Software usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

This paper reports the findings of an experimental study in which the effect of informational load, direction of move and task difficulty on performance time were investigated. A mathematical model based on the empirical findings is presented.

Traffic sign detection and recognition plays an important part in today’s technology driven world. The purpose of traffic signs is to help drivers as well as pedestrians for safe navigation. The two major phases involved in traffic sign detection and recognition are : identifying the region of interest and proceeding to detect any and all signs that might be present, and further, classifying the detected signs into their respective classes. This paper attempts to review all the existing methods/practices for the detection of signs(real-time).

Traffic sign detection and recognition plays an important part in today’s technology driven world. The purpose of traffic signs is to help drivers as well as pedestrians for safe navigation. The two major phases involved in traffic sign detection and recognition are : identifying the region of interest and proceeding to detect any and all signs that might be present, and further, classifying the detected signs into their respective classes. This paper attempts to review all the existing methods/practices for the detection of signs(real-time).