標(biāo)題: Titlebook: Development and Analysis of Deep Learning Architectures; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Compu [打印本頁] 作者: formation 時間: 2025-3-21 17:47
書目名稱Development and Analysis of Deep Learning Architectures影響因子(影響力)
書目名稱Development and Analysis of Deep Learning Architectures影響因子(影響力)學(xué)科排名
書目名稱Development and Analysis of Deep Learning Architectures網(wǎng)絡(luò)公開度
書目名稱Development and Analysis of Deep Learning Architectures網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Development and Analysis of Deep Learning Architectures被引頻次
書目名稱Development and Analysis of Deep Learning Architectures被引頻次學(xué)科排名
書目名稱Development and Analysis of Deep Learning Architectures年度引用
書目名稱Development and Analysis of Deep Learning Architectures年度引用學(xué)科排名
書目名稱Development and Analysis of Deep Learning Architectures讀者反饋
書目名稱Development and Analysis of Deep Learning Architectures讀者反饋學(xué)科排名
作者: 貧窮地活 時間: 2025-3-21 21:08 作者: 動作謎 時間: 2025-3-22 03:34 作者: mediocrity 時間: 2025-3-22 06:46
Gehetzte Erben, hektische Epigonenecent years. Domain adaptation or transfer learning algorithms alleviate this challenge by transferring relevant knowledge from a source domain to induce a model for a related target domain, where labeled data are scarce. Further, deep learning algorithms are instrumental in learning informative fea作者: 無法破譯 時間: 2025-3-22 11:18
Joan Kristin Bleicher,Bernhard P?rksenecially on the perception of the environment by camera, Radar, and Lidar sensors and fusion concepts. Camera-based perception includes the detection of road users. Highest detection performance is especially required for detecting vulnerable road users such as pedestrians and bicycle drivers. Here, 作者: 小隔間 時間: 2025-3-22 14:45 作者: 小隔間 時間: 2025-3-22 17:26
Zusammenfassung des Analytischen Rahmens signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network?(CNN) architectures for baby cry detection, and compare thei作者: 太空 時間: 2025-3-22 22:49
https://doi.org/10.1007/978-3-031-35096-2rms of attacks are constantly emerging to exploit vulnerabilities in system compromising the security parameters such as Confidentiality, Integrity and Availability (CIA). Injection attacks also termed as False Data Injection Attacks (FDIA) are the complex attacks on the ICS. FDIA affects the data i作者: Observe 時間: 2025-3-23 03:20
Migration im Kontext des EU-Grenzmanagementsg wireless applications with high degrees of freedom. Deep learning?has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems?in utilizing limited spectrum resources. In this chapter, we first describe how deep learning?is used to des作者: amnesia 時間: 2025-3-23 07:02
https://doi.org/10.1007/978-3-031-35096-2 spread propaganda, communicate and organize has increased. However, techniques to effectively identify such material are lacking. This chapter explores an approach which can classify any piece of text as belonging to one of four extremist groups: Sunni Islamic, Antifascist Groups, White Nationalist作者: 聯(lián)邦 時間: 2025-3-23 12:20 作者: 新星 時間: 2025-3-23 16:37
978-3-030-31766-9Springer Nature Switzerland AG 2020作者: 鎮(zhèn)痛劑 時間: 2025-3-23 20:14 作者: BRIBE 時間: 2025-3-23 22:35 作者: Certainty 時間: 2025-3-24 04:43
Development and Analysis of Deep Learning Architectures978-3-030-31764-5Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 上下連貫 時間: 2025-3-24 09:07 作者: Parley 時間: 2025-3-24 14:31
Case Study: Deep Convolutional Networks in Healthcare,modified in accordance with the requirements of the problems. Through this chapter the most popular and up-to-date deep learning solutions to biomedical problems are discussed. Studies are analyzed according to problem characteristic, importance of solution, requirements and deep learning approaches作者: 通情達(dá)理 時間: 2025-3-24 15:05
Deep Domain Adaptation for Regression,ssion applications using deep neural networks has not been explored in the literature. Our extensive empirical studies on two popular regression applications (age estimation and head pose estimation from images) depict the merit of our framework over competing baselines.作者: 無底 時間: 2025-3-24 19:06
Deep Learning-Based Pedestrian Detection for Automated Driving: Achievements and Future Challenges, detection performance reaches human performance? As false detections can lead to hazardous situations in traffic scenarios, the expectations on the performance of artificial intelligence for advanced driver assistance systems and automated driving often go beyond human performance. Challenges are p作者: deceive 時間: 2025-3-24 23:19 作者: Graves’-disease 時間: 2025-3-25 03:25
Deep Learning for Wireless Communications,ance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning?provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning?in pro作者: 美色花錢 時間: 2025-3-25 10:22
Tomer Gardi und sein (un)bekannter Onkel,ly removed in each group (transformation stage). The two-stage NDR procedure is repeated until a user-defined criterion controlling information loss is satisfied. The reduced dimensional data is finally used for classification with a DNN-based framework where direct error-driven learning regime?is i作者: 該得 時間: 2025-3-25 15:28 作者: 逃避責(zé)任 時間: 2025-3-25 19:28 作者: 思考才皺眉 時間: 2025-3-25 21:41 作者: Psychogenic 時間: 2025-3-26 01:11 作者: MAUVE 時間: 2025-3-26 04:20
Migration im Kontext des EU-Grenzmanagementsance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning?provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning?in pro作者: badinage 時間: 2025-3-26 08:36
Development and Analysis of Deep Learning Architectures作者: Acetabulum 時間: 2025-3-26 12:38 作者: 斗志 時間: 2025-3-26 20:08 作者: construct 時間: 2025-3-26 22:10 作者: corpus-callosum 時間: 2025-3-27 03:41 作者: vascular 時間: 2025-3-27 06:52 作者: 手榴彈 時間: 2025-3-27 12:37
Book 2020 heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical und作者: AMITY 時間: 2025-3-27 15:03 作者: Insul島 時間: 2025-3-27 18:39
Zusammenfassung des Analytischen RahmensNNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized non-symmetric kernels, better results are obtained compared to common CNN architectures.作者: companion 時間: 2025-3-27 22:10
https://doi.org/10.1007/978-3-031-35096-2aches. This chapter will describe the performance of various models in detail. The process of creating good quality datasets for each extremist category and the unique challenges such a task presents will also be explored.作者: 路標(biāo) 時間: 2025-3-28 04:46 作者: optic-nerve 時間: 2025-3-28 06:23
,Baby Cry Detection: Deep Learning and?Classical Approaches,NNs, we analyze the performance of recurrent neural network (RNN) architectures, which are able to capture temporal behavior of acoustic events. We show that by carefully designing CNN architectures with specialized non-symmetric kernels, better results are obtained compared to common CNN architectures.作者: 大洪水 時間: 2025-3-28 13:29
Identifying Extremism in Text Using Deep Learning,aches. This chapter will describe the performance of various models in detail. The process of creating good quality datasets for each extremist category and the unique challenges such a task presents will also be explored.作者: interference 時間: 2025-3-28 16:53 作者: 無脊椎 時間: 2025-3-28 19:52
Deep Learning for Soft Sensor Design, the process industry. Many Soft Sensors are designed by using data-driven approaches and exploiting historical databases. Machine learning is widely used for this aim. Here, the potentialities of deep learning in solving some challenges raising in industrial applications are introduced. More specif作者: 拋物線 時間: 2025-3-28 23:56
Case Study: Deep Convolutional Networks in Healthcare,complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis me作者: 圖表證明 時間: 2025-3-29 04:37
Deep Domain Adaptation for Regression,ecent years. Domain adaptation or transfer learning algorithms alleviate this challenge by transferring relevant knowledge from a source domain to induce a model for a related target domain, where labeled data are scarce. Further, deep learning algorithms are instrumental in learning informative fea作者: Abjure 時間: 2025-3-29 10:36 作者: 彩色 時間: 2025-3-29 12:39 作者: chance 時間: 2025-3-29 17:49
,Baby Cry Detection: Deep Learning and?Classical Approaches, signal-to-noise ratio conditions. Automatic cry detection has applications in commercial products (such as baby remote monitors) as well as in medical and psycho-social research. We design and evaluate several convolutional neural network?(CNN) architectures for baby cry detection, and compare thei作者: 止痛藥 時間: 2025-3-29 22:02
Securing Industrial Control Systems from False Data Injection Attacks with Convolutional Neural Netrms of attacks are constantly emerging to exploit vulnerabilities in system compromising the security parameters such as Confidentiality, Integrity and Availability (CIA). Injection attacks also termed as False Data Injection Attacks (FDIA) are the complex attacks on the ICS. FDIA affects the data i
