Computational Web Intelligence for Wired and Wireless Applications

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Computational Web Intelligence for Wired and
Wireless Applications

• Yan-Qing Zhang
• Department of Computer Science
• Georgia State University
• Atlanta, GA 30302-4110
  yzhang_at_cs.gsu.edu

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Outline

• Introduction
• Computational Intelligence
• Web Technology
• Computational Web Intelligence (CWI)
• Wired and Wireless Applications
• Conclusion and Future Work

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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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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Computational Intelligence

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

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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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Computational Web Intelligence (Zhang and Lin,
2002)

• Uncertainty on the Web (FLINT 2001 at BISC at UC
  Berkeley http//www-bisc.cs.berkeley.edu/)
  (Zhang, et al, 2001 (a), (b) (c))
• CWI CI WT (Zhang and Lin, 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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Computational Web Intelligence (cont.) (Zhang and
Lin 2002)

• 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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• 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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• 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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• 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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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).

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Fuzzy Neural Web Agents for Stock Prediction
(Zhang, et al, 2001)

•  

 To implement this stock prediction system, Java
Servlets, Java Script and Jdbc are used. SQL is
used as the back-end database.
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Fig 1. Graph of Predicted and Real values for
dow stock using complete data (Zhang, et al,
2001)
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Personalized Wireless Information Agents for
Mobile Phones
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Personalized Weather Agent
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• Mobile Wireless Shopping Agents

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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

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Truck to Truck Communication

• 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.

SyD listener
SyD Listener
Truck AppO
Truck AppO
SyD Engine
SyD Engine
TDB
TDB
DBS Database service TDB Truck database
Truck1
Truck2
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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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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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References

• 1 Y.-Q. Zhang, A. Kandel, T.Y. Lin and Y.Y. Yao
  (eds.), Computational Web Intelligence
  Intelligent Technology for Web Applications,
  Series in Machine Perception and Artificial
  Intelligence, volume 58, World Scientific, 2004.
• 2 Y.-Q. Zhang and T.Y. Lin, Computational Web
  Intelligence (CWI) Synergy of Computational
  Intelligence and Web Technology, Proc. of
  FUZZ-IEEE2002 of World Congress on Computational
  Intelligence 2002 Special Session on
  Computational Web Intelligence, pp. 1104-1107,
  Honolulu, May 2002.
• 3 M. Atlas and Y.-Q. Zhang, Fuzzy Neural Web
  Agents for Efficient NBA Scouting, Web
  Intelligence and Agent Systems An International
  Journal, vol. 6, no. 1, pp. 83-91, 2008.
• 4 Y.-Q.  Zhang, S. Hang, T.Y. Lin and Y.Y. Yao,
  Granular Fuzzy Web Search Agents, Proc. of
  FLINT2001, pp. 95-100, UC Berkeley, Aug. 14-18,
  2001.
• 5 Y.-Q. Zhang, S. Akkaladevi, G. Vachtsevanos
  and T.Y. Lin,  Fuzzy Neural Web Agents for Stock
  Prediction, Proc. of FLINT2001, pp. 101-105, UC
  Berkeley, Aug. 14-18, 2001.
• 6 Y. Tang and Y.-Q. Zhang, Personalized
  Library Search Agents Using Data Mining
  Techniques, Proc. of FLINT2001, pp. 119-124, UC
  Berkeley, Aug. 14-18, 2001.

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Thank you!

• Any Question?