標(biāo)題: Titlebook: Network Data Analytics; A Hands-On Approach K. G. Srinivasa,Siddesh G. M.,Srinidhi H. Book 2018 Springer International Publishing AG 2018 [打印本頁] 作者: Mosquito 時間: 2025-3-21 18:05
書目名稱Network Data Analytics影響因子(影響力)
書目名稱Network Data Analytics影響因子(影響力)學(xué)科排名
書目名稱Network Data Analytics網(wǎng)絡(luò)公開度
書目名稱Network Data Analytics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Network Data Analytics被引頻次
書目名稱Network Data Analytics被引頻次學(xué)科排名
書目名稱Network Data Analytics年度引用
書目名稱Network Data Analytics年度引用學(xué)科排名
書目名稱Network Data Analytics讀者反饋
書目名稱Network Data Analytics讀者反饋學(xué)科排名
作者: Iatrogenic 時間: 2025-3-22 00:00 作者: 同步信息 時間: 2025-3-22 02:05 作者: 宣稱 時間: 2025-3-22 07:28
Basics of Machine LearningIn this chapter, the basics of machine learning are introduced with its key terminologies and its tasks. The different types of tasks that are involved in machine learning are data acquisition, data cleaning, data modeling, and data visualization. These tasks are discussed in this chapter with steps on getting started with machine learning.作者: 原始 時間: 2025-3-22 11:15
Other Analytical Techniquesroviding the suitable offers for the customers. Random forest is a classification technique that builds a number of decision trees based on the decision points in the classification. In this chapter, these analytical techniques are discussed with examples.作者: GUILT 時間: 2025-3-22 16:11 作者: 增減字母法 時間: 2025-3-22 20:04 作者: 柳樹；枯黃 時間: 2025-3-22 22:06 作者: 驚惶 時間: 2025-3-23 05:27 作者: 平常 時間: 2025-3-23 08:03
Apache Hiver processing the data. In this chapter, Hive and its architectural components are discussed first. Later, the chapter is followed with different kinds of operations that can be executed in Hive and examples on it. The chapter concludes with the network log and call log case studies with Hive.作者: disciplined 時間: 2025-3-23 10:59
Apache Sparkg a cache mechanism for analytics in a distributed manner. In this chapter, Apache Spark is discussed with its architectural elements and examples. The core elements of the Spark, text search, and retail analytics examples are discussed with Spark in this chapter.作者: BLANC 時間: 2025-3-23 16:52
Apache Flumem that provides a platform for real-time data analytics. In this chapter, an overview of Apache Flume and its architectural components with workflow is discussed. Later, the configuration of Flume with Twitter social network is discussed as an example for real-time analytics.作者: 整潔漂亮 時間: 2025-3-23 19:54
Stormlysis of real-time data, Apache Storm needs to be used. In this chapter, Apache Storm is discussed with its architectural elements and examples. The configuration of Apache Storm with Twitter networking site is discussed as an example of collection and analysis of hashtags.作者: Deference 時間: 2025-3-23 23:38 作者: pulmonary 時間: 2025-3-24 02:40 作者: 暴發(fā)戶 時間: 2025-3-24 06:36 作者: 優(yōu)雅 時間: 2025-3-24 12:47 作者: HAVOC 時間: 2025-3-24 17:31 作者: ACE-inhibitor 時間: 2025-3-24 22:32
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.g the energy bills payable by the householders and their consequences on household energy consumption and carbon emissions. Lastly, an ‘integrated’ scenario combines the assumptions in the first three scenarios and then analyses their effects on household energy consumption and carbon emissions. The作者: 整體 時間: 2025-3-24 23:28
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.g the energy bills payable by the householders and their consequences on household energy consumption and carbon emissions. Lastly, an ‘integrated’ scenario combines the assumptions in the first three scenarios and then analyses their effects on household energy consumption and carbon emissions. The作者: Urgency 時間: 2025-3-25 05:54
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.iables. It accounts for the question which knowledge states have to be expected when specified analysis-based learning mechanisms are applied to given instructional information. The Sepia model shows which and how qualitative physics knowledge facilitates quantitative physics problem solving. Sepia 作者: PANG 時間: 2025-3-25 10:17
lerance for free dimensions and mating dimensions. Finally, it explains the classification of materials, material mechanical properties, definitions of mechanical failures, typical failure modes, and typical failure theories for ductile and brittle materials. Therefore, this chapter is a concise sum作者: licence 時間: 2025-3-25 15:35 作者: Meager 時間: 2025-3-25 18:10 作者: 蝕刻術(shù) 時間: 2025-3-25 21:51 作者: 卷發(fā) 時間: 2025-3-26 03:19
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.nteraction by the acquisition of usage patterns. Thus, the three Sects.?., ., and . will present the state-of-the-art which is related to Chap.?.. Section . will describe existing approaches for describing interactions between human users and interactive dialogue systems. Existing approaches for the作者: 澄清 時間: 2025-3-26 05:45
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.e of low-fidelity modeling, including the low-fidelity model setup and finding a proper balance between the model speed and accuracy. More detailed exposition of various response correction methods and their applications for solving computationally expensive design problems in various engineering fi作者: Adulate 時間: 2025-3-26 10:48
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.on using response correction techniques can be enhanced with adjoint sensitivities. In particular, we start by discussing how to incorporate derivative data into the surrogate modeling and optimization process. Then, we provide the formulations for adjoint-enhanced versions of space mapping (Chap. .作者: 猛擊 時間: 2025-3-26 13:35
ty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA that can efficiently create multiple solution alternatives to problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in th作者: insecticide 時間: 2025-3-26 19:21
Introduction to Data Analyticse analytical architecture is first discussed. It is later followed by the different phases that are involved in the lifecycle of the data analytics project and the interconnection of Big data and Hadoop ecosystem.作者: Manifest 時間: 2025-3-26 22:37
Text Analyticsodels with basic examples were discussed. In this chapter, the different stages of text analytics are first discussed and later followed by the case studies. The case studies that are discussed as a part of this chapter are automatic summarization, spam classification, question classification, and s作者: 深淵 時間: 2025-3-27 03:18 作者: Pigeon 時間: 2025-3-27 07:21 作者: 極肥胖 時間: 2025-3-27 10:33
1617-7975 for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics..978-3-030-08544-5978-3-319-77800-6Series ISSN 1617-7975 Series E-ISSN 2197-8433 作者: figure 時間: 2025-3-27 13:57 作者: jagged 時間: 2025-3-27 21:41 作者: 啤酒 時間: 2025-3-28 00:41 作者: dictator 時間: 2025-3-28 02:16
978-3-030-08544-5Springer International Publishing AG 2018作者: Indebted 時間: 2025-3-28 10:04
Network Data Analytics978-3-319-77800-6Series ISSN 1617-7975 Series E-ISSN 2197-8433 作者: Encoding 時間: 2025-3-28 12:20
K. G. Srinivasa,Siddesh G. M.,Srinidhi H.Introduces tools for data analytics, machine learning for data analytics, and for exploring and visualizing data.Suitable as both a practical guide and a reference for researchers and students.Provide作者: 狂熱文化 時間: 2025-3-28 16:03 作者: 神圣將軍 時間: 2025-3-28 20:57
https://doi.org/10.1007/978-3-319-77800-6Hadoop; Data Analytics; Data Visualization; High Performance Computing; Machine Learning Algorithms作者: Salivary-Gland 時間: 2025-3-29 02:27
Introduction to Data Analyticselligence, computer security, Web technology, and Big data analytics. Data analytics refer to broad term where many of the areas such as cyber-physical systems (CPS), Internet of Things (IoT), Big data, machine learning and data mining overlap among each other. However, there are subtle differences 作者: extinguish 時間: 2025-3-29 04:44 作者: 眼界 時間: 2025-3-29 07:56 作者: 含沙射影 時間: 2025-3-29 14:31 作者: Acetaldehyde 時間: 2025-3-29 16:38 作者: Eviction 時間: 2025-3-29 20:13 作者: 我不重要 時間: 2025-3-30 00:34 作者: allergy 時間: 2025-3-30 05:58 作者: 陰謀小團體 時間: 2025-3-30 08:13
Regressionnce of a variable on the other set of variables in the dataset. It involves two types of variables namely dependent and independent variables. Regression analysis is used for the applications like forecasting performance in cars, predicting the profit in stocks and others. In this chapter, an overvi作者: Colonnade 時間: 2025-3-30 14:18
Classificationd learning technique, wherein a classifier assigns label or class to a set of data points based on the data that are classified with labels. The process of classification starts with a training set of labeled data and assigned classes and these are used to predict the class of unknown data points or作者: ABOUT 時間: 2025-3-30 18:41 作者: conjunctivitis 時間: 2025-3-30 23:51 作者: Pruritus 時間: 2025-3-31 04:43 作者: Atrium 時間: 2025-3-31 08:18
Advanced Analytics with TensorFlowced analytical applications and machine learning-based classification models cannot be used. Advanced classification models include neural networks as the basis for analytics. Neural networks consist of input, hidden layers, and the output. TensorFlow is one of the analytical tools that help in deve作者: Irritate 時間: 2025-3-31 10:34
