標(biāo)題: Titlebook: Deep Learning and Missing Data in Engineering Systems; Collins Achepsah Leke,Tshilidzi Marwala Book 2019 Springer Nature Switzerland AG 20 [打印本頁(yè)] 作者: negation 時(shí)間: 2025-3-21 17:19
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems影響因子(影響力)
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems被引頻次
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems被引頻次學(xué)科排名
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems年度引用
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems年度引用學(xué)科排名
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems讀者反饋
書(shū)目名稱(chēng)Deep Learning and Missing Data in Engineering Systems讀者反饋學(xué)科排名
作者: GNAT 時(shí)間: 2025-3-21 23:46 作者: 發(fā)源 時(shí)間: 2025-3-22 02:34
Missing Data Estimation Using Invasive Weed Optimization Algorithm,itute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number of rows. We propose a framework for the imputation procedure that uses a deep learning method with a swarm intelligence algorithm called deep learning-invasive weed optimization (DL-IWO) approach.作者: 浮夸 時(shí)間: 2025-3-22 07:09
Missing Data Estimation Using Swarm Intelligence Algorithms from Reduced Dimensions,ained from the bottleneck layer of the deep autoencoder network; in this case, the number of reduced features is 30. The aim is to observe whether this approach preserves accuracy while minimizing execution time.作者: Muffle 時(shí)間: 2025-3-22 10:59 作者: 運(yùn)氣 時(shí)間: 2025-3-22 16:43 作者: 運(yùn)氣 時(shí)間: 2025-3-22 20:20 作者: Outshine 時(shí)間: 2025-3-22 23:46 作者: 無(wú)脊椎 時(shí)間: 2025-3-23 05:13
Missing Data Estimation Using Firefly Algorithm,izing an error function based on the interrelationship and correlation between features in the dataset. The proposed methodology in this chapter, therefore, has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.作者: molest 時(shí)間: 2025-3-23 07:00
Book 2019ing systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including:.deep autoencoder neural networks;.deep denoising autoenco作者: 思鄉(xiāng)病 時(shí)間: 2025-3-23 13:31
2197-6503 es new paradigms of machine learning and computational intel.Deep Learning and Missing Data in Engineering Systems. uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in 作者: 亞麻制品 時(shí)間: 2025-3-23 15:02
Networking Humans and Non-Humansitute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number of rows. We propose a framework for the imputation procedure that uses a deep learning method with a swarm intelligence algorithm called deep learning-invasive weed optimization (DL-IWO) approach.作者: 不溶解 時(shí)間: 2025-3-23 20:02
Networking Individuals and Groupsained from the bottleneck layer of the deep autoencoder network; in this case, the number of reduced features is 30. The aim is to observe whether this approach preserves accuracy while minimizing execution time.作者: POINT 時(shí)間: 2025-3-24 00:42
Engineering for Children Curriculumization algorithm and deep learning with cuckoo search algorithm, to name a few. Also presented in this book are experiments that show the impact of using lower dimensions and different numbers of hidden layers in the deep autoencoder networks.作者: Basilar-Artery 時(shí)間: 2025-3-24 05:31 作者: Pastry 時(shí)間: 2025-3-24 08:13
https://doi.org/10.1007/978-3-030-00317-3ates for the missing data entries surpasses that of existing methods, but this is considered a worthy bargain when the accuracy of the said estimates in a high-dimensional setting is taken into consideration.作者: myelography 時(shí)間: 2025-3-24 12:51 作者: Inordinate 時(shí)間: 2025-3-24 18:41
Missing Data Estimation Using Cuckoo Search Algorithm,ates for the missing data entries surpasses that of existing methods, but this is considered a worthy bargain when the accuracy of the said estimates in a high-dimensional setting is taken into consideration.作者: 兩棲動(dòng)物 時(shí)間: 2025-3-24 22:06 作者: exhibit 時(shí)間: 2025-3-25 01:52 作者: 柏樹(shù) 時(shí)間: 2025-3-25 04:10
Deep Learning and Missing Data in Engineering Systems作者: 純樸 時(shí)間: 2025-3-25 09:52
Deep Learning and Missing Data in Engineering Systems978-3-030-01180-2Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: landfill 時(shí)間: 2025-3-25 14:49 作者: hair-bulb 時(shí)間: 2025-3-25 17:21 作者: 障礙物 時(shí)間: 2025-3-25 20:49
Networking Individuals and Groupsn combination with optimization algorithms to perform missing data estimation tasks. The results from these networks will be compared against those obtained from using the seven hidden-layered deep autoencoder network from the literature. The network training times are observed to increase with the increasing number of hidden layers.作者: Engaging 時(shí)間: 2025-3-26 01:55
https://doi.org/10.1007/978-3-030-01180-2Artificial Intelligence; Missing Data Estimation; Deep Learning; Swarm Intelligence; Machine Learning; Mo作者: 反對(duì) 時(shí)間: 2025-3-26 04:49
Springer Nature Switzerland AG 2019作者: 尾隨 時(shí)間: 2025-3-26 12:09
Introduction to Missing Data Estimation,y a discussion of the classical missing data techniques ensued by a presentation of machine learning approaches to address the missing data problem. Subsequently, machine learning optimization techniques are presented for missing data estimation tasks.作者: exquisite 時(shí)間: 2025-3-26 13:29 作者: PHONE 時(shí)間: 2025-3-26 18:12
Deep Learning Framework Analysis,n combination with optimization algorithms to perform missing data estimation tasks. The results from these networks will be compared against those obtained from using the seven hidden-layered deep autoencoder network from the literature. The network training times are observed to increase with the increasing number of hidden layers.作者: 小蟲(chóng) 時(shí)間: 2025-3-26 22:01 作者: 勤勉 時(shí)間: 2025-3-27 03:03
Studies in Big Datahttp://image.papertrans.cn/d/image/264595.jpg作者: ORE 時(shí)間: 2025-3-27 06:18
Industrial Process Emission Policiesy a discussion of the classical missing data techniques ensued by a presentation of machine learning approaches to address the missing data problem. Subsequently, machine learning optimization techniques are presented for missing data estimation tasks.作者: Microaneurysm 時(shí)間: 2025-3-27 09:28 作者: 要素 時(shí)間: 2025-3-27 14:47 作者: 模仿 時(shí)間: 2025-3-27 18:29
https://doi.org/10.1007/978-3-030-00317-3wing number of studies in the deep learning area warrants a closer look at its possible application in the domain. Missing data being an unavoidable scenario in present-day datasets results in different challenges, which are nontrivial for existing techniques that constitute narrow artificial intell作者: insincerity 時(shí)間: 2025-3-27 23:02 作者: FAST 時(shí)間: 2025-3-28 05:50 作者: growth-factor 時(shí)間: 2025-3-28 08:34 作者: upstart 時(shí)間: 2025-3-28 11:52
Networking Humans and Non-Humansing data is a recurrent issue in day-to-day datasets, resulting in a variety of setbacks which are often difficult for existing techniques which constitute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number作者: GIST 時(shí)間: 2025-3-28 18:17 作者: 無(wú)辜 時(shí)間: 2025-3-28 22:30 作者: NATTY 時(shí)間: 2025-3-29 02:29 作者: 情節(jié)劇 時(shí)間: 2025-3-29 05:57 作者: 放棄 時(shí)間: 2025-3-29 10:13 作者: GLADE 時(shí)間: 2025-3-29 12:56
