Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i) MATLAB optimization toolbox, (ii) genetic algorithm (GA), and (iii) multiobjective optimization (NSGA-II) technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives.

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An investor would like to build a balanced portfolio with stocks representing different sectors. Several researchers have attempted the portfolio selection problem by different methods. Many of these methods consider companies of different sectors together. However, it can be argued that the attributes affecting the company's growth vary for different sectors. Therefore, it is advisable to compare a company with the companies of the same sector. There are many options for the selection of a stock from a particular sector. A stock ranking method is proposed by using MADM methods based on overall performance under a stochastic environment. Of many MADM methods, SAW, AHP, TOPSIS, and VIKOR are applied. Usually, Euclidean distances (2-norm) are considered in the implementation of TOPSIS and VIKOR methods. In this work, this norm is generalized to p-norm, where p > 1. The model is tested for 13 companies in the field of Information Technology sector (IT) listed on National Stock Exchange in India and 13 criteria as performance indicators of a company. A MATLAB GUI system is developed and the results are obtained for several values of p in case of TOPSIS and VIKOR methods besides other methods. As the result indicates, the ordering is not much affected by different values of p in certain range. Moreover, higher values of p have adverse effect on the ordering. The proposed model is able to provide better information on the overall performance of a particular stock in comparison with its peers. The results obtained by various methods clearly separate good companies from inferior companies though the exact ordering slightly differs.

With the increase in the popularity of using huge image databases in various image retrieval applications, a need arises to develop an efficient, robust and automatic system which provides output in the form of similar images with respect to the input or query image. In this paper, three Image Retrieval Systems using very basic but novel descriptors, which can retrieve black and white (binary) image, grayscale image and color image from the binary, grayscale and color image databases, respectively, were implemented. It should be noted that the features used for the binary image and grayscale image may not be efficiently applicable to the color images. Obtained results indicate the same and it additionally shows that irrespective of numbers of images in database, developed Image Retrieval Systems retrieve images in very short period of time with high accuracy. Accuracy in color image retrieval is less compared to binary and grayscale image retrieval. So, it creates a need to design highly effective features for the color image retrieval.