標題: Titlebook: Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing; Simon James Fong,Richard C. Millham [打印本頁] 作者: 拿著錫 時間: 2025-3-21 17:54
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing影響因子(影響力)
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing影響因子(影響力)學科排名
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing網(wǎng)絡公開度
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing網(wǎng)絡公開度學科排名
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing被引頻次
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing被引頻次學科排名
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing年度引用
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing年度引用學科排名
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing讀者反饋
書目名稱Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing讀者反饋學科排名
作者: 闡明 時間: 2025-3-21 20:36
Data Visualization Techniques and Algorithms,o consideration the volume of data attributes involved. In this chapter, the behavior of animals is explored to help create a method and an algorithm for data visualization suited for big data visualization.作者: 吹牛大王 時間: 2025-3-22 00:35
Business Intelligence,epth, ethnographical view of a tiny aspect of this operation. Data modelling may be denoted as a method to determine and evaluate the data needs required to support the business processes rooted within the information systems of an organization.作者: VEIL 時間: 2025-3-22 04:40 作者: 合乎習俗 時間: 2025-3-22 10:02 作者: 令人不快 時間: 2025-3-22 15:12 作者: 磨碎 時間: 2025-3-22 17:27
Conclusion Action: The Final Synthesis,epth, ethnographical view of a tiny aspect of this operation. Data modelling may be denoted as a method to determine and evaluate the data needs required to support the business processes rooted within the information systems of an organization.作者: plasma-cells 時間: 2025-3-23 00:26 作者: sterilization 時間: 2025-3-23 03:12 作者: Airtight 時間: 2025-3-23 08:55 作者: 幾何學家 時間: 2025-3-23 10:00 作者: cataract 時間: 2025-3-23 14:14
https://doi.org/10.1007/978-3-663-12183-1hat is able to manage both the essential characteristics (velocity, variety, volume) and the quality of use characteristics (veracity, value) of big data. The “quality of use” dimensions of this data could be assessed through the use of a set of metrics along with expert knowledge that determines “high value” data.作者: 小蟲 時間: 2025-3-23 19:00 作者: 課程 時間: 2025-3-24 00:04
Comparison of Contemporary Meta-Heuristic Algorithms for Solving Economic Load Dispatch Problem, depth, our evaluation is on the typical meta-heuristics algorithms which have certain history and application track records. Extensive simulation experiments are performed. It is found that good results are achieved among these algorithms under different cases experiment, especially the CS, FPA, FA, Maniac Fireflies Algorithm (MFA).作者: 預知 時間: 2025-3-24 03:34 作者: acquisition 時間: 2025-3-24 08:04 作者: nitroglycerin 時間: 2025-3-24 13:51 作者: overwrought 時間: 2025-3-24 17:21 作者: Ibd810 時間: 2025-3-24 22:38 作者: 大氣層 時間: 2025-3-25 00:54
978-981-15-6697-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: MARS 時間: 2025-3-25 05:51 作者: condemn 時間: 2025-3-25 08:24 作者: 不理會 時間: 2025-3-25 14:19
Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing978-981-15-6695-0Series ISSN 2524-552X Series E-ISSN 2524-5538 作者: 強化 時間: 2025-3-25 17:44
James Stirling’s Methodus Differentialisnd disadvantages, of different mining algorithms that are suited for both traditional and big data sources. These algorithms include those designed for both sequential and closed sequential pattern mining for both the sequential and parallel processing environments.作者: 大喘氣 時間: 2025-3-25 20:03
,Tobin’s Legacy and Modern Macroeconomics,between data attributes by counting the number of occurrence without focusing on the closeness of time dimension. In this chapter, we focus on how closeness preference model can be applied in discovering association rules instead of only using support and confidence value which are the traditional method of discovering association rules.作者: REP 時間: 2025-3-26 01:49 作者: neutral-posture 時間: 2025-3-26 04:19 作者: 長處 時間: 2025-3-26 08:43
James Stirling’s Methodus Differentialistics for Internet of Things (IoT) applications where data analytics from cloud servers are handled at the edge of a sensor network. Hence, data collected by sensor-enabled device are processed by the edge of a network rather than on the central cloud server. When data stream is processed at central 作者: armistice 時間: 2025-3-26 14:33
James Stirling’s Methodus Differentialisnd disadvantages, of different mining algorithms that are suited for both traditional and big data sources. These algorithms include those designed for both sequential and closed sequential pattern mining for both the sequential and parallel processing environments.作者: 脾氣暴躁的人 時間: 2025-3-26 17:48 作者: 壓倒 時間: 2025-3-26 22:29
Taming Speculation: The Tobin Tax,ances in machine learning if outliers are interpreted as noise. In the past decades, there are many outlier detection algorithms that have been developed and reported in the literature. Many have been implemented as software programs across a spectrum of various applications ranging from identifying作者: babble 時間: 2025-3-27 02:48
,Als Mechaniker an der Universit?t Glasgow,ional methods, researchers have been investigating new methods ranging from artificial intelligent methods to optimization algorithms to further extend the quality of the solution. Among the methods, the meta-heuristic method is a popular choice for their unique searching power collectively and iter作者: 范例 時間: 2025-3-27 06:31
https://doi.org/10.1007/978-3-663-12183-1a (volume, velocity, and variety) produced within the Internet of Things. Fog computing is shown to be highly advantageous in areas such as smart city and healthcare monitoring. Given its architecture, the fog computing model has significant energy savings over its traditional cloud computing model.作者: 甜瓜 時間: 2025-3-27 12:25
https://doi.org/10.1007/978-3-642-91514-7pinions of its users on issues. In this context, private individuals become the sources of information through online sharing of opinions and thoughts. These thoughts and opinions can be extracted to show patterns on sentiments of its users. Sentiment analysis, also referred as opinion mining, studi作者: DAMP 時間: 2025-3-27 16:16 作者: 羽毛長成 時間: 2025-3-27 19:00 作者: exclusice 時間: 2025-3-28 01:05
Conclusion Action: The Final Synthesis,d rely on their outputs for meaningful information. As the various aspects of big data developed, different tools developed to assist with the utilisation of these aspects. Various types of users had different requirements for the same aspect of data mining. Furthermore, these tools began as tools f作者: Clinch 時間: 2025-3-28 02:14 作者: RUPT 時間: 2025-3-28 09:05
James Stirling’s Methodus Differentialisam mining algorithms in fog environment. During the experiment, swarm search methods are applied to pre-process data stream in order to select accurate feature subsets and speed the local fog data analytics. The basis for apply swarm search methods is to find the best data streaming algorithm that g作者: slow-wave-sleep 時間: 2025-3-28 11:06
Taming Speculation: The Tobin Tax, or lightweight analysis with sliding window, other than global analysis only.” These three mechanisms are then combined with the existing outlier measurements such as “interquartile, local outlier factor and Mahalanobis distance range.” In this study, the computer simulation experiments show encour作者: 教唆 時間: 2025-3-28 14:38
2524-552X e and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase su978-981-15-6697-4978-981-15-6695-0Series ISSN 2524-552X Series E-ISSN 2524-5538 作者: 啤酒 時間: 2025-3-28 20:40 作者: Nmda-Receptor 時間: 2025-3-29 01:59 作者: 為敵 時間: 2025-3-29 07:03 作者: maladorit 時間: 2025-3-29 09:32
Lightweight Classifier-Based Outlier Detection Algorithms from Multivariate Data Stream, or lightweight analysis with sliding window, other than global analysis only.” These three mechanisms are then combined with the existing outlier measurements such as “interquartile, local outlier factor and Mahalanobis distance range.” In this study, the computer simulation experiments show encour作者: Fillet,Filet 時間: 2025-3-29 12:52 作者: subordinate 時間: 2025-3-29 18:44 作者: 忘川河 時間: 2025-3-29 20:40
Parameter Tuning onto Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) Network for Feave little value on output feature set. Deep learning methods have been applied to select relevant features in the classification problem; however, the current approach (i.e., search strategies) to the learning of a parameter can either grow out of bound or shrink (they decay exponentially in the nu作者: angiography 時間: 2025-3-30 01:19
