The variations of day light intensity must be taken into account to recognize the color of the agriculture product when using camera vision system. In this study, the development of outdoor image analysis for oil palm fruit fresh bunches (FFB) was developed to analyses image of oil palm FFB. The software analysis will generates the mathematical model and correlation factor between the light intensity in relation to value of FFB from Red, Green and Blue component (RGB) of image taken. The visual basic programming language was used. An Excel worksheet programming software was developed to draw a chart and modify the charts location and dimensions. The API (Application Programming Interface) called GDI32 (Graphic Device Interface: 32-bit version) was applied to read and manipulate the pixel value of image. The software automation technique was used in the development of outdoor image analysis for agriculture product. The automation concept is to describe the automatic generation of the software to manipulate the data for producing the graph, mathematical analysis and equation, automatically from a bundle of data collected by only a click of mouse button. The result generated was used to develop mathematical model for color-based recognition for outdoor image of agriculture product. Linear regression function was used to find the equation of mathematical model for outdoors image analysis. To find the best equation of mathematical model, various trendline of data manipulation can be chosen manually from option provided in Microsoft Excel. The images of FFB were taken by using sony digital camera Handycam at various times of day from morning to afternoon in the oil palm plantation.

Ad hoc networks, a subset of wireless networks, allow the formation of a wireless network without the need for access point. All participating users in an ad hoc network agree to accept and forward messages, to and from each other. Mobility management with reference to handoff management has widely been recognized as one of the most important and challenging issue in ad hoc network. Mobility of node is defined by various mobility models. Evaluating mobility models within an ad hoc network gives solution to find out performance measures like blocking probability, handoff probability, dropping probability, hop count, delay etc. This research is an investigation in search of the effect of Random Walk, Random Waypoint and Gauss Markov mobility models on performance of mobile ad hoc network. Power of nodes in ad hoc networks is supplied with batteries hence power-saving is an important issue. This paper also focuses on energy calculations.

A digestive system disorders are the very important problem in human being universally. As a panacea, a preliminary investigation method is proposed before the endoscopy procedure is called Electrogastrography. Electrogastrogram [EGG] is obtained from the human being cutaneously, which is noninvasive, easy procedure over endoscopy. EGG recorded for a 180 human being includes the normal individual and patients with digestive system disorder such us Nausea, Dyspepsia, Vomiting, ulcer, etc. A database is created for the analysis purpose. During the acquisition of EGG, the analog signal’s numeric values are recorded as .bio file format using the data scope. Wavelet transform is used for the analysis of EGG to find the deviation in frequency and Power from the power spectrum estimate plot obtained for disorders patients compare to normal Individual. Wavelet Transform analysis includes the principle component analysis, denosing of the signal and power spectrum estimation. In the proposed wavelet analysis, the data of a patient or normal individual is included for analysis. The EGG signal is reconstructed with a data obtained from the data scope. This signal undergoes principle component analysis to remove the noise in the EGG signal to obtain de-noised signal and it is plotted for power spectral density estimation with Welch power spectral Density Estimation. As a result of the proposed method the power variation is found to be a range of 40-52dB and frequency is detected as 0.06 Hz to 0.075Hz for ulcer patient 0.02 Hz to 0.04 Hz for dyspepsia patient and 0.05Hz to 0.058 Hz for Normal Individual. The above said finding supports the physician in the diagnosis of digestive system disorders at fair amount of accuracy.

This paper introduces fuzzy goal programming approach to unbalanced transportation problem with additive multiple fuzzy goals, when the goals are considered to be of equal importance. But in reality all goals may not be of equal importance. Here we have discussed two different ways of assigning weights to additional model described in the paper. The direct weights are used in fuzzy goal programming model for unbalanced multi-objective transportation problem.

In this paper, the concept based on entropy analysis is proposed. To determine the attribute weights under the conditions that the attribute weights are completely unknown. Furthermore, in order to make a decision or choose the optimum alternative, Using entropy analysis method attribute weights are completely known. To take a decision or choose optimal alternative we proposed the vertex distance method and calculate closeness for obtaining best ideal alternative solution. Compare this method using Additive Utility Function to check the feasibility and validity of the proposed study. Finally, a numerical example has been provided to illustrate the solution procedure on the proposed method.

Computer Science is largely concerned about a single question - how long does it take to execute a given algorithm? But computer scientists don’t give the answer in minutes or milliseconds; they give it relative to the number of elements the algorithm has to manipulate resulting into formation of computational complexity theory. For entry level students in Data Structures and Analysis of Algorithms, understanding this theory is quite hard as literature review seen so far is dedicated to classifying problems by how hard they are. We have attempted for rigorous analysis of literature review and discovered different types of problems. A division of problem space is suggested in order to put these concepts in simple and conceptually understandable format.

In a transportation problem generally a single criterion of minimizing the total transportation cost is considered but in certain practical situations two or more objectives are relevant. For example, the objectives may be minimization of total cost, consumption of certain scarce resources such as energy, total deterioration of goods during transportation etc. Clearly, this problem can be solved using any of the multiobjective linear programming techniques, but the computational efforts needed would be prohibitive in many cases. In this paper, The Bi-objective transportation problem, where only objectives are considered as fuzzy. We apply the fuzzy programming technique with hyperbolic membership function to solve a biobjective transportation problem as vector minimum problem.

In scenarios where data chunks arrive temporally, a good algorithm for exploratory analysis should be able to generate the knowledge and with the next chunk of data arriving, the process should be the one of just updating online by accumulating the knowledge derived from the recent chunk. Such an incremental learning process in most of the cases indent a lot of memory requiring to carry all earlier data in the process of updating the knowledge successively. In this research work we propose to employ a novel Cluster-Histo-Regression analysis of the chunk to extract the knowledge for the temporal instant and fuse this knowledge through Histo-Regression-Distance analysis with the already accumulated knowledge. We have designed a methodology which (i) discards all those data samples from the chunk which have participated in the knowledge generation process (ii) indents minimum amount of memory to carry the accumulated knowledge and (iii) proposes to carry forward only those limited data samples (referred to as hard samples) which could not contribute to knowledge generated at that moment. Knowledge of each cluster is represented in the form of a histogram for each dimension of the clustered data and is transformed to regression line for the compact representation of the knowledge. The regression line parameters of the clusters obtained by incremental augmentation have shown an accuracy of up to 100% for some of the data sets that are considered for experimentation.

The aim of this paper is to develop monotone method for nonlinear time degenerate parabolic Neumann initial boundary value problems. These Problems are important in the Study of Physics, Ecology, Heat and Mass Transfer and any other branches of science and Engineering. Monotone Method is one of the important method in the theory of differential equations The main idea of this method is to develop iterative scheme based on the notion of upper and lower solutions. Using upper and lower solutions as distinct initial iterations, two monotone sequences are constructed. It is shown that these two sequences converge monotonically from about and below to maximal and minimal solutions respectively which lead to the existence – comparison and uniqueness results for the solution of nonlinear time degenerate Neumann IBVP. Positivity lemma is also obtained which play an important role in the proofs of these results.

Rough set model with lower and upper approximations based on lattice theory is defined and for the new model some properties are given. Lattice theory plays an important role in rough set theory and fuzzy set theory, so the new rough set model over lattice theory in this paper may be a new tool to study the relationship between fuzzy set theory and rough set theory.

This paper deals with study of the evolution of social conventions or norms that selects one equilibrium over others based on repeated distributed interactions between agents in a society. To study the phenomenon of emergence of social norms, we have assumed that the interactions between the agents are private, i.e.; not observable to the other agents not involved in the interactions. We consider a population of agents, where, in each interaction each agent is paired with another agent selected randomly from its neighborhood or from the population in a non-uniform manner. Each agent is learning concurrently over repeated interactions with selected opponents from the society. An agent learns a policy to play the game from repeated interactions with multiple agents. We are particularly interested in finding out if the entire population learns to converge to a consistent norm when multiple action combinations yield the same optimal payoff. In addition to this, we also would like to explore the effects of heterogeneous populations where different agents may be using different learning algorithms.