Proceedings of the Third International Conference on Soft Computing for Problem Solving

Genetic Algorithm (GA) mimics natural evolutionary process. Since dying of an organism is important part of natural evolutionary process, GA should have some mechanism for dying of solutions just like GA have crossover operator for birth of solutions. In nature, occurrence of event of dying of an organism has some reasons like aging, disease, malnutrition and so on. In this work we propose three strategies of dying or removal of solution from next generation population. Multi-objective Genetic Algorithm (MOGA) takes decision of removal of solution, based on one of these three strategies. Experiments were performed to show impact of dying of solutions and dying strategies on the performance of MOGA.

In the past recent years, a growing environmental consciousness has been shaping the way society looks on green. Society’s attitude towards environmental issues has been changing and hence recoverable product environments are becoming an increasingly important segment of the overall push in industry. We have proposed a model for the design of a closed loop supply chain with green components. We have investigated a joint economic production quantity model for a single vendor, single buyer system considering lot-splitting. The effect of deterioration is taken into consideration. Here we have assumed that the vendor fulfils the buyer’s demand with the produced and remanufactured units, where the remanufactured items are considered as good as those of new items. Mathematical and numerical analysis are presented to describe the situation.

The Static Synchronous Compensator (STATCOM) is the typical Flexible AC Transmission System (FACTS) devices playing a vital role as a stability aid for the large transient disturbances in a multi-machine power system. This paper deals with the design of STATCOM with two different controllers installed in a multi-machine power system. The disturbances are created in the multi-machine system and the results obtained are analyzed. The results proved the supremacy of the feedback linearizing controller tuned by Bacterial Foraging Optimization Algorithm (BFOA) of STATCOM over the power system stabilizer (PSS) equipped with PI Proportional-Integral controller of STATCOM.

In last some decades, Fuzzy topology has been extensively used in logic programming. It has been noticed by several researchers that, this theory was applied on various logical program to find more truthful result. The strength of fuzzy mathematics lies in its usefulness and having fruitful applications especially outside mathematics. In this paper, we prove some common fixed point theorem by using E.A. property in fuzzy metric spaces. We prove our results in fuzzy metric spaces in the sense of Kramosil and Michalek [

1

]. Our result generalize and extend relevant result of Mihet [

2

] and Vijayaraju [

3

]. An application of finite families of self mappings is given to support our result.

The objective of this paper is to study the historical development of computer programmers for playing the game of checkers. Since the game-playing is a NP-hard problem, it would be interesting to use evolutionary algorithms to solve them. The question is can a programme be developed which can beat humans with complete success, it may appears that some challenges may also be formed which may substantiate the argument of the paper. Further, these challenges also form a part of this study.

This paper presents a feature based Computer Aided Process Planning (CAPP) system for rotational components. While developing a feature based CAPP system, a set of flexible process plans were encountered and a population based heuristics namely Genetic Algorithm (GA) is used to obtain a most optimal process plan for a defined objective function. The objective function for optimization of process plan is the minimization of manufacturing score. Proposed methodology has been implemented for an example part having different rotational features.

The efficiency of Wireless Sensor Networks (WSNs) depends on the routing protocols used, since routing protocol provide the best possible data transmission route from sensor nodes to sink to save energy of nodes in the network. The clustering schemes enhance the network lifetime, raise the scalability and reduce the energy consumption of the sensor network. The work in this paper presents the comparative analysis of the energy efficient routing protocols for WSN such as SEP, TSEP and DSEP. The optimized routing protocol has been proposed on the basis of the network life time, stability and cluster head selection for efficient working of the sensor networks.

This paper addresses a new approach for rotor parameter estimation of induction motors. Condition monitoring of electric motors avoids unexpected motor failures and greatly improves system reliability and maintainability. These are very important issues in motor-driven and power-electronics systems since they are very important issues in motor-driven and power-electronics systems since they can greatly improve the reliability, availability, and maintainability of the system. Induction motors are critical components in many industrial processes early fault diagnosis and Condition monitoring can increase machinery availability and performance, reduces consequential damage, prolong machine life (Mirahl et al., Condition monitoring of squirrel-cage induction motors fed PWM -based drives using a parameter estimation approach, 2004) [

1

], reduce spare parts and breakdown maintenance (Siddique et al., IEEE Trans. Energy Convers., 20:106–114, 2005) [

2

]. A reliable parameter estimation technique for induction motors is critical for the development of high-performance drive systems, and it can also be utilized for condition monitoring applications as well. An accurate parameter estimation technique can also be used for motor condition monitoring purposes. In this paper, a simple and reliable technique, based on parameter estimation methods, is introduced for rotor broken bar fault detection (Watson, The use of line current as a condition monitoring tool for three phase induction motors) [

3

].

Speech Recognition is the process of automatically recognizing the spoken words of person based on information in speech signal. Recognition technique makes it possible to the speaker’s voice to be used in verifying their identity and control access to services such as voice dialing, telephone shopping, banking by telephone, database access services, information service, voice mail, remote access to computers and security control for the confidential information areas. Digital filter plays an important role in digital signal processing applications. Digital filter can also be applied in speech processing applications, such as speech enhancement, speech filtering, noise reduction and automatic speech recognition among others. Filtering is a widely researched topic in the present era of communications. As the received signal is continuously corrupted by noise where both the received signal and noise change continuously, and then arises the need for filtering. This paper provides introduction to Barthannwin Wave Filter based on Barthannwin window for speech signal modeling suited to high performance and robust isolated word recognition. It provides efficient performances with less computational complexity. A Barthannwin Wave filter is designed based on the estimated noise statistics, and it is useful for noise reduction of the speech. The proposed filtering scheme outperforms other existing speech enhancement methods in terms of accuracy in a word recognition system.

Graph coloring is a widely studied method of assigning labels or colors to elements of a graph. This can also be mapped with bio-inspired bird flocking algorithms to solve the NP complete graph coloring problem in optimum time complexity. This paper proposes an application of the Bird flocking algorithm that uses the concepts of a flock of agents, e.g. birds moving together in a complex manner with simple local rules namely cohesion, alignment, separation and avoidance. Each bird representing one data, move with the aim of creating homogeneous groups of data in a two dimensional environment producing a spatial distribution that can be used to solve a particular computational problem. The combination of these characteristics can be used to design and solve the task of 3 coloring graphs. This graph labeling can hierarchically or linearly be applied on a domain specific network or set of items.

In present world, we must use assorted high tech devices and equipments to get our jobs done and make the life simpler. These devices can be controlled by home care taker from any place since the home care taker might not present at home. Thus we need a remote interaction system with our every day essential devices for a technology blended state of the art life of present time. Smart home is equipped with such system so that we can control our home appliances from any location. In order to examine a true remote and enough secure solution to be really favorable and practicable, mobile technology is better than any other solution. In this paper we introduce new criteria so that the unremarkable services of the mobile phones can enlarged to communicate with and operate the home appliances and make our home a really well groomed one with obvious cost effective technology.

Resource Management in Grid computing system is a fundamental issue in achieving high performance due to the distributed and heterogeneous nature of the resources. The efficiency and effectiveness of Grid resource management greatly depend on the scheduling algorithm. In this paper, the problem of scheduling is represented by a weighted directed acyclic graph (DAG). Ant Colony Optimization is used for scheduling tasks on resources in Grid which simultaneously pay attention to two objectives of makespan (schedule length) and the failure probability (reliability). These objectives are conflicting and it is not possible to minimize both objectives at the same time. With the help of concept of non-dominance, we are able to choose a trade-off between makespan minimization and reliability maximization. For evaluating the algorithm, ACO is compared with NSGA-II. The metrics for evaluating the convergence and diversity of the obtained non-dominated solutions by the two algorithms are reported. The results of simulation using JAVA programming language manifest that proposed approach can be used more efficiently for allocating the tasks as compared to NSGA-II.

In this paper, we consider the problem of evaluating system reliability using Markov modeling approach, in which Times to failure and Times to repair of the operating units are assumed to follow exponential distribution. For this purpose, a method has been developed to construct a fuzzy set as an estimator for unknown parameters in the proposed statistical model. Using α-cut approach, the membership functions of MTTF and Availability are then constructed using Non-Linear Programming approach. With the proposed approach, explicit closed-form expressions of the system characteristics are obtained by inverting the interval limits of α-cuts of membership functions.

The traditional inventory model assumes that a retailer accepts the offer of delay in payments since he does not have the capital with him. Even when he has to make the payments at the end of credit limit, he takes a loan to pay off the supplier. The model focuses on commodities having quadratic demand with trade credit policies. The commodities considered in this model are perishable stock whose deterioration starts immediately as soon as you store the items. We have considered all the factors which one retailer must kept in mind deciding his inventory level, the concept of inflation and time value of money is also considered.

The occurrence of Cholelithiasis is the commonest biliary disease to be reported in India. Our research is aimed to assess the potential association between the gallbladder stone and the blood analysis through a cohort study. An attempt is also made to illustrate the importance of image enhancement technique by grey level mapping in digital image processing for the better analysis of biliary system and its related complications.

The present study represents a computational prediction for the effect of thermal gradient on the vibrations of non-homogeneous four sided clamped skew plate with variable thickness. Authors assumed that temperature varies bi-linearly, density of the plate’s material varies linearly in one direction due to non-homogeneity and thickness of plate varies exponentially in one direction. The general equation of motion and consecutive equations are solved by using the Rayleigh–Ritz method. Calculations are made for natural frequencies for first two modes of vibration of a parallelogram plate (a special type of skew plate) at the different combinations of parameters. Results are shown in graphs.

In this paper reconstruction algorithm of convex binary image in discrete tomography made efficient by implementing branch and bound method. We focus on diagonal and anti-diagonal (dad) projections and comparison done with the conventional horizontal and vertical (hv) projections. It was shown that proposed strategy is computationally strong and gives fast reconstruction.

Sentiment classification is classification of reviews into positive or negative depends on the sentiment words expressed in reviews. Automatic sentiment classification is necessary in various applications such as market analysis, opinion mining, contextual advertisement and opinion summarization. However, sentiments are expressed differently in different domain and annotating label for every domain of interest is expensive and time consuming. In cross domain sentiment classification, a sentiment classifier trained in source domain is applied to classify reviews of target domain, always produce low performance due to the occurrence of features mismatch between source domain and target domain. The proposed method develops solution to feature mismatch problem in cross domain sentiment classification by creating enhanced sentiment sensitive thesaurus using wiktionary. The enhanced sentiment sensitive thesaurus aligns different words in expressing the same sentiment not only from different domains of reviews and from wiktionary to increase the classification performance in target domain. In this paper, the proposed method describes the method of construction of enhanced sentiment sensitive thesaurus which will be useful for cross domain sentiment classification.

Wireless sensor network is crowded network, which consists lots of nodes. There is some constrained in wireless sensor network like awareness of energy, environmental constraints like temperature, pressure, sound. The aim of this research is to analyze the energy efficient operation in the sensor node. For this purpose, Low energy adaptive clustering hierarchy (LEACH), Stable Election Protocol (SEP) and Gateway Cluster Head Election-FL (GCHE-FL) protocols were analyzed and compared Most of the researchers have checked topologies and architectures that allow energy efficient operation in the wireless sensor node. Clustering is one of the most popular techniques to reduce the energy in the sensor network. Various node clustering methods have been reported in the literature. In the present research article, the fuzzy logic technique is used in Cluster Head Election and Protocol Gateway for heterogeneous sensor network. It is observed that in GCHE-FL, the sensor node being alive for much more time as compared to LEACH, SEP.

Currently search engines are using keyword based approach which becomes problematic when the user is not aware about the way to write query such that desirable results only appear because for that he must know the semantic concepts that are used in that particular domain in which he is interested. To overcome this problem, we introduced the concept of Semantic Web which is an extension of the current Web that allows the meaning of information to be precisely described in terms of well-defined vocabularies that are understood by people and computers. Ontology is one of the fundamental ingredients used in the semantic web infrastructure. This paper focuses on the problem of internet users who most of the time looks for the good hotels available beforehand whenever they plan any trip. At times they go for image search option to have a view of hotels in a particular location. But problem is user gets confused whether search results displayed online by currently available search engines are reliable or not. This paper aims to implement a tool which is based on Semantic Web for searching the images of hotels and displaying the results. Semantic web refines the search in such a way that only relevant images are returned. The crux is to develop ontology for various hotels along with their locations and the facilities they provide. User enters his query from an interface and corresponding SPARQL query is generated. This query with the help of JENA API is searched in the data present in Ontology.

This paper presents 1-Error Linear Complexity Test (1-ELCT) which is based on Linear Complexity Test—LCT described in (Rukhin et al., NIST Special Publication 800–822, 2001). 1-ELCT is improved version of Bit Flipping Linear Complexity Test (BFLCT). In BFLCT, it is checked that wether the sequence remains random or not after flipping one bit with respect to the LCT. 1-ELCT is for block length of the form

$$ M = 2^{q} ,\;q \in {\mathbb{N}}\,\& \;q > 8 $$

M

=

2

q

,

q

∈

N

&

q

>

8

and it is of practical use for binary sequences of length 10

6

.

In the recent years, a variety of mathematical models relating to crop yield have been proposed. A study on Neural Method for Site –Specific Yield Prediction was undertaken for Jabalpur district using Artificial Neural Networks (ANN). The input dataset for crop yield modeling includes weekly rainfall, maximum and minimum temperature and relative humidity (morning, evening) from 1969 to 2010. ANN models were developed in Neural Network Module of MATLAB (7.6 versions, 2008). Model performance has been evaluated in terms of MSE, RMSE and MAE. The basic ANN architecture was optimized in terms of training algorithm, number of neurons in the hidden layer, input variables for training of the model. Twelve algorithms for training the neural network have been evaluated. Performance of the model was evaluated with number of neurons varied from 1 to 25 in the hidden layer. A good correlation was observed between predicted and observed yield (r = 0.898 and 0.648).

Military target recognition in IR images is a complex area of computing where most of the existing computation methods do not provide satisfactory deployable solutions. Present paper brings out the different aspects of this problem and discusses how computational intelligence can be helpful in solving many of these complex issues. Computational Intelligence itself consists of different disciplines like Evolutionary Computation, Fuzzy Logic and Artificial Neural Networks which further have their own branches. These all combined can become a powerful tool to create an effective military target recognition solution.

Web page segmentation is an important step for many applications such as Information Retrieval, Noise Removal, Full Text Search, Information Extraction, and Automatic page adaptation and so on can benefit from this structure. Many segmentation methods have been proposed on HTML Web page segmentation whereas Flash Web pages have been omitted because of their less availability. But in recent days, we can see many Flash Web pages taking their appearance. In this paper, we are proposing segmentation method by using image processing techniques after processing Web pages as images, because of their unavailability of semantic structure. We perform the experimental analysis based on ground truth analysis (actual blocks in Web page as per human perception) and obtained the better performance level. We also measure the usefulness of Flash Web page blocks.

The Complexity of higher order linear systems are large and order of matrices are higher. Matrices of higher order are difficult to deal with. The main objectives of order reduction is to design a controller of lower order which can effectively control the original high order system so that the overall system is of lower order and easy to understand. Analysis and design of reduced order model becomes simpler and economic. Parametric uncertainties exist in practical systems for entire range of the operating conditions. To overcome this, time interval system is employed. This paper presents a survey on design of reduced order model for large scale time interval systems.

In a country like India, which has large number of routes between two given places, it is difficult to identify the most convenient and best route for travelling. Also, deciding the mode of transport according to the comfort of the person will require deep knowledge of the routes and the variables which affect the travel. To solve these problems, many algorithms have been suggested, but there are very few models that incorporate all the factors affecting the convenience of the routes and the modes of transport. In the developed Fuzzy Rule Based System, multiple modes are evaluated for all the existing routes incorporating all the factors and the favorable outcome is determined. I observed that finding the optimal route and the best transport mode becomes very easy with this system; also it emphasizes the convenience factor that is of utmost importance in any kind of transport applications. This system has additional advantage of economy of the time consumed for finding the results.

In this research article, an integrated system with variable production is developed in fuzzy environment. The demand rate is regarded as an increasing function of time. In developing the proposed model, it is assumed that the manufacturer takes the raw materials in multiple batches from the supplier, process them to produce finished products and deliver to the buyer in multiple shipments. The effect of inflation and time value of money is also taken into consideration. All the cost parameters are considered as triangular fuzzy numbers and signed distance method is used for defuzzification. The model is illustrated with numerical example and sensitivity analysis with respect to the system parameters is also executed.

The purpose of the study is to examine the efficiency of R&D and non-R&D firms in Indian pharmaceutical firms from 2000 to 2010 comprising both pre and post product patent periods by applying data envelopment analysis technique. The analysis based on a panel sample data set of 141 firms, drawn from PROWESS database of CMIE, measures efficiency by using one output and four inputs. Net sales revenue is taken as output and raw material cost, salaries and wages, advertisement and marketing cost and capital cost as inputs. The study found that efficiency of R&D firms was higher than that of non-R&D intensive firms for all the years. Nevertheless, both types of firms were found to have a good scope for improvement in their resource use efficiency without affecting their level of output.

In this paper, we developed a two-warehouse imperfect production model under the fuzzy environment. Demand rate is time dependent. Production rate is demand dependent and greater than the demand rate. Inflation is also taken into this consideration. Shortages are allowed and partially backlogged. The model is developed in both crisp and fuzzy sense. All the costs are taken as fuzzy triangular numbers. Numerical examples for both the models and sensitivity analysis are illustrated this model.

In the highly competitive global market the growth of Indian economy substantially depends on its knowledge resources. A competency-based approach towards human resources management (HRM) is one of the key success factors in modern organizations. Today, academia faces much the same situation which manufacturing and service companies “in the real world” have been encountering for decades. It seems that institutions only with explicit quality assurance and the ability to produce talents needed by the industry will be able to survive in the academic market. The need for interaction with the competence data for the continuous monitoring and identification of individual competencies create the opportunity for application of soft computing tool in general and that of Expert Systems in particular. This paper covers the investigation of the outcome of the realization of the need for the soft computing tool for the competence management and performance assessment in the educational institutions to improve quality in the teaching and learning process through the case study methodology on various parameters.

Odor leak localization is playing a critical role in industry, especially in the case of combustible and toxic gas as well as security applications. This research has tried to address the need of a sniffing system to improve the gas sensor (TGS 2620) response. The novel algorithm named “Breath ADJ” based on biological sniffing and smelling is implemented in a sniffing system designed. And tested at various distances from the source. The Breath ADJ algorithm is aimed at realization of breath inhalation and exhalation to suck the odor on the sensor for improving the response and clean off the odor during recovery period of sensor response. The improvement in the system response has been evaluated by three features of slope during the rise of sensor response, Odor reach time and a maximum value of sensor response. The results based on an optimum Breath ADJ algorithm are promising. The algorithm is successful in sensing/detecting odor at a distance of 90 cm from the odor source in an indoor arena and 60 cm in outdoor door arena.

Enzyme streptokinase is produced by

streptococcus

sp. in native form and useful to treat acute myocardial infarction, being an essential drug it is necessary to enhance its production utilizing its recombinant strain for thrombolytic therapy. Various fundamental models incorporating indispensable parameters are found to apparently describe the entire existence of employed cells in the bioreactor environment. The unstructured system features can be defined by dynamical system using a composite model. The endeavour would be to establish the fundamental constraints that affect the plasmid instability criterion and hold a relevant role in dynamics of batch and continuous culture system. On performing statistical analysis, screening of production media components and culture condition optimization has been achieved; the data obtained noticeably illustrates the role of few significant parameters governing the culture system. A useful technique has been further implemented using neural network simulation which on the other hand serves as soft computing tool for optimizing factors controlling the process dynamics.

The landuse change has considerable impact on the surface run-off of a catchment. With the changing landuse there is reduction in the initial abstraction which results in increasing run-off. This also has effect on future because of constant change in landuse due to urbanization. The Soil Conservation Service Curve Number (SCS-CN) model was used in the study for calculating run-off in a sub-catchment of Narmada River basin for the years 1990, 2000 and 2011 which was further validated with the observed data from the gauges. Stream flow of future for 2020 and 2030 was estimated by this method to observe the impact of landuse change on run-off. The landuse classification was done by Fuzzy C-Mean algorithm. The future landuse prediction for 2020 and 2030 was performed with the Markov Chain Model with 2011 validation. Future run-off was generated on the basis of changing landuse which shows increasing rate of run-off.

A LabVIEW based data acquisition system (LV-DAS) is developed for Electrical Impedance Tomography (EIT) for automatic current injection and boundary data collection. The developed LV-DAS consists of a NIUSB-6251 DAQ card, NISCB-68 connector module and an automatic electrode switching module (A-ESM). A LabVIEW based graphical user interface (LV-GUI) is develop to control the current injection and data acquisition by LV-DAS through A-ESM. Boundary data are collected for a number of practical phantoms and the boundary data profiles are studied to assess the LV-DAS. Results show that the high resolution NIDAQ card of the DAS improves its data acquisition performance with accurate measurement and high signal to noise ratio (SNR).

Information regarding spatial distribution of different crops in a region of multi-cropping system is required for management and planning. In the present study, multi dated LISS-III and AWiFS data were used for crop identification. The cultivable land area extracted from the landuse classification of LISS-III image was used to generate spectral-temporal profile of crops according to their growth stages with Normalised Difference Vegetation Index (NDVI) method. The reflectance from the crops on 9 different dates identified separate spectral behavior. This combined NDVI image was then classified by Fuzzy C-Mean (FCM) method again to get 5 crop types for around 12,000 km

2

area on Narmada river basin of Madhya Pradesh. The accuracy assessment of the classification showed overall accuracy of 88 % and overall Kappa of 0.83. The crop identification was done for one entire Ravi season from 23 October 2011 to 10 March 2012.

Particle swarm is a stochastic optimization paradigm inspired by the concepts of social psychology and artificial intelligence. Population topology plays significant role in the performance of PSO. It determines the way in which particles communicate and share information with each other. Topology can be depicted as a network model. Regular networks are highly clustered but the characteristic path length grows linearly with the increase in number of vertices. On the contrary, random networks are not highly clustered but they have small characteristic path length. Small world networks have a distinctive combination of regular and random networks i.e. highly clustered and small characteristic path length. This paper presents a novel algorithm for data clustering by incorporating the concept of small world in particle swarm optimization. Efficiency of the proposed methodology is tested by applying it on five standard benchmark data set. Results obtained are compared with another PSO variant. Comparative study demonstrates the effectiveness of the proposed approach.

Fast Marching Method is an efficient numerical technique for estimation and tracking monotonically advancing interfaces such as wavefronts. The method works by addressing the theory of viscosity solutions for Hamilton–Jacobi equations, entropy conditions for propagating interfaces and narrow band technique for recovering shapes from images. The entropy conditions constrain the method to give first arrival wavefronts and make the algorithms based on this method stable in heterogeneous media. The use of narrow band approach makes the method extremely fast in computation. This method has a wide range of applications including computation of wavefronts in seismology, photolithographic developments in microchip manufacturing, problems of search and optimal path planning and many other areas such as graphics and medical imaging. This paper presents Fast Marching Method in context of solving Eikonal equation in three dimensions and its utilization to obtain traveltime responses of a variety of numerical models of subsurface. Study of patterns of such traveltime responses gives a comprehensive idea about anomalous geological structure present in the Earth’s subsurface.

As the problem related to minimizing makespan as objective is NP-hard for more than two machines in flowshop scheduling, therefore need for heuristics have been felt to yield optimal or near optimal solutions in polynomial time. In the present paper, we propose an alternative heuristic algorithm which is compared with the benchmark Palmer’s, CDS and NEH algorithm for the processing of n-jobs through m-machines. The proposed heuristic gives solution for solving n-job and m-machine flowshop scheduling problem with minimizing makespan as criteria. Comparisons have been made for tested instances.

With the emergence of web as one of the primary mode of information sharing and searching, it is a challenge posed to the researchers and developers to design the information retrieval system which can effectively and efficiently returns the query result as per user’s requirement. This survey paper tends to find out some challenges posed by information retrieval and how the concept of fuzzy helps to solve those challenges.

Inter-vehicle communications have great potential to induce great interest in research and industry. Vehicular ad hoc networks (VANETs) may significantly improve passenger safety and comfort. The deployment of VANETs is a challenging task in weakly interconnected and in highly overloaded networks both. A good back off technique can reduce a large number of collisions in the MAC layer I VANET. This will reduce the collision probability and hence increases the utilization of network resources. A uniform random distribution has been employed to choose the back off value in the Binary Exponential Back off (BEB) technique used in the IEEE 802.11 MAC protocol. This random choosing VANETs leads to unnecessary idle times and reduced throughput. This paper proposes a new back off technique called “Squaring Back off (SB)” in which the differences between the consecutive contention window sizes are reduced to a negligible value. Here, the value of the back off timer is based on the size of the contention window. The size of the contention window is varied in accordance with the result of the previous transmission. A successful transmission reduces the size of the contention window whereas a failure leads to the increase in the contention window size. Simulation results indicate that the proposed technique provides better throughputs and less idle times than the logarithmic and Fibonacci based techniques when used in a mobile ad-hoc environment. Squaring back off based media access control can prove very useful for vehicular ad-hoc networks.

Adjunct Array Token Petri Net structure (AATPNS) to generate rectangular pictures has been defined in Lalitha et al (Indian J. Math. Math. Sci. 8(1):11–19, 2012)

7

]. AATPNS with inhibitor arcs generated context free and context sensitive Kolam Array languages and Tabled 0L/1L languages. In this paper we study the expressiveness of this model by comparing with some other interesting array generating grammar devices like Pure 2D context free grammars with regular control, Regional tile rewriting Grammars, Prusa Grammars and also comparing with local languages.

This paper addresses the fault section estimation as a real multi-objective optimization problem. Non-dominated sorting particle swarm optimization (NSPSO) based on the concept of NSGA-II, has been proposed to alleviate the problems associated with conventional multi objective evolutionary algorithms. An analytical fault analysis and iterative procedure to get the multiple estimates of fault location and NSPSO based optimization algorithm to further nail down the exact fault location has been presented. The techniques fully consider loads, laterals and customer trouble calls in radial distribution systems, take into account for all types of fault. Due to the presence of various conflicting objective functions, the fault location task is a multi-objective, optimization problem. In the proposed technique, the multi-objective nature of the fault section estimation problem is retained using non-dominated sorting approach. As a result, the proposed methodology is generalized enough to be applicable to any radial distribution systems. The applicability of the proposed methodology has been demonstrated through detail simulation studies on standard test systems. Results are used to reduce the possible number of potential fault location which helps and equips the operators to locate the fault accurately.

The aim of present article is to find the approximate solution of integral equation using Bernstein multiwavelets approximation. Bernstein polynomial multiwavelets are constructed using orthonormal Bernstein polynomials. These Bernstein polynomial multiwavelets approximate the solution of integral equation. Using orthogonality property of Bernstein polynomial muliwavelets operational matrix of integration is obtained which reduces the integral equation in the system of algebraic equation and can be solved easily. The examples of different profiles are illustrated.

Solving non-linear optimization with more accuracy has become a challenge for the researchers. Evolutionary global search techniques today are treated as the alternate paradigm over the traditional methods for their simplicity and robust nature. However, if an evolutionary problem is computationally burdened both the human efforts and time will be wasted. In this paper a much simpler and more robust optimization algorithm called Drosophila Food-Search Optimization (DFO) Algorithm is proposed. This new technique is based on the food search behavior of the fruit fly called ‘Drosophila’. In order to evaluate the efficiency and efficacy of the DFO-algorithm, a set of 20 unconstrained benchmark problems have been used. The numerical results confirms the supremacy of DFO over the algorithms namely Hybrid Ant Colony-Genetic Algorithm (GAAPI), Level-Set evolution and Latin squares Algorithm (LEA), which are reported as the most efficient algorithms in the recent literature.

The use of basic crypto-primitives or building blocks has a vital role in the design of secure crypto algorithms. Such crypto primitives must be analyzed prior to be incorporated in crypto algorithm. In cryptanalysis of any crypto algorithm, a cryptanalyst generally deals with intercepted crypts without much auxiliary information available to recover plaintext or key information. As brute force attack utilizes all possible trials exhaustively, it has high computing time complexity due to huge search space and hence is practically infeasible to mount on secure crypto algorithms. The Geffe generator is a non-linear binary key sequence generator. It consists of three linear feedback shift registers and a nonlinear combiner. In this paper, we attempt Geffe generator to find initial states of all three shift registers used. The initial states are the secret key bits that maintain the security of Geffe generator. To find secret key bits, one has to search huge key space exhaustively. We consider divide-and-conquer attack and genetic algorithm to reduce exhaustive searches significantly. Simulation results show that correct initial states of all shift registers can be obtained efficiently.

Computers have proved to be an inevitable part of our modern life. Every work of our modern life involve directly or indirectly the use of computers. A lot of our personal as well as confidential work related information is stored in computer systems. Therefore, it is an important task to secure this information and protect it. In this paper, the aim is to establish the sense in computer system that could differentiate between the self process (i.e. processes that are not harmful to our computer system) and the non-self process (i.e. processes that are harmful and dangerous to our computer system). A process coming in the system is identified whether the process is part of the stable system i.e. self process or is it a harmful process which can destabilize a system i.e. non-self process. This is done with the help of the detectors generated by the genetic algorithm. This technique would be used to classify the processes at process level into SELF (non-harmful) and NON-SELF (harmful or dangerous). This would help the system to sense the processes before the harmful processes do any harm to the system.

Load flow problem has a great significance to analyze the power system network due to its roll in planning and operation of network. Generally, Newton–Raphson (NR) method is used to analyze the load flow problems due to its efficiency and accuracy. But, NR method has some inherent drawbacks like in-efficient for highly loaded network, assumption are required for initial values and abnormal operating conditions. To overcome the existing drawbacks of NR method, a recently developed swarm intelligence based algorithm, namely Gbest guided Artificial Bee Colony algorithm (GABC) is applied to solve the load flow problem for five bus network. The reported results of GABC are compared to the results of NR method and basic ABC algorithm, which show that the accuracy of unknown parameters such as voltage, angle and power produced by GABC is competitive method to the NR method and basic ABC algorithm based method.

In industries, more than 85 % of the motors are Induction Motors, because of the low maintenance and robustness. Maximum torque and efficiency is obtained by the speed control of induction motor. Using Artificial Intelligence (AI) techniques, particularly the neural networks, performance and operation of induction motor drives is improved. This paper presents dynamic simulation of induction motor drive using neuro controller. The integrated environment allows users to compare simulation results between conventional, Fuzzy and Neural Network controller (NNW). The performance of fuzzy logic and artificial neural network based controller’s are compared with that of the conventional proportional integral controller. The dynamic modeling and simulation of Induction motor is done using MATLAB/SIMULINK and the dynamic performance of induction motor drive has been analyzed for artificial intelligent controller.

Pervasive systems are usually highly dynamic, heterogeneous, and resource-restricted where small and powerful dissimilar devices have to establish independent network unknown by the user. There is no fixed infrastructure and centralized access control. The set of connections relies on the convergence of wireless technologies, advanced electronics and the Internet to communicate seamlessly with other devices as tiny sensors. Trusted and Security-critical communication is the key concern in such decentralized and unpredictable environment. Bio-Inspired systems are increasing significant adaptation, reliability and strength in the dynamic and heterogeneous networks where information is ubiquitous. Some specific characteristics of swarms, like their lightweight, transient nature and indirect communication, make this adaptation more demanding. In this paper we explore bio-Inspired systems to look at the trust computation factors and opportunities in autonomic computing environments like mobile pervasive environment and evaluate their trustworthiness. We use standard clustering technique and propose a trust metric in which we observe the node behavior through various trust parameters. In winding up, we put our efforts to represent the cluster formation with honey bee mating to set up general vulnerabilities requirements for compromised node behavior to the system under exploration.

Test problems play an important role when it comes to evaluate the performance of multi-objective evolutionary algorithms (MOEAs). Among a number of test problems available in literature, DTLZ toolkit has its distinct place. In this paper, this toolkit has been used to measure the potential of a newly developed algorithm based on social evolution, namely, Adaptive Social Evolution (ASE) algorithm. The results are compared with those of binary-coded elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and found to be better in terms of convergence and computational time.

This paper presents a reference watermarking scheme in which both original and watermark images are RGB color images. Reference images are obtained from red, green and blue components of the original image by using Discrete Wavelet Transform (DWT) and Directive contrast. Singular Value Decomposition (SVD) is applied on each of the component images (Red channel, Green channel and Blue channel) of watermark and reference images (red, green and blue). Watermark is embedded by modifying the singular values of the reference images with the singular values of the watermark component images. A reliable watermark extraction scheme is developed. Experimental results show that the proposed scheme survives against common signal/image processing attacks.

In this paper, we are interested in the winner determination problem in the multi-unit combinatorial auctions (MU-WDP). In this type of auctions, a set of units of goods has to be auctioned. Bidders request the number of desired units of each good and the total price for the complete bid. Each bid has to be discarded or fully accepted. The objective of the auctioneer is to maximize its revenue. The MU-WDP is known to be NP-hard. In this paper, we propose three local search methods: a local search (LS), a tabu search (TS) and a stochastic local search (SLS) for the MU-WDP. The proposed methods are evaluated on some benchmark problems. The experimental study shows that the SLS algorithm is able to find good solutions for the multi-unit winner allocation compared to both LS and TS methods. Further, experiments against the CPLEX 12.5, show the effectiveness of the proposed SLS method in finding good quality solutions in faster time.

The performance of crossover operator is a complex interplay of the various characteristics of the genetic algorithm (GA) and sometimes also of the problem under question. The fundamental design choices in a GA therefore, are its representation of candidate solutions and the operators that will act on that representation. This paper suggests a new strongly biased multiparent crossover operator that offers a strong locality and provides a strong heritability quotient but can still escape the local optima when the sample space is over represented by similar building blocks. The proposed operator uses a dynamic 2-D vector representation for the chromosomes and this data structure may evolve as the execution of the crossover operator proceeds. On a population which consists of solutions near to optimal and mostly lying in the basin of attraction of single optima, the bias towards the optima in the generated offsprings is proportional to the sample size from the search space. Based on this reasoning, the effect of arity of the proposed crossover operator is tested using a population in which all the candidates lie in close neighborhood of each other. To analyze the dynamic search behavior of the proposed crossover operator and the impact of the representation scheme on the locality, heritability and exploratory power of the operator, we suggest a no mutation, zero selection pressure GA model that generates bounded diameter minimum spanning trees from the underlying complete graphs on random and Euclidean instances.

The paper shows performance comparison of DST-DCT, Haar-DCT, Walsh-DCT, Hartley-DCT, Slant-DCT and Kekre-DCT Hybrid Wavelet Transforms using Normalization for ‘Color to Gray and Back’. The color information of the image is embedded into its gray scale version using hybrid wavelet transform [HWT] and normalization method. Instead of using the original color image for storage and transmission, gray image (Gray scale version with embedded color information) can be used, resulting into better bandwidth or storage utilization. Among the three algorithms considered the second algorithm give better performance as compared to first and third algorithm. In our experimental results second algorithm for Haar-DCT HWT and Walsh-DCT HWT using Normalization gives better performance in ‘Color to gray and Back’ w.r.t all other transforms in method 1, method 2 and method 3. The intent is to achieve compression of 1/3 and to store and send color images as gray image and to be able to recover the color information afterwards.

Recent times have seen a wide proliferation of use of Genre as basis of music classification and documentation. This paper proposes a novel approach of music genre classification using unsupervised Neural Network method—Kohonen Self Organizing Maps (SOM). Various music features like timbrel features (attack slope), spectral distribution of energy (brightness) and tonality (major or minor) were extracted from the audio files using MATLAB toolbox. An inventory of a number of musical pieces was developed for building clusters of different genre. A database containing the extracted musical features was then clustered using SOM. Each cluster encapsulated a different genre of music. The centroid of each cluster was taken as the representative point of that genre in the considered n dimensional musical feature space. Then a number of songs were then mapped onto the said space and classified into different genre depending on their Euclidean distance from the center of each cluster.

Availability of Cloud environment setup is sometimes difficult at personal level for a researcher, especially a beginner. In this case, open source tools, softwares can help a great deal to build and deploy a private cloud. Deploying cloud for research purpose is quiet time consuming due to scattered information and variety of options. In this paper, an attempt has been done for providing collective information of various measures to deploy a private cloud. In-depth analysis of different real-time errors during installation of a cloud setup along with solutions is also reported in this paper. This attempt will certainly helpful in minimizing the time and efforts to setup the cloud and makes deployment easier. Open source operating system (OS) Ubuntu-12.04 long time support (LTS) and OpenNebula cloud computing framework are used for deployment of this private cloud.

Artificial Bee Colony (ABC) algorithm is a bio-inspired technique motivated by the intelligent foraging behavior of honey bee swarm. ABC mainly depends on the activity of employed bees, onlooker bees and scout bees. It is a practice that during simulation, if no further improvement in the population is found within an allowable number of cycles, the employed bee becomes scout and reinitializes the population by its standard equation. But there is a chance of losing the best individuals achieved so far. In this paper, a modification in scout bee activity is proposed, with an insertion of a modified Quadratic Approximation namely qABC. The effectiveness of the proposed qABC over most recent variants of ABC is analyzed through a set of Benchmark problems. The experimental confirms that qABC outperforms its other variants.

In this paper, a multistage model for software defect density indicator is proposed using the top most reliability relevant metrics and Fuzzy Inference System (FIS). Prediction of defect in each phase of software development life cycle (SDLC) is desirable for effective decision-support and trade-off analysis during early development phases. The predictive accuracy of proposed model is validated using nine real software projects data. Validation results are satisfactory.

The research in this paper is organized in two parts. In the first part, a generalized second-order dual is formulated for a class of continuous programming problems with square roots of positive semi-definite quadratic forms, hence it is nondifferentiable. Under second-order pseudoinvexity and second-order quasi-invexity, various duality theorems are proved for this pair of dual nondifferentiable continuous programming problems. Lastly, it is pointed out that our duality results can be regarded as dynamic generalizations of those for a nondifferentiable nonlinear programming problem, already treated in the literature. In the second part, a generalized second-order dual is formulated for a continuous programming problem with support functions. Duality results similar to those of the first part are validated for this class of problems. Clearly the results of this part are more general in sense that a support function extends a square root of a positive semidefinite quadratic form.

A low cost Electrical Impedance Tomography (EIT) instrumentation is developed for studying the impedance imaging of practical phantoms. The EIT instrumentation is developed with a constant current injector, signal conditioner block and a DIP switch based multiplexer module. The constant current injector consists of a variable frequency Voltage Controlled Oscillator (VCO) and a modified Howland based Voltage Control Current Source (VCCS). The signal conditioner block is made up of an instrumentation amplifier (IAmp), a 50 Hz notch filter (NF) and a narrow band pass filter (NBPF) developed by cascading one lowpass filter and a highpass filter. The electrode switching module is developed using DIP switch based multiplexers to switch the electrodes sequentially for injecting current and measuring the boundary voltage data. Load response, frequency response and the Fast Fourier Transform (FFT) studies are conducted to evaluate the VCO, VCCS, IAmp, NF and NBPF performance. A number of practical phantoms are developed and the resistivity imaging is studied in EIDORS to evaluate the instrumentation. Result shows that the instrumentation is suitable for laboratory based practical phantom imaging studies.

Electrical Impedance Tomography (EIT) essentially needs the Jacobean matrix to reconstruct the conductivity distribution of the domain under test. A Broyden’s method based high speed Jacobean matrix (J) calculator is proposed for Electrical Impedance Tomography (EIT). The Gauss-Newton-based EIT image reconstruction algorithm repetitively calculates the Jacobian matrix (J) which needs a lot of computation time and cost. Broyden’s method based high speed Jacobean matrix calculator (JMC) makes explicit use of secant and adjoint information that can be obtained from the forward solution of the EIT. The Broyden’s method based high speed Jacobean matrix calculator (JMC) approaches reduce the computational time remarkably by approximating the system Jacobian (J) successively through low-rank updates. The performance of the JMC is studied with simulated EIT data and the results are compared with Gauss-Newton method based EIT reconstruction. Simulated results show that the Broyden’s method based image reconstruction algorithm accelerates the reconstruction speed remarkably.

For a software development project, management often faces the dilemma of when to stop testing the software and release it for operation. Estimating the remaining defects (or failures) in software can help test management to make release decisions. Several methods exist to estimate the defect content in software; among them are also a variety of software reliability growth models (SRGMs). SRGMs have underlying assumptions that are often violated in practice, but empirical evidence has shown that a number of models are quite robust despite these assumption violations. However it is often difficult to know which model to apply in practice. In the present study a method for selecting SRGMs to predict total number of defects in a software is proposed. The method is applied to a case study containing 3 datasets of defect reports from system testing of three releases of a large medical record system to see how well it predicts the expected total number of failures in a software.

Shell and tube heat exchangers (STHE) are the most common type of heat exchangers widely used in various kinds of industrial applications. Cost minimization of these heat exchangers is of prime concern for designers as well as for users. Heat exchanger design involves processes such as selection of geometric and operating parameters. Generally, different exchangers geometries are rated to identify those that satisfy a given heat duty and a set of geometric and operational constraints. In the present study we have considered minimization of total annual cost as an objective function. The different variables used include shell internal diameter, outer tube diameter and baffle spacing for which two tube layout viz. triangle and square are considered. The optimization tool used is differential evolution (DE) algorithm, a nontraditional stochastic optimization technique. Numerical results indicate that, DE can be used effectively for dealing with such types of problems.

In this paper, image data compression algorithm is presented using back propagation neural networks. Back propagation is used to compress printed English characters by training a net to function as an auto associative net (the training input vector and the target output vector are the same) with fewer hidden units than there are in the input or output units. The input and the output data files are formed in +1 and −1 form. The network parameters are adjusted using different learning rates and momentum factors. Mainly, the input pixels are used as target values so that assigned mean square error (MSE) can be obtained, and then the hidden layer output will be the compressed image. The proposed algorithm has been implemented in MATLAB to simulate the algorithm for the English characters A, B, C, D, E. The results obtained, such as compression ratio, mean square error, number of epoch for different learning rates and momentum factors are presented in this paper. Hebbian learning rule and Delta learning rules are used to train the network. Sigmoidal function, binary sigmoidal function and bipolar sigmoidal function are used in feed forward net and back propagation net respectively.

The VME based Ion Cyclotron Resonance Heating (ICRH) Data Acquisition Control system (DAC) is commissioned for remote operation of heating experiment on SST-1 Tokamak. ICRH-DAC is physically distributed into two sections at RF Lab and SST-1 hall. RF Generation section at RF Lab and Transmission line, interface and antenna section at the SST-1 hall having its own independent VME based DAC system.VME system of both section is running on master/slave configuration when synchronization mode operation is needed. This synchronization of both DAC could be possible with EPICS (Experimental Physics and Industrial Control System) process variables, which broadcast and describe itself in Ethernet network. The existing system uses the TCP/IP Ethernet network for the same. The proposed program is used for real-time state parameters transmission and storage, dynamic graphical display, modification of the interactive system and synchronization. This paper will describe the conceptual design of EPICS based ICRH DAC software to achieve desired goal of experiment for generic development platform oriented toward complex data acquisition is proposed.

In distributed systems a computer generally process information of distributed application or provide service in distributed system. Therefore, computers connected in distributed system need to keep on all time. It leads to the concept of Wake-on-LAN (Local Area Network). However, keeping on during the idle period is the wastage of power. Therefore, it is essential to save the power while being efficient in network management. In this paper we propose an improved Wake-on-LAN device that incorporates the usage of basic Wake-on-LAN technology into a network management and power saving product.

The security is one of the indispensable concerns in the cloud computing. Different authentication and access mechanisms are implemented for different services in the cloud. For uniform strong authentication and obviate the need for password registration, two factor authentication (T-FA) has been proposed. The data owner provides one of the credentials in this is a two tier mechanism. T-FA needs two identities based on what the user knows and what he possesses which is implemented through Software as a Service (SaaS) in the cloud computing environment. However, key-compromise impersonation makes the software only mechanism vulnerable if a server is compromised. This study examines this vulnerability and proposes an identity based signature technique to make T-FA resilient to impersonation attack. Analysis shows that the proposed technique is able to make T-FA mechanism robust to key-compromise impersonation attacks.

Artificial Bee Colony (ABC) is one of the latest and emerging swarm intelligence algorithms. Though, there are some areas where ABC works better than other optimization techniques but, the drawbacks like stucking at local optima and preferring exploration at the cost of exploitation, are also associated with it. This paper uses position update equation in ABC as in Gbest-guided ABC (GABC) and attempts to improve ABC algorithm by balancing its exploration and exploitation capabilities. The proposed algorithm is named as Expedited Artificial Bee Colony (EABC). We altered the onlooker bee phase of ABC by forcing the individual bee to take positive direction towards the random bee if this selected random bee has better fitness than the current bee and if it is not the case then the current bee will move in reverse direction. In this way, ABC colony members will not follow only global best bee but also a random bee which has better fitness than the current bee which is going to be modified. So the mentioned drawbacks of the ABC may be resolved. To analyze the performance of the proposed modification, 14 unbiased benchmark optimization functions have been considered and experimental results reflect its superiority over the Basic ABC and GABC.

Advancement in Information and communication technology has made the healthcare information and services globally accessible. The paper throws light on the impact of e-health services in a developing nation like India, where densely populated communities spread over vast distances. The healthcare delivery systems are being overloaded in the developing and densely populated countries, so it is imperative to have an efficient and cost effective systems. The paper highlights the various social and technical issues in the successful deployment of e-health services in India. Further the inclusion of “Aadhaar” (A unique Identification number provided by UIDAI, an agency of Government of India) has been suggested to make the services more streamlined and secured.

The term weight is based on the frequency with which the term appears in that document. The term weighting scheme measures the importance of a term with respect to a document and a collection. A term with higher weight is more important than a term with lower weight. A document ranking model uses these term weights to find the rank of a document in a collection. We propose a cluster-based term weighting models based on the TF-IDF model. This term weighting model update the inter-cluster and intra-cluster frequency components uses the generated clusters as a reference in improving the retrieved relevant documents. These inter cluster and intra-cluster frequency components are used for weighting the importance of a term in addition to the term and document frequency components.

In this work we have studied the influence of applied DC electric field on the SHG conversion efficiency of poled organic polymeric films. Regression analysis is carried out to get the desired relationship between the input and output parameters. Statistical testing of the models are performed with F-test to obtain the mathematical relationship between input and output parameters. To examine the goodness of fit of a model, the test for significance of regression model is performed and ANOVA is applied to the response data. Curve fitting analysis has been done to perform the accuracy of observed data.

Increasing popularity of nature inspired meta-heuristics and novel additions in the pool of these techniques at a rapid pace results in a need to categorize and explore these meta-heuristics from different point of views. This paper attempts to compare three broad categories of bio-inspired techniques- Swarm Intelligence methods, Evolutionary techniques and Ecology based approaches via three renowned algorithms, Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Biogeography Based Optimization (BBO) that fall under three categories respectively, based on few varied characteristics. Six benchmark functions are considered for comparison. The paper further suggests a taxonomy of nature inspired methods based on the source of inspiration.

Fritz John and Karush-Kuhn-Tucker type optimality conditions for a nondifferentiable multiobjective variational problem with equality and inequality constraints are obtained. Using Karush-Kuhn-Tucker type optimality conditions, a Wolfe type second-order nondifferentiable multiobjective dual variational problem is constructed. Various duality results for the pair of Wolfe type second-order dual variational problems are proved under second-order pseudoinvexity. A pair of Wolfe type dual variational problems having equality and inequality constraints with natural boundary values is also formulated to prove various duality results. Finally, the linkage between our results and those of their static counterparts existing in the literature is briefly outlined.

An effective supply chain management (SCM) facilitates companies to react to changing demand by swiftly communicating their needs to the supplier. Optimizing a supply chain (SC) performance is a key factor for success in long term SC relationships. Substantial information such as price, delivery time percentage and acceptance percentage are discussed in the process. Imprecise demand as one of the factors is added in the same process that fuzzifies coordination between buyer and supplier. The paper considers non-deterministic conditions in the environment of business, coordination in procurement and distribution in a supplier selection problem and a fuzzy model with two objectives is defined. The proposed model is a “fuzzy bi-objective mixed integer nonlinear” problem. To process the solution the fuzzy model is converted into crisp and further fuzzy goal programming approach is employed. The model is validated with the help of a real case problem.

Increase in environmental concerns and consumer awareness together with imposition of government regulations are forcing electronic industries to set up their own reverse logistics network. An evaluation of the impact of their reverse logistics activities is also imperative for the design of an effective and efficient reverse supply chain. This paper presents a bi objective mixed integer linear programming model for a reverse logistics network design that considers value added recovery of return of end of life (EOL) products and also focuses on controlling the transportation activities involved in the reverse logistics system, a major contributor to the increase in carbon emission. The objectives are to maximise the product’s value recovery by determining the optimum flow of products and components across facilities in the network and minimise the carbon emission by determining the optimum routes to be taken by vehicles and by appropriate selection of vehicles. The reverse logistics model developed is goal programming model and it takes into account two objectives with almost equal weightage. The model captures the trade-offs between total profit and emission of CO

2

. The model is justified by a case study in the context of the reverse logistics network design of an Indian company manufacturing air conditioners and refrigerators.

A product passes through different life cycle phases once it comes in the market. During the launch phase it is promoted on mass level, second phase stresses on capturing maximum potential, and in later phases company emphasizes on retention of the product in the minds of the customers until its continuation in the market. The advertising budget and media mix used to advertise the product must be planned according to the current phase of the product. Thus it becomes imperative for a company to look at media planning problems in a dynamic manner over a planning period as the market conditions change and the product moves through its cycle with time. In this paper we have formulated a multi-period mathematical programming problem that dynamically computes the optimal number of insertions and allocates advertising budget in various media channels of a segmented market. The aim is to maximize the reach obtained through each media from all the segments in each time period under budgetary constraints and bounds on the decision variables. The reach in any period for a media is taken as a sum of current period reach and retention of the previous periods reach. Goal programming technique is used to solve the problem. A case study is presented to show the real life application of the model.

With the rising awareness of advancements in Information technology amongst various industries, the predilection to selection of commercial-off the shelf (COTS) components have increased invariably. It provides the ability to reuse the software components, thereby, maximizing the reliability while reducing the developmental cost. The decision of whether to buy the component or build from scratch, is known as build-or-buy decision. In order to prevent the software from failure, redundant components have to be incorporated which can be ascertained using fault tolerant schemes. Further, the innovation in the field of Application Package Software (APS) has supplemented the industry with highly configurable, sophisticated applications. Through this paper, we shall discuss a framework concentrating upon whether to build or buy the software components while designing a fault-tolerant modular software system. The objective of the paper is to maximize the reliability of the software while minimizing the overall cost. Further, the components with comparatively less execution time are chosen over the ones which require more time for executing the software. Hence the objective of the paper shall further be elaborated upon minimizing the execution time with the aid of a case study on supplementing an APS for Airline Industry.

This paper presents a novel Lossless ECG Compression using Symbol substitution (LECS) deployable on low computational devices (LCD) like mobile phones for effective use in telecardiology. Of the few LCD deployable compression algorithms, even losslessly compressed ECG suffers transmission loss in Global System for Mobile (GSM) networks due to the reduced character set supported by the SMS protocols. The LECS encodes using the Standard GSM Character ETSI GSM 03.38 set for un-trimmed ECG transmission. The evaluation using MIT-BIH Arrhythmia database showed an average compression-ratio (CR) of 7.03, Percentage-Root-mean-square-Distortion (PRD) as low as 0.0211 proving superior performance in both compression and quality for real-time mobile based telecardiology applications.

Humanitarian supply chain and logistics has succeeded in attracting research attention in recent years as the special field of attention. Purpose of this paper is to introduce the difference between the Continuous Aid programmes from the disaster relief chains in a Humanitarian Supply System. So far the literature was so biased towards only Disaster relief chains. Through the case study and other research, this paper is to establish and advocate that all Humanitarian Supply networks are not having the disaster management orientation and also to establish scope for further discussions and analysis to the area of Continuous aid category. This paper is primarily the conceptual framework for researching the internal operations strategy of any human Supply Network Operations. It reviews the existing literature in Humanitarian supply network and disaster relief measure to establish the current meaning of Humanitarian Supply network. It adopts the micro case based approach to authenticate the concept of including the Continuous Aid programs in “Humanitarian Supply network” which is currently biased towards Disaster relief management. The paper has found the usage of Humanitarian Supply network term in a complement way with Disaster Relief operations/management so far. However, there are operations which do not support the disaster relief measures; On the other hand—it supports the livelihood of the society. Those operations should also be called and classified under the Continuous Aid Category under Humanitarian Supply network operations. Further research and arguments are indispensable to differentiate the Operations dynamics of Continuous Aid Program from the disaster relief management. The discussions can help the re-orientation of research to strengthen and streamline the definitions and areas of Human Supply network. The conceptual paper is analyzing the operations of Humanitarian Supply Chains in detail and gives a new dimension and school of thought with a definition towards Humanitarian Supply networks. This is the original paper written based on the author’s experience with NGOs operating in this field. The concept “Humanitarian Supply Networks” should be established and explored carefully, not to be used extensively only on the disaster relief measures, based on the facts established through this paper. The points discussed in the paper will help to widen the discussions on the Humanitarian Supply Networks field.

Neural classification systems are widely used in many fields for making logical decisions. This paper envisages a neural classification system based on back propagation algorithm to suggest an advisory model for graduate school admissions. It uses real and synthetically generated data to advise the students about the group of graduate schools where they have the maximum probability of getting selected. The system takes into consideration all the important aspects of the student’s application such as: the GPA, GRE score, number of publications, professor recommendation, parent institute rating and work experience in order as to suggest the group of potential schools. A new parameter named Student Rating Index (SRI) is also defined for a better representation of the quality of professor recommendation. The system comprises of a two-layer feed-forward network, with sigmoid hidden and output neurons to classify the data sets. The results are verified using mean square error method, Receiver Operator Characteristic (ROC) curve and confusion matrices. The verification confirms that the proposed system is an accurate and reliable representation. Thus the proposed advisory system can be used by the students to make more focused applications in the graduate schools.