authors : Ekle, Francis Adoba; Shidali, Vincent; Ochogwu, Richard Emoche

Subjects: SUPERVISED learning; MACHINE learning; DASH diet

• Source: Discover Artificial Intelligence; 12/19/2024, Vol. 4 Issue 1, p1-20, 20p

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Predicting intensive care need in women with preeclampsia using machine learning – a pilot study.

• Authors : Edvinsson, Camilla; Björnsson, Ola; Erlandsson, Lena

Subjects: MACHINE learning; HIGH-risk pregnancy; ASPARTATE aminotransferase

• Source: Hypertension in Pregnancy; Dec2024, Vol. 43 Issue 1, p1-10, 10p

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Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems.

• Authors : Shern, Siow Jat; Sarker, Md Tanjil; Haram, Mohammed Hussein Saleh Mohammed

Subjects: MACHINE learning; STANDARD deviations; ARTIFICIAL intelligence

• Source: Energies (19961073); Nov2024, Vol. 17 Issue 22, p5772, 25p

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Approximate planning in spatial search.

• Authors : Kryven, Marta; Yu, Suhyoun; Kleiman-Weiner, Max

Subjects: ARTIFICIAL intelligence; EXPECTED utility; COGNITIVE science

• Source: PLoS Computational Biology; 11/12/2024, Vol. 20 Issue 11, p1-21, 21p

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