標(biāo)題: Titlebook: Artificial Neural Networks in Pattern Recognition; 10th IAPR TC3 Worksh Neamat El Gayar,Edmondo Trentin,Hazem Abbas Conference proceedings [打印本頁(yè)] 作者: incompatible 時(shí)間: 2025-3-21 20:08
書目名稱Artificial Neural Networks in Pattern Recognition影響因子(影響力)
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: 鄙視 時(shí)間: 2025-3-21 23:10 作者: finale 時(shí)間: 2025-3-22 02:27
Fetal Morph Functional Diagnosisent SI. To compete the state-of-the-art (SOTA), we propose a fusion method between WST and x-vectors architecture, we show that this structure outperforms HWSTCNN by . on TIMIT dataset sampled at 8?kHz and makes the same performance in the SOTA at 16?kHz.作者: 很像弓] 時(shí)間: 2025-3-22 06:54
General Remarks About Autosomal DiseasesN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.作者: Connotation 時(shí)間: 2025-3-22 11:56 作者: 采納 時(shí)間: 2025-3-22 15:58
A Novel Representation of?Graphical Patterns for?Graph Convolution Networksal Neural Networks (CNNs) in image processing. To this end we propose a new representation for graphs, called GrapHisto, in the form of unique tensors encapsulating the features of any given graph to then process the new data using the CNN paradigm.作者: institute 時(shí)間: 2025-3-22 18:12
Wavelet Scattering Transform Depth Benefit, An?Application for?Speaker Identificationent SI. To compete the state-of-the-art (SOTA), we propose a fusion method between WST and x-vectors architecture, we show that this structure outperforms HWSTCNN by . on TIMIT dataset sampled at 8?kHz and makes the same performance in the SOTA at 16?kHz.作者: 樸素 時(shí)間: 2025-3-23 00:23
Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from?Raw SpeechN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.作者: 過(guò)多 時(shí)間: 2025-3-23 02:02
https://doi.org/10.1007/978-1-4615-1981-2tic program, alternatingly. According to the computer experiments for two-class and multiclass problems, the MLS SVM does not outperform the LS SVM for the test data although it does for the cross-validation data.作者: 斗志 時(shí)間: 2025-3-23 05:53
https://doi.org/10.1007/978-1-4684-1191-1aring the aforementioned two models, the performance of the most widely used optimization functions, including SGD, Adam, and AdamW is studied as well. The methods are evaluated using mAP and mAR to verify whether YOLOv6 potentially outperforms YOLOv5, and whether AdamW is capable to generalize better than its peer optimizers.作者: 詞匯 時(shí)間: 2025-3-23 13:40 作者: Mangle 時(shí)間: 2025-3-23 16:03
A Study on?the?Autonomous Detection of?Impact Cratersaring the aforementioned two models, the performance of the most widely used optimization functions, including SGD, Adam, and AdamW is studied as well. The methods are evaluated using mAP and mAR to verify whether YOLOv6 potentially outperforms YOLOv5, and whether AdamW is capable to generalize better than its peer optimizers.作者: 殘酷的地方 時(shí)間: 2025-3-23 20:11 作者: 地牢 時(shí)間: 2025-3-23 22:41 作者: larder 時(shí)間: 2025-3-24 03:09 作者: 北極熊 時(shí)間: 2025-3-24 07:47
Artificial Neural Networks in Pattern Recognition978-3-031-20650-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: diabetes 時(shí)間: 2025-3-24 14:06 作者: 愉快嗎 時(shí)間: 2025-3-24 16:48 作者: 肉體 時(shí)間: 2025-3-24 19:53 作者: 一回合 時(shí)間: 2025-3-25 00:19 作者: 上漲 時(shí)間: 2025-3-25 05:23
Fetal Islet Transplantation and Pregnancy,oncerns that this increasing popularity has exacerbated issues of unfairness and discrimination toward individuals. Researchers in this field have proposed a wide variety of fairness-enhanced classifiers and fairness matrices to address these issues, but very few fairness techniques have been transl作者: 種植,培養(yǎng) 時(shí)間: 2025-3-25 08:40
https://doi.org/10.1007/978-1-4615-1981-2 maximizing the minimum margin and minimizing the maximum margin. It works to improve the generalization ability of the L1 SVM (standard SVM) and LP (Linear Programming) SVM. In this paper, we discuss whether it also works for the LS (Least Squares) SVM. The minimal complexity LS SVM (MLS SVM) is tr作者: adroit 時(shí)間: 2025-3-25 12:20
Fetal Neuroimaging: Ultrasound or MRI?,ic decision-making through the ability to recognise patterns in medical images. Such technologies started showing promising results in their ability to match or outperform physicians in certain specialities and improve the quality of medical diagnosis. Convolutional neural networks are one state-of-作者: patriarch 時(shí)間: 2025-3-25 17:20
,The “First Trimester (11–14?Weeks) Scan”,set is a good example of visually similar paired images to figure out how humans compare images. In this research, we consider these more generic annotated categories to build a semantic manifold distance. We introduce an atypical triplet-loss using the inverse Kullback-Leibler divergence to model t作者: 人造 時(shí)間: 2025-3-25 22:02
Fetal Morph Functional Diagnosisvariance scales. Our primary purpose is to present an approach to optimally design the WST to enhance the identification accuracy for short utterances. We describe the invariant features offered by the depth of this transform by performing simple experiments based on text-independent and text-depend作者: Liberate 時(shí)間: 2025-3-26 01:51
General Remarks About Autosomal Diseases the potential of recurrent neural networks (RNNs) to handle this problem. We compare the RNN architecture to widely used convolutional neural networks (CNNs) and to some other machine learning-based and traditional non-learning algorithms on several publicly available databases. We show that the RN作者: 失望昨天 時(shí)間: 2025-3-26 07:05 作者: TAIN 時(shí)間: 2025-3-26 09:54 作者: 死亡率 時(shí)間: 2025-3-26 16:19
Bruce J. Kelman,Melvin R. Sikov and possibly millions of dollars of investment. It is crucial that both patent producers and consumers are able to assess the novelty of such patents and perform basic processing on them. In this work, we review approaches in the literature in patent analysis and novelty assessment that range from 作者: 單色 時(shí)間: 2025-3-26 18:36 作者: neutral-posture 時(shí)間: 2025-3-26 23:50 作者: 美色花錢 時(shí)間: 2025-3-27 02:44
https://doi.org/10.1007/978-1-4684-1191-1and geology without directly landing on its surface. Autonomous detection of craters has been of particular interest lately, especially for Mars and Lunar surfaces. This review study deals with the technical implementation, training, and testing of YOLOv5 and YOLOv6 to gauge their efficiency in dete作者: certitude 時(shí)間: 2025-3-27 06:17 作者: 儀式 時(shí)間: 2025-3-27 11:56
https://doi.org/10.1007/978-3-031-20650-4artificial intelligence; computer networks; computer science; computer systems; computer vision; database作者: figment 時(shí)間: 2025-3-27 17:28
978-3-031-20649-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 省略 時(shí)間: 2025-3-27 20:26 作者: chemical-peel 時(shí)間: 2025-3-27 22:15
Jeffrey E. Christiaansen,Charles M. Petersonthe-art graph neural networks. In an experimental evaluation on five graph data sets, we show that this novel augmentation technique is able to significantly improve the classification accuracy of three different neural network models.作者: 火海 時(shí)間: 2025-3-28 02:25
Fetal Islet Transplantation and Pregnancy,ossing edges introducing linear splines, and threat the control points as novel “fake” vertices that can be optimized via the underlying layout optimization process. We provide qualitative and quantitative analysis over multiple graphs and optimizing different aesthetic losses, that show how the pro作者: 混合物 時(shí)間: 2025-3-28 09:14
Fetal Islet Transplantation and Pregnancy,ividuals who are distinguished from other pairs in the records by data-driven similarity measures between each individual in the transformed data. Such a design identifies the bias and mitigates it at the data preprocessing stage of the machine learning pipeline to ensure individual fairness. Our me作者: 漸變 時(shí)間: 2025-3-28 13:01
Fetal Neuroimaging: Ultrasound or MRI?, results with those of convolutional neural networks employed for the same tasks. Our findings support the use of Capsule Networks over Convolutional Neural Networks for Computer-Aided Diagnosis due to their superiority in performance but more importantly for their better interpretability and their 作者: overwrought 時(shí)間: 2025-3-28 16:11
,The “First Trimester (11–14?Weeks) Scan”,d images of the TLL dataset showed that the retrieving score from the first candidate guess (top-1) is . which is . higher compared to the recall score of the baseline triplet-loss which is limited to ., and with a top-5 pairing score as high as . which represents a gain of ..作者: Ablation 時(shí)間: 2025-3-28 20:36
https://doi.org/10.1007/978-981-15-8171-7encoder layers. We show that monolingual MahaBERT-based models provide rich representations as compared to sentence embeddings from multi-lingual counterparts. However, we observe that these embeddings are not generic enough and do not work well on out-of-domain social media datasets. We consider tw作者: 濕潤(rùn) 時(shí)間: 2025-3-29 01:05
Comprehensive Gynecology and Obstetricsransformer-encoder model that takes character-level input of words and their context to achieve this. The embeddings are generated as the outputs of the model. The model is then trained to minimize triplet loss, which ensures that spell variants of a word are embedded close to the word, and that unr作者: Hot-Flash 時(shí)間: 2025-3-29 06:49 作者: 漂亮才會(huì)豪華 時(shí)間: 2025-3-29 09:29 作者: 種族被根除 時(shí)間: 2025-3-29 14:42
Richard K. Miller,Henry A. Thiedeesses the problem of multi-label distortion classification and ranking. A vision transformer was used for feature learning. The experiment showed that the proposed solution performed well in terms of F1 score of single distortion (77.9%) and F1 score of single and multiple distortions (69.9%). Moreo作者: 同謀 時(shí)間: 2025-3-29 16:09 作者: conservative 時(shí)間: 2025-3-29 22:39 作者: puzzle 時(shí)間: 2025-3-29 23:53
Multi-stage Bias Mitigation for?Individual Fairness in?Algorithmic Decisionsividuals who are distinguished from other pairs in the records by data-driven similarity measures between each individual in the transformed data. Such a design identifies the bias and mitigates it at the data preprocessing stage of the machine learning pipeline to ensure individual fairness. Our me作者: 蚊子 時(shí)間: 2025-3-30 05:29
A Review of?Capsule Networks in?Medical Image Analysis results with those of convolutional neural networks employed for the same tasks. Our findings support the use of Capsule Networks over Convolutional Neural Networks for Computer-Aided Diagnosis due to their superiority in performance but more importantly for their better interpretability and their 作者: 混合物 時(shí)間: 2025-3-30 08:54 作者: BAN 時(shí)間: 2025-3-30 16:23
Mono vs Multilingual BERT for?Hate Speech Detection and?Text Classification: A Case Study in?Marathiencoder layers. We show that monolingual MahaBERT-based models provide rich representations as compared to sentence embeddings from multi-lingual counterparts. However, we observe that these embeddings are not generic enough and do not work well on out-of-domain social media datasets. We consider tw作者: 抵押貸款 時(shí)間: 2025-3-30 18:18 作者: Postmenopause 時(shí)間: 2025-3-30 23:47 作者: Organonitrile 時(shí)間: 2025-3-31 02:31 作者: 上釉彩 時(shí)間: 2025-3-31 06:39 作者: 抵押貸款 時(shí)間: 2025-3-31 11:17 作者: Coordinate 時(shí)間: 2025-3-31 15:47
