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CS-534 Artificial Intelligence Showcase Neural Networks: ASAP and 20Q

CS-534 Artificial Intelligence Showcase Neural Networks: ASAP and 20Q. Andrew Foltan WPI CS534 . Neural Networks. Traditional computers are von -Neumann machines suited to solving problems using well defined algorithms i nput data must be precise Neural N etworks

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CS-534 Artificial Intelligence Showcase Neural Networks: ASAP and 20Q

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  1. CS-534 Artificial Intelligence ShowcaseNeural Networks: ASAP and 20Q Andrew Foltan WPI CS534

  2. Neural Networks • Traditional computers are von-Neumann machines • suited to solving problems using well defined algorithms • input data must be precise • Neural Networks • well suited to situations with no clear algorithmic solutions • predicts solutions based on imprecise input data

  3. NN Applications • Business • Stocks prediction • Health • Breast cancer cell analysis • Sports • Horse racing • Science • Speech recognition • Weather forecasting • Solar Flare prediction

  4. Neural Network Historical Summary

  5. Automated Solar Activity Predictor • Automated real-time prediction of Solar flares using image processing and machine learning- based system • Near-real time detection and classification ofsunspot groups • Forecasts solar flares and their intensity using Neural Networks • Why? • Geomagnetic storms • Communications disruption • Satellite damage • University of Bradford (UOB), Space Weather Research Group • 2007-2009, RamiQahwaji / TufanColak • http://spaceweather.inf.brad.ac.uk/

  6. NN Learning • Input Data Set • National Geophysical Data Center (NOAA) publicly available sunspots and flares catalogues used to associate sunspots to flares • The association is determined based on the location and timing information. • The Neural Networks were optimized andtrained using this association information • Input data comprised sunspot group classification (McIntosh) and sunspot areas. • The Neural Networks are combined to produce a hybrid system to give flaring probability and intensity of each sunspot group

  7. The Flares Prediction Model ASAP’s Flares Prediction “Automated Computer-Based Prediction of Solar Flares: The ASAP System”, RamiQahwaji and TufanColak, Bradford University, England, UK

  8. Results • Accuracy depends upon each stage • ~95% on sunspot grouping • ~80% on sunspot group classification • ~90% on flare prediction depending on correct classification • Overall success rate of ~70% on final flare prediction • “AUTOMATED PREDICTION OF SOLAR FLARES: Integrating Image Processing and Machine Learning for the Creation of a Hybrid Computer Platform that Provides Real-Time Prediction of Solar Flares”, Amazon $78

  9. 20Q • Based on “20 Questions” game to identify person, place or thing • Predictive neural network • 20Q.net website created in 1995 • 20Q started out knowing one object, a cat, and one question • Trained by online played its 44,000,000th game in September 2006 • 20Q guesses correct answer 76 percent of the time (98 percent of the time with 25 questions) • “Because 20Q does not simply follow a binary decision tree, answering a question incorrectly will not throw it completely off” - 20Q.net

  10. 20Q Game Think of Something - “A bicycle” • Q1: Is it Animal, Vegetable, or Mineral? Mineral • Q2: Is it heavy? Y • Q3: Is it mechanical? Y • Q4: Can it protect you from the rain? N • Q5: Can you find it in a house? N • Q6: Does it shine? Y • Q7: Do you use it at night? Sometimes • Q8: Is it used in a sport? Y • Q9: Can you make sounds with it? N • Q10: Can it float? N • Q11: Can you walk on it? N I think your mind is giving me mixed signals… • Q12: Can you hold it when you use it? N • Q13: Do you put things in it? N • Q14: Would you be lost without it? N • Q15: Can it be easily moved? Y • Q16: Can it be refilled? N • Q17: Is it very large? N Drum roll please … It’s a bike! Online at http://20Q.net

  11. References • Automated Solar Activity Predictor http://spaceweather.inf.brad.ac.uk • Neural Networks versus Conventional Computing http://www.neuralnetworksolutions.com • Neural Networks History http://www-cs-faculty.stanford.edu/~eroberts/courses/soco/projects/neural-networks/History/history1.html • Wikipedia: Neural Networks http://en.wikipedia.org/wiki/Neural_network • “Automated Computer-Based Prediction of Solar Flares: The ASAP System”, RamiQahwaji and TufanColak, Bradford University, England, UK http://www.google.com/url?sa=t&rct=j&q=3qahwaji.ppt&source=web&cd=1&cad=rja&ved=0CDUQFjAA&url=http%3A%2F%2Fwww.spaceweather.eu%2Fro%2Frepository%2Fdownload%3Fid%3D3Qahwaji-1261139631.ppt%26file%3D3Qahwaji.ppt&ei=fb50UajqKvPy0QG4wIDADQ&usg=AFQjCNEAReW5MKb57Lm1loWRjnRfydMHCA&bvm=bv.45512109,d.dmQ • Publication, “AUTOMATED PREDICTION OF SOLAR FLARES”, Amazon.com, $78 http://www.amazon.com/AUTOMATED-PREDICTION-SOLAR-FLARES-Integrating/dp/3838370309 • SpaceWeather.net - http://esa-spaceweather.net/sda/asap/ • 20Q.net - http://www.20q.net/flat/history.html • Twenty Questions, Ten Million Synapses - http://scienceline.org/2006/07/tech-schrock-20q/ • National Geophysical Data Center, Space Weather Data - http://www.ngdc.noaa.gov/stp/spaceweather.html • Sunspot Classification - http://theastronomer.tripod.com/SolarObserving202.html#sclassifications

  12. McIntosh Sunspot Group Classification • Sunspot groups are classified by a three letter code. • The first code letter deals with the group type. • The second code letter describes the penumbra of the largest spot of the group. • The third code letter describes the compactness of the spots in the immediate part of a group. • Group Type: • A: Unipolar group without penumbra. • B: Bipolar group without penumbra on any spots. • C: Bipolar group with penumbra on one end of group, usually surrounding largest of leading umbra. • D: Bipolar group with penumbrae on spots at both ends of group and with longitudinal extent less than 10°. • E: Bipolar group with penumbrae on spots at both ends of group and with longitudinal length between 10° and 15°. • F: Bipolar group with penumbrae on spots at both ends of group and with longitudinal length more than 15°. • H: Unipolar group with penumbra. • Penumbra of Largest Spot: • x: No penumbra (class A or B) • r: Rudimentary penumbra partly surrounds largest spot. • s: Small, symmetric penumbra, elliptical or circular and N-S size smaller than 2.5". • a: Small, asymmetric penumbra, irregular in outline and N-S size smaller than 2.5°. • h: Large, symmetric penumbra, N-S size larger than 2.5°. • k: Large, asymmetric penumbra, N-S size larger than 2.5. • Spot Compactness: • x: Assigned to (but undefined for) unipolar groups (types A and H). • o: Open - few, if any, spots between leader and follower. • i: Intermediate - numerous spots between leader and follower, all without mature penumbra. • c: Compact - many large spots between leader and follower, with at least one mature penumbra. • So, for example, if you were looking up data on a particular sunspot, and found the classification "Fki", you would know that this particular spot was: • F - bipolar (containing both a positive and negative charge) with penumbra on both ends exceeding a longitudinal length of 15 degrees. • k- the leader has a large, asymmetic penumbra with a N/S size larger the 2.5. • i- and that the group is intermediate with many spots between the leader and followers without mature penumbra.

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