Somsubhra Gupta

A Goal Programming approach for solving Interval valued Multiobjective Fractional Programming problems using Genetic Algorithm

In this article, the efficient use of a genetic algorithm (GA) to the goal programming (GP) formulation of interval valued multiobjective fractional programming problems (MOFPPs) is presented. In the proposed approach, first the interval arithmetic technique [1] is used to transform the fractional objectives with interval coefficients into the standard form of an interval programming problem with fractional criteria. Then, the redefined problem is converted into the conventional fractional goal objectives by using interval programming approach [2] and then introducing under-and over-deviational variables to each of the objectives. In the model formulation of the problem, both the aspects of GP methodologies, minsum GP and minimax GP [3] are taken into consideration to construct the interval function (achievement function) for accommodation within the ranges of the goal intervals specified in the decision situation where minimization of the regrets (deviations from the goal levels) to the extent possible within the decision environment is considered. In the solution process, instead of using conventional transformation approaches [4, 5, 6] to fractional programming, a GA approach is introduced directly into the GP framework of the proposed problem. In using the proposed GA, based on mechanism of natural selection and natural genetics, the conventional roulette wheel selection scheme and arithmetic crossover are used for achievement of the goal levels in the solution space specified in the decision environment. Here the chromosome representation of a candidate solution in the population of the GA method is encoded in binary form. Again, the interval function defined for the achievement of the fractional goal objectives is considered the fitness function in the reproduction process of the proposed GA. A numerical example is solved to illustrate the proposed approach and the model solution is compared with the solutions of the approaches [6, 7] studied previously.

Composite Transposition Substitution Chaining Based Cipher Technique

In this paper, variable block length based character / bit level transformation has been proposed for encryption, where a block of 16 characters/128 bits has been taken into account. The block is passed through a substitution operation followed by various transpositions using multi dimensional array. The block is operated with its one-time sub key which produces intermediate output of the same length. Consecutive 8 characters/64 bits are taken from the stream of plain text with the previous text block which produces a block containing stream of 192 bits. This is used as current text block in order to produce a new text block containing 24 characters using same techniques. Another 8 characters are taken with previous block and the same operations are performed to produce a block of 256 bits. If the plain text contains greater than 32 characters i.e. 256 bits, then every 256 bit block is XORed with the previous 256 bit block except the first block. Finally bits are selected randomly from MSB position and the selected bits are processed through a special substitution technique to generate the final encrypted block.

A genetic algorithm approach to fuzzy goal programming formulation of fractional multiobjective decision making problems

This paper presents a genetic algorithm (GA) based fuzzy goal programming (FGP) solution method to multiobjective decision making (MODM) problems with fractional criteria.

A genetic algorithm based stochastic simulation approach to chance constrained interval valued multiobjective decision making problems

This article presents how the stochastic simulation through genetic algorithm (GA) can be used to modeling and solving chance constrained interval valued multiobjective decision making (MODM) problems. In the proposed method, a stochastic simulation approach to the chance constraints is employed for interval valued goal representation of the objectives as well as decision identification through the use of an GA method in an inexact decision making context. In the executable goal programming (GP) model of the problem, both the aspects of the GP, minsum GP and minmax GP [1], are addressed within goal achievement function for minimizing possible regrets associated with the deviational variables of the defined goals for goal achievement within the target intervals specified in the decision making environment. A numerical example is solved and a comparison is made with the conventional GP approach.

Text Dependent Voice Based Biometric Authentication System Using Spectrum Analysis and Image Acquisition

Biometrics is concerned with identifying a person based on the physical or behavioral traits of him such as face, fingerprints, voice and iris. With the pronounced need for robust human recognition techniques in critical applications such as secure access control, international border crossing and law enforcement. Biometrics is a viable technology that can be used into large-scale identity management systems. Biometric systems work under the assumption that many of the physical or behavioral traits of humans are distinctive to an individual, and that they can be precisely acquired using sensors and represented in a numerical format that helps in automatic decision-making in the context of authentication. In the presented approach effort has been made to design a Voice based Biometric Authentication system with desired aspiration level.

A genetic algorithm-based fuzzy goal programming approach for solving fractional bilevel programming problems

This paper presents a genetic algorithm (GA) based fuzzy goal programming procedure for modelling and solving bilevel programming problems having fractional objectives in a hierarchical decision system. In the proposed approach, the concept of tolerance membership functions in fuzzy sets for measuring the degree of satisfactions of the decision-makers (DMs) regarding achievements of fuzzily described objective goals as well as the degree of optimality of the decision vector controlled by the upper-level DM are considered in the decision-making context. The proposed approach leads to achieve the highest membership value (unity) of each of the defined fuzzy goals to the extent possible in the decision-making situation. In the GA search process, the fitter codon selection scheme, two-point crossover and random mutation are adopted to reach a satisfactory solution in the decision-making environment. To illustrate the potential use of the approach, a numerical example is solved.

A genetic algorithm approach for fuzzy goal programming formulation of chance constrained problems using stochastic simulation

This paper presents how the stochastic simulation based genetic algorithm (GA) can be used to the fuzzy goal programming (FGP) formulation of a chance constrained multiobjective decision making (MODM) problem.

An Energy-Efficient Congestion Avoidance Priority-Based Routing Algorithm for Body Area Network

In this paper, a routing algorithm for human body area network has been proposed. This algorithm is energy efficient and further, avoids congestions to some extent. One of the main challenges of WBAN is the node life time, i.e. energy level of the nodes. The nodes are selected in this algorithm for communication depending upon three factors that are energy level, number of hops the packet needs to travel while opting that node, and finally the request queue length which is actually the total number of packets traversed through the node for last t time interval. The first factor is for the shortest path to the destination as human body area network consisting of critical data that is needed to be sent fast, however, annexed by other factors as well,namely life time of nodes and congestion-free communication since the shortest path may be congested sometime because of over burden of data. The second factor actually considers the energy level of nodes and the third factor tries to avoid the path which may already been followed by huge numbers of packets.

Proposed Intelligent System to Identify the Level of Risk of Cardiovascular Diseases Under the Framework of Bioinformatics

This paper proposed a method to implement an intelligent system to find out the risk of cardiovascular diseases in human being. Genetics play a direct and indirect role in increasing the risks of cardiovascular diseases. Habits and individual symptom viz. suffering from diabetes, obesity and hypertension also can influence the risk of the said diseases. Excessive energy accumulation in ones body can create fatal problem in health. In this paper, method has been proposed to the proposed to investigate three major factors i.e. family history of CVD, Other diseases and Average Energy Expenditure and find out the of level of risks of cardiovascular diseases.

Fuzzy goal programming approach to chance constrained multiobjective decision making problems using genetic algorithm

This paper presents how genetic algorithm (GA) can be used in fuzzy goal programming (FGP) formulation of multiobjective stochastic programming (SP) problems.