標(biāo)題: Titlebook: Artificial Intelligence and Soft Computing; 21st International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2023 [打印本頁] 作者: aspirant 時(shí)間: 2025-3-21 17:06
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: Tidious 時(shí)間: 2025-3-21 21:03 作者: Memorial 時(shí)間: 2025-3-22 03:35 作者: CLAMP 時(shí)間: 2025-3-22 06:02 作者: Integrate 時(shí)間: 2025-3-22 10:06 作者: Tdd526 時(shí)間: 2025-3-22 13:25
Type-2 Fuzzy Classifier with?Smooth Type-Reductionome to obtain a feedforward type-2 fuzzy network structure. Moreover, the KM algorithm and its modifications complicate the learning process due to the non-differentiability of the maximum and minimum functions. Therefore, this paper proposes to use the smooth maximum function to develop a new structure of the fuzzy type-2 classifier.作者: ATP861 時(shí)間: 2025-3-22 19:24 作者: ablate 時(shí)間: 2025-3-22 22:58
K.-H. Hanne,U. Schmidt,K.-P. F?hnrichof the contrastive divergence pre-training is analyzed on the accuracy of the trained networks. The results are promising for decision support in the production process to minimize the influence of subjectivity by human evaluators.作者: 蕨類 時(shí)間: 2025-3-23 04:20 作者: mastoid-bone 時(shí)間: 2025-3-23 08:00
,Bau und Einrichtung von Krüppelheimen,ke. In the following work, an analysis of the detection of DDoS Backscatter with the use of neural networks is performed. To this end, a novel dataset is collected and described, on which a hyperband-optimized neural network is trained, and the decision process of the classifier is explained using LIME and SHAP.作者: 內(nèi)閣 時(shí)間: 2025-3-23 10:22
K. Biesalski,H. Eckhardt,K. Wickelearch experiment we have examined also other latest algorithms to select the best configuration of proposed model. Results show that our proposed BiLSTM deep learning neural network archived over 99% of accuracy.作者: 消毒 時(shí)間: 2025-3-23 15:21 作者: 神圣在玷污 時(shí)間: 2025-3-23 19:20 作者: 厚顏 時(shí)間: 2025-3-24 00:38 作者: 暗諷 時(shí)間: 2025-3-24 04:55 作者: 芭蕾舞女演員 時(shí)間: 2025-3-24 07:38
Training Subjective Perception Biased Images of?Vehicle Ambient Lights with?Deep Belief Networks Usiof the contrastive divergence pre-training is analyzed on the accuracy of the trained networks. The results are promising for decision support in the production process to minimize the influence of subjectivity by human evaluators.作者: Hay-Fever 時(shí)間: 2025-3-24 14:27 作者: 時(shí)代 時(shí)間: 2025-3-24 15:38
Analysis and?Detection of?DDoS Backscatter Using NetFlow Data, Hyperband-Optimised Deep Learning andke. In the following work, an analysis of the detection of DDoS Backscatter with the use of neural networks is performed. To this end, a novel dataset is collected and described, on which a hyperband-optimized neural network is trained, and the decision process of the classifier is explained using LIME and SHAP.作者: 濕潤 時(shí)間: 2025-3-24 20:50
BiLSTM Deep Learning Model for?Heart Problems Detectionearch experiment we have examined also other latest algorithms to select the best configuration of proposed model. Results show that our proposed BiLSTM deep learning neural network archived over 99% of accuracy.作者: 沐浴 時(shí)間: 2025-3-25 00:01 作者: 北京人起源 時(shí)間: 2025-3-25 06:26
Multi-objective Bayesian Optimization for?Neural Architecture Searchod is applied to combine classification accuracy with network size on two benchmark datasets here. The results indicate that MO-BayONet is able to outperform an available genetic algorithm based approach.作者: overwrought 時(shí)間: 2025-3-25 07:57
Multilayer Perceptrons with?Banach-Like Perceptrons Based on?Semi-inner Products – About Approximatiroducts are related either to uniformly convex or to reflexive Banach-spaces. Most famous examples of uniformly convex Banach spaces are the spaces . and . for .. The result is valid for all discriminatory activation functions including the sigmoid and the . activation.作者: 調(diào)整校對 時(shí)間: 2025-3-25 12:36 作者: Sinus-Node 時(shí)間: 2025-3-25 18:15 作者: Misgiving 時(shí)間: 2025-3-25 22:29
A Fast Learning Algorithm for?the?Multi-layer Neural Networks boosts the algorithm significantly due to the elimination of the computation of the square root. In a classic variant scaled rotations utilize so-called scale factors — .. It turns out that the scale factors can be omitted during the computation which boosts the overall algorithm performance even 作者: languor 時(shí)間: 2025-3-26 04:02
A New Computational Approach to?the?Levenberg-Marquardt Learning Algorithmns to effectively reduce the high computational load of this algorithm. Detailed parallel neural network computations are explicitly discussed. Additionally obtained acceleration is shown based on a few test problems.作者: entice 時(shí)間: 2025-3-26 05:05 作者: 向宇宙 時(shí)間: 2025-3-26 10:52
An Empirical Study of?Adversarial Domain Adaptation on?Time Series Datan various time series domains. Although several domain-adversarial models have been proposed in the past, there is a lack of empirical results with different types of time series. This paper provides an empirical analysis with multiple models, datasets and evaluation objectives. Two models known fro作者: Communicate 時(shí)間: 2025-3-26 14:50
Human-AI Collaboration to?Increase the?Perception of?VRsic functionality for immersing yourself in a virtual environment. In this paper, we propose a human-AI collaboration for analyzing the newly generated images that can be used for creating worlds. The presented method is based on analyzing different scenes (from simulation and real environment) usin作者: 高度表 時(shí)間: 2025-3-26 18:24
Portfolio Transformer for?Attention-Based Asset Allocationeights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The . (PT) network, introduced here, circumvents the need to predict asset returns and instead directly optimizes the Sharpe ratio, a risk-adjusted p作者: 認(rèn)識(shí) 時(shí)間: 2025-3-26 21:00
Transfer Learning with?Deep Neural Embeddings for?Music Classification Tasksssification tasks with several datasets. The tasks include genre recognition, speech/music distinguishing, predominant instrument recognition and performer identification. We propose the usage of pre-trained . neural networks for feature extraction and apply several supervised classification algorit作者: Adrenaline 時(shí)間: 2025-3-27 03:01 作者: 獨(dú)白 時(shí)間: 2025-3-27 06:41
BiLSTM Deep Learning Model for?Heart Problems Detectionare many types of deep learning models, however the most important to fit architecture and training model to the input data. In this article we propose a model of deep learning based on architecture in which we use BiLSTM neural network. Proposed model is trained by using Adam algorithm. For the res作者: Myocarditis 時(shí)間: 2025-3-27 11:21 作者: 整潔 時(shí)間: 2025-3-27 16:24 作者: 兩種語言 時(shí)間: 2025-3-27 17:58 作者: 上釉彩 時(shí)間: 2025-3-27 23:53 作者: 財(cái)主 時(shí)間: 2025-3-28 02:23 作者: entrance 時(shí)間: 2025-3-28 10:16
An Application of?Information Granules to?Detect Anomalies in?COVID-19 Reportsschool education have changed. A significant part of worldwide business has migrated to the virtual world, and the global supply chains have been disrupted. On the other hand, this new situation created opportunities for a much faster development of some areas of business and science. For example, t作者: amplitude 時(shí)間: 2025-3-28 14:12
Type-2 Fuzzy Classifier with?Smooth Type-Reduction type-1 set. All accurate type reduction methods used to build fuzzy classifiers are based on the recursive Karnik-Mendel algorithm, which is troublesome to obtain a feedforward type-2 fuzzy network structure. Moreover, the KM algorithm and its modifications complicate the learning process due to th作者: 使入迷 時(shí)間: 2025-3-28 17:43 作者: 公共汽車 時(shí)間: 2025-3-28 22:19 作者: COMMA 時(shí)間: 2025-3-29 02:30 作者: nonchalance 時(shí)間: 2025-3-29 03:37
K.-H. Hanne,U. Schmidt,K.-P. F?hnrichn various time series domains. Although several domain-adversarial models have been proposed in the past, there is a lack of empirical results with different types of time series. This paper provides an empirical analysis with multiple models, datasets and evaluation objectives. Two models known fro作者: lanugo 時(shí)間: 2025-3-29 08:20 作者: 用手捏 時(shí)間: 2025-3-29 12:57
https://doi.org/10.1007/978-3-642-77659-5eights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The . (PT) network, introduced here, circumvents the need to predict asset returns and instead directly optimizes the Sharpe ratio, a risk-adjusted p作者: 曲解 時(shí)間: 2025-3-29 19:08 作者: 溺愛 時(shí)間: 2025-3-29 20:42
,Bau und Einrichtung von Krüppelheimen, network resource with the intent to obstruct the utility of a service is associated with hacktivism, blackmailing and extortion attempts. Intrusion Prevention Systems are an essential line of defence against this problem, strengthening public institutions, industrial and critical infrastructure ali作者: 不合 時(shí)間: 2025-3-30 00:46
K. Biesalski,H. Eckhardt,K. Wickelare many types of deep learning models, however the most important to fit architecture and training model to the input data. In this article we propose a model of deep learning based on architecture in which we use BiLSTM neural network. Proposed model is trained by using Adam algorithm. For the res作者: Proponent 時(shí)間: 2025-3-30 07:52 作者: 經(jīng)典 時(shí)間: 2025-3-30 10:38 作者: BADGE 時(shí)間: 2025-3-30 14:54
Konrad Biesalski,Hellmut Eckhardte (ARIMA), Support vector machines (SVM), hybrid ARIMA-SVM, and Long short-term memory (LSTM). Depending on the different types of data and the period, various models are used for prediction. A single model may be the best fit in the short term but may not be the best in long-term series data. Thus,作者: BUMP 時(shí)間: 2025-3-30 19:15 作者: Phenothiazines 時(shí)間: 2025-3-30 22:54
Fachkr?ftemangel im Pflegesektor of interest, if the dissimilarity measure between data is given by a general norm such that the Euclidean inner product is not longer consistent to that situation. We prove mathematically that the universal approximation completeness is guaranteed also for those networks where the used semi-inner p作者: Buttress 時(shí)間: 2025-3-31 03:24 作者: GRE 時(shí)間: 2025-3-31 07:56 作者: Hdl348 時(shí)間: 2025-3-31 10:42
Die Berechnung des GegenstandswertesIn this paper we introduce a new Knowledge Representation model, the Similarity Fuzzy Semantic Networks. It is an extension of Fuzzy Semantic Networks that incorporates . through a Similarity Inference Rule. Moreover, we show as it can be effectively applied to a trending and complex problem like the analysis of radical discourse in Twitter.作者: compel 時(shí)間: 2025-3-31 16:33
Similarity Fuzzy Semantic Networks and?Inference. An?Application to?Analysis of?Radical Discourse inIn this paper we introduce a new Knowledge Representation model, the Similarity Fuzzy Semantic Networks. It is an extension of Fuzzy Semantic Networks that incorporates . through a Similarity Inference Rule. Moreover, we show as it can be effectively applied to a trending and complex problem like the analysis of radical discourse in Twitter.作者: 震驚 時(shí)間: 2025-3-31 19:42
Artificial Intelligence and Soft Computing978-3-031-23492-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 帳單 時(shí)間: 2025-3-31 23:45
