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  • 1
    ISBN: 9780128154588
    Language: English
    Pages: xviii, 256 Seiten , Diagramme, Graphen , 24 cm
    Parallel Title: Erscheint auch als Dey, Nilanjan, - 1984- Social network analytics
    DDC: 302.30721
    RVK:
    Keywords: Social networks Research ; Methodology ; Social networks Research ; Data processing ; Aufsatzsammlung
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  • 2
    ISBN: 9780128156414
    Language: English
    Pages: 1 Online-Ressource (276 pages)
    Parallel Title: Print version Dey, Nilanjan Social Network Analytics : Computational Research Methods and Techniques
    DDC: 302.30721
    Keywords: Online social networks.. ; Social sciences-Network analysis.. ; Data mining.. ; Computational linguistics ; Electronic books
    Abstract: Front Cover -- Social Network Analytics: Computational Research Methods and Techniques -- Copyright -- Contents -- Contributors -- Editors Biography -- Preface -- Chapter 1: Classification and Analysis of Facebook Metrics Dataset Using Supervised Classifiers -- 1. Introduction -- 2. Literature Review -- 2.1. Bayes Classifiers -- 2.2. Function Classifiers -- 2.3. Lazy Classifiers -- 2.4. Metaclassifiers -- 2.5. Rule Classifiers -- 2.6. Tree Classifiers -- 2.7. Other Classifiers -- 3. Dataset Analysis -- 4. Results and Discussion -- 5. Conclusion -- References -- Chapter 2: An Overview on Social Networking: Design, Issues, Emerging Trends, and Security -- 1. Introduction -- 2. Literature Review -- 3. Promising Issues and Security Challenges in Social Networking -- 3.1. Security Threat in Educational Social Network -- 3.2. Security, Issues, and Challenges in Social Network [2] -- 3.3. Influence Maximization in Social Networks -- 3.4. Data Communication in VSN -- 3.5. Crowd Sourcing Complex Tasks -- 3.6. Polar Opinion Dynamic in Social Network -- 3.7. Privacy Preservation Location Sharing -- 3.8. Social Networking in Educational Networks -- 3.9. The Role of Social Network in a Disaster Scenario -- 3.10. Delegation Model in Social Networks -- 3.11. Social Network Formation Model -- 3.12. Financial Outcome of Social Networks -- 3.13. Pervasive Social Networking -- 3.14. Security and Privacy in Wireless Body Area Network -- 3.15. Analysis of Social Networking Issues Related to Area of Focus -- 3.16. Cooperation in Social Networking Members Under a VANET -- 4. Challenging Aspects in Social Networking -- 5. Static and Dynamic Social Network Model -- 5.1. Static Network Model -- 5.2. Dynamic Social Network Model -- 5.3. A Framework for Dynamic Social Model -- 5.4. Use of Dynamic Social Network: Social-Aware Routing in MANET
    Abstract: 6. Factors That Affect the Design of a Social Network -- 6.1. Management of Relationship Among Users in a Social Network -- 6.2. Profit by Social Network -- 6.3. Type of Content Floating in Social Networks -- 6.4. Audience of Social Network Site -- 7. Security Prospective in Social Networking -- 7.1. Social Networking: Information Leakage and Theft Mechanism -- 7.2. Trust Relationship in Facebook -- 7.3. Phishing Technique of Attack in Facebook -- 7.4. Source Identification of Spread Rumor in Social Networks -- 8. Impact of SNSs-Facebook as a Case Study -- 8.1. Facebook Clients Are More Trusting Than Others -- 8.2. Facebook Users Have All the More Pleasant Connections -- 8.3. Facebook Clients Get More Social Help Than Other Individuals -- 9. Conclusion -- References -- Further Reading -- Chapter 3: Emergence of Stable and Glassy States in Dynamics of Social Networks -- 1. Introduction -- 2. Current State and Historical Development -- 2.1. Heider Balanced Theory -- 2.2. Energy Reduction -- 2.3. Evolving Networks -- 3. Pseudo-Paths Toward Jammed States -- 3.1. Discussion -- 3.1.1. Role of Individuals Participation -- 3.1.2. Inverse Participation Ratio -- 4. Effect of Gain and Loss of Esteem in Heider's Balance Theory -- 4.1. Balanced States -- 4.2. Differential Equation -- 4.3. Discussion -- 5. Glassy States in Aging Social Networks -- 5.1. Evolution of Aging Networks -- 5.2. Discussion -- 5.2.1. Simulation Model -- 5.2.2. Formation of Aged Networks -- 6. Conclusion -- References -- Chapter 4: De-Anonymization Techniques for Social Networks -- 1. Introduction -- 1.1. Necessity of Publishing of Social Networks -- 1.1.1. Academic and Government Data Mining -- 1.1.2. Advertising -- 1.1.3. Third-Party Applications -- 1.1.4. Aggregation -- 1.1.5. Other Data-Release Scenarios -- 1.2. Overview of Anonymization Techniques -- 2. De-Anonymization Techniques
    Abstract: 3. De-Anonymization Attacks -- 3.1. Privacy Breach -- 3.2. Passive Attack -- 3.3. Active Attack -- 3.3.1. Broad Category of Active Attacks -- 4. Two-Stage De-Anonymization Algorithm [1] -- 4.1. Seed Identification -- 4.2. Propagation -- 4.3. Some Concepts -- 5. Other De-Anonymization Techniques -- 6. Conclusions -- References -- Further Reading -- Chapter 5: An Analysis of Demographic and Behavior Trends Using Social Media: Facebook, Twitter, and Instagram -- 1. Introduction -- 2. Material and Methods -- 2.1. Data Collection -- 2.2. Data Inclusion Criteria -- 2.3. Analysis of Raw Data -- 3. Results -- 3.1. Statistical Analysis -- 3.2. Research Papers According to Year -- 3.3. Data Collection Method and Behavior Analysis Methods Used -- 3.4. Classification Based on Different Methods -- 4. Discussion -- 5. Conclusion -- Author Contribution -- References -- Further Reading -- Chapter 6: Social Network Influence on Mode Choice and Carpooling During Special Events: The Case of Purdue Game Day -- 1. Introduction -- 2. Background and Related Work -- 3. Data -- 4. Modeling Framework -- 5. Model Estimation Results -- 5.1. Model 1 (Car Owners) -- 5.1.1. Density, Network Size, and Ego-Alter Tie Attributes -- 5.1.2. Homophily Indexes -- 5.1.3. Heterogeneity Indexes -- 5.1.4. Location Variables -- 5.2. Model 2 (Non-Car Owners) -- 5.2.1. Density, Network Size, and Ego-Alter Tie Attributes -- 5.2.2. Homophily Indexes -- 5.2.3. Heterogeneity Indexes and Location Variables -- 6. Conclusions -- References -- Chapter 7: Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Techniques -- 1. Introduction -- 2. Deep Learning -- 2.1. Learning Neural Networks -- 2.2. Curse of Dimensionality -- 2.3. Essence of Deep Learning -- 2.4. Convolution Neural Networks -- 3. Sentiment Analysis -- 3.1. Challenges Faced While Analyzing Sentiments -- 4. Related Works
    Abstract: 5. The Proposed Methodology -- 5.1. Movie Reviews Used -- 5.1.1. File Descriptions -- 5.1.2. Data Fields -- 6. Results and Discussion -- 7. Discussions and Conclusion -- References -- Chapter 8: Sentiment Analysis for Airlines Services Based on Twitter Dataset -- 1. Introduction -- 2. Literature Survey -- 3. Concept and Architecture of Sentiment Analysis -- 3.1. Description of Dataset -- 4. Proposed Methodologies -- 4.1. BIRCH Clustering (Balanced Iterative Reducing and Clustering Using Hierarchies) -- 4.2. Association Rule Mining -- 4.2.1. Interestingness Computation -- 5. Result and Discussion -- 5.1. Word Frequency Consideration and Cloud of Words Formation for Every Sentiments -- 5.2. Determining the Association Between Words -- 5.3. Cluster Analysis of Words and Their Association -- 6. Conclusion and Future Work -- References -- Further Reading -- Chapter 9: Multilateral Interactions and Isolation in Middlemen-Driven Network Games -- 1. Introduction -- 2. Preliminaries -- 3. Network Game With Middlemen -- 4. The NMMI Value for the Class VMg -- 5. Empirical Illustration -- 6. Conclusion -- Acknowledgments -- References -- Chapter 10: The Interplay of Identity and Social Network: A Methodological and Empirical Study -- 1. Introduction -- 2. Openness and Awareness -- 3. Survey Details -- 4. Openness and Awareness Metric -- 5. Results and Discussion -- 5.1. Descriptive Statistics -- 5.2. Interaction Pattern -- 5.3. Distribution of Openness -- 6. Conclusion -- Acknowledgments -- References -- Chapter 11: Social Networks and Their Uses in the Field of Secondary Education -- 1. Introduction -- 2. Social Networks in Secondary Education: A Critical Look From a Triple Multilevel View -- 2.1. Microlevel: Uses of Social Networks for Teaching and Learning -- 2.2. Mesolevel: Institutional Uses of Social Networks
    Abstract: 2.3. Macrolevel: Policies That Promote the Development of Digital Skills and the Incorporation of Information and Communi ... -- 3. Analysis of the Use Made of Social Networks Among Students in the Fourth Year of Secondary Education -- 4. Discussion and Conclusions -- Acknowledgments -- References -- Further Reading -- Chapter 12: NGOs' Communication and Youth Engagement in the Digital Ecosystem -- 1. Introduction -- 2. NGOs in the Digital Ecosystem -- 3. The Mobilization of Young People -- 4. Methodology -- 5. Results -- 5.1. NGO Communication Activities in Social Networks -- 5.2. The Role of Influencers -- 6. Conclusions -- References -- Further Reading -- Index -- Back Cover
    URL: Volltext  (lizenzpflichtig)
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  • 3
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Academic Press | Boston, MA : Safari
    ISBN: 9780128181492
    Language: English
    Pages: 1 online resource (370 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Neutrosophic Set in Medical Image Analysis gives an understanding of the concepts of NS, along with knowledge on how to gather, interpret, analyze and handle medical images using NS methods. It presents the latest cutting-edge research that gives insight into neutrosophic set’s novel techniques, strategies and challenges, showing how it can be used in biomedical diagnoses systems. The neutrosophic set (NS), which is a generalization of fuzzy set, offers the prospect of overcoming the restrictions of fuzzy-based approaches to medical image analysis. Introduces the mathematical model and concepts of neutrosophic theory and methods Highlights the different techniques of neutrosophic theory, focusing on applying the neutrosophic set in image analysis to support computer- aided diagnosis (CAD) systems, including approaches from soft computing and machine learning Shows how NS techniques can be applied to medical image denoising, segmentation and classification Provides challenges and future directions in neutrosophic set based medical image analysis
    Note: Online resource; Title from title page (viewed August 8, 2019)
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