This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on real data from the Kadhimiya teaching hospital shows that the proposed CUHF is a better method when compared to the accuracy of the other integrated filters.
The UN organization is considered one of the most important organizations at the international level. It has accomplished multiple tasks and roles of many different issues and events that hit the developing and advanced world countries. It has performed a series of procedures and laws that have had an impact on ending the wars and conflicts that plagued some countries and continued for a period of time in the past. Moreover, it has improved the level of the international relations between a number of countries due to the problems and incidents took place between them. It has relied on finding solutions and treatments for humanitarian problems such as the preservation of the environment, preventing the spread of epidemics and diseases Thi
... Show MoreThe main idea of this paper is to define other types of a fuzzy local function and study the advantages and differences between them in addition to discussing some definitions of finding new fuzzy topologies. Also in this research, a new type of fuzzy closure has been defined, where the relation between the new type and different types of fuzzy local function has been studied
The present study focuses on the deformation of neutron-rich nuclei near the neutron drip line. The nuclei of interest include 28O, 42Si, 58Ca, 80Ni, 100Kr, 122Ru, 152Ba, 166Sm, and 176Er. The relativistic Hartree - Bogoliubov (RHB) approach with effective density-dependent point coupling is utilized to investigate the triaxial deformation, and Skyrme - Hartree - Fock + Bardeen - Cooper - Schrieffer is used to analyze the axial deformation. The study aimed to understand the interplay between nuclear forces, particle interactions, and shell structure to gain insights into the unique behavior of neutron-rich nuclei. Despite these nuclei containing magic numbers, their shapes are still affected by the nucleons' collective behavior and
... Show MoreThe wound healing process is incredibly intricate, consisting of a series of cellular activities. Although, this complex process has the potential to degenerate and result in chronic wound problems that are resistant to biological healing mechanisms. Nanoparticles can help to reduce inflammation, promote tissue regeneration, and accelerate wound healing. The proteolytic enzymes are believed to break down proteins and other molecules that can cause inflammation and impede the healing process. Wound was created in vivo using adult mice, and by taking blood samples the hematological parameters were evaluated to detected the effects of bromelain, silver nanoparticles and Br-AgNPs. The results shows an increased in white blood cells WBC, RBC, MC
... Show MoreIn this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.
Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a
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