標題: Titlebook: Intelligent Systems; 12th Brazilian Confe Murilo C. Naldi,Reinaldo A. C. Bianchi Conference proceedings 2023 The Editor(s) (if applicable) [打印本頁] 作者: opioid 時間: 2025-3-21 16:58
書目名稱Intelligent Systems影響因子(影響力)
書目名稱Intelligent Systems影響因子(影響力)學科排名
書目名稱Intelligent Systems網絡公開度
書目名稱Intelligent Systems網絡公開度學科排名
書目名稱Intelligent Systems被引頻次
書目名稱Intelligent Systems被引頻次學科排名
書目名稱Intelligent Systems年度引用
書目名稱Intelligent Systems年度引用學科排名
書目名稱Intelligent Systems讀者反饋
書目名稱Intelligent Systems讀者反饋學科排名
作者: Munificent 時間: 2025-3-21 21:57
Letícia Bomfim,Oton Cunha,Michelle Kuroda,Alexandre Vidal,Helio Pedrinieginning graduate courses, and will complement the Applied Math- ematical Sciences (AMS) series, which will focus on advanced textbooks and research level monographs. Preface As in Part I, this book concentrates on understanding the behavior of dif- ferential equations, rather than on solving the eq作者: PARA 時間: 2025-3-22 01:22
Victor Hugo Braguim Canto,Jo?o Renato Ribeiro Manesco,Gustavo Botelho de Souza,Aparecido Nilceu Marabeginning graduate courses, and will complement the Applied Mathe- matical Sciences (AMS) series, which will focus on advanced textbooks and research level monographs. Preface Consider a first order differential equation of form x‘ = f ( t, x). In elemen- tary courses one frequently gets the impress作者: Vasoconstrictor 時間: 2025-3-22 07:39
Anisio P. Santos Jr.,Anage C. Mundim Filho,Robinson Sabino-Silva,Murillo G. Carneiroelp when dealing with nonlinear problems, since many physical systems can be described by nonlinear differential equations. This is known as the qualitative study of differential equations and it is one of the major aspects and the biggest achievement of the modern analysis of differential equations作者: MORPH 時間: 2025-3-22 12:29 作者: 繁重 時間: 2025-3-22 14:37
Dieine Estela Bernieri Schiavon,Carla Diniz Lopes Becker,Viviane Rodrigues Botelho,Thatiane Alves Piro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: insightful 時間: 2025-3-22 18:25 作者: Hyperalgesia 時間: 2025-3-22 21:54
ro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: Flagging 時間: 2025-3-23 01:29
Jo?o P. B. Andrade,Leonardo F. Costa,Lucas S. Fernandes,Paulo A. L. Rego,José G. R. Maiaro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: Emasculate 時間: 2025-3-23 08:48
Alison Corrêa Mendes,Alexandre César Pinto Pessoa,Anselmo Cardoso de Paivaro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: 事物的方面 時間: 2025-3-23 09:52
Bruno M. Pacheco,Victor H. R. de Oliveira,Augusto B. F. Antunes,Saulo D. S. Pedro,Danilo Silva,for tro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: probate 時間: 2025-3-23 16:06
André A. V. Escorel Ribeiro,Rodrigo Cesar Lira,Mariana Macedo,Hugo Valadares Siqueira,Carmelo Bastosro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: 沒血色 時間: 2025-3-23 19:49
Mauricio W. Barg,Barbara S. Rodrigues,Gabriela T. Justino,Kleyton Pontes Cotta,Hugo R. V. Portuita,Fro?t too by study of this text. An important, but optional component of the book (based on the - structor’s or reader’s preferences) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?作者: Hemiparesis 時間: 2025-3-23 23:44 作者: 親愛 時間: 2025-3-24 04:59 作者: cavity 時間: 2025-3-24 09:03
Cícero L. Costa,Danielli A. Lima,Celia A. Zorzo Barcelos,Bruno A. N. Traven?olonces) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?978-1-4899-8265-0978-1-4419-1163-6作者: RAG 時間: 2025-3-24 11:29
Diego Pinheiro da Silva,William da Rosa Fr?hlich,Marco Antonio Schwertner,Sandro José Rigonces) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?978-1-4899-8265-0978-1-4419-1163-6作者: Increment 時間: 2025-3-24 15:11
Arthur Guilherme Santos Fernandes,Geraldo Braz Junior,Jo?o Otávio Bandeira Diniz,Aristófanes Correa nces) is its computer material. The book is one of the few graduate di?erential equations texts that use the computer to enhance the concepts and theory normally taught to ?978-1-4899-8265-0978-1-4419-1163-6作者: gusher 時間: 2025-3-24 19:05 作者: sparse 時間: 2025-3-25 01:37
0302-9743 ization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications.?.978-3-031-45388-5978-3-031-45389-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: CERE 時間: 2025-3-25 05:43 作者: Meager 時間: 2025-3-25 09:26
Convolutional Neural Networks for?the?Molecular Detection of?COVID-19of the spectrum sample. The predictive performance of the CNN was compared against several other algorithms widely adopted in the literature. The CNN architecture discriminates COVID-19 with Raman spectroscopy of blood samples with 96.8% accuracy, 95.5% sensitivity, and 98.2% of specificity, represe作者: COUCH 時間: 2025-3-25 12:56 作者: Spartan 時間: 2025-3-25 18:38
Enhancing Stock Market Predictions Through the?Integration of?Convolutional and?Recursive LSTM Blockotable improvement in prediction accuracy (MAPE reduction of 2.22%), strongly suggesting that pre-processing data via Convolutional Neural Networks (CNN) benefits LSTM blocks and can enhance the performance of stock market prediction methodologies.作者: FLAGR 時間: 2025-3-25 21:39 作者: 遺忘 時間: 2025-3-26 03:20 作者: 幼稚 時間: 2025-3-26 06:43
Conference proceedings 2023p learning applications; reinforcement learning and GAN; classification; machine learning analysis;.Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications.?.作者: deforestation 時間: 2025-3-26 11:49 作者: PET-scan 時間: 2025-3-26 16:11 作者: 幻影 時間: 2025-3-26 20:11 作者: 語言學 時間: 2025-3-27 00:18
Deep Reinforcement Learning for?Voltage Control in?Power Systemsoach, three novel reinforcement learning variations named windowed, ensemble and windowed ensemble Q-Learning, which alter the agent’s learning process for voltage control, are presented and tested on IEEE 13, 37 and 123 bus systems, simulated on OpenDSS.作者: 束以馬具 時間: 2025-3-27 03:42 作者: Femine 時間: 2025-3-27 08:28
Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by?Transformer Encoders on data augmentation and positional encoders do not provide performance gains to our models. Conversely, the models based on the DFCSC approach overcome the appropriate baselines, and they do remarkably well as the lowest and highest improvements respectively are 5.9% and 10.4%.作者: fulcrum 時間: 2025-3-27 11:57
Dog Face Recognition Using Vision Transformerelationships between them. Results obtained on DogFaceNet, a public database of dog face images, show that the proposed method, which uses the EfficientFormer-L1 architecture, outperforms the state-of-the-art method proposed previously in literature based on ResNet, a deep convolutional neural network.作者: Arresting 時間: 2025-3-27 15:33 作者: 輕快走過 時間: 2025-3-27 19:26
EfficientDeepLab for?Automated Trachea Segmentation on?Medical Imagesmatic segmentation methodologies can speed up organ delimiting during radiotherapy planning. This work designs a method, EfficientDeepLab, a convolutional neural network architecture trained on CT scans for trachea segmentation, and obtained an 88.6% dice score.作者: 換話題 時間: 2025-3-27 22:52 作者: 直覺好 時間: 2025-3-28 04:50 作者: Onerous 時間: 2025-3-28 06:40 作者: 不連貫 時間: 2025-3-28 14:26 作者: Oafishness 時間: 2025-3-28 16:27
978-3-031-45388-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 牽索 時間: 2025-3-28 19:01 作者: 敬禮 時間: 2025-3-29 02:22 作者: nonradioactive 時間: 2025-3-29 06:54 作者: 得罪人 時間: 2025-3-29 08:14 作者: 啪心兒跳動 時間: 2025-3-29 11:50 作者: 貞潔 時間: 2025-3-29 19:20
Hierarchical Graph Convolutional Networks for?Image Classificationor converting images to graphs often fail to preserve the hierarchical information of the image elements and produce sub-optimal or poor regions. To address these limitations, we propose a novel approach that uses a hierarchical image segmentation technique to generate graphs at multiple segmentatio作者: GEST 時間: 2025-3-29 22:18
Interpreting Convolutional Neural Networks for?Brain Tumor Classification: An Explainable Artificialficantly improve outcomes and quality of life for patients with brain tumors. Magnetic resonance (MRI) is a powerful diagnostic tool, and convolutional neural networks (CNNs) are efficient deep learning algorithms for image analysis. In this study, we explored using two CNN models for brain tumor cl作者: Noctambulant 時間: 2025-3-30 03:18
Enhancing Stock Market Predictions Through the?Integration of?Convolutional and?Recursive LSTM Blockgrates convolutional networks, which learn to process signals through filters, with recursive LSTM blocks to account for critical temporal information often overlooked in convolutional approaches. Our investigation primarily revolves around two research questions: (1) Can integrating convolutional a作者: callous 時間: 2025-3-30 06:16 作者: 翻動 時間: 2025-3-30 10:28
Dog Face Recognition Using Deep Features Embeddingsbout . of households own at least one pet. Lost and missing dogs are a severe source of suffering and problems for their families. So, this paper addresses the problem of facial dog identification. This technology can benefit many applications, such as handling the missing pet problem, granting pets作者: 在駕駛 時間: 2025-3-30 14:34 作者: agonist 時間: 2025-3-30 20:24 作者: Anal-Canal 時間: 2025-3-30 20:56
Multi-label Classification of?Pathologies in?Chest Radiograph Images Using DenseNetnce of a DenseNet in a multi-label classification task on radiography images, using focal loss as the loss function to address the class imbalance problem. For the experiments, 14 different types of findings were considered. Satisfactory results were obtained using the area under the ROC curve (AUC-作者: 彩色的蠟筆 時間: 2025-3-31 01:00
Does Pre-training on?Brain-Related Tasks Results in?Better Deep-Learning-Based Brain Age Biomarkers?ase biomarker. Deep learning models have been established as reliable and efficient brain age estimators, being trained to predict the chronological age of healthy subjects. In this paper, we investigate the impact of a pre-training step on deep learning models for brain age prediction. More precise
