Neutrosophic Hough Transform

Budak, Ümit; Guo, Yanhui; Şengür, Abdulkadir; Smarandache, Florentín

doi:10.3390/axioms6040035

Cited by 3 publications

(1 citation statement)

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“…A final voting scheme as given in fuzzy k-NN was considered for class label determination. Budak et al proposed an NS-based efficient Hough transform [20]. The authors initially transferred the Hough space into the NS space by calculating the NS membership triples.…”

Section: Related Workmentioning

confidence: 99%

Neutrosophic Weighted Support Vector Machines for the Determination of School Administrators Who Attended an Action Learning Course Based on Their Conflict-Handling Styles

et al. 2018

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In the recent years, school administrators often come across various problems while teaching, counseling, and promoting and providing other services which engender disagreements and interpersonal conflicts between students, the administrative staff, and others. Action learning is an effective way to train school administrators in order to improve their conflict-handling styles. In this paper, a novel approach is used to determine the effectiveness of training in school administrators who attended an action learning course based on their conflict-handling styles. To this end, a Rahim Organization Conflict Inventory II (ROCI-II) instrument is used that consists of both the demographic information and the conflict-handling styles of the school administrators. The proposed method uses the Neutrosophic Set (NS) and Support Vector Machines (SVMs) to construct an efficient classification scheme neutrosophic support vector machine (NS-SVM). The neutrosophic c-means (NCM) clustering algorithm is used to determine the neutrosophic memberships and then a weighting parameter is calculated from the neutrosophic memberships. The calculated weight value is then used in SVM as handled in the Fuzzy SVM (FSVM) approach. Various experimental works are carried in a computer environment out to validate the proposed idea. All experimental works are simulated in a MATLAB environment with a five-fold cross-validation technique. The classification performance is measured by accuracy criteria. The prediction experiments are conducted based on two scenarios. In the first one, all statements are used to predict if a school administrator is trained or not after attending an action learning program. In the second scenario, five independent dimensions are used individually to predict if a school administrator is trained or not after attending an action learning program. According to the obtained results, the proposed NS-SVM outperforms for all experimental works.

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Section: Related Workmentioning

confidence: 99%

Neutrosophic Weighted Support Vector Machines for the Determination of School Administrators Who Attended an Action Learning Course Based on Their Conflict-Handling Styles

et al. 2018

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Track initialisation for multiple formations based on neutrosophic Hough transform

Penggang,

Kun,

Guangdong

2024

IET Radar Sonar & Navi

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Track crossing is a major issue in the initialisation of multiple‐flight trajectories. To solve this problem, the authors propose a track initialisation algorithm. It uses characteristics of formation flight to find centroids of each formation with DBSCAN algorithm. Then, it initialises the track based on the centroid. The author use a Neutrosophic Hough Transform (NHT) method to improve accuracy and computational speed. That helps address errors caused by the approximation of straight lines between true points resulting from the clustering algorithm. The authors made three experiments using track initialisation data from two flight formations with five target aircraft each, over a span of three frames. NHT, Fuzzy HT, Improved Hough Transform (HT) and HT are compared. Results revealed that the average runtime of NHT was 10.2153 s. The F‐measure of NHT was 100.00%, while that of Fuzzy HT was 9.8347 s. The F‐measure of Fuzzy HT was 80.00%. The Improved HT was 12.0723 s. The F‐measure of Improved HT was 11.76% and HT was 13.783 s. And the F‐measure of HT was 6.87%. The authors lost some computation speed to achieve higher prediction accuracy. The accuracy of the NHT is higher than other methods.

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