標題: Titlebook: Context-Aware Machine Learning and Mobile Data Analytics; Automated Rule-based Iqbal Sarker,Alan Colman,Paul Watters Book 2021 The Editor(s [打印本頁] 作者: 可樂 時間: 2025-3-21 17:53
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作者: contrast-medium 時間: 2025-3-21 22:29
Context-Aware Machine Learning and Mobile Data Analytics978-3-030-88530-4作者: 玉米棒子 時間: 2025-3-22 01:21
Introduction to Context-Aware Machine Learning and Mobile Data Analyticsre many, but not limited to personalized assistance services, recommendation systems, human-centric computing, adaptive and intelligent systems, IoT services, smart cities as well as mobile privacy and security systems. Thus a study on context-aware machine learning modeling utilizing users’ mobile 作者: 率直 時間: 2025-3-22 06:26 作者: Intact 時間: 2025-3-22 09:28
Powen Yao,Zhankai Ye,Michael Zydare many, but not limited to personalized assistance services, recommendation systems, human-centric computing, adaptive and intelligent systems, IoT services, smart cities as well as mobile privacy and security systems. Thus a study on context-aware machine learning modeling utilizing users’ mobile 作者: 約會 時間: 2025-3-22 14:30
Nikolaos Karagiannis,Debbie A. Mohammedctive rule-based machine learning method that minimizes the issue and generates a set of non-redundant behavioral rules by taking into account the precedence of relevant contexts. Finally, the effectiveness of the technique presented in this chapter, has been provided through experimental results.作者: 約會 時間: 2025-3-22 19:34
icationdevelopers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-cente978-3-030-88532-8978-3-030-88530-4作者: 因無茶而冷淡 時間: 2025-3-22 22:43
Book 2021 discovering rules from contextual raw data can make this book more impactful for the applicationdevelopers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-cente作者: 輕推 時間: 2025-3-23 05:26 作者: Commodious 時間: 2025-3-23 07:08 作者: 挖掘 時間: 2025-3-23 12:40 作者: interlude 時間: 2025-3-23 17:46
Sustainability and Social Policy Nexusnted various components of context-aware machine learning framework and systems with their related issues, where contextual data acquisition is the primary step for context-aware machine learning modeling. In this chapter, we present several contextual datasets that can be utilized to build a machin作者: 沙漠 時間: 2025-3-23 21:20 作者: BOOST 時間: 2025-3-23 22:14
Nikolaos Karagiannis,Debbie A. Mohammedavioral patterns. In this chapter, we focus on discovering behavioral rules of individual mobile phone users by taking into account multi-dimensional contexts—for example temporal, spatial, or social context. Association rule mining is the most prominent rule-based machine learning method for genera作者: insidious 時間: 2025-3-24 03:25
Proper Future Economic Policiesntexts (temporal, spatial, and social context) utilizing their phone log data. However, user behavior is not static, may change over time in the real world. The discovered rules from mobile phone data, therefore, need to be dynamically updated and managed according to the recent behavioral patterns 作者: candle 時間: 2025-3-24 10:31
Global Institute for Sustainable Prosperityrather than using traditional procedural code, are structured to solve complex problems by reasoning through sources of knowledge, which are primarily interpreted as if–then rules. In this chapter, we explore primarily on context-aware rule-based expert system modeling, which is considered one of th作者: iodides 時間: 2025-3-24 14:46 作者: maladorit 時間: 2025-3-24 18:36
Finland: Vocational Guidance in Finland and availability in various real-world applications, there has been a lot of development in the domain of context-aware computing systems in recent years. However, building a context-aware machine learning system still poses a variety of genuine challenges. This chapter addresses the most important作者: Annotate 時間: 2025-3-24 19:30
https://doi.org/10.1007/978-3-030-88530-4mobile data analytics; user behavior modeling; context-aware mobile computing; personalization; mac作者: JAMB 時間: 2025-3-25 02:01
978-3-030-88532-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: CROAK 時間: 2025-3-25 03:39
Iqbal Sarker,Alan Colman,Paul WattersPresents a comprehensive study and highlights the usefulness of the concept of context-aware machine learning.Introduces an automated rule-based machine learning framework to effectively analyze and d作者: ANA 時間: 2025-3-25 09:37
http://image.papertrans.cn/c/image/236886.jpg作者: 音樂學者 時間: 2025-3-25 11:44 作者: 監(jiān)禁 時間: 2025-3-25 16:14 作者: heckle 時間: 2025-3-25 21:05
A Literature Review on Context-Aware Machine Learning and Mobile Data Analyticscontext-aware machine learning framework presented in the earlier chapter. It covers contextual information in mobile phone data, context discretization, and time-series modeling techniques, rule discovery techniques including association rules and classification rules, dynamic rule updating and man作者: 波動 時間: 2025-3-26 04:09 作者: 小平面 時間: 2025-3-26 07:48
Discretization of Time-Series Behavioral Data and Rule Generation based on Temporal Contextf the users, which is used as the basis of generating rules based on temporal context. Although a static segmentation approach is straightforward to comprehend and can be beneficial for analyzing population behavior by comparing across individuals, the generated static segments do not always map to 作者: ascetic 時間: 2025-3-26 11:34
Discovering User Behavioral Rules Based on Multi-Dimensional Contextsavioral patterns. In this chapter, we focus on discovering behavioral rules of individual mobile phone users by taking into account multi-dimensional contexts—for example temporal, spatial, or social context. Association rule mining is the most prominent rule-based machine learning method for genera作者: FRAUD 時間: 2025-3-26 13:31
Recency-Based Updating and Dynamic Management of Contextual Rulesntexts (temporal, spatial, and social context) utilizing their phone log data. However, user behavior is not static, may change over time in the real world. The discovered rules from mobile phone data, therefore, need to be dynamically updated and managed according to the recent behavioral patterns 作者: 要塞 時間: 2025-3-26 19:05 作者: myalgia 時間: 2025-3-26 21:22
Deep Learning for Contextual Mobile Data Analyticsion learning. In the earlier chapters, we have presented methodologies to build context-aware machine learning systems through pre-processing steps of contextual raw data, extracting contextual rules, recent pattern-based rule updating and management, as well as rule-based expert system modeling and作者: CLEFT 時間: 2025-3-27 03:19 作者: EPT 時間: 2025-3-27 08:23 作者: 自戀 時間: 2025-3-27 11:31 作者: dissolution 時間: 2025-3-27 15:27
Application Scenarios and Basic Structure for Context-Aware Machine Learning Frameworkial context-aware applications, which motivates research into context-aware machine learning framework and systems. The framework consists of several data processing layers starting from raw contextual data to application development, which has been presented in this chapter.作者: GORGE 時間: 2025-3-27 20:11 作者: 混合,攙雜 時間: 2025-3-27 22:08 作者: MEEK 時間: 2025-3-28 05:42 作者: 尾隨 時間: 2025-3-28 09:13
Sustainability and Social Policy Nexusstances, the pre-processing steps have also been analyzed to clean and remove noises from raw data. Finally, the basic feature selection and extraction methods for efficient processing has also been provided in this chapter.作者: Condense 時間: 2025-3-28 14:06
Proper Future Economic Policiesrecency-based updating and management of rules for mobile phone users has come to represent an important field of research. In this chapter, we present a recency-based approach for modeling individual’s behavior to resolve this issue.作者: 叫喊 時間: 2025-3-28 17:27
Contextual Mobile Datasets, Pre-processing and Feature Selectionstances, the pre-processing steps have also been analyzed to clean and remove noises from raw data. Finally, the basic feature selection and extraction methods for efficient processing has also been provided in this chapter.作者: single 時間: 2025-3-28 20:40
Recency-Based Updating and Dynamic Management of Contextual Rulesrecency-based updating and management of rules for mobile phone users has come to represent an important field of research. In this chapter, we present a recency-based approach for modeling individual’s behavior to resolve this issue.作者: 著名 時間: 2025-3-29 02:09
Wei Guo,Xiaoli Wang,Zhi Deng,Hongpeng Liof individual mobile phone users. We also highlight the limitations of previous work in the field of context-aware computing, which motivates the need for further study based on machine learning techniques.作者: Obscure 時間: 2025-3-29 05:38
https://doi.org/10.1007/978-3-031-06493-7ate optimal time segments of individuals with similar behavioral characteristics. Moreover, how the generated segments can be used to produce a set of temporal behavioral rules according to users’ preferences, has been presented. Finally, some experimental results to show the effectiveness of the technique have also been provided.
