| Cool team/project name |
Students (link names to email) |
Brief description of project idea |
Final idea or still exploring? |
Need additional team members? |
| Music Genie |
Suman Manjunath
Ramkumar
Mrunal Upadhyay
Mansi Sharma
|
A song recommender system on the lines of last.fm, pandora |
Finalized |
Have: 4
Need: 0 |
| Xformer's |
Subhav Mital, Bharathi Ravishanker
|
Suggests which stocks to purchase based on an intelligent
analysis/algorithms. This includes determining the probability of
stock prices rising or falling, sensing current market trends/economy,
digging data from a repository of previous successful buy ins, ability
to customize it based on current needs. FOCUS: User Profiling and Adaptation. May involve some other non-trivial computations for
arriving at decisions |
Finalized |
Have: 2
Need: 0 |
| Stock Marketer's |
S B Harish, Shirpaa Manoharan |
Suggests which stocks to purchase based on an intelligent
analysis/algorithms. This includes determining the probability of
stock prices rising or falling, sensing current market trends/economy,
digging data from a repository of previous successful buy ins, ability
to customize it based on current needs.FOCUS: Time Series Prediction and Adaptation. May involve some other non-trivial computations for
arriving at decisions |
Finalized. |
Have: 2
Need: 0 |
| Robot Sword Fighting Simulation |
Jiuguang Wang, Douglas Brooks,
Hae Won Park |
Retrieving past defense moves for Robot Sword Fighting simulation. Memorize past attacks so that when a new attack initiates,
past defensive move is retrieved and modified to defend the attack. Essentially, this involves building a dataset from
simulations and adapting a CBR system with very fast retrieval/revise step. |
Finalized |
Have: 3
Need: 0 |
| Performance driven automatic system configuration |
Pengcheng Xiong, Deepal Jayasinghe, Aayush Garg |
We plan to create a CBR system for performance driven automatic system configuration. We firstly collect the data to build a case base. The case include the performance data like response time, throughput as well as the configuration data. The configuration data may include attributes like, the hardware of machines, the software configurations, e.g., web server (Apache) configuration, application server (Tomcat) configuration, cluster (CJDBC) configuration and also DB(mysql) configuration. We may also specify other attributes like the workload and number of replicas. Then we let the customer specify the case, like what is their configuration. By doing retrieval in the CBR system that we built, we can give the solution to customer, e.g., what response time or throughput the customer can expect. The customers can deploy the configuration and do the revision and reuse step of the CBR. Finally, they retained their case in the CBR system. |
Finalized |
Have: 3
Need: 0 |
| CBR-based Attitudes for Robotic Behaviors |
Abhishek Shroff,
Sung Hyun Park,
Chien-Ming Huang
|
If a robot can dynamically alter (create, maintain, change, or adapt) its attitudes toward different objects, it would be taken as more “personal” to the users, and we can expect more emotional attachment from the users for their own robots. We will be building a CBR system for retrieving appropriate attitude when certain objects are in view of the robot based on what features they have.
|
Exploring |
Have 3,
Need 0 |
| SuperME! |
Akshit Proothi,Apurva,
Dev Priya,Ramakrishnan |
To try and improve the performance of games as the number of traces keeps increasing.Key focus would be on better retrieval and thus, try to create a "Super ME" which could collect information from multiple players and try to be the ultimate opponent |
exploring |
Have: 4
Need:0 |
| Text summarization using CBR |
Hrishikesh Mantri , Deepak Zambre , Hrishikesh Pathak , Shreyansh Gandhi |
The idea is to use pairs of as case base for summarizing a new document given as query. The summarization can be thought of as evaluating the similarity in structure/domain/relationship to existing cases, then using the most similar case's summary to identify the focus points of the query document. |
exploring |
Have :4
Need : 0 |
| CBArch |
Andres Cavieres, Radhika Shivapurkar, Urjit Singh Bhatia, Preetam Joshi |
The main goal is to assist architects and engineers to design more sustainable buildings. Sustainable building depends on the tuning of various features such as location, spatial configuration, solar orientation, shapes, size, construction materials, main activities, energy type, etc. For that purpose case retrieval will be based on extensive data sets provided by Energy Information Administration, which is a government department responsible for generating official energy statistics. Case adaptation is planned to be done within CAD environments using parametric modeling techniques.proposal here |
Finalized |
Have :4
Need :0 |
| A Case Based Mind Engine (ACME) |
Parth Parekh, Will Wagstaff, Sethuraman Krishnan, Akash Shah |
Improve the performance of D2 for the BattleCity like games. |
exploring |
Have :4
Need : 0 |
| SpamAssassins |
Shantanu Gupta, Pranesh P Ranganathan,Ashwin Raghunathan |
Filters Spam messages using a CBR framework. A wide variety of Word, character and special character occurences can be analyzed to filter out Spam and non-Spam email messages. |
Finalized. |
Have: 3
Need: 0 |
| Zeus |
Swaroop Butala, Siddharth Mehta |
Anonymous Microsoft Web Data Database: (can be found here)
ftp://ftp.ics.uci.edu/pub/machine-learning-databases/anonymous
The data is created by sampling and processing the www.microsoft.com logs.The data records the use of www.microsoft.com by 38000 anonymous, randomly-selected users. For each user, the data lists all the areas of the web site that user visited in a one week timeframe.
One can auto suggest links to user based on his history and what page he is on at that moment.
|
Finalized |
Have :2
Need : 0 |
