Part of Speech Tagging of Marathi Text Using Trigram Method

In this paper we present a Marathi part of speech tagger. It is a morphologically rich language. It is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using trigram Method. The main concept of trigram is to explore the most likely POS for a token based on given information of previous two tags by calculating probabilities to determine which is the best sequence of a tag. In this paper we show the development of the tagger. Moreover we have also shown the evaluation done

Extreme Learning Machine Based Prognostics of Battery Life

This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions, using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques

On Finding Minimally Unsatisfiable Cores of CSPs

International audienceWhen a Constraint Satisfaction Problem (CSP) admits no solution, it can be useful to pinpoint which constraints are actually contradicting one another and make the problem infeasible. In this paper, a recent heuristic-based approach to compute infeasible min- imal subparts of discrete CSPs, also called Minimally Unsatisfiable Cores (MUCs), is improved. The approach is based on the heuristic exploitation of the number of times each constraint has been falsified during previous failed search steps. It appears to en- hance the performance of the initial technique, which was the most efficient one until now

A new linear genetic programming approach based on straight line programs: Some theoretical and experimental aspects

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representation

A SOFTWARE AGENT FRAMEWORK TO OVERCOME MALICIOUS HOST THREATS AND UNCONTROLLED AGENT CLONES

ABSTRACT An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. Enormous number of researches is going on by comparing the functional similarities of the Human Immune System for making the agents more adaptable in regard with security. In this research work, the functional similarities of Human Nervous system are given to the agents by proposing an agent-based framework where the agents can adapt themselves from one of the threats, the malicious host attack. The agents become aware of the malicious hosts' attack by learning and coordination is maintained by a Co-Agent to make this system work successfully. The concept of learning and coordination are taken from the Human Nervous system functionality. This system has shown a better functioning in maintaining the system performance by making the agents aware of malicious hosts and by producing limited number of clones

Data Mining: How Popular Is It?

Data Mining is a process used in the industry, to facilitate decision making. As the name implies, large volumes of data is mined or sifted, to find useful information for decision making. With the advent of E-business, Data Mining has become more important to practitioners. The purpose of this paper is to find out the importance of Data Mining by looking at the different application areas that have used data mining for decision making

A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research