標(biāo)題: Titlebook: Artificial Intelligence and Soft Computing; 19th International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2020 [打印本頁(yè)] 作者: 烈酒 時(shí)間: 2025-3-21 16:47
書目名稱Artificial Intelligence and Soft Computing影響因子(影響力)
書目名稱Artificial Intelligence and Soft Computing影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence and Soft Computing網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence and Soft Computing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence and Soft Computing被引頻次
書目名稱Artificial Intelligence and Soft Computing被引頻次學(xué)科排名
書目名稱Artificial Intelligence and Soft Computing年度引用
書目名稱Artificial Intelligence and Soft Computing年度引用學(xué)科排名
書目名稱Artificial Intelligence and Soft Computing讀者反饋
書目名稱Artificial Intelligence and Soft Computing讀者反饋學(xué)科排名
作者: inquisitive 時(shí)間: 2025-3-21 21:49
https://doi.org/10.1007/978-3-031-45312-0works. The proposed strategy is to limit the modification of the weights of the networks entering a randomly selected set of neurons (activation function). Randomizing a fragment of the network whose parameters are modified is carried out in parallel with the selection of a mini-batch based on which作者: Receive 時(shí)間: 2025-3-22 00:25 作者: 全神貫注于 時(shí)間: 2025-3-22 05:24
Fundamental Theories of Physicsion. In this paper a new modification of the conjugate gradient algorithm is presented. The proposed solution speeds up the directional minimization, which result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performanc作者: 一罵死割除 時(shí)間: 2025-3-22 11:29
Fundamental Theories of Physicsf spam, detection of phishing attempts or for detection of fake messages. One of the areas where document classification is of growing importance is automatic assessment of civic, legal and government documents. Using automated classification, a governmental institution might scan all its incoming c作者: septicemia 時(shí)間: 2025-3-22 15:48 作者: 航海太平洋 時(shí)間: 2025-3-22 20:47
https://doi.org/10.1007/978-1-4615-1331-5e of RVFL is fast training without backpropagation. This is because the weights and biases of hidden nodes are selected randomly and stay untrained. Recently, alternative architectures with randomized learning are developed which differ from RVFL in that they have no direct links and a bias term in 作者: myocardium 時(shí)間: 2025-3-22 23:45
https://doi.org/10.1007/978-1-4615-1331-5ically and resource-wise) services in the network so that no of the clients will be victimized and all of them will receive the best possible time of response. Also - there must be a balance not to instantiate a service on every possible machine - which would take too many resources. The task which 作者: gnarled 時(shí)間: 2025-3-23 02:52
Systems Science in Retrospect and Prospectrecognition and image classification than classic machine learning approach. Among the most used methods in DL, CNNs are for a special interest. In this work, we have developed an automatic classifier that permits to classify a large number of fashion clothing articles based on ML and DL approaches.作者: Intruder 時(shí)間: 2025-3-23 08:37 作者: 蠟燭 時(shí)間: 2025-3-23 12:33 作者: 法律 時(shí)間: 2025-3-23 16:30 作者: 認(rèn)識(shí) 時(shí)間: 2025-3-23 21:29 作者: FEIGN 時(shí)間: 2025-3-23 22:34
Monoranjan Maiti,Samir Maity,Arindam RoyBoth classes of algorithms have their history, principles and represent two different biological areas, converted to computer technology. Despite fact that scientists already exhibited that both systems exhibit almost the same behavior dynamics (chaotic regimes etc.), researchers still take both cla作者: conceal 時(shí)間: 2025-3-24 05:08
Facets of Uncertainties and Applicationstongue. Currently, deep neural networks are the most successful technology for this task. The efficient solution requires methods that do not simply process single images, but are able to extract the tongue movement information from a sequence of video frames. One option for this is to apply recurre作者: Mobile 時(shí)間: 2025-3-24 09:29
Facets of Uncertainties and Applicationss. The order flow is the microsecond stream of orders arriving at the exchange, driving the formation of prices seen on the price chart of a stock or currency. To test the stationarity of our proposed model we train our model on data before the 2017 Bitcoin bubble period and test our model during an作者: 量被毀壞 時(shí)間: 2025-3-24 13:03
Facets of Uncertainties and Applicationsned two neural networks to achieve reliable 6D object pose estimation on such images. The first neural network detects fiducial points of objects, which are then fed to a PnP algorithm responsible for pose estimation. The second one is an rotation regression network delivering at the output the quat作者: 貴族 時(shí)間: 2025-3-24 15:12
https://doi.org/10.1007/978-81-322-2301-6raphical representations: chromograms and spectrograms. We have used a large dataset of music divided into eight genres, with certain overlapping musical features. Key, style-defining elements and the overall character of specific genres are represented in our proposed visual representation and reco作者: Organonitrile 時(shí)間: 2025-3-24 20:59
https://doi.org/10.1007/978-3-030-61401-0artificial intelligence; classification; computer networks; computer vision; correlation analysis; evolut作者: hyperuricemia 時(shí)間: 2025-3-25 00:49 作者: 窩轉(zhuǎn)脊椎動(dòng)物 時(shí)間: 2025-3-25 05:15 作者: 污穢 時(shí)間: 2025-3-25 09:22 作者: 單獨(dú) 時(shí)間: 2025-3-25 12:58 作者: recede 時(shí)間: 2025-3-25 17:45 作者: 隱語 時(shí)間: 2025-3-25 22:07
A New Algorithm with a Line Search for Feedforward Neural Networks Trainingplication of a line search method. Similar algorithms based on the QR decomposition utilize a runtime fixed size of the training step. In some situations that might result in inaccurate weight corrections in a given step. The proposed algorithm solves this issue by finding the exact spot of the opti作者: AMITY 時(shí)間: 2025-3-26 03:17
Fast Conjugate Gradient Algorithm for Feedforward Neural Networksion. In this paper a new modification of the conjugate gradient algorithm is presented. The proposed solution speeds up the directional minimization, which result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performanc作者: Limousine 時(shí)間: 2025-3-26 08:16 作者: BRACE 時(shí)間: 2025-3-26 12:12 作者: 光滑 時(shí)間: 2025-3-26 13:38
Are Direct Links Necessary in Random Vector Functional Link Networks for Regression?e of RVFL is fast training without backpropagation. This is because the weights and biases of hidden nodes are selected randomly and stay untrained. Recently, alternative architectures with randomized learning are developed which differ from RVFL in that they have no direct links and a bias term in 作者: 使成波狀 時(shí)間: 2025-3-26 18:31 作者: 胰臟 時(shí)間: 2025-3-26 21:38 作者: DEI 時(shí)間: 2025-3-27 02:47
Method of Real Time Calculation of Learning Rate Value to Improve Convergence of Neural Network Trairparameters, which helps to increase a convergence rate of a training process. There are known techniques of time-based decay, step decay and exponential decay, in which the learning rate is initialized manually and then corrected downwards proportionally to some value. In contrast, in this paper, i作者: 羽毛長(zhǎng)成 時(shí)間: 2025-3-27 08:06
Application of an Improved Focal Loss in Vehicle Detections in object detection. Deep neural network object detectors can be grouped in two broad categories: the two-stage detector and the one-stage detector. One-stage detectors are faster than two-stage detectors. However, they suffer from a severe foreground-backg-round class imbalance during training th作者: bromide 時(shí)間: 2025-3-27 11:07
Concept Drift Detection Using Autoencoders in Data Streams Processingrift detector. The autoencoders are neural networks that are learned how to reconstruct input data. As a side effect, they are able to learn compact nonlinear codes, which summarize the most important features of input data. We suspect that the properly learned autoencoder on one part of the data st作者: thalamus 時(shí)間: 2025-3-27 14:47 作者: SLAY 時(shí)間: 2025-3-27 18:20
On the Similarity Between Neural Network and Evolutionary AlgorithmBoth classes of algorithms have their history, principles and represent two different biological areas, converted to computer technology. Despite fact that scientists already exhibited that both systems exhibit almost the same behavior dynamics (chaotic regimes etc.), researchers still take both cla作者: Resign 時(shí)間: 2025-3-28 00:28
3D Convolutional Neural Networks for Ultrasound-Based Silent Speech Interfacestongue. Currently, deep neural networks are the most successful technology for this task. The efficient solution requires methods that do not simply process single images, but are able to extract the tongue movement information from a sequence of video frames. One option for this is to apply recurre作者: frivolous 時(shí)間: 2025-3-28 05:17 作者: Deject 時(shí)間: 2025-3-28 06:43
6D Pose Estimation of Texture-Less Objects on RGB Images Using CNNsned two neural networks to achieve reliable 6D object pose estimation on such images. The first neural network detects fiducial points of objects, which are then fed to a PnP algorithm responsible for pose estimation. The second one is an rotation regression network delivering at the output the quat作者: 虛構(gòu)的東西 時(shí)間: 2025-3-28 12:53
Application of Neural Networks and Graphical Representations for Musical Genre Classificationraphical representations: chromograms and spectrograms. We have used a large dataset of music divided into eight genres, with certain overlapping musical features. Key, style-defining elements and the overall character of specific genres are represented in our proposed visual representation and reco作者: judicial 時(shí)間: 2025-3-28 18:29
Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flowd after the bubble. We show that without any retraining, the proposed model is temporally stable even as Bitcoin trading shifts into an extremely volatile “bubble trouble” period. The significance of the result is shown by benchmarking against existing state-of-the-art models in the literature for modelling price formation using deep learning.作者: lacrimal-gland 時(shí)間: 2025-3-28 19:36
Application of Neural Networks and Graphical Representations for Musical Genre Classificationgnized by the networks. We show that the networks have learned to distinguish between genres upon features observable by a human listener and compare the metrics for the network models. Results of the conducted experiments are described and discussed, along with our conclusions and comparison with similar solutions.作者: COMA 時(shí)間: 2025-3-29 02:19
0302-9743 nce and Soft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020..The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts:??neural networks and their applications;作者: accessory 時(shí)間: 2025-3-29 04:29 作者: 改進(jìn) 時(shí)間: 2025-3-29 09:52
Fundamental Theories of Physicswhich result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performance of our method and the classic CG method was compared.作者: 發(fā)電機(jī) 時(shí)間: 2025-3-29 11:55
Monoranjan Maiti,Samir Maity,Arindam Roy that scientists already exhibited that both systems exhibit almost the same behavior dynamics (chaotic regimes etc.), researchers still take both classes of algorithms as two different classes. We show in this paper, that there are some similarities, that can help to understand evolutionary algorithms as neural networks and vice versa.作者: 黃瓜 時(shí)間: 2025-3-29 15:55 作者: 流浪者 時(shí)間: 2025-3-29 23:32
Fast Conjugate Gradient Algorithm for Feedforward Neural Networkswhich result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performance of our method and the classic CG method was compared.作者: 淺灘 時(shí)間: 2025-3-30 02:39 作者: fatty-acids 時(shí)間: 2025-3-30 07:23 作者: 開玩笑 時(shí)間: 2025-3-30 09:49
https://doi.org/10.1007/978-81-322-2301-6gnized by the networks. We show that the networks have learned to distinguish between genres upon features observable by a human listener and compare the metrics for the network models. Results of the conducted experiments are described and discussed, along with our conclusions and comparison with similar solutions.作者: 南極 時(shí)間: 2025-3-30 12:58
Conference proceedings 2020ft Computing, ICAISC 2020, held in Zakopane, Poland*, in October 2020..The 112 revised full papers presented were carefully reviewed and selected from 265 submissions. The papers included in the first volume are organized in the following six parts:??neural networks and their applications; fuzzy sys作者: buoyant 時(shí)間: 2025-3-30 18:26
Conference proceedings 2020ns; artificial intelligence in modeling and simulation..The papers included in the second volume?are organized in the following four parts: computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control..*The conference was held virtually due to the COVID-19 pandemic..作者: Injunction 時(shí)間: 2025-3-30 22:51 作者: Circumscribe 時(shí)間: 2025-3-31 02:40
Systems Science in Retrospect and Prospect In this paper, we reviewed the recent findings in adversarial attacks and defense strategies. We also analyzed the effects of attacks and defense strategies applied, using the local and global analyzing methods from the family of explainable artificial intelligence.作者: misanthrope 時(shí)間: 2025-3-31 08:06 作者: Certainty 時(shí)間: 2025-3-31 12:11 作者: amorphous 時(shí)間: 2025-3-31 14:44
Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks In this paper, we reviewed the recent findings in adversarial attacks and defense strategies. We also analyzed the effects of attacks and defense strategies applied, using the local and global analyzing methods from the family of explainable artificial intelligence.作者: 難解 時(shí)間: 2025-3-31 19:33
3D Convolutional Neural Networks for Ultrasound-Based Silent Speech Interfacestial and temporal convolutions in a decomposed form, which proved very successful recently in video action recognition. We find experimentally that our 3D network outperforms the CNN+LSTM model, indicating that 3D CNNs may be a feasible alternative to CNN+LSTM networks in SSI systems.
