Classification System. Hybrid Intelligence classification system is a system that

combines at least two intelligent technologies. Specifically, the focus of this project is to

apply hybrid Neuro-Fuzzy system to the IBM Watson data and Innocentive

Trustworthiness challenge data for prediction and classification. Neural network are low-

level computational structure which has ability to learn and performs well on the raw

data. On the other hand, fuzzy logic deals with reasoning on higher level using If-then

rules and linguistic variables. So combining these two methods can provide us with a

very powerful classification system. ]]>

declarative representation and use it as a basis for predictive analysis. It is a framework that

allows us to express complex probability distributions in a simple way. PGMs can be applied to a

variety of scenarios wherein a model is built to reflect the conditional dependencies between

random variables and then used to simulate the interactions between them to draw conclusions.

The framework further provides many algorithms to analyze these models and extract

information.

One of the applications of PGMs is in solving mathematical puzzles such as Sudoku.

Sudoku is a popular number puzzle that involves filling in empty cells in an ‘N x N’ grid in such

a way that numbers 1 to N appear only once in each row, column and ‘N 1/2 x N 1/2 ’ sub-grid. We

can model this problem as a PGM and represent it in the form of a bipartite graph. The main

concepts we employ to obtain an algorithm to solve Sudoku puzzles are factor graphs and

message passing algorithms. In this project we attempt to modify the sum-product message

passing algorithm to solve the puzzle. Additionally, we implement a solution using Sinkhorn

balancing to overcome the impact of loopy propagation and compare its performance with the

former. ]]>

a larger text. In search engines, Text summarization can be used for

generating compressed descriptions of web pages. For indexing, these can

be used rather than whole pages when building inverted indexes. For query

results, summaries can be used for snippet generation. In this project, we

research on several techniques of text summarization. We evaluate these

techniques for quality of the generated summary and time required to

generate it. We implement the technique chosen from the evaluation in

Yioop, an open source, PHP-based search engine. ]]>

for modeling huge datasets involving lots of uncertainties among its various interdependent

feature sets. Some of the most common applications of these models are image segmentation,

medical diagnosis and various other data clustering and data classification applications. A

classification problem deals with identifying to which category a particular instance belongs to,

based on previous knowledge acquired by analysis of various such instances. The instances are

described using a set of variables called attributes or features. A Naive Bayes model assumes that

all the attributes of an instance are independent of each other given the class of that instance.

This is a very simple representation of the system, but the independence assumptions made in

this model are incorrect and unrealistic. The TAN model improves on the Naive Bayes model by

adding one more level of interaction among attributes of the system. In the TAN model, every

attribute is dependent on its class and one other attribute from the feature set. Since this model

incorporates the dependencies among the attributes, it is more realistic than a Naive Bayes

model. This project analyzes the performance of these two models on various datasets. The TAN

model gives better performance results if there are correlations between the attributes but the

performance is almost the same as that of Naive Bayes model, if there are not enough

correlations between the attributes of the system. ]]>