Cybernetics and Information Technologies
De Gruyter Open
About: Cybernetics and Information Technologies is an academic journal published by De Gruyter Open. The journal publishes majorly in the area(s): Computer science & Cloud computing. It has an ISSN identifier of 1311-9702. It is also open access. Over the lifetime, 640 publications have been published receiving 4034 citations. The journal is also known as: CIT.
Topics: Computer science, Cloud computing, Information technology, Routing protocol, Artificial intelligence
TL;DR: This paper focuses on a survey of feature selection methods and can conclude that most of the FS methods use static data, while the existing DR algorithms do not address the issues with the dynamic data.
Abstract: Abstract Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.
TL;DR: The initial results from a data mining research project implemented at a Bulgarian university are presented, aimed at revealing the high potential of data mining applications for university management.
Abstract: Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.
TL;DR: A survey of the main approaches employed in gamification and the emerging new directions in the context of the relevant motivational psychology and pedagogy is presented, with a focus on the motivational factors that impact learning and understanding of behavior change.
Abstract: Learning is a goal driven social activity determined by motivational factors. To be able to efficiently gamify learning for improved student motivation and engagement, the educators have to understand the related aspects studied in games, motivational psychology and pedagogy. This will help them to identify the factors that drive and explain desired learning behaviors. This paper presents a survey of the main approaches employed in gamification and the emerging new directions in the context of the relevant motivational psychology and pedagogy. The focus is on the motivational factors that impact learning and understanding of behavior change. The purpose of the paper is two-fold: on one side, to provide analysis and guide to relevant works related to gamification along with outlining the emerging trends, and on the other, to provide foundation for evaluation and identification of the areas of possible improvements.
TL;DR: A simple method to detect and remove shadows from a single RGB image by multiplying the shadow region by a constant andShadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.
Abstract: A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a pre-processing task in many computer vision applications. This paper proposes a simple method to detect and remove shadows from a single RGB image. A shadow detection method is selected on the basis of the mean value of RGB image in A and B planes of LAB equivalent of the image. The shadow removal is done by multiplying the shadow region by a constant. Shadow edge correction is done to reduce the errors due to diffusion in the shadow boundary.
TL;DR: The proposed method notably raises the capacity as well as bits per pixel that can be hidden in the image compared to existing bit flipping method.
Abstract: Abstract This article proposes bit flipping method to conceal secret data in the original image. Here a block consists of 2 pixels and thereby flipping one or two LSBs of the pixels to hide secret information in it. It exists in two variants. Variant-1 and Variant-2 both use 7th and 8th bit of a pixel to conceal the secret data. Variant-1 hides 3 bits per a pair of pixels and the Variant-2 hides 4 bits per a pair of pixels. Our proposed method notably raises the capacity as well as bits per pixel that can be hidden in the image compared to existing bit flipping method. The image steganographic parameters such as, Peak Signal to Noise Ratio (PSNR), hiding capacity, and the Quality Index (Q.I) of the proposed techniques has been compared with the results of the existing bit flipping technique and some of the state of art article.