標(biāo)題: Titlebook: Advances in Machine Learning for Big Data Analysis; Satchidananda Dehuri,Yen-Wei Chen Book 2022 The Editor(s) (if applicable) and The Auth [打印本頁(yè)] 作者: 我贊成 時(shí)間: 2025-3-21 16:40
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書目名稱Advances in Machine Learning for Big Data Analysis讀者反饋學(xué)科排名
作者: 提煉 時(shí)間: 2025-3-21 22:01
A Counter-Based Profiling Scheme for Improving Locality Through Data and Reducer Placement,e Fachgeschichtsschreibung dort an, wo sich eine spezialisierte Kommunikation mit deutschsprachigen Texten besch?ftigt.. Der Zusammenhang von Fachgeschichte und Deutschsprachigkeit z?hlt gleichsam von selbst zur Sache und hat überdies den Vorteil, da? die Geschichte der Germanistik sich nicht in ein作者: 飛鏢 時(shí)間: 2025-3-22 03:47
Supply Chain Management (SCM): Employing Various Big Data and Metaheuristic Strategies, sind wissenschaftliche Leistungen? Was sind gute oder schlechte Beitr?ge zur Wissenschaft? Welche Fragestellungen sind relevant? Welchen Stellenwert hat eine Theorie oder eine Hypothese im Verh?ltnis zum bestehenden Wissen? Ist eine neue Theorie wissenschaftlich fortschrittlich, oder handelt es sic作者: accrete 時(shí)間: 2025-3-22 06:18
Hybrid Approach to Prevent Accidents at Railway: An Assimilation of Big Data, IoT and Cloud,s, sondern auch Museen, Schule, Wissenschaftsfeste etc..Verb.Was istWissenschaftskommunikation? .Ob Zeitung oderInternet, Museum oder Science Café: Es gibt zahlreiche M?glichkeiten, wieWissenschaft und ?ffentlichkeit zusammenkommen. Wie aber werden komplexeSachverhalte aus Forschung und Technik inte作者: 和平主義者 時(shí)間: 2025-3-22 11:03
Book 2022 this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be le作者: 盡責(zé) 時(shí)間: 2025-3-22 15:46 作者: crucial 時(shí)間: 2025-3-22 20:40
Ricardo A. Madeira,Marcos A. RangelThe current chapter details the background study, followed by introducing big data and its various sources. Afterwards, data analytics and processing are elaborated. We have discussed big data, supply chain management in case of agriculture, analytics and the types of challenges, etc. encountered in this context.作者: etiquette 時(shí)間: 2025-3-22 22:34 作者: Lumbar-Stenosis 時(shí)間: 2025-3-23 04:20
Book 2022ancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems. .作者: CROW 時(shí)間: 2025-3-23 08:15
1868-4394 machine learning and data science.Offers a reference guide f.This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems...In this book, various advancements of machine learning algorithms to extract data-driven dec作者: parsimony 時(shí)間: 2025-3-23 12:14 作者: 我悲傷 時(shí)間: 2025-3-23 15:16 作者: 解決 時(shí)間: 2025-3-23 20:03
Supply Chain Management (SCM): Employing Various Big Data and Metaheuristic Strategies,tellung ist ein Teilaspekt von besonderem Interesse, n?mlich die Frage, ob es gelingen kann, unabh?ngig von den h?ufig kontroversen Einsch?tzungen der betroffenen Wissenschaftler und den im weitesten Sinne sozialen Bedingungen ihres wissenschaftlichen Handelns, methodologische Regeln zu formulieren,作者: affluent 時(shí)間: 2025-3-23 23:03
https://doi.org/10.1007/0-306-47984-2y, we also derive a refined taxonomy of the types of ACO variants that have been used for multi-objective optimization and we include a review of some of their real-world applications. In the last part of the chapter, we provide some potential paths for further research in this area.作者: gustation 時(shí)間: 2025-3-24 05:15
https://doi.org/10.1007/0-306-47984-2e. A feature selection module called FSRA (feature selection using rank aggregation) has also been presented to support cost-effective classification of CPS attacks. CPSAD has been tested on several CPS benchmark datasets and the results have been found highly satisfactory.作者: 小口啜飲 時(shí)間: 2025-3-24 08:37 作者: athlete’s-foot 時(shí)間: 2025-3-24 14:19 作者: evaculate 時(shí)間: 2025-3-24 18:17 作者: Proclaim 時(shí)間: 2025-3-24 22:59 作者: 殺死 時(shí)間: 2025-3-25 00:49 作者: 狼群 時(shí)間: 2025-3-25 04:57 作者: 消滅 時(shí)間: 2025-3-25 10:55
,Hybridization of the Higher Order Neural Networks with the Evolutionary Optimization Algorithms—An 978-3-7091-3970-7作者: 牛馬之尿 時(shí)間: 2025-3-25 11:57
Value of Random Vector Functional Link Neural Networks in Software Development Effort Estimation,978-3-658-21157-8作者: Deadpan 時(shí)間: 2025-3-25 19:46 作者: Legend 時(shí)間: 2025-3-25 22:56 作者: strdulate 時(shí)間: 2025-3-26 02:05
1868-4394 t analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems. .978-981-16-8932-1978-981-16-8930-7Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: 昆蟲 時(shí)間: 2025-3-26 06:29 作者: TOXIN 時(shí)間: 2025-3-26 08:53
https://doi.org/10.1007/0-306-47984-2he behavior of social insects is the main source of inspiration. Being a particular SI approach, the ACO metaheuristic is mainly characterized by its distributiveness, flexibility, capacity of interaction among simple agents, and its robustness. The ACO metaheuristic has been successfully applied to作者: EVEN 時(shí)間: 2025-3-26 13:45 作者: debase 時(shí)間: 2025-3-26 17:59 作者: 拖債 時(shí)間: 2025-3-27 00:00 作者: 字的誤用 時(shí)間: 2025-3-27 02:44 作者: 大暴雨 時(shí)間: 2025-3-27 08:18 作者: Detoxification 時(shí)間: 2025-3-27 12:12 作者: 調(diào)情 時(shí)間: 2025-3-27 17:21 作者: ANTH 時(shí)間: 2025-3-27 20:02
Closing the Achievement Gap: A Systemic Viewucation, biology, etc. The performance increases when it deals with big data. ML algorithms challenged by big data make more accurate and timely predictions and unveil more concrete patterns than ever before. A decision tree is one of the most successful models in data science, especially when big d作者: FLINT 時(shí)間: 2025-3-28 01:27
Advances in Machine Learning for Big Data Analysis978-981-16-8930-7Series ISSN 1868-4394 Series E-ISSN 1868-4408 作者: 易于交談 時(shí)間: 2025-3-28 03:00
https://doi.org/10.1007/978-981-16-8930-7Machine Learning; Big Data Analysis; Data Mining; Pattern Recognition; Supervised Learning; Classificatio作者: conception 時(shí)間: 2025-3-28 09:49 作者: resuscitation 時(shí)間: 2025-3-28 12:30 作者: OUTRE 時(shí)間: 2025-3-28 14:56
Intelligent Systems Reference Libraryhttp://image.papertrans.cn/a/image/148709.jpg作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-3-28 19:00
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