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Titlebook: Applications of Artificial Intelligence and Neural Systems to Data Science; Anna Esposito,Marcos Faundez-Zanuy,Eros Pasero Book 2023 The E

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41#
發(fā)表于 2025-3-28 14:45:27 | 只看該作者
Empirische Analyse sozialer Problemetions. Genomics is the technique most frequently used for precisely identifying variants. The ongoing global gathering of RNA samples of the virus has made such an approach possible. Nevertheless, variant identification techniques are frequently resource-intensive. As a result, the diagnostic capabi
42#
發(fā)表于 2025-3-28 21:04:16 | 只看該作者
Akteure: Typen, Interessen, Kooperationenall, we consider a set C of . and assume that each expert is characterized by one or more of them. We define a suitable set function in order to evaluate the competence of each coalition. This set function turns out to be a ., see [., .]. As a first proposal, competencies will be represented by Bool
43#
發(fā)表于 2025-3-29 00:35:56 | 只看該作者
44#
發(fā)表于 2025-3-29 06:19:04 | 只看該作者
45#
發(fā)表于 2025-3-29 09:49:28 | 只看該作者
Applications of Artificial Intelligence and Neural Systems to Data Science978-981-99-3592-5Series ISSN 2190-3018 Series E-ISSN 2190-3026
46#
發(fā)表于 2025-3-29 15:19:46 | 只看該作者
47#
發(fā)表于 2025-3-29 16:47:50 | 只看該作者
48#
發(fā)表于 2025-3-29 21:29:24 | 只看該作者
Learning-Based Approach to Predict Fatal Events in Brugada Syndromem. Cardiologists manually measured 24 features per ECG. Then, a multi-layer perceptron (MLP), a boosted decision tree (BDT) model, a decision tree, a Support Vector Machine (SVM), and a Na?ve Bayes (NB) classifier were employed to classify the ECGs. All models show a high negative predictive value:
49#
發(fā)表于 2025-3-29 23:56:45 | 只看該作者
Deep Acoustic Emission Detection Trained on?Seismic Signalsion. The foremost is related to adopting a convolutional neural network (faster R-CNN) with a pre-training on a very large dataset, it was possible to employ the transfer learning (TL) technique. The main benefits of TL include: speed up training considerably, saving of resources, improving the effi
50#
發(fā)表于 2025-3-30 06:07:42 | 只看該作者
A Deep Learning Framework for the Classification of Pre-prodromal and Prodromal Alzheimer’s Disease 35 SCD subjects and 32 MCI patients have been acquired at resting state. After preprocessing, a graphical representation of the input data has been generated splitting the EEG signal into non-overlapping epochs of 5?s and applying a Continuous Wavelet Transform (CWT). The images have been used to tr
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