An Efficient Approach Formulation of Social Groups of User Calls of GSM

We are living in a world of wireless technology. The most widely used wireless i.e. mobile computing device today is the Mobile phone, can be used not only for voice and data communications but also as a computing device running context aware applications. In this paper we present a model that based on GSM data base. The objective of this paper identifies social and suspicious groups based on Cell Id, IMSI, IMEI, date and time, Location Area, MCC and MNC. This information can be used by applications for the detection of users, user context, discovering of groups and relation between them using clustering technique of data mining. One of the vital means in dealing with these data is to classify or group them into a set of categories or clusters. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collector

Valsartan (Profiles of Drugs Substances, Excipients and Related Methodology)

Valsartan is an antihypertensive drug which selectively inhibits angiotensin receptor type II. This tetrazole derivative was first developed by Novartis and marketed under brand name Diovan® . This compound is orally active and is rapidly absorbed after oral doses, having a bioavailability of approximately 23% . Valsartan appears as a white or almost white hygroscopic powder. This compound must be kept in an air-tight container and should be protected from light and heat. It is available in film-coated tablets containing valsartan 40, 80, 160, or 320 mg, and capsules with dosage of 80 or 160 mg. Tablet combinations of valsartan with hydrochlorothiazide or amlodipine are also availabl

Performance Evaluation of Dynamic Particle Swarm Optimization

Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Unconstrained optimization problems can be formulated as a D-dimensional minimization problem as follows: Min f (x) x=[x1+x2+……..xD] where D is the number of the parameters to be optimized. subjected to: Gi(x) <=0, i=1…q Hj(x) =0, j=q+1,……m Xε [Xmin, Xmax]D, q is the number of inequality constraints and m-q is the number of equality constraints. The particle swarm optimizer (PSO) is a relatively new technique. Particle swarm optimizer (PSO), introduced by Kennedy and Eberhart in 1995, [1] emulates flocking behavior of birds to solve the optimization problems.In this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO

Dynamic Particle Swarm Optimization to Solve Multi-objective Optimization Problem

AbstractMulti-objective optimization problem is reaching better understanding of the properties and techniques of evolutionary algorithms. This paper presents the Dynamic Particle Swarm Optimization algorithm for solving multiobjective optimization problem. This Dynamic PSO is different from the existing PSO's and some local version of PSO in terms of swarm size, topology and search space. In this paper swarm size criteria for dynamic PSO is considered. Experiment conducted for standard benchmark functions of multi-objective optimization problem, which shows the better performance rather the basic PSO

An Efficient Approach Formulation of Social Groups of User Calls of

Abstract -We are living in a world of wireless technology. The most widely used wireless i.e. mobile computing device today is the Mobile phone, can be used not only for voice and data communications but also as a computing device running context aware applications. In this paper we present a model that based on GSM data base. The objective of this paper identifies social and suspicious groups based on Cell Id, IMSI, IMEI, date and time, Location Area, MCC and MNC. This information can be used by applications for the detection of users, user context, discovering of groups and relation between them using clustering technique of data mining. One of the vital means in dealing with these data is to classify or group them into a set of categories or clusters. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collectors