This article re-defines traditional requirement engineering processes by performing exhaustive requirement gathering through an interactive and collaborative communication, where stakeholders are... Show More

This article re-defines traditional requirement engineering processes by performing exhaustive requirement gathering through an interactive and collaborative communication, where stakeholders are equally involved in capturing and finalizing requirements in the scope of a project. It strongly supports concept of re-usability using principals of situational method engineering where methods are tailor-made per requirements from the method repository (maintained in the cloud). Search and selection of appropriate methods meeting stakeholder's interest is performed using concepts of knowledge discovery and data mining methods over a web interface. Knowledge extraction, in the form of a matched set of requirements is performed at every level of a proposed multi-layered framework, as progression towards a desirable method for reuse. This approach will help in overcoming the challenging tasks of identifying relevant requirements, incorporating change requests and minimizing the number of disagreements between stakeholders and developers.

Image Enhancement works as a first mandatory criteria for an efficient image analysis task. Removing noises and managing the contrast are the two major tasks that need to be accomplished in an image... Show More

Image Enhancement works as a first mandatory criteria for an efficient image analysis task. Removing noises and managing the contrast are the two major tasks that need to be accomplished in an image enhancement process. In this article, an innovative approach for color image enhancement is proposed. The proposed approach is a two-step technique. The first step is the noise removal step. Here, an improved median filter, Improved_Median(), is introduced to smooth the noises which exist in the original color image. Then, in the second step, local contrast enhancement is done. For that, an improved version of CLAHE, AA_CLAHE() is proposed for the local contrast management of the filtered image. The V-channel of HSV color space is used for the color computations involved in the local contrast management process. The overall enhancement done by the proposed approach is found to be satisfactory and outperforms the same produced by other state-of-the-art algorithms through experiments on several noisy and poor contrast color images obtained from different standards databases.

Motion estimation has traditionally been used in video encoding only, however, it can also be used to solve various real-life problems. Nowadays, researchers from different fields are turning... Show More

Motion estimation has traditionally been used in video encoding only, however, it can also be used to solve various real-life problems. Nowadays, researchers from different fields are turning towards motion estimation. Motion estimation has become a serious problem in many video applications. It is a very important part of video compression technique and it provides improved bit rate reduction and coding efficiency. The process of motion estimation is used to improve compression quality and it also reduces computation time. Block-based motion estimation algorithms are used as they require less memory for processing of any video file. It also reduces the complexity involved in computations. In this article, various block-matching motion estimation algorithms are discussed such as Full search (FS) or Exhaust Search, Three-Step search (TSS), New Three-Step search (NTSS), Four-Step search (FSS), Diamond search (DS) etc.

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest... Show More

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

The shape, length, and size of the vocal tract and vocal folds vary with the age of the human being. The variation may be of different age or sickness or some other conditions. Arguably, the... Show More

The shape, length, and size of the vocal tract and vocal folds vary with the age of the human being. The variation may be of different age or sickness or some other conditions. Arguably, the features extracted from the utterances for the recognition task may differ for different age group. It complicates further for different emotions. The recognition system demands suitable feature extraction and clustering techniques that can separate their emotional utterances. Psychologists, criminal investigators, professional counselors, law enforcement agencies and a host of other such entities may find such analysis useful. In this article, the emotion study has been evaluated for three different age groups of people using the basic age- dependent features like pitch, speech rate, and log energy. The feature sets have been clustered for different age groups by utilizing K-means and Fuzzy c-means (FCM) algorithm for the boredom, sadness, and anger states. K-means algorithm has outperformed the FCM algorithm in terms of better clustering and lower computation time as the authors' results suggest.

Development and progress mainly depends on education and its solid dissemination. Technologies as well as engineering solutions are important for the business and corporate houses. In this context... Show More

Development and progress mainly depends on education and its solid dissemination. Technologies as well as engineering solutions are important for the business and corporate houses. In this context, educational initiatives and programs play a vital role. Developing countries are suffering from many problems and therefore fostering new academic innovation and researches on economic development in today's context. Information Technologies and management science are important for solid business solutions. Therefore, education and knowledge dissemination play an important and valuable role. In many developing countries, gaps between industrial needs and the availability of skilled labor are limited. Information Sciences and Computing are the most valuable areas of study in today's knowledge world. The components, subsets, and subfields of Information Sciences and Technology are rapidly emerging worldwide. Among the emerging and popular areas, a few include Cloud Computing, Green Computing, Green Systems, Big-Data Science, Internet, Business Analytics, and Business Intelligence. Developing countries (like China, Colombia, Malaysia, Mauritius, India, Brazil, South Africa) depend in many ways on knowledge dissemination and solid manpower for their development. Thus, there is an urgent need to introduce such programs and the majority of these programs have been proposed here. Information Science and Technology (IST) with programs such as Bachelors, Masters, and Doctoral Degrees have been listed here with academic and industrial contexts. This article highlights these programs with proper SWOT analysis.