Sudip Sanyal

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.

A Multifactor Secure Authentication System for Wireless Payment

Organizations are deploying wireless based online payment applications to expand their business globally, it increases the growing need of regulatory requirements for the protection of confidential data, and especially in internet based financial areas. Existing internet based authentication systems often use either the Web or the Mobile channel individually to confirm the claimed identity of the remote user. The vulnerability is that access is based on only single factor authentication which is not secure to protect user data, there is a need of multifactor authentication. This paper proposes a new protocol based on multifactor authentication system that is both secure and highly usable. It uses a novel approach based on Transaction Identification Code and SMS to enforce another security level with the traditional Login/password system. The system provides a highly secure environment that is simple to use and deploy with in a limited resources that does not require any change in infrastructure or underline protocol of wireless network. This Protocol for Wireless Payment is extended as a two way authentications system to satisfy the emerging market need of mutual authentication and also supports secure B2B communication which increases faith of the user and business organizations on wireless financial transaction using mobile devices.

Hybrid Approach for Parallelization of Sequential Code with Function Level and Block Level Parallelization

Automatic parallelization of a sequential code is about finding parallel segments in the code and executing these segments parallely by sending them to different computers in a grid. Basically, parallel segments in the code can be found by doing block level analysis, instruction level analysis or function level analysis. Block is any continuous part of the code that performs a particular task. This paper talks about a hybrid approach that combines the block level analysis with functional level analysis for parallelization of sequential code and its illustrates its advantages over block level parallelization and function level parallelization performed independently. In this approach, segments of code are identified as basic blocks. These blocks are analyzed to identify them as parallelizable or dependent. Loops which are also identified as blocks are parallelized using existing loop parallelization techniques. This information would be used for automatic parallel processing of the set of independent blocks on different nodes in the grid using message passing interface (MPI). The system will annotate the MPI library functions to the program at appropriate positions in the source code to proceed with the automatic parallelization and execution of the program

Quantum dynamics: Path integral approach to time correlation functions in finite temperature

We propose a method to calculate time correlation functions using path integral formulation of quantum mechanics. The accuracy of the proposed method is examined by comparing the calculated result with exact and centroid molecular dynamics results.

A Hybrid Approach for Word Alignment in English-Hindi Parallel Corpora with Scarce Resources

This paper presents an approach which improves the performance of the word alignment with scarce resources for English-Hindi language pair. We obtain an improvement in the performance of IBM Model 1-2 algorithm by applying part of speech (POS) tag prior to the computation of word alignment probability. This paper demonstrates the increase of precision, recall and F-measure by approximately 15%, 11%, 14% respectively and reduction in Alignment Error Rate (AER) by approximately 14% with IBM Model 1. Similarly it shows an increase of precision, recall and F-measure by approximately 6%, 6% and 6% respectively and reduction in Alignment Error Rate (AER) by approximately 6% with IBM Model 2. Experiments of this paper are based on TDIL corpus.

Software Installation on a Huge Heterogeneous Network using Mobile Agents

In this paper we describe a system that performs software installation on a vast heterogeneous network using mobile agents. More specifically, the Java aglets platform has been used to achieve this purpose. We require that a one time installation of the Java aglet server is done on all nodes in the network. When new software is to be installed, the central server is responsible to send agents over the network that performs the installation. Error handling and security features have also been addressed in our system.

Statistics and linguistic rules in multiword extraction:a comparative analysis

A hybrid methodology is proposed for extracting multiword expressions based on linguistic and statistical information. In the proposed methodology, N-grams are extracted by linguistic patterns and then various statistical measures are applied for classifying these N-grams as multiword expressions. To solve the problem of deciding cut-off boundary threshold in statistical filtering phase, a novel method for calculating boundary threshold is designed. Comparative analysis between the baseline method and the proposed methodology is presented. In the baseline method, firstly, N-grams are filtered by statistical measures and then linguistic filtering is applied. Precision, recall and ƒ-Score are calculated on manually annotated corpus. Observed results show that the proposed methodology provides good results for certain types of multiword expressions like compound nouns, verb-particles and verb-verb.

Segmenting Long Sentence Pairs to Improve Word Alignment in English-Hindi Parallel Corpora

This paper presents an approach which improves the performance of word alignment for English-Hindi language pair. Longer sentences in the corpus create severe problems like the high computational requirements and poor quality of resulting word alignment. Here, we present a method to solve these problems by breaking the longer sentence pairs into shorter ones. Our approach first breaks the source and target sentences into clauses and then treats the resulting clause pairs as sentence pairs to train word alignment model. We also report preliminary work on automatically identifying clause boundaries which are appropriate for improvement of word alignment. This paper demonstrates the increase of precision, recall and F-measure by approximately 11%, 7%, 10% respectively and reduction in Alignment Error Rate (AER) by approximately 10% in the performance of IBM Model 1 for word alignment. These results are obtained by training on 270 sentence pair and testing on 30 sentence pairs. Experiments of this paper are based on TDIL corpus.

POS-based word alignment for small corpus

A good quality word alignment system needs a high quality, domain specific and large size of the parallel corpus for training. Finding a high quality parallel corpus for a particular language pair in a specific domain is expensive and hard to find. Conversely, using a small corpus has some advantages like less training time and low memory requirement. It can be created or corrected manually. So this paper is an effort to achieve good quality word alignment with a small size of parallel corpus. This paper describes use of part of speech (POS) tag to improve the performance of statistical word alignment. This approach works well with small size of the corpus. Experiments were conducted on TDIL sample tourism corpus of 1000 sentences for English-Hindi language pair. Out of these 1000 sentences 950 sentences are used for training and 50 sentences are used for testing. F-measure is increased by approximately 4% and Alignment Error Rate (AER) decreased by approximately 4% in comparison to baseline system for word alignment GIZA++.

A Hybrid Approach for Word Alignment with Statistical Modeling and Chunker

This paper presents a hybrid approach to improve word alignment with Statistical Modeling and Chunker for English-Hindi language pair. We first apply the standard word alignment technique to get an approximate alignment. The source and target language sentences are divided into chunks. The approximate word alignment is then used to align the chunks. The aligned chunks are then used to improve the original word alignment.