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Titlebook: Applied Artificial Intelligence 2: Medicine, Biology, Chemistry, Financial, Games, Engineering; The Second Serbian I Nenad Filipovi? Confer

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期刊全稱Applied Artificial Intelligence 2: Medicine, Biology, Chemistry, Financial, Games, Engineering
期刊簡稱The Second Serbian I
影響因子2023Nenad Filipovi?
視頻videohttp://file.papertrans.cn/160/159645/159645.mp4
發(fā)行地址Presents applications of AI in Medicine, Biology, Chemistry, Financial, Games, Engineering.Results of the 2nd Serbian International Conference on Applied Artificial Intelligence.Presents theory, metho
學(xué)科分類Lecture Notes in Networks and Systems
圖書封面Titlebook: Applied Artificial Intelligence 2: Medicine, Biology, Chemistry, Financial, Games, Engineering; The Second Serbian I Nenad Filipovi? Confer
影響因子.The book Applied Artificial Intelligence 2: Medicine, Biology, Chemistry, Financial, Games, Engineering is providing exceptional chapters of the state-of-the-art research knowledge and results on the innovative theories, methodology and applications of artificial intelligence and its sub-domain like deep learning, machine learning in different areas such as medicine, economy, education, law, smart city, government, industry etc. Innovative research ideas on how to solve problems using artificial intelligence, both in R&D and real-time applications are presented. Chapters describe the advanced prototypes, systems, methodologies, tools and techniques and general survey papers, which indicate future directions. These Chapters are extended papers from the Second Serbian International Conference on Applied Artificial Intelligence (SICAAI), which was held in Kragujevac, Serbia, on May 19-20, 2023.
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Energy Issues: Framing and Policy ChangeVGG19, DenseNet121, Xception). The results reveal competitive accuracy, as well as other metrics such as sensitivity, specificity, precision etc. Although the acquired performance is quite positive, additional investigations on larger datasets are necessary to get more robust conclusions.
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Fast Breeder Reactors - Lecture 1,penetrating radar recordings are used as input to the deep convolutional neural networks. In order to estimate the performances of the models, stratified 5-fold cross-validation is utilized along with the area under the ROC curve and confusion matrix. The results showed that the best performing mode
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