The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present study proposes the use of a hybrid image segmentation technique to acquire precise segmentation outcomes, while engaging “Alpha Shape (α-Shape)” in supposition to derive the original contour, followed by a refining process through engaging a conventional active contour model. Empirical results show high potential in the suggested computational method. Trials indicate that the primary contour is capable of being precisely set next to the objective contour and effectively have these objective contours extracted, devoid of any contour instigation. Some of the benefits associated with the novel hybrid contour include minimized cost of computation, enhanced anti-jamming capability, as well as enlarged utilization array of snake model.
This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estima
... Show MoreIn probability theory generalizing distribution is an important area. Several distributions are inappropriate for data modeling, either symmetrical, semi-symmetrical, or heavily skewed. In this paper, a new compound distribution with four parameters called Marshall Olkin Marshall Olkin Weibull (MOMOWe) is introduced. Several important statistical properties of new distribution were studied and examined. The estimation of unknown four parameters was carried out according to the maximum likelihood estimation method. The flexibility of MOMOWe distribution is demonstrated by the adoption of two real datasets (semi-symmetric and right-skewed) with different information fitting criteria. Su
The musical templates are the fundamental reason for admiration and interest among a lot of cultural and societal medias because of the beauty of it's melodic value, where a lot of Iraqi singing and music specialists and composers try to consolidate daily life idea and translate it into music in a manner that preserve the template and rhythm horizontally and vertically, through the involvement of the scientific and philosophical concepts and theories of modern thought to reach the recipient,, as is the theory of form (Gestalt), one of the most important theories that stretched to their interpretations to the field in general art and in music science, especially, as an area that can be manifested as partial components template music, conn
... Show MoreThis study was conducted in College of Science \ Computer Science Department \ University of Baghdad to compare between automatic sorting and manual sorting, which is more efficient and accurate, as well as the use of artificial intelligence in automated sorting, which included artificial neural network, image processing, study of external characteristics, defects and impurities and physical characteristics; grading and sorting speed, and fruits weigh. the results shown value of impurities and defects. the highest value of the regression is 0.40 and the error-approximation algorithm has recorded the value 06-1 and weight fruits fruit recorded the highest value and was 138.20 g, Gradin
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
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The aim of the current research is to identify the effect of the effective reading strategy on the achievement of second-middle students in biology, as well as the imaginative thinking skills of students. The researcher adopted the experimental design with partial control to achieve the goal of the research. The current research community identified the second-middle students in the government schools of the Baghdad Education Directorate / Rusafa I for the academic year (2021-2022 AD). The Safina Intermediate School for Girls was chosen to be the research sample in the form of intentionally, two classes were randomly selected from a total of four classes, one of them is experimental and the other is con
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