Sunita Garhwal

Conversion of fuzzy automata into fuzzy regular expressions using transitive closure

The concept of transitive closure is useful for the conversion of classical finite automata into regular expressions. In this paper, we generalize and extend the concept of transitive closure for the conversion of fuzzy automata into fuzzy regular expressions. We prove that, for a fuzzy automaton M where r is a fuzzy regular expression obtained using the proposed approach, L(M)=L(r). Finally, a numerical example illustrates this conversion.

Antispammer for Mobile Messages

SMS spams are dramatically increasing year by year due to the growth of mobile phone users around the world. Recent reports have clearly indicated the same. Mobile or SMS spam is a physical and thriving problem due to the fact that bulk pre-pay SMS packages are conveniently available these days and SMS is considered as a trusted and personal service, so it gathers more response rate from the customers. SMS spam filtering is a comparatively recent errand to deal such a problem. It inherits many concerns and quick fixes from Email spam filtering. However it fronts its own certain issues and problems. This paper inspires to work on the task of filtering mobile messages as Ham or Spam. The paper analyses different machine learning classifiers on large corpus of SMS spam.

M-ITRS: Mathematical Model for Identification of Tandem Repeats in DNA Sequence

In DNA, tandem repeat consists of two or more contiguous copies of a pattern of nucleotides. Tandem repeats of the motif are useful in many applications like molecular biology (related to genetic information of inherited diseases), forensic medicines, DNA fingerprinting and molecular markers for cancer. Various researchers designed formal models and grammars to identify two contiguous copies of the pattern. Tree-adjoining grammar cannot be designed for k-copy language. There is a need to design a formal model which will work for more than two contiguous copies of the pattern. In this paper, we have designed deep pushdown automata for k-continuous copies of the pattern for . The proposed formal model will also identify the tandem repeats without specifying the pattern and its size.In this paper, Enriched Big Bang-Big Crunch (EBC) algorithm is proposed to solve the reactive power problem. The problem of converging to local optimum solutions occurred for the Bang-Big Crunch (BB-BC) approach due to greedily looking around the best ever found solutions. The proposed algorithm takes advantages of typical Big Bang-Big Crunch (BB-BC) algorithm and enhances it with the proper balance between exploration and exploitation factors. Proposed EBC algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the improved performance of the proposed algorithm in reducing the real power loss.In this paper, Enriched Big Bang-Big Crunch (EBC) algorithm is proposed to solve the reactive power problem. The problem of converging to local optimum solutions occurred for the Bang-Big Crunch (BB-BC) approach due to greedily looking around the best ever found solutions. The proposed algorithm takes advantages of typical Big Bang-Big Crunch (BB-BC) algorithm and enhances it with the proper balance between exploration and exploitation factors. Proposed EBC algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the improved performance of the proposed algorithm in reducing the real power loss.In this paper, Enriched Big Bang-Big Crunch (EBC) algorithm is proposed to solve the reactive power problem. The problem of converging to local optimum solutions occurred for the Bang-Big Crunch (BB-BC) approach due to greedily looking around the best ever found solutions. The proposed algorithm takes advantages of typical Big Bang-Big Crunch (BB-BC) algorithm and enhances it with the proper balance between exploration and exploitation factors. Proposed EBC algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the improved performance of the proposed algorithm in reducing the real power loss.Optimization algorithms are search methods to find an optimal solution to a problem with a set of constraints. Bio-Inspired Algorithms (BIAs) are based on biological behavior to solve a real world problem. BIA with optimization technique is to improve the overall performance of BIA. The aim of this paper is to introduce a novel optimization algorithm which is inspired by natural stinging behavior of honey bee to find the optimal solution. This algorithm performs both monitor and sting if any occurrence of predators. By applying a novel optimization algorithm based on stinging behavior of bee, used to solve the intrusion detection problems. In this paper, a new host intrusion detection system based on novel optimization algorithm has been proposed and implemented. The performance of the proposed Anomaly-based Host Intrusion Detection System (A-HIDS) using a novel optimization algorithm based on stinging behavior of bee has been tested. In this paper, after an explanation of the natural stinging behavior of honey bee, a novel optimization algorithm and A-HIDS are described and implemented. The results show that the novel optimization algorithm offers some advantage according to the nature of the problem.

HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records

Objectives One of the most important functions for a medical practitioner while treating a patient is to study the patients complete medical history by going through all records, from test results to doctors notes. With the increasing use of technology in medicine, these records are mostly digital, alleviating the problem of looking through a stack of papers, which are easily misplaced, but some of these are in an unstructured form. Large parts of clinical reports are in written text form and are tedious to use directly without appropriate pre-processing. In medical research, such health records may be a good, convenient source of medical data; however, lack of structure means that the data is unfit for statistical evaluation. In this paper, we introduce a system to extract, store, retrieve, and analyse information from health records, with a focus on the Indian healthcare scene. Methods A Python-based tool, Healthcare Data Extraction and Analysis (HEDEA), has been designed to extract structured information from various medical records using a regular expression-based approach. Results The HEDEA system is working, covering a large set of formats, to extract and analyse health information. Conclusions This tool can be used to generate analysis report and charts using the central database. This information is only provided after prior approval has been received from the patient for medical research purposes.

Revising Antimirov's Partial Derivatives for Fuzzy Regular Expressions

Antimirovs partial derivatives are used in classical automata theory for the conversion of regular expressions to finite automata, tree regular expressions to tree automata, and ω-regular expressions to Buchi automata. In this paper, we describe a new variant of the Antimirovs partial derivatives for the conversion of fuzzy regular expressions to ε-free fuzzy non-deterministic finite automata. The membership values in fuzzy automata are assigned by using integral lattice-ordered monoids. The resulting automaton turns out to be an improvement over the existing approaches, because it is ε -free. Furthermore, herein it is formally proved that a non-deterministic fuzzy automaton M is obtained from a fuzzy regular expression r such that LR(M) LR(r).

Comprehensive analysis of various rough set tools for data mining

Rough set theory is a present day scientific way to deal with imperfect information. Rough sets have been prescribed for a wide assortment of uses. Unequivocally, the rough set methodology is by all accounts basic and critical for Artificial Intelligence and subjective sciences, especially in data mining, knowledge discovery, machine learning, expert systems and pattern acknowledgment. In this paper, we examine data mining programming frameworks inside of the system of rough sets against a few perspectives, for example, the technical specifications and specializations alongside its constraints. By studying the analysis, the decision and choice of tools can be made simple.

SMS spam detection for Indian messages

The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. Though in most parts of the world, mobile messaging channel is currently regarded as “clean” and trusted, on the contrast recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. It is an evolving setback especially in the Middle East and Asia. SMS spam filtering is a comparatively recent errand to deal such a problem. It inherits many concerns and quick fixes from Email spam filtering. However it fronts its own certain issues and problems. This paper inspires to work on the task of filtering mobile messages as Ham or Spam for the Indian Users by adding Indian messages to the worldwide available SMS dataset. The paper analyses different machine learning classifiers on large corpus of SMS messages for Indian people.

Closure Properties of Prefix-free Regular Languages

Regular languages are closed under union, intersection, complementation, Kleene-closure and reversal operations. Regular languages can be classified into infix-free, prefixfree and suffix-free. In this paper various closure properties of prefix-free regular languages are investigated and result shows that prefix-free regular languages are closed under union and concatenation. Under complementation, reverse, Kleene-closure and intersection operations prefix-free regular languages are not closed. General Terms Theoretical Computer Science