We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). Standard Deviation, Mean, Energy and Entropy are extorted using the histogram approach for each merger space. These features are found to be higher in occurrence in the tumor region than the non-tumor one. MRI scans of the five brains with 60 slices from each are utilized for testing the proposed method’s authenticity. These brain images (230 slices as normal and 70 abnormal) are accessed from the Internet Brain Segmentation Repository (IBSR) dataset. 60% images for training and 40% for testing phase are used. Average classification accuracy as much as 98.02% (training) and 98.19% (testing) are achieved.
Background: The primary stability of the dental implant is a crucial factor determining the ability to initiate temporary implant-supported prosthesis and for subsequent successful osseointegration, especially in the maxillary non-molar sites. This study assessed the reliability of the insertion torque of dental implants by relating it to the implant stability quotient values measured by the Osstell device. Material and methods: This study included healthy, non-smoker patients with no history of diabetes or other metabolic, or debilitating diseases that may affect bone healing, having non-restorable fractured teeth and retained roots in the maxillary non-molar sites. Primary dental implant stability was evaluated using a torque ratc
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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
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Objective: To identification environmental and psychological violence's components among collegians’ students of different stages, and gender throughout creating specific questionnaire, and estimating regression of environmental domain effect on psychological domain, as well as measuring powerful of the association contingency between violence's domains in admixed form with respondent characteristics, such that (Demographics, Economics, and Behaviors), and extracting model of estimates impact of studied domains in studying risks, and protective factors among collegians’ students in Baghdad city. Methodolog
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In this study; the genus of Sinoxylon Duftschmid, 1825 (Coleoptera, Bostrichidae) was revised. There were 3 species registered in our investigations: S. anale Lesne, 1897; S. ceratoniae (Linnaeus, 1758) and S. muricatum (Olivier, 1790), the last species was redescribed as being found for the first time for the Iraqi faunal insects. Key to the species were constructed and supported by figures of the main diagnostic characters and some morphological features.
A total of 30 specimens of house sparrow Passer domesticus biblicus Hartert, 1904 (15 females and 15 males) were collected from gardens of some houses in Baghdad city; all birds were dissected to identify the parasites in vesicle, gizzard, intestine, gall bladder and caecum. One species of trematodes Brachydistomum microscelis (Yamaguti, 1933) was found in the gall bladder and two species of cestodes Anonchotaenia globata (von Linstow, 1879) and Raillietina tetragona (Molin, 1858) were found in the small intestine of house sparrow. Morphologic and morphometric measurements were considered.
The genus Brachydistomum Travassos, 1944 is being recorded for the first time in Iraq in the gall bladder of house sparr
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In May 20th. 1985 two species of aphids were found on the roots of Vicia faba L. in Hammam region 30 Kilometer south of Mosul. Samples of these aphids were sent to the Commonwealth Institute of Entomology, London. ( No. 17002/9804 Asia ) and identified as being Smynthurodes betas westwood and Dysaphis crataegi (Kaltenbach) (APhididae : Homoptera). The first species was dominant. The latter species was also noticed on the roots of the common bishop's weed (Ammi majus L.) and on the wide carrot (Daucus carrota L.) of the family Umbelliferae (Bodenheimer & Swirski, 1957).
This paper presents the implementation of a complex fractional order proportional integral derivative (CPID) and a real fractional order PID (RPID) controllers. The analysis and design of both controllers were carried out in a previous work done by the author, where the design specifications were classified into easy (case 1) and hard (case 2) design specifications. The main contribution of this paper is combining CRONE approximation and linear phase CRONE approximation to implement the CPID controller. The designed controllers-RPID and CPID-are implemented to control flowing water with low pressure circuit, which is a first order plus dead time system. Simulation results demonstrate that while the implemented RPID controller fails to stabi
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