標(biāo)題: Titlebook: Big Data and Visual Analytics; Sang C. Suh,Thomas Anthony Conference proceedings 2017 Springer International Publishing AG 2017 Big data.B [打印本頁] 作者: 延展 時間: 2025-3-21 16:48
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書目名稱Big Data and Visual Analytics讀者反饋學(xué)科排名
作者: 與野獸博斗者 時間: 2025-3-21 21:22
Swarm Intelligence Applied to Big Data Analytics for Rescue Operations with RASEN Sensor Networks,d technology, cyber-physical system platforms, and algorithms exist which can coordinate rescue operations utilizing swarm intelligence with Rapid Alert Sensor for Enhanced Night Vision (RASEN). We will also introduce biologically inspired algorithms combined with proposed fusion night vision techno作者: enfeeble 時間: 2025-3-22 00:49 作者: 美食家 時間: 2025-3-22 07:12 作者: Blatant 時間: 2025-3-22 10:33
Scalable Gene Sequence Analysis on Spark,equences, and analysis of such large-scale sequencing data is the primary concern. This chapter introduces a scalable genome sequence analysis system, which makes use of parallel computing features of Apache Spark and its relational processing module called Spark Structured Query Language (Spark SQL作者: 結(jié)構(gòu) 時間: 2025-3-22 15:13
Big Sensor Data Acquisition and Archiving with Compression,tion of sensor data and the storage of massive sensor data collection. These data sources generate so much data quickly that data compression is essential to reduce storage requirement or transmission capacity of devices. This work first discusses a low complexity sensing framework which enables to 作者: Hiatus 時間: 2025-3-22 17:44 作者: 頂點 時間: 2025-3-22 23:09 作者: Mammal 時間: 2025-3-23 02:37
A Big Data Analytics Approach in Medical Imaging Segmentation Using Deep Convolutional Neural Netwoloping solutions from large datasets, and is an important tool in the big data analytics toolbox. Deep Learning has been recently employed to solve various problems in computer vision and demonstrated state-of-the-art performance on visual recognition tasks. In medical imaging, especially in brain t作者: 迫擊炮 時間: 2025-3-23 07:51
Big Data in Libraries,he three Vs): Volume, Velocity and Variety. Laney [1, 2] Each of these has different challenges. Huge volumes of data require the ability to store and retrieve the data efficiently. High velocity data requires the ability to ingest the data as it is created, essentially very fast internet connection作者: Malaise 時間: 2025-3-23 09:52 作者: CAMEO 時間: 2025-3-23 17:20
Study of Hardware Trojans in a Closed Loop Control System for an Internet-of-Things Application,ndability is challenged by probable vulnerabilities in the core computing system. These vulnerabilities can appear on both front (software) and back (hardware) ends of the computing system. While the software vulnerabilities are well researched and documented, the hardware ones are normally overlook作者: 膽汁 時間: 2025-3-23 20:47 作者: 大笑 時間: 2025-3-24 00:26 作者: Inelasticity 時間: 2025-3-24 02:59
Introduction to Evolutionary ComputingKorea’s compressed economic growth during its industrialization process was made possible by an abundance of high quality labor but since then a low birthrate and its rapidly aging society have caused a continued decrease that ultimately led to its current shortage.作者: 招惹 時間: 2025-3-24 08:23 作者: 繁榮中國 時間: 2025-3-24 12:58
Conference proceedings 2017ata and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics...Each chap作者: painkillers 時間: 2025-3-24 15:38
Interactive Evolutionary Algorithmsules: business decisions, Data—technology and analytics, user experience-operational excellence, quality dimensions and people—capabilities. As such, BDFAB exhibits an interdisciplinary approach to Big Data adoption in practice.作者: 枯燥 時間: 2025-3-24 20:05 作者: Audiometry 時間: 2025-3-24 23:09
What is an Evolutionary Algorithm?,ed. Next, a quality-adjustable sensor data archiving is discussed, which compresses an entire collection of sensor data efficiently without compromising key features. Considering data aging aspect, this archiving scheme is capable of decreasing data fidelity gradually to secure more storage space.作者: Insubordinate 時間: 2025-3-25 04:51 作者: 雪上輕舟飛過 時間: 2025-3-25 11:02
Scalable Gene Sequence Analysis on Spark,in memory. Experiments detailed in this chapter make use of publicly available 1000 genome Variant Calling Format (VCF) data (Size 1.2TB) as input. The input data are analyzed using Spark and the end results are evaluated to measure the scalability and performance of the system.作者: 利用 時間: 2025-3-25 12:48
Big Sensor Data Acquisition and Archiving with Compression,ed. Next, a quality-adjustable sensor data archiving is discussed, which compresses an entire collection of sensor data efficiently without compromising key features. Considering data aging aspect, this archiving scheme is capable of decreasing data fidelity gradually to secure more storage space.作者: entreat 時間: 2025-3-25 18:03
Conference proceedings 2017 and visual analytics...This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference..作者: 我說不重要 時間: 2025-3-25 21:24 作者: AER 時間: 2025-3-26 03:34 作者: CAB 時間: 2025-3-26 04:58
https://doi.org/10.1007/978-3-319-63917-8Big data; Big data applications; Big data architecture; Business analytics; Data mining and knowledge di作者: Antagonist 時間: 2025-3-26 12:24 作者: heterogeneous 時間: 2025-3-26 13:35
Introduction to Evolutionary GenomicsBig data Analytics and data science helps finance in combining business research expertise, scientific processes, quantitative analytics and system infrastructure to distill knowledge and insights from internal/external, structured/unstructured data.作者: 碎石 時間: 2025-3-26 18:45
Big Data Analytics and Visualization: Finance,Big data Analytics and data science helps finance in combining business research expertise, scientific processes, quantitative analytics and system infrastructure to distill knowledge and insights from internal/external, structured/unstructured data.作者: Senescent 時間: 2025-3-27 00:05 作者: installment 時間: 2025-3-27 02:23 作者: nominal 時間: 2025-3-27 06:43 作者: Ancestor 時間: 2025-3-27 11:54
https://doi.org/10.1007/978-1-84996-129-5d technology, cyber-physical system platforms, and algorithms exist which can coordinate rescue operations utilizing swarm intelligence with Rapid Alert Sensor for Enhanced Night Vision (RASEN). We will also introduce biologically inspired algorithms combined with proposed fusion night vision techno作者: faucet 時間: 2025-3-27 15:45 作者: 表示問 時間: 2025-3-27 20:06 作者: cardiovascular 時間: 2025-3-27 23:57 作者: 必死 時間: 2025-3-28 05:03
What is an Evolutionary Algorithm?,tion of sensor data and the storage of massive sensor data collection. These data sources generate so much data quickly that data compression is essential to reduce storage requirement or transmission capacity of devices. This work first discusses a low complexity sensing framework which enables to 作者: Sinus-Rhythm 時間: 2025-3-28 08:05
Introduction to Evolutionary Computingrding to the Behaviors Questionnaire performed in 2015 by K.D. Nuggets, around one fourth of 459 participants tried to interpret data clusters that exceed 1 terabyte and 100 petabytes. One of the subjects of previous studies is the Canonic Method, which is used to form the meanings of big data in a 作者: 甜食 時間: 2025-3-28 12:57 作者: Neonatal 時間: 2025-3-28 18:35
Genome and Transcriptome Sequencingloping solutions from large datasets, and is an important tool in the big data analytics toolbox. Deep Learning has been recently employed to solve various problems in computer vision and demonstrated state-of-the-art performance on visual recognition tasks. In medical imaging, especially in brain t作者: Annotate 時間: 2025-3-28 21:23 作者: enterprise 時間: 2025-3-28 23:35
Introduction to Evolutionary Genomicsthe last two decades with the proliferation of Web to the social media sites such as Twitter, Facebook, and Tumblr, social media are increasingly becoming the platform of choice for people to express their views or opinions. With an account of over two billion users, social media provides a major so作者: Trypsin 時間: 2025-3-29 07:03 作者: 逗它小傻瓜 時間: 2025-3-29 10:52 作者: glucagon 時間: 2025-3-29 14:15 作者: ingenue 時間: 2025-3-29 18:25 作者: Classify 時間: 2025-3-29 23:28 作者: Adornment 時間: 2025-3-30 00:06
Transdisciplinary Benefits of Convergence in Big Data Analytics,other key factors imposed by data velocity that limits immediate inferencing, data variability that limits data consistency, and data veracity that limits data quality. As these challenges are resolved in other disciplinary domains, the spillover benefits can be profound when transdisciplinary advan作者: Permanent 時間: 2025-3-30 04:07
A Big Data Analytics Approach in Medical Imaging Segmentation Using Deep Convolutional Neural Netwohens the feature propagation from layer to layer. We show how this arrangement increases the efficiency during training and the accuracy of the results. We have trained and evaluated our method based on the imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2017作者: 珊瑚 時間: 2025-3-30 08:23
A Framework for Social Network Sentiment Analysis Using Big Data Analytics,tous, the bottlenecks here are processing speed, storage, and time, which are involved with the traditional storage system. So to deal with the data processing of these massive amounts of data, some special tools and techniques are offered by Big Data framework.作者: 集聚成團(tuán) 時間: 2025-3-30 13:01
Study of Hardware Trojans in a Closed Loop Control System for an Internet-of-Things Application,em is represented as a digital hardware. Through this platform, a comprehensive runtime analysis of hardware Trojan is accomplished and we developed four threat models that can lead to destabilization of the closed-loop system causing hazardous conditions. Since the primary objective is to study the作者: happiness 時間: 2025-3-30 19:25
High Performance/Throughput Computing Workflow for a Neuro-Imaging Application: Provenance and Apprlly expensive) may further be required for some purposes, such as generating classifiers. We show that distributing the preprocessing of the images using a compute cluster and running multiple preprocessings in parallel can substantially reduced the time required for the images to be ready for quali作者: CRAFT 時間: 2025-3-30 23:10 作者: Vsd168 時間: 2025-3-31 03:46
https://doi.org/10.1007/978-1-84996-129-5e scout approach with dynamic programming ability enables appropriate relief supplies to be deployed intelligently by networked convoys to survivors continuously throughout the night, within critical constraints calculated in advance, such as projected time, cost, and reliability per mission. Rescue作者: Chandelier 時間: 2025-3-31 05:24
