Abstract: There are various methods established on time series data having linguistic values for forecasting the future values with the help of fuzzy time series forecasting. However, the major problem in fuzzy time series forecasting is the accuracy in the forecasted values. In the present paper we propose a computational method of forecasting for fuzzy time series. In the proposed method the notion of intuitionistic fuzzy set is used in fuzzy time series forecasting with simplified computational approach. The proposed method is implemented to forecast the movement of share market prices of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India.

Abstract: Wireless ad hoc network enable new and exciting applications, such as decision making in the battlefield, emergency, search-and-rescue operations, but also pose significant technical challenges. Topology control problems are concerned with the assignment of power values to the nodes of an ad hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. In this paper we give a brief overview of topology (arrangement of devices) control problem in wireless ad hoc networks, up to some extent. Given a set of nodes in a 2-D plane, end-to-end traffic load (i.e amount of data to be transmitted between mobile hosts) and delay between a pairs of node, the problem is to find a network topology that can meet the optimal QoS requirements. We can solve the problem, by formulating as a linear programming problem for the traffic loads. and an optimal solution has been proposed to solve the problem.

Abstract: The observation of peak flows into river or stream system is not straight forward but complex function of hydrology and geology of the region. There are well established statistical approach to predict the flood events with their magnitude and frequency. Development of models based on temporal observations may improve understanding the underlying hydrological processes in such complex phenomena. Present work utilized temporal patterns extracted from temporal observations of annual peak series using wavelet theory. These patterns are then utilized by an artificial neural network (ANN). The wavelet-ANN conjunction model is then able to predict the flood event comparable to statistical approach. The application of the proposed methodology is illustrated with real data. The limited performance evaluation of the methodology show potential application of the developed methodology.

Abstract: Learners often get overwhelmed by the availability of large volumes of learning content in web-based educational systems. This content is presented to the learners either statically or with numerous hyper-links for navigation. The choice of content varies according to the requirements. Hence a static sequence of contents cannot satiate different learners enrolled in a course. Sequencing content according to the learners’ needs is the objective of designing adaptive systems. Ant Colony Optimization (ACO) is an evolutionary technique that takes into account the dynamic nature of the problem and employs collective intelligence to provide optimized solutions. An ACO based algorithm Adaptive Content Sequencing in eLearning (ACSeL) is proposed in this paper that evaluates the level of a learner and recommends appropriate concepts to him/her. It is sensitive to the changes in learning behaviours of each learner and fine-tunes its strategies to recommend the next concept accordingly. The behaviours of past learners are captured and utilized to recommend content to prospective learners.

Abstract: This paper presents the hybrid approach of Cuckoo Search (CS) and Genetic Algorithm (GA) algorithms for solving optimization problems. In standard CS, each cuckoo lays one egg at a time, but in the proposed hybrid algorithm, in order to lay more eggs we used the genetic algorithms’ strategy (Crossover) for their reproduction. According to the cuckoos breeding style, each nest will have one cuckoo at a time. Since there is limitation in number of nests we will have a selection for all cuckoos. Furthermore, we added mutation in order to reduce the chance of eggs to be discovered, because cuckoo birds are specialized in mimicry in color and pattern of the host birds. This theory gets us closer to their real living style. Experimental results are examined with some standard benchmark functions and the results are reported.

Abstract: This paper presents a diagnostic system for the detection of presence and absence of cardiac Arrhythmia from the Electrocardiogram (ECG) data using the methods of Feature Selection, Feature Extraction and Binary Classification Technique. A hybrid approach of three algorithms namely Rank Correlation, Principal Component Analysis (PCA) and Support Vector Machine (SVM) are applied on the UCI Cardiac Arrhythmia data set for the automatic arrhythmia detection in Arrhythmia Diagnostic System. Spearman Rank Correlation aids the process of dimension reduction and increases the accuracy of the classifier. In this study, SVM has been widely used for classification based diagnosis of diseases. The results obtained after implementation of all the three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier depends on the cost and kernel parameter sigma classification frequency upon the number of attributes selected by Rank Correlation. The experimental method shows that hybrid approach is superior to other approaches.

Abstract: Clustering methods divide the dataset into groups called clusters such that the objects in the same cluster are more similar and objects in the different clusters are dissimilar. Clustering algorithms can be hierarchical or partitional. Partitional clustering methods decompose the dataset into set of disjoint clusters. Most partitional approaches assume that the number of clusters are known a priori. Moreover, they are sensitive to initialization. Hierarchical clustering methods produce a complete sequence of clustering solutions, either from singleton clusters to a cluster including all individuals or vice versa. Hierarchical clustering can be represented by help of a dendrogram that can be cut at different levels to obtain different number of clusters of corresponding granularities. If dataset has large multilevel hierarchies then it becomes difficult to determine optimal clustering by cutting the dendrogram at every level and validating clusters obtained for each level. Genetic Algorithms (GAs) have proven to be a promising technique for solving complex optimization problems. In this paper, we propose an Optimal Clustering Genetic Algorithm (OCGA) to find optimal number of clusters. The proposed method has been applied on some artificially generated datasets. It has been observed that it took less number of iterations of cluster validation to arrive at optimal number of clusters.

Abstract: Mango quality assessment is important in meeting market requirements The quality of the mango can be judge by its length, thickness, width, area, etc In this paper on the basis of simple mathematical calculations different parameters of a number of mango are calculated The present paper focused on the classification of mangoes using morphological Operations A video containing mangoes hanging from the trees is made and used as the input to this algorithm The video is read frame by frame and the within one frame morphological operations, watershed algorithm and analysis and segmentation are applied The mango types used in this study were Ripe Mango, Unripe Mango In this paper the application of neural network is used for assessment of mango The contours of ripe and unripe mangoes have been extracted, precisely normalised and then used as input data for the neural network The network optimisation has been carried out and then the results have been analysed in the context of response values worked out by the output neurons

Abstract: FMS Scheduling problem is one of the most difficult NP-hard combinatorial optimization problems.

Abstract: Swarm intelligence systems are typically made up of a population of simple agents or bodies interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm. In this work, we present an extended form of ABC algorithm, where we have added mutation operator of Genetic algorithm (GA) in the classical ABC algorithm. We have used mutation operator of real coded genetic algorithm. We have added mutation operator after the employed bee phase of ABC algorithm. This paper presents some experiments on ABC with real coded mutation operator on benchmark optimization problems.

Abstract: Unsupervised learning is the process to partition the given data set into number of clusters where similar data objects belongs same cluster and dissimilar data objects belongs to another cluster k-Means is the partition based unsupervised learning algorithm which is popular for its simplicity and ease of use Yet, k-Means suffers from the major shortcoming of passing number of clusters and centroids in advance Decimal scaling is one of the normalization approaches which standardize the features of the dataset and improve the effectiveness of the algorithm Integrating different distance measures with modified k-Means algorithm help to select the proper distance measure for specific data mining application This paper compare the results of modified k-Means with different distance measures like Euclidean Distance, Manhattan Distance, Minkowski Distance, Cosine Measure Distance and the Decimal Scaling normalization approach Result Analysis is taken on various datasets from UCI machine dataset repository and shows that Mk-Means is advantageous and improve the effectiveness with normalized approach and Minkowski distance measure

Abstract: Weather forecasting is a challenging area. To protect life and property weather warnings is important forecast. The model performance is contrasted with Multi-layered perceptron network. The proposed network is trained with actual data of the past 5years and tested which comes from meteorological department. For weather forecasting the data of temperature, wind speed and relative humidity were used to train and test. The future weather conditions is predicted by trained ANN. The proposed enhanced method for weather forecasting has advantages over other techniques. This model produced the most accurate forecasts in comparison with other existing methods.

Abstract: This paper presents an accelerated version of copy-move image forgery detection scheme on the Graphics Processing Units or GPUs. With the replacement of analog cameras with their digital counterparts and availability of powerful image processing software packages, authentication of digital images has gained importance in the recent past. This paper focuses on improving the performance of a copy-move forgery detection scheme based on radix sort by porting it onto the GPUs. This scheme has enhanced performance and is much more efficient compared to other methods without degradation of detection results. The CPU version of the radix-sort based detection scheme was developed in Matlab and critical sections of the CPU version were coded in C-language using Matlab’s Mex interface to get the maximum performance. The GPU version was developed using Jacket GPU Engine for Matlab and performs over twelve times faster than its optimized CPU variant. The contribution this paper makes towards blind image forensics is the use of integral images for computing feature vectors of overlapping blocks in block-matching technique and acceleration of the entire copy-move forgery detection scheme on the GPUs, not found in literature.

Abstract: Differential evolution (DE) is a well known and simple population based probabilistic approach used to solve nonlinear and complex problems. It has reportedly outperformed a few evolutionary algorithms when tested over both benchmark and real world problems. DE, like other probabilistic optimization algorithms, has inherent drawback of premature convergence and stagnation. Therefore, in order to find a trade-off between exploration and exploitation capability of DE algorithm, scaling factor in mutation process is modified. In mutation process, trial vector is calculated by perturbing the target vector. In this paper, a dynamic scale factor is proposed which controls the perturbation rate in mutation process. The proposed strategy is named as Dynamic Scaling Factor based Differential Evolution Algorithm (DSFDE). To prove efficiency of DSFDE, it is tested over 10 benchmark problems.

Abstract: Often real world provides some complex optimization problems that can not be easily dealt with available mathematical optimization methods. If the user is not very conscious about the exact solution of the problem in hand then intelligence emerged from social behavior of social colony members may be used to solve these kind of problems. Based on this concept, Passino proposed an optimization technique known as the bacterial foraging optimization algorithm (BFOA). The foraging behavior of bacteria produces an intelligent social behavior, called as swarm intelligence. Social foraging behavior of Escherichia coli is studied by researchers and developed a new algorithm named Bacterial foraging optimization algorithm (BFOA). BFOA is a widely accepted optimization algorithm and currently it is a growing field of research for distributed optimization and control. Since its inception, a lot of research has been carried out to make BFOA more and more efficient and to apply BFOA for different types of problems. This paper presents a review of BFOA modifications and it application areas.

Abstract: Peak ground acceleration (PGA) plays an important role in assessing effects of earthquakes on the built environment, persons, and the natural environment. It is a basic parameter of seismic wave motion based on which earthquake resistant building design and construction are made. The level of damage is, among other factors, directly proportional to the severity of the ground acceleration, and it is important information for disaster-risk prevention and mitigation programs. In this study, a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) is proposed for predicting Peak Ground Acceleration (PGA). Artificial neural network and Fuzzy logic provide attractive ways to capture nonlinearities present in a complex system. Neuro-Fuzzy modelling, which is a newly emerging versatile area, is a judicious integration of merits of above mentioned two approaches. In ANFIS, both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic are combined in order to give enhanced prediction capabilities, as compared to using a single methodology alone. The input variables in the developed ANFIS model are the earthquake magnitude, epi-central distance, focal depth, and site conditions, and the output is the PGA values. Results of ANFIS model are compared with earlier results based on artificial neural network (ANN) model. It has been observed that ANN model performs better for PGA prediction in comparison to ANFIS model.

Abstract: This paper proposes an efficient face recognition system which is invariant to pose. It presents a transformation to generate features of the frontal face from a given posed image of a subject. The proposed system has been tested on three databases viz. IITK, FERET and CMU-PIE. It has been observed that it performs better than the existing well known system.

Abstract: In this study, a new mutation operator for real coded genetic algorithms called the Log Logistic Mutation (LLM) is proposed. The performance of LLM is compared with existing real coded mutation operator namely Power Mutation (PM). LLM is used in conjunction with a well known crossover operator; Laplace Crossover (LX), to obtain a new generational real coded genetic algorithm called LX-LLM. LX-LLM is compared with the existing LX-PM. The performance of both the genetic algorithms is compared on the basis of success rate, average function evaluation, average error and computational time, and the supremacy of the proposed mutation operator is established.

Abstract: Multimodal optimization is used to find multiple global & local optima which is very useful in many real world optimization problems. But often evolutionary algorithms fail to locate multiple optima as required by the system. Also they fail to store those optima by themselves. So we have to use other selection scheme that can detect & store multiple optima along with evolutionary algorithms. Hence we use niching which is a very powerful tool in detecting & storing multiple optima. Niching methods were introduced to EAs to allow maintenance of a population of diverse individuals so that multiple optima within a single population can be located .Crowding which is a very primitive branch of niching is used here as the selection scheme with Invasive Weed Optimization (IWO) which is a ecologically inspired algorithms depicting behaviors of plants . For multimodal optimization the total search space is divided into several niches in which separately IWO is applied to find the optima in niches. The niches will also store this optima within themselves.

Abstract: Keystroke dynamics is a biometric technique to identify a user based on the analysis of his/her typing rhythm The steps of keystroke dynamics include feature extraction, feature subset selection and classifier In the experiment mean, median and standard deviation of feature values such as latency, duration and digraph are measured in feature extraction A meta-heuristic based on artificial bee colony algorithm is proposed for feature selection Neural network is used for classification The performance of artificial bee colony algorithm is analyzed with regard to feature reduction rate The obtained result shows comparable quality with faster convergence

Abstract: Artificial Bee Colony (ABC) optimization algorithm is a powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. In ABC each bee stores candidate solution; and stochastically modifies its candidate over time, based on the best solution found by neighboring bees,and based on the best solution found by the bee itself. When tested over various benchmark function and real life problems, it has performed better than a few evolutionary algorithms and other search heuristics . ABC, like other probabilistic optimization algorithms, has inherent drawback of premature convergence or stagnation that leads to loss of exploration and exploitation capability . Therefore, in order to balance between exploration and exploitation capability of ABC a new search strategy is proposed. In the proposed strategy, search process in ABC is performed by smaller group of independent swarms of bees. The experiments with 10 test functions of different complexities show that the proposed strategy has better diversity and faster convergence than the basic ABC.

Abstract: Both parallel and distributed network environment systems play a vital role in the improvement of high performance computing. The primary concern when analyzing these systems is multiprocessor task scheduling. This paper addresses the problem of efficient multiprocessor task scheduling. A multiprocessor task scheduling problem is represented as directed acyclic task graph (DAG), for execution on multiprocessors with communication costs. In this paper we have investigated the effectiveness of a proposed paradigm based on genetic algorithms (GAs). GAs is a class of robust stochastic search algorithms for various combinatorial optimization problems. We have designed a GA based encoding mechanism that uses multi-chromosome encoding scheme. The implementation of the technique is simple. The performance of the designed algorithm has been tested on a variety of multiprocessor systems both heterogeneous as well as homogeneous.

Abstract: In this paper,we study a linear fractional programming problem in which the objective function coefficients, technological coefficients and the right-handside coefficients are fuzzy numbersWe present a vertex-following solution method using a linear ranking function In fact, the proposed method is similar to the simplex method used for solving crisp linear fractional programming problems

Abstract: In the basic Artificial Bee Colony (ABC) algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to which its value is not improving. In the present study, we propose two new mechanisms for the movements of scout bees. In the first method, the scout bee follows a non-linear interpolated path while in the second one, scout bee follows Gaussian movement. Numerical results and statistical analysis of benchmark problems indicate that the proposed modifications enhance the performance of ABC.

Abstract: Supply chain management is a very dynamic operation research problem where one has to quickly adapt according to the changes perceived in environment in order to maximize the benefit or minimize the loss. Therefore we require a system which changes as per the changing requirements. Multi agent system technology in recent times has emerged as a possible way of efficient solution implementation for many such complex problems. Our research here focuses on building a Multi Agent System (MAS), which implements a modified version of Gravitational Search swarm intelligence Algorithm (GSA) to find out an optimal strategy in managing the demand supply chain. We target the grains distribution system among various centers of Food Corporation of India (FCI) as application domain. We assume centers with larger stocks as objects of greater mass and vice versa. Applying Newtonian law of gravity as suggested in GSA, larger objects attract objects of smaller mass towards itself, creating a virtual grain supply source. As heavier object sheds its mass by supplying some to the one in demand, it loses its gravitational pull and thus keeps the whole system of supply chain perfectly in balance. The multi agent system helps in continuous updation of the whole system with the help of autonomous agents which react to the change in environment and act accordingly. This model also reduces the communication bottleneck to greater extents.

Abstract: In the forthcoming time where the coverage area of the Wi-Fi networks would increase drastically, the transfer of call between two users present in a Wi-Fi network can be achieved thus reducing the effective cost of call rates significantly. The following article presents a view of how people in any Wi-Fi network can call each other via the network. Once the user is registered as a genuine user using any authentication methodology, the ASTERISK SERVER which has DHCP server configured provides an IP to any user requesting having a Wi-Fi enabled cell phone. Now, this user can talk to any other user in a Wi-Fi network by sending and receiving speech signals as data packets over the network. Since the dependence over the service provider is removed, overall cost is drastically reduced.

Abstract: Software watermarking is the process of hiding information in a program or source code to protect the piracy of the software. Software piracy is a great threat for software industry because of every country losing millions of dollars every year. To protect the software piracy of the software’s, variety of prevention techniques have been developed for copyright protection of software codes using both hardware and software. But, unfortunately no single technique is currently strong enough to protect the piracy of software codes. However, through a combination of techniques software developers are using for better protection of their software codes. In this paper, we have explained various static and dynamic techniques of software watermarking. In static watermarking techniques, the watermarks are stored in the source code, either in data section or code section where as in dynamic watermarking techniques the watermarks are generated during program execution. In this paper we have proposed a combination of static software watermarking techniques for structural programming by equation reordering and function dependency oriented sequencing (FDOS). In this method we embed the watermark in source code by a nested procedure wherein we firstly interchange the safe operands of mathematical equation and secondly impose an ordering on the mutual independent functions by introducing bogus dependency.

Abstract: Image segmentation played a vital role in medical imaging system. With the help of image segmentation pulmonary parenchyma can be detected from multisliced CT images. Pulmonary diseases such as lung cancer, tumor, and mass cells can be detected with 2D Otsu algorithm. 2D Otsu algorithm is a well-known image segmentation method. In CT images segmentation 2D Otsu play a vital role. Main drawback of 2D Otsu method is its computation complexity and computational time. In this paper 2D Otsu algorithm has implemented with Differential Evolution (DE) Algorithm. This results in reducing the computational complexity as well as computational time. Further the results are compared with 2D Otsu and 2D Otsu with PSO, which proves the efficiency of using DE with 2D Otsu.

Abstract: Cognitive Radio (CR) is an intelligent Software Defined Radio (SDR) that can alter its transmission parameters according to predefined objectives by sensing the dynamic wireless environment. In this paper, we propose a method to determine the necessary transmission parameters for a multicarrier system based on multiple scenarios using a multi-objective evolutionary algorithm like Non-dominated Sorting based Genetic Algorithm (NSGA-II). Each scenario is represented by a fitness function and represented as a composite function of one or more radio parameters. We model the CR parameter adaptation problem as an unconstrained multi-objective optimization problem and then propose an algorithm to optimize the CR transmission parameters based on NSGA-II. We compute the fitness score by considering multiple scenarios at a time and then evolving the solution until optimal value is reached. The final results are represented as a set of optimal solutions referred as pareto-front for the given scenarios. We performed multi-objective optimization considering two objectives and the best individual fitness values which are obtained after final iteration are reported here as the pareto-front.