Predicting a T20 cricket match result while the match is in progress

There has been research done on ODI and Test match cricket but very few on T20 cricket, which is currently more favorite than its older brothers. And that’s why we decided to do research on this format of the game. The result of a T20 cricket match depends on lots of in game and pre-game attributes. Pre-game attributes like condition, venue, pitch, team strength etc. and in game attributes like wickets in hand, run rate, total run, strike rate etc. influence a match result predominantly. We gave more emphasis on in game attributes as our prediction will be when match is in progress. Our intentions would be to finding out the attributes which is most affecting the result in different phases of the game. We broke an innings into three phases: Power-play (1-6 overs), Mid-overs (7- 16) and final overs (17-20). Prediction will be active till the last over of mid overs phase. We consider an entire cycle of process of data mining, decision making and preparing a model to predict. Mining the data according to the attributes and different phases we have divided important to construct meaningful statistics. Modeling a problem for prediction requires several intelligent assumptions and molding the problem with collected data-sets. As we already mentioned cricket is a game of uncertainty and T20 format is the most unpredictable format rather than the other two format because it is the shortest format of the game and one over can change the result of a game. In this project we tried to design a prediction model which can go with this unpredictability and try to make a result prediction

Evaluation of Alternative Face Detection Techniques and Video Segment Lengths on Sign Language Detection

Sign language is the primary medium of communication for people who are hearing impaired. Sign language videos are hard to discover in video sharing sites as the text-based search is based on metadata rather than the content of the videos. The sign language community currently shares content through ad-hoc mechanisms as no library meets their requirements. Low cost or even real-time classification techniques are valuable to create a sign language digital library with its content being updated as new videos are uploaded to YouTube and other video sharing sites. Prior research was able to detect sign language videos using face detection and background subtraction with recall and precision that is suitable to create a digital library. This approach analyzed one minute of each video being classified. Polar Motion Profiles achieved better recall with videos containing multiple signers but at a significant computational cost as it included five face trackers. This thesis explores techniques to reduce the computation time involved in feature extraction without overly impacting precision and recall deeply. This thesis explores three optimizations to the above techniques. First, we compared the individual performance of the five face detectors and determined the best performing single face detector. Second, we evaluated the performance detection using Polar Motion Profiles when face detection was performed on sampled frames rather than detecting in every frame. From our results, Polar Motion Profiles performed well even when the information between frames is sacrificed. Finally, we looked at the effect of using shorter video segment lengths for feature extraction. We found that the drop in precision is minor as video segments were made shorter from the initial empirical length of a minute. Through our work, we found an empirical configuration that can classify videos with close to two orders of magnitude less computation but with precision and recall not too much below the original voting scheme. Our model improves detection time of sign language videos that in turn would help enrich the digital library with fresh content quickly. Future work can be focused on enabling diarization by segmenting the video to find sign language content and non-sign language content with effective background subtraction techniques for shorter videos

Flu Trend Prediction Using Social Media Network Data

Ali Al Essa's, Miad Faezipour's, Jeongkyu Lee's, and Gopala Duggina's poster using Hadoop and MapReduce programming to analyze Twitter data about influenza to analyze flu trends