標(biāo)題: Titlebook: Big Data Analytics for Smart Urban Systems; Saeid Pourroostaei Ardakani,Ali Cheshmehzangi Book 2023 The Editor(s) (if applicable) and The [打印本頁(yè)] 作者: Philanthropist 時(shí)間: 2025-3-21 17:20
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書目名稱Big Data Analytics for Smart Urban Systems讀者反饋
書目名稱Big Data Analytics for Smart Urban Systems讀者反饋學(xué)科排名
作者: Indent 時(shí)間: 2025-3-21 22:35 作者: 災(zāi)禍 時(shí)間: 2025-3-22 02:34
Urban Sustainabilityhttp://image.papertrans.cn/b/image/185616.jpg作者: 難取悅 時(shí)間: 2025-3-22 06:40 作者: modest 時(shí)間: 2025-3-22 09:07
Showing Your Spring Application on the WebBy questioning ‘what smartness does in the smart city’, Baykurt and Raetzsch (2020) provide suggestions for analytical approaches to investigatess the complexity of urban systems, such as relationships and networks in the built and natural environments.作者: BRACE 時(shí)間: 2025-3-22 14:13
Big Data Analytics: An Introduction to Their Applications for Smart Urban Systems,Over the last two decades, we have witnessed how analytics and data science have changed how we think about our urban systems. With the rise of smart cities and smart urban systems, in particular, we see the emergence of big data analytics to be more progressive and towards changes in management, governance, and culture of urban thinking.作者: Exterior 時(shí)間: 2025-3-22 18:50
Moving Forward with Big Data Analytics and Smartness,By questioning ‘what smartness does in the smart city’, Baykurt and Raetzsch (2020) provide suggestions for analytical approaches to investigatess the complexity of urban systems, such as relationships and networks in the built and natural environments.作者: tolerance 時(shí)間: 2025-3-22 22:53 作者: 惡臭 時(shí)間: 2025-3-23 03:24
978-981-99-5545-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 船員 時(shí)間: 2025-3-23 06:44 作者: paleolithic 時(shí)間: 2025-3-23 11:00
https://doi.org/10.1007/978-1-4614-5383-3 economic solutions. This chapter aims to propose a time-series?machine learning?approach to forecast cryptocurrency?market trends-mainly Bitcoin. It is comprised of three steps, including data preprocessing, pattern?recognition, and price prediction. The data preprocessing approach aims to handle m作者: 發(fā)現(xiàn) 時(shí)間: 2025-3-23 13:53
Tobias Heinroth,Wolfgang Minkerinformation. However, they still suffer from the lack of a solid Big Data solutions to recognise, model and predict credit risk data patterns. This chapter aims to propose machine learning?pipelines which are capable of extracting principal information from a huge and public credit risk dataset. For作者: 重疊 時(shí)間: 2025-3-23 20:30
Working with Collections and Custom Typests the impact of the pandemic on people’s mobility trends at the early stages. It uses a correlation matrix method to find the correlations of mobility trends and six commonly-used places, including retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential作者: 完成才能戰(zhàn)勝 時(shí)間: 2025-3-23 22:41
Spring Data Within Your Spring Applicationoisy or unlabeled samples, they could produce incorrect or inaccurate conclusions. This chapter refers to a single target prediction analysis on Google’s App rating. This aspect is relevant to smart urban management and systems and could help optimize data analysis for multiple uses. After conductin作者: BILE 時(shí)間: 2025-3-24 02:55
Adding E-mail and Scheduling Tasks-CIS Fraud Detection dataset was raised for technique innovation to tackle this problem. For the consideration of the size of the available fraud dataset, this chapter proposes a solution with the combination of the big data technique and the machine learning algorithms. Aside from the raised pipeli作者: 乳汁 時(shí)間: 2025-3-24 07:33 作者: 矛盾心理 時(shí)間: 2025-3-24 13:00 作者: 小故事 時(shí)間: 2025-3-24 16:47 作者: Isthmus 時(shí)間: 2025-3-24 22:48
Improve the Daily Societal Operations Using Credit Fraud Detection: A Big Data Classification Solutne of the solution, other identified works include a comparison of four implemented machine learning algorithms, three surveys into the computing time with respect to the data size, number of executors, and number of cores. Findings from this chapter help finding solutions for more efficient credit fraud detection.作者: 睨視 時(shí)間: 2025-3-25 01:50 作者: 雪崩 時(shí)間: 2025-3-25 05:56 作者: 斜谷 時(shí)間: 2025-3-25 10:32 作者: cringe 時(shí)間: 2025-3-25 14:37
2731-6483 n sustainability applications.Discusses the capacities of thBig Data Analytics for Smart Urban Systems aims to introduce Big data solutions for urban sustainability smart applications, particularly for smart urban systems. It focuses on intelligent big data which takes the benefits of machine learni作者: 剝皮 時(shí)間: 2025-3-25 15:59 作者: restrain 時(shí)間: 2025-3-25 21:38 作者: 白楊 時(shí)間: 2025-3-26 00:30 作者: 易改變 時(shí)間: 2025-3-26 06:08 作者: pulmonary-edema 時(shí)間: 2025-3-26 09:39
,Big Data Analytics for?Credit Risk Prediction: Machine Learning Techniques ?and?Data Processing Appon Tree?and Gradient Boosting?are trained, tested and evaluated to find the best-fitted?techniques?for credit risk prediction. According to the results, Gradient Boosting?(AUC of 0.987) gives?a better performance as compared to?Decision Tree?(AUC of 0.488).作者: calamity 時(shí)間: 2025-3-26 13:19 作者: FIR 時(shí)間: 2025-3-26 17:41
Book 2023er the many years of collaboration between academia and industry, we noticed the common language is ‘big data’; with that, we have developed novel ideas to bridge the gaps and help promote innovation, technologies, and science’..- Tian Tang, Independent Researcher, China.?‘Big Data Analytics is a fa作者: 哥哥噴涌而出 時(shí)間: 2025-3-26 20:57
2731-6483 nguage is ‘big data’; with that, we have developed novel ideas to bridge the gaps and help promote innovation, technologies, and science’..- Tian Tang, Independent Researcher, China.?‘Big Data Analytics is a fa978-981-99-5545-9978-981-99-5543-5Series ISSN 2731-6483 Series E-ISSN 2731-6491 作者: 高原 時(shí)間: 2025-3-27 03:16
Stock Market Prediction During COVID-19 Pandemic: A Time-Series Big Data Analysis Method,o greatly affected the stock market. Because stock prices sometimes show similar patterns?and are determined by a variety?of factors, we propose identifying comparable patterns?in past stock data of daily stock prices, as well as selecting the primary components that significantly affect the price, 作者: Invertebrate 時(shí)間: 2025-3-27 05:58 作者: 放大 時(shí)間: 2025-3-27 10:45 作者: ASTER 時(shí)間: 2025-3-27 16:28 作者: Merited 時(shí)間: 2025-3-27 21:21
Adaptive Feature Selection for Google App Rating in Smart Urban Management: A Big Data Analysis Appoisy or unlabeled samples, they could produce incorrect or inaccurate conclusions. This chapter refers to a single target prediction analysis on Google’s App rating. This aspect is relevant to smart urban management and systems and could help optimize data analysis for multiple uses. After conductin作者: GNAW 時(shí)間: 2025-3-28 01:14
Improve the Daily Societal Operations Using Credit Fraud Detection: A Big Data Classification Solut-CIS Fraud Detection dataset was raised for technique innovation to tackle this problem. For the consideration of the size of the available fraud dataset, this chapter proposes a solution with the combination of the big data technique and the machine learning algorithms. Aside from the raised pipeli作者: 官僚統(tǒng)治 時(shí)間: 2025-3-28 05:01
10樓作者: 削減 時(shí)間: 2025-3-28 09:23
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