書目名稱Artificial Neural Networks in Pattern Recognition影響因子(影響力)學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition網(wǎng)絡(luò)公開度
書目名稱Artificial Neural Networks in Pattern Recognition網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition被引頻次
書目名稱Artificial Neural Networks in Pattern Recognition被引頻次學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition年度引用
書目名稱Artificial Neural Networks in Pattern Recognition年度引用學(xué)科排名
書目名稱Artificial Neural Networks in Pattern Recognition讀者反饋
書目名稱Artificial Neural Networks in Pattern Recognition讀者反饋學(xué)科排名
作者: Contend 時間: 2025-3-21 21:28 作者: 殺死 時間: 2025-3-22 00:33 作者: Initial 時間: 2025-3-22 06:09 作者: Mitigate 時間: 2025-3-22 10:14 作者: Acetabulum 時間: 2025-3-22 13:57 作者: GREEN 時間: 2025-3-22 19:16
,China’s Responsible Investment Pathway,former-based models and Recurrent Neural Networks (RNNs) have excelled in processing long sequences, yet face challenges in transitioning to processing infinite-length sequences online, a crucial step in mimicking human learning over continuous data streams. While Transformer models handle large con作者: 相反放置 時間: 2025-3-23 00:15 作者: MIR 時間: 2025-3-23 04:13
,China’s Responsible Investment Pathway,s and is optimized in three-stage cross-validation: In the first stage, the hyperparameter values for the L1 SVM (MK) are determined fixing the hyperparameter values for multiple kernels. In the second stage the hyperparameter values for the multiple kernels are determined, and in the third stage, t作者: 連鎖 時間: 2025-3-23 07:07 作者: declamation 時間: 2025-3-23 12:47
Timothy P Hughes,David M Ross,Junia V Melollenge. In this paper, we introduce a hybrid evolutionary algorithm that advances the design of CNNs for medical image segmentation. By integrating Cartesian Genetic Programming with Simulated Annealing, our approach efficiently explores the architectural design space, yielding CNN architectures tha作者: Ornithologist 時間: 2025-3-23 16:57 作者: Legion 時間: 2025-3-23 21:35
Handbook of Classical Conditioningre called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of comput作者: NOVA 時間: 2025-3-23 23:25
Esther Hoffmann,Patrick Sch?pflin. Melanoma, a malignant skin cancer, has the highest mortality rate among all skin cancer types. Early detection of melanoma significantly enhances the chances of effective treatment and survival rates. This research evaluates advanced deep learning techniques in medical imaging, specifically Vision作者: Cytokines 時間: 2025-3-24 05:57
Cynthia Arantes Ferreira Ludererge analysis-based computer-aided diagnostic (CAD) systems for oral cancer. To this end, we propose a novel model that we name Gray Wolf Optimization (GWO) based deep Feature Selection Network (GFS-Net). Initially, we use an attention-aided NASNet Mobile, a convolutional neural network (CNN) architec作者: 無瑕疵 時間: 2025-3-24 09:18
Leticia Andrea Chechi,Cátia Grisaachine learning approach to predict the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson’s Progression Markers Initiative (PPMI) dataset, we examined the impact of da作者: Narcissist 時間: 2025-3-24 12:31 作者: BALK 時間: 2025-3-24 17:20 作者: Torrid 時間: 2025-3-24 19:41
Konstantinos Demertzis,Lazaros Iliadishallenges. To bridge this gap, we introduce VAeViT, a pioneering hybrid model that seamlessly integrates the strengths of Vision Transformers and Variational Autoencoders (VAE). VAeViT leverages VAE’s efficiency in feature representation to encode the 3D object views into a lower-dimensional latent 作者: parasite 時間: 2025-3-25 01:47
Artificial Neural Networks in Pattern Recognition978-3-031-71602-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: ciliary-body 時間: 2025-3-25 04:06 作者: BATE 時間: 2025-3-25 09:57
Gaussian-Mixture Neural Networks to its intrinsic difficulties and to the many shortcomings of statistical parametric and non-parametric techniques. Artificial neural networks (ANN) have long been applied to the estimation of posterior probabilities for pattern classification (a simple supervised learning task), yet only a few att作者: spondylosis 時間: 2025-3-25 12:56 作者: Enliven 時間: 2025-3-25 18:44 作者: Aprope 時間: 2025-3-25 20:04 作者: 陶瓷 時間: 2025-3-26 03:49 作者: 知識分子 時間: 2025-3-26 05:33
Automatic Interpretation of?,F-Fluorocholine PET/CT Findings in?Patients with Primary Hyperparathyroarathyroid glands. This paper aims to enhance the diagnostic accuracy of PET/CT using .F-fluorocholine (FCH) by employing radiomic image analysis to differentiate hyperfunctioning parathyroid glands (HPTG) from thyroid gland (TG) normal or adenomatous tissue. To this aim, the paper contributes a nov作者: 殘忍 時間: 2025-3-26 08:49 作者: FID 時間: 2025-3-26 12:41 作者: Palate 時間: 2025-3-26 18:34
Vision Transformer Features-Based Leukemia Classificationre called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of comput作者: 廚房里面 時間: 2025-3-26 23:44 作者: 代理人 時間: 2025-3-27 02:13 作者: N斯巴達人 時間: 2025-3-27 08:36
Machine Learning for?Clinical Score Prediction from?Longitudinal Dataset: A?Case Study on?Parkinson’achine learning approach to predict the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson’s Progression Markers Initiative (PPMI) dataset, we examined the impact of da作者: 邊緣 時間: 2025-3-27 11:03 作者: 輕打 時間: 2025-3-27 14:24
Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networksform traditional machine learning, especially for Brain-Computer Interface (BCI) applications. By processing also other recording modalities (e.g., electromyography, EMG) together with EEG signals, motor decoding improved. However, multi-modal algorithms for decoding hand movements are mainly applie作者: alcohol-abuse 時間: 2025-3-27 19:10
VAeViT: Fusing Multi-views for Complete 3D Object Recognitionhallenges. To bridge this gap, we introduce VAeViT, a pioneering hybrid model that seamlessly integrates the strengths of Vision Transformers and Variational Autoencoders (VAE). VAeViT leverages VAE’s efficiency in feature representation to encode the 3D object views into a lower-dimensional latent 作者: 重畫只能放棄 時間: 2025-3-27 22:14 作者: 采納 時間: 2025-3-28 05:04 作者: figure 時間: 2025-3-28 07:12 作者: Trypsin 時間: 2025-3-28 10:58
Executive Women and Glass Ceiling in China,ly determined while estimating the parameters. Our suggested approach is utilized in medical settings, specifically to focus medication for individuals with heart disease based on clinical data and analyze breast tissue taking into account histological scans. When it comes to data with strictly boun作者: jaunty 時間: 2025-3-28 16:22 作者: 不規(guī)則 時間: 2025-3-28 19:39
Handbook of Chlor-Alkali Technologys (k-nearest neighbor and random forest) and deep neural networks were applied to the task of discriminating between HPTG and TG over the dataset in order to fix baseline results for the Community to challenge. Moreover, two ensemble methods are proposed that combine the aforementioned classifiers, 作者: cardiopulmonary 時間: 2025-3-28 23:54
David G. Lavond,Joseph E. Steinmetzgradient loss term in the loss function. Additionally, real CT images are input into the generator to help the model learn the target modality’s style features. Using Conditional GANs, mismatched data pairs are input into the discriminator with false labels to improve content matching sensitivity. E作者: 卡死偷電 時間: 2025-3-29 06:31 作者: fulcrum 時間: 2025-3-29 10:11
Cynthia Arantes Ferreira Luderert exploring CNNs. Moreover, XAI approaches have never been applied to analyze EEG-based functional connectivity. To overcome these limitations, we design and apply a CNN for processing directed connectivity measures estimated via spectral Granger causality. The CNN automatically learns features in t作者: 無底 時間: 2025-3-29 12:02 作者: 水槽 時間: 2025-3-29 17:50 作者: 發(fā)出眩目光芒 時間: 2025-3-29 19:58 作者: LANCE 時間: 2025-3-30 01:28
Gaussian-Mixture Neural Networks of Gaussian mixture models. Therefore, the proposed machine is termed Gaussian-mixture Neural Network (GNN). The best selling points of the GNN lie in its simplicity and effectiveness. Preliminary experimental results are reported and analyzed that involve data samples of variable size randomly dra作者: 評論者 時間: 2025-3-30 05:55 作者: 豎琴 時間: 2025-3-30 09:11
Robust Clustering with?McDonald’s Beta-Liouville Mixture Models for?Proportional Dataly determined while estimating the parameters. Our suggested approach is utilized in medical settings, specifically to focus medication for individuals with heart disease based on clinical data and analyze breast tissue taking into account histological scans. When it comes to data with strictly boun作者: Salivary-Gland 時間: 2025-3-30 16:25
Evaluating Support Vector Machines with?Multiple Kernels by?Random Searchzation abilities by the three-stage cross-validation for the reduced kernel structure is statistically comparable to or better than random search for the two-class problems and most of the multiclass problems tested.作者: HUMP 時間: 2025-3-30 20:19
Automatic Interpretation of?,F-Fluorocholine PET/CT Findings in?Patients with Primary Hyperparathyros (k-nearest neighbor and random forest) and deep neural networks were applied to the task of discriminating between HPTG and TG over the dataset in order to fix baseline results for the Community to challenge. Moreover, two ensemble methods are proposed that combine the aforementioned classifiers,
