Journal•ISSN: 0066-5452
Applied mathematical sciences
Springer Nature
About: Applied mathematical sciences is an academic journal. The journal publishes majorly in the area(s): Mathematics Subject Classification & Nonlinear system. It has an ISSN identifier of 0066-5452. Over the lifetime, 3103 publications have been published receiving 12902 citations.
Topics: Mathematics Subject Classification, Nonlinear system, Orthogonal polynomials, Fuzzy logic, Fuzzy number
Papers published on a yearly basis
Papers
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544 citations
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TL;DR: Several techniques for edge detection in imageprocessing are compared and various well-known measuring metrics used in image processing applied to standard images are considered in this comparison.
Abstract: Edge detection is one of the most commonly used operations in image analysis, and there are probably more algorithms in the literature for enhancing and detecting edges than any other single subject. The reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detections is an essential tool. In this paper, we have compared several techniques for edge detection in image processing. We consider various well-known measuring metrics used in image processing applied to standard images in this comparison.
258 citations
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TL;DR: This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course and shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value.
Abstract: Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable pattern pertaining students’ learning behaviour. This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students’ demographics, previous academic records, and family background information. Decision Tree, Naive Bayes, and Rule Based classification techniques are applied to the students’ data in order to produce the best students’ academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students’ level of success in the first semester.
112 citations
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TL;DR: In this article, a generalization of Molodtsov's soft set, called soft multiset, is introduced, with its basic operations such as complement, union and intersection.
Abstract: In 1999 Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. The solutions of such problems involve the use of mathematical principles based on uncertainty and imprecision. In this paper we recall the definition of a soft set, its properties and its operations. As a generalization of Molodtsov’s soft set we introduce the definitions of a soft multiset, its basic operations such as complement, union and intersection. We give examples for these concepts. Basic properties of the operations are also given.
95 citations