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Sample Research Areas in

Wired and Wireless Applications. 3. Introduction. QoI (Quality of Intelligence) of ... with uncertain data on the Internet and wireless networks. ...

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Sample Research Areas in

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    Slide 1:Sample Research Areas in Advanced Operating Systems

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    Slide 2:Outline

    Computational Web Intelligence (CWI) Wired and Wireless Applications

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    Slide 3:Introduction

    QoI (Quality of Intelligence) of e-Business WI = AI + IT WI (Web Intelligence) exploits Artificial Intelligence (AI) and advanced Information Technology (IT) on the Web and Internet . (Zhong, Liu, Yao and Ohsuga) at Proc. the 24th IEEE Computer Society International Computer Software and Applications Conference (COMPSAC 2000),

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    Slide 4:Introduction (cont.)

    “CI is a subset of AI”, “CI is not a subset of AI, there is an overlap between AI and CI”. In general, CI?AI. crisp logic and rules in AI, and fuzzy logic and rules in CI (Zadeh). Motivation: “Input CI onto Web?”

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    Slide 5:Computational Intelligence

    fuzzy computing (FC) neural computing (NC), evolutionary computing (EC), probabilistic computing (PC), granular computing (GrC) rough computing (RC). …

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    Slide 6:Web Technology

    a hybrid technology including computer networks, the Internet, wireless networks, databases, search engines, client-server, programming languages, Web-based software, security, agents, e-business systems, and other relevant techniques.

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    Slide 7:Computational Web Intelligence (Zhang and Lin, 2002)

    Uncertainty on the Web (FLINT 2001 at BISC at UC Berkeley http://www-bisc.cs.berkeley.edu/) CWI = CI + WT (2002) CWI is a hybrid technology of Computational Intelligence (CI) and Web Technology (WT) on wired and wireless networks. CWI is dedicating to increasing QoI of e-Business applications with uncertain data on the Internet and wireless networks.

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    Slide 8:Computational Web Intelligence (cont.)

    Fuzzy Web Intelligence Neural Web Intelligence Evolutionary Web Intelligence Probabilistic Web Intelligence Granular Web Intelligence Rough Web Intelligence Hybrid Web Intelligence

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    Slide 10:Preface. . . . . . . . . . . . . . . . . . . . . . . . . v Introduction to Computational Web Intelligence and Hybrid Web Intelligence. . .. . . . . . . . . . . . . xviii Part I: Fuzzy Web Intelligence, Rough Web Intelligence and Probabilistic Web Intelligence. . . . ... . . . . . . . . . . . . . . . . . . 1 Chapter 1. Recommender Systems Based on Representations. .. . . 3 Chapter 2. Web Intelligence: Concept-based Web Search. . . . . . . 19 Chapter 3. A Fuzzy Logic Approach to Answer Retrieval from the World-Wide-Web .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Chapter 4. Fuzzy Inference Based Server Selection in Content Distribution Networks. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . 77 Chapter 5. Recommendation Based on Personal Preference. . . …..101 Chapter 6. Fuzzy Clustering and Intelligent Search for a Web-based Fabric Database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Chapter 7. Web Usage Mining: Comparison of Conventional, Fuzzy and Rough Set Clustering . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . 133 Chapter 8. Towards Web Search Using Contextual Probabilistic Independencies. . . . .. . . . . . . . . . . . . . . .. . . . . . . 149

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    Slide 11:Part II: Neural Web Intelligence, Evolutionary Web Intelligence and Granular Web Intelligence 167   Chapter 9. Neural Expert System for Vehicle Fault Diagnosis via The WWW. . . .. . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .169 Chapter 10. Dynamic Documents in The Wired World.. ... . . . .183 Chapter 11. Proximity-based Supervision for Flexible Web Page Categorization. . . . .. . . . . . .. . . . .. . . . . . . . . . 205 Chapter 12. Web Usage Mining: Business Intelligence From Web Logs. . . . 229 Chapter 13. Intelligent Content-Based Audio Classification and Retrieval for Web Application. . . . . . . . . . . . . . . . . . . . . . . . . . . 257  

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    Slide 12:Part III: Hybrid Web Intelligence and e-Applications 283 Chapter 14. Developing an Intelligent Multi-Regional Chinese Medical Portal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .285 Chapter 15. Multiplicative Adaptive User Preference Retrieval and Its Applications to Web Search. . . . . . . . . . . . . . . . . . . . . . . . . . . . .303 Chapter 16. Scalable Learning Method to Extract Biological Information from Huge Online Biomedical Literature. . . . . . . . . . . . . . . . . . .329 Chapter 17. iMASS: An Intelligent Multi-resolution Agent-based Surveillance System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .347 Chapter 18. Networking Support for Neural Network-based Web Monitoring and Filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Chapter 19. Web Intelligence: Web-based BISC Decision Support System (WBICS-DSS) . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . .391 Chapter 20. Content and Link Structure Analysis for Searching the Web. 431 Chapter 21. Mobile Agent Technology for Web Applications. . . . 453 Chapter 22. Intelligent Virtual Agents and the WEB. . . . . . . . . . .481 Chapter 23. Data Mining in Network Security. . . . . . . . . . . . . . . .501 Chapter 24. Agent-supported WI Infrastructure: Case Studies in Peer-to-peer Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Chapter 25. Intelligent Technology for Content Monitoring on the Web. .539

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    Slide 13:Wired and Wireless Applications

    CWI has various applications in intelligent e-Business on the Internet and on wireless mobile networks. 1. Neural-Net-based online Stock Agents, 2. Personalized Mobile Phone Agents, 3. Mobile Wireless Shopping Agents, 4. Mobile Wireless Fleet Application (Yamacraw Research Project).

    Slide 14:Fuzzy Neural Web Agents for Stock Prediction

       To implement this stock prediction system, Java Servlets, Java Script and Jdbc are used. SQL is used as the back-end database.

    Slide 15:Fig 1. Graph of Predicted and Real values for dow stock using complete data

    Slide 16:Personalized Wireless Information Agents for Mobile Phones

    Slide 17:Personalized Weather Agent

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    Slide 18:Mobile Wireless Shopping Agents

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    Slide 19:Mobile Fleet Application (Yamacraw Research Project)

    Automated scheduling of pickups and deliveries Distributed design Emergency Handling: On-the-fly scheduling of package exchanges between trucks (rendezvous – peer-to-peer interaction) Demo

    20 21 SyD listener TDB SyD Listener A truck (Truck1) sends a request to the SyD Listener on a peer truck using SyD Engine “invoke” method. A selected (Truck2) peer resolves the request using Its own SyD Listener and Engine. Sends the result back to the calling peer (Truck1). IP address of peers are resolved using the SyD directory service running in a central location Each device is capable of functioning as client or server. Truck1 Truck2 DBS: Database service TDB: Truck database TDB Truck AppO Truck AppO SyD Engine SyD Engine

    Slide 21:Truck to Truck Communication

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    Slide 22:Conclusion

    CWI based on CI and WT, a new research area, is proposed to increase the QoI of e-Business applications. CWI has a lot of wired and wireless applications in intelligent e-Business. FWI, NWI, EWI, PWI, GWI, RWI, and HWI are major CWI techniques currently.

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    Slide 23:Future Work

    CWI on wired and mobile wireless networks. Web Data Mining and Knowledge Discovery. Intelligent wireless mobile PDAs (do smart e-Business, Homeland Security, etc.) Intelligent Wireless Mobile Agents (in cars, houses, offices, etc.) Intelligent Bioinformatics on the Web CWI and Grid Computing.

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