標(biāo)題: Titlebook: Data Science and Big Data: An Environment of Computational Intelligence; Witold Pedrycz,Shyi-Ming Chen Book 2017 Springer International Pu [打印本頁] 作者: clot-buster 時間: 2025-3-21 19:02
書目名稱Data Science and Big Data: An Environment of Computational Intelligence影響因子(影響力)
書目名稱Data Science and Big Data: An Environment of Computational Intelligence影響因子(影響力)學(xué)科排名
書目名稱Data Science and Big Data: An Environment of Computational Intelligence網(wǎng)絡(luò)公開度
書目名稱Data Science and Big Data: An Environment of Computational Intelligence網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science and Big Data: An Environment of Computational Intelligence被引頻次
書目名稱Data Science and Big Data: An Environment of Computational Intelligence被引頻次學(xué)科排名
書目名稱Data Science and Big Data: An Environment of Computational Intelligence年度引用
書目名稱Data Science and Big Data: An Environment of Computational Intelligence年度引用學(xué)科排名
書目名稱Data Science and Big Data: An Environment of Computational Intelligence讀者反饋
書目名稱Data Science and Big Data: An Environment of Computational Intelligence讀者反饋學(xué)科排名
作者: 歸功于 時間: 2025-3-21 22:13 作者: ATRIA 時間: 2025-3-22 03:10 作者: 尊嚴(yán) 時間: 2025-3-22 07:25 作者: Guaff豪情痛飲 時間: 2025-3-22 11:13
Developing Modified Classifier for Big Data Paradigm: An Approach Through Bio-Inspired Soft Computind on supervised features following conventional data mining principle. However, the classification of majority or positive class is over-sampled by taking each minority class sample. Definitely, significant computationally intelligent methodologies have been introduced. Following the philosophy of d作者: 有斑點(diǎn) 時間: 2025-3-22 13:36
Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing oachieve effective selection of data pre-processing techniques towards effective selection of relevant attributes, sampling of representative training and test data, and appropriate dealing with missing values and noise. More importantly, this framework allows the employment of suitable machine learn作者: 有斑點(diǎn) 時間: 2025-3-22 20:59 作者: lethargy 時間: 2025-3-23 00:43
Event Detection in Location-Based Social Networksause of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of作者: ascend 時間: 2025-3-23 05:27 作者: 真實(shí)的人 時間: 2025-3-23 06:30
Big Data for Effective Management of Smart Gridsata, and user interaction data are collected. Then, as described in several scientific papers, many data analysis techniques, including optimization, forecasting, classification and other, can be applied on the large amounts of smart grid big data. There are several techniques, based on Big Data ana作者: jettison 時間: 2025-3-23 09:41
Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analyticsd neural network) mapped on smart-gateway networks. Scalability and robustness are considered to perform real-time data analytics. Furthermore, as the success of system depends on the trust of users, network intrusion detection for smart gateway has also been developed to provide system security. Ex作者: arcane 時間: 2025-3-23 14:51
Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Sciencelarge amount of data needs to be performed. The problem addressed in this chapter is how such Big Data can be integrated, spatio-temporally correlated, and analyzed in real-time, in order to improve capabilities of modern electricity network in dealing with weather caused emergencies.作者: tariff 時間: 2025-3-23 19:50
Osmud Rahman,Benjamin C. M. Fungtational time from cubic to linear. In the second part, a distributed clustering approach with fixed computational budget is illustrated. This method extends the k-means algorithm by applying regularization at the level of prototype vectors. An optimal stochastic gradient descent scheme for learning作者: 朦朧 時間: 2025-3-24 00:35
Svetlana Karelskaia,Ekaterina Zugals for the upper and lower boundaries for data objects with respect to each cluster. BFPM facilitates algorithms to converge and also inherits the abilities of conventional fuzzy and possibilistic methods. In Big Data applications knowing the exact type of data objects and selecting the most accurat作者: SCORE 時間: 2025-3-24 03:27 作者: MELON 時間: 2025-3-24 08:19
Anita Sadeghpour MD, FACC, FASEon detection approaches, such as rule-based, signature-based and computer intelligence based approaches were developed. Out of these, computational intelligence based anomaly detection mechanisms show the ability to handle hitherto unknown intrusions and attacks. However, these approaches suffer fro作者: ATP861 時間: 2025-3-24 12:28
Anita Sadeghpour MD, FACC, FASEd on supervised features following conventional data mining principle. However, the classification of majority or positive class is over-sampled by taking each minority class sample. Definitely, significant computationally intelligent methodologies have been introduced. Following the philosophy of d作者: 前奏曲 時間: 2025-3-24 18:44
Andreas Vogel,Oktay Ergünay,Müfiz Alpmenachieve effective selection of data pre-processing techniques towards effective selection of relevant attributes, sampling of representative training and test data, and appropriate dealing with missing values and noise. More importantly, this framework allows the employment of suitable machine learn作者: 提煉 時間: 2025-3-24 21:30 作者: Mirage 時間: 2025-3-24 23:36
N. Bayülke,E. Inan,A. Dogan,A. Hürataause of this, we propose a probabilistic machine learning approach to event detection which explicitly models the data generation process and enables reasoning about the discovered events. With the aim to set forth the differences between both approaches, we present two techniques for the problem of作者: convert 時間: 2025-3-25 03:21
Palpable Pigmented Lesions on the Trunk, of the data collected by these tools is staggering. Relying on traditional pipeline safety assessment techniques to analyze such huge data is neither efficient nor effective. Intelligent techniques such as data mining techniques, neural networks, and hybrid neuro-fuzzy systems are promising alterna作者: N防腐劑 時間: 2025-3-25 10:28
Aimilios Lallas,Horacio Cabo,Gabriel Salerniata, and user interaction data are collected. Then, as described in several scientific papers, many data analysis techniques, including optimization, forecasting, classification and other, can be applied on the large amounts of smart grid big data. There are several techniques, based on Big Data ana作者: Oscillate 時間: 2025-3-25 13:49
Digestion and Absorption of Carbohydratesd neural network) mapped on smart-gateway networks. Scalability and robustness are considered to perform real-time data analytics. Furthermore, as the success of system depends on the trust of users, network intrusion detection for smart gateway has also been developed to provide system security. Ex作者: Needlework 時間: 2025-3-25 16:32 作者: 勾引 時間: 2025-3-25 22:34 作者: commensurate 時間: 2025-3-26 00:44 作者: FOVEA 時間: 2025-3-26 07:58 作者: lambaste 時間: 2025-3-26 10:40 作者: A保存的 時間: 2025-3-26 15:06
Studies in Big Datahttp://image.papertrans.cn/d/image/263087.jpg作者: ORBIT 時間: 2025-3-26 17:21
https://doi.org/10.1007/978-3-319-53474-9Big Data; Data Science; Computational Intelligence; Data Analytics; Internet of Things作者: 胎兒 時間: 2025-3-27 00:50
978-3-319-85162-4Springer International Publishing AG 2017作者: 壓迫 時間: 2025-3-27 03:25
Osmud Rahman,Benjamin C. M. Fungossible: (i) adapt available algorithms or design new approaches such that they can run on a distributed computing environment (ii) develop model-based learning techniques that can be trained efficiently on a small subset of the data and make reliable predictions. In this chapter two recent algorith作者: Graduated 時間: 2025-3-27 06:21
Svetlana Karelskaia,Ekaterina Zugas, the membership assignments, and distance or similarity functions. In this chapter we describe different data types, membership functions, and similarity functions and discuss the pros and cons of using each of them. Conventional similarity functions evaluate objects in the vector space. Contraril作者: 無思維能力 時間: 2025-3-27 11:17
Osmud Rahman,Benjamin C. M. Fungbeyond the capability of commonly used software tools to capture, curate and manage within a tolerable elapsed time and also beyond the processing feasibility of the single machine architecture. In addition to the traditional structured data, the new avenue of NoSQL Big Data has urged a call to expe作者: 亞當(dāng)心理陰影 時間: 2025-3-27 17:01 作者: CHYME 時間: 2025-3-27 18:48 作者: Parallel 時間: 2025-3-28 00:53
Andreas Vogel,Oktay Ergünay,Müfiz Alpmenearning as a powerful tool of big data processing. In machine learning context, learning algorithms are typically evaluated in terms of accuracy, efficiency, interpretability and stability. These four dimensions can be strongly related to veracity, volume, variety and variability and are impacted by作者: 運(yùn)動的我 時間: 2025-3-28 02:54 作者: 詼諧 時間: 2025-3-28 10:07
N. Bayülke,E. Inan,A. Dogan,A. Hürataevents in a crowd-sourced manner. Location-based social networks have proven to be faster than traditional media channels in reporting and geo-locating breaking news, i.e. Osama Bin Laden’s death was first confirmed on Twitter even before the announcement from the communication department at the Whi作者: 性學(xué)院 時間: 2025-3-28 11:03 作者: 陪審團(tuán) 時間: 2025-3-28 17:02
Aimilios Lallas,Horacio Cabo,Gabriel Salernitrol of their consumption. On the other hand, the ever-increasing pervasiveness of technology together with the smart paradigm, are becoming the reference point of anyone involved in innovation, and energy management issues. In this context, the information that can potentially be made available by 作者: 壓艙物 時間: 2025-3-28 22:04
Digestion and Absorption of Carbohydratesmple is to design a smart agent to make decisions within environment in response to the presence of human beings. Smart building/home is a typical computational intelligence based system enriched with sensors to gather information and processors to analyze it. Indoor computational intelligence based作者: 不來 時間: 2025-3-28 23:44
Digestion and Absorption of Lipidsramatically escalating, mainly due to the high level of exposure of the network components to weather elements. Combined, 75% of power outages are either directly caused by weather-inflicted faults (e.g., lightning, wind impact), or indirectly by equipment failures due to wear and tear combined with作者: 毛細(xì)血管 時間: 2025-3-29 03:25
Data Science and Big Data: An Environment of Computational Intelligence978-3-319-53474-9Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: 定點(diǎn) 時間: 2025-3-29 07:45
Large-Scale Clustering Algorithmsossible: (i) adapt available algorithms or design new approaches such that they can run on a distributed computing environment (ii) develop model-based learning techniques that can be trained efficiently on a small subset of the data and make reliable predictions. In this chapter two recent algorith作者: 高爾夫 時間: 2025-3-29 11:40
On High Dimensional Searching Spaces and Learning Methodss, the membership assignments, and distance or similarity functions. In this chapter we describe different data types, membership functions, and similarity functions and discuss the pros and cons of using each of them. Conventional similarity functions evaluate objects in the vector space. Contraril作者: transdermal 時間: 2025-3-29 17:42 作者: foreign 時間: 2025-3-29 21:49 作者: 小母馬 時間: 2025-3-30 03:58 作者: Affectation 時間: 2025-3-30 05:39 作者: Unsaturated-Fat 時間: 2025-3-30 11:45 作者: 比賽用背帶 時間: 2025-3-30 14:49 作者: 歡笑 時間: 2025-3-30 18:05
Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Surveys are usually transmitted through metallic pipelines. Working under unforgiving environments, these pipelines may extend to hundreds of kilometers, which make them very susceptible to physical damage such as dents, cracks, corrosion, etc. These defects, if not managed properly, can lead to catastrop作者: 裁決 時間: 2025-3-30 23:55
Big Data for Effective Management of Smart Gridstrol of their consumption. On the other hand, the ever-increasing pervasiveness of technology together with the smart paradigm, are becoming the reference point of anyone involved in innovation, and energy management issues. In this context, the information that can potentially be made available by
