標(biāo)題: Titlebook: Explainable AI Within the Digital Transformation and Cyber Physical Systems; XAI Methods and Appl Moamar Sayed-Mouchaweh Book 2021 The Edit [打印本頁] 作者: VER 時間: 2025-3-21 18:47
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書目名稱Explainable AI Within the Digital Transformation and Cyber Physical Systems讀者反饋
書目名稱Explainable AI Within the Digital Transformation and Cyber Physical Systems讀者反饋學(xué)科排名
作者: 結(jié)果 時間: 2025-3-21 23:21 作者: Abominate 時間: 2025-3-22 03:38
Science of Data: A New Ladder for Causation, first, we need to integrate reasoning into the machine. Recently, many researchers in artificial intelligence (AI) have been interested in implementing causality in the machine. Some researchers suggest inferential logic as the best way to implement causality in the machine. Other researchers such 作者: exigent 時間: 2025-3-22 04:57 作者: 眼界 時間: 2025-3-22 09:18 作者: Agronomy 時間: 2025-3-22 13:52 作者: Agronomy 時間: 2025-3-22 20:05
Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Stade in the development and administration of tests, the time required to obtain a confident response can be prohibitive in areas of dense outbreaks. For these reasons, there has been great interest in adopting machine learning solutions to detect COVID-19 in prospective patients and aid clinicians i作者: choleretic 時間: 2025-3-22 22:20
A Critical Study on the Importance of Feature Selection for Diagnosing Cyber-Attacks in Water Crition system (IDS). However, the performance and efficiency of IDS can be affected by a number of factors such as the quality of data, high dimensionality of the data, and computational cost. Feature selection techniques can eliminate most of these challenges by eliminating the redundant and non-inform作者: HARP 時間: 2025-3-23 02:43 作者: 藕床生厭倦 時間: 2025-3-23 09:31
n and cyber-physical systems.Provides a textual scientific dThis book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in maki作者: 富足女人 時間: 2025-3-23 10:04 作者: 未完成 時間: 2025-3-23 17:35 作者: 分發(fā) 時間: 2025-3-23 20:47 作者: 和音 時間: 2025-3-24 02:11 作者: 對手 時間: 2025-3-24 03:28
Wieslawa Kaczor,Ma?gorzata Koter-Mórgowskacan help design tailored and just-in-time interventions. We demonstrate that the prediction performance provided by the transparent models is comparable to that of the other tested classification algorithms. We summarize several ways in which the constructed models can be used during and after the data collection.作者: 王得到 時間: 2025-3-24 08:36 作者: 挫敗 時間: 2025-3-24 14:44 作者: 貴族 時間: 2025-3-24 16:16
Principles of Explainable Artificial Intelligence,ssing which are the principles of XAI. In addition, we illustrate different types of explanations returned by established explainers. Finally, we discuss their usability and how they can be exploited in real-world applications.作者: 老人病學(xué) 時間: 2025-3-24 21:27
Science of Data: A New Ladder for Causation,ent domains and techniques such as logic, deep learning algorithms, and cognitive architectures. Furthermore, we will discuss a theoretical framework that integrates causality in the machine which is a crucial step toward creating explainable AI.作者: airborne 時間: 2025-3-25 01:25 作者: Obituary 時間: 2025-3-25 06:01 作者: 形容詞 時間: 2025-3-25 10:33
A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage T reduction techniques are combined with IDS. The utilized IDS also improves efficiency by reducing the memory usage and using data with better quality, which in turn increases the detection accuracy. The results have been analyzed in terms of F1-score and accuracy w.r.t. two classifiers.作者: 肉身 時間: 2025-3-25 13:00
Book 2021ropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities etc.) with different levels of technical knowl作者: 拒絕 時間: 2025-3-25 17:44 作者: Negligible 時間: 2025-3-26 00:01
Book 2021healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefitsand requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions..Includ作者: 有其法作用 時間: 2025-3-26 03:18
Explainable AI Within the Digital Transformation and Cyber Physical SystemsXAI Methods and Appl作者: 搖晃 時間: 2025-3-26 06:52 作者: 一大群 時間: 2025-3-26 11:11
Adolescent Sex Offender Programs,d explanations. It summarizes the gathered research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization.作者: Coronary 時間: 2025-3-26 15:57
Alice Ordean MD, CCFP, MHSc, FCFPnerated summaries. Conducted experiments show that our approach’s scores are highly positively correlated to the state-of-the-art ROUGE ones. This research’s salient outcome is that it proposes a cognitive and explainable model of AGSE, and it pushes toward cognitive natural language processing. It 作者: Limpid 時間: 2025-3-26 17:31
Post-Field Data Quality Control,fference in scaled activations of the features in the last convolutional layer of the CNN for both classes. In doing so, we exploit the feature similarity between the two diseases for training purposes, thereby mitigating issues arising due to the scarcity of COVID-19 data. In models with overlappin作者: FUSE 時間: 2025-3-27 00:32
Prologue: Introduction to Explainable Artificial Intelligence,d explanations. It summarizes the gathered research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization.作者: 讓步 時間: 2025-3-27 01:52 作者: watertight, 時間: 2025-3-27 06:51
Mitigating the Class Overlap Problem in Discriminative Localization: COVID-19 and Pneumonia Case Stfference in scaled activations of the features in the last convolutional layer of the CNN for both classes. In doing so, we exploit the feature similarity between the two diseases for training purposes, thereby mitigating issues arising due to the scarcity of COVID-19 data. In models with overlappin作者: 不朽中國 時間: 2025-3-27 13:22 作者: 無目標(biāo) 時間: 2025-3-27 17:33
Moamar Sayed-MouchawehIncludes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems.Provides a textual scientific d作者: cardiac-arrest 時間: 2025-3-27 21:48
http://image.papertrans.cn/e/image/319279.jpg作者: 自制 時間: 2025-3-28 01:28
https://doi.org/10.1007/978-3-030-76409-8Explainable Artificial Intelligence (XAI); Digital Transformation and Cyber Physical Systems; XAI appl作者: adjacent 時間: 2025-3-28 02:49
Adolescent Sex Offender Programs,edictive maintenance, bioinformatics, e-commerce, healthcare, etc.), and challenges (accuracy/transparency trade-off, adapted to different levels of users, generalized to different application domains, etc.) of Explainable Artificial Intelligence (XAI) within the context of Digital Transformation an作者: expunge 時間: 2025-3-28 06:52 作者: 隨意 時間: 2025-3-28 14:03 作者: 障礙 時間: 2025-3-28 18:27
