Many approaches of different complexity already exist to edge detection in
color images. Nevertheless, the question remains of how different are the results
when employing computational costly techniques instead of simple ones. This
paper presents a comparative study on two approaches to color edge detection to
reduce noise in image. The approaches are based on the Sobel operator and the
Laplace operator. Furthermore, an efficient algorithm for implementing the two
operators is presented. The operators have been applied to real images. The results
are presented in this paper. It is shown that the quality of the results increases by
using second derivative operator (Laplace operator). And noise reduced in a good
range by using mean filter for smoothing before applying Laplace operator.
Moreover, thresholding is used for image in both cases to see results in clearer
manner.
Researchers are interested in the issue of children abuse and they look for its cause in the past and present. Their interest is limited to identifying penal liability which is caused by children abuse away from focusing on civil liability. So, the study is going to clarify the parents' responsibility for children abuse rather than civil liability of the medic in case he wouldn’t notify the authorities about the case according to the American law rules and the attitude of Iraqi law rather than some judicial application of civil cases that were exposed to American judiciary concerning children abuse.
Background: Restoration of the gingival margin of Class II cavities with composite resin continues to be problematic, especially where no enamel exists for bonding to the gingival margin. The aim of study is to evaluate the marginal leakage at enamel and cementum margin of class II MOD cavities using amalgam restoration and modern composite restorations Filtek™ P90, Filtek™ Z250 XT (Nano Hybrid Universal Restorative) and SDR bulk fill with different restoratives techniques. Materials and method: Eighty sound maxillary first premolar teeth were collected and divided into two main groups, enamel group and cementum group (40 teeth) for each group. The enamel group was prepared with standardized Class II MOD cavity with gingival margin (1 m
... Show MoreBackground: Suffering from recurrent boils (furunclosis) is a common problem in our locality as it is noticed by many dermatologists especially in association with increasingly hot weather. The most common causative organisms are staphylococci. Objective: The aim of the study was to shed the light upon this problem and compare two systemic therapeutic agents for the prevention of recurrence, doxycycline and rifampicin. Patient and method: One hundred thirty-five (135) Patients with recurrent boils from Al-Yarmouk teaching hospital dermatology outpatient department were included in this study; age ranged from 10 to 64 years old and out of total patients 32 were males and 103 were females. Patients were assessed by full history and cl
... Show MoreClavicle fractures are common injuries in young, active individuals. The majority of the fractures (80% to 85%) occur in the mid-shaft of the bone and account for approximately 2.6% of all fractures. For many years the standard treatment of fracture clavicle was the "figure-of-eight" bandage but several indications for operative fixation, the position of plate fixation for clavicle fracture remains controversial. The aim of this study is to compare between anterio—inferior and anterior-superior plating of mid-shaft displaced clavicle fracture in complications, plate prominence, nonunion, malunion and range of motion. This study is a prospective study conducted in Al-Kindy teaching hospital .A total of 30 patients with fracture mid –sh
... Show MoreThe purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an
... Show MoreThe research aims to identify the extent to which Iraqi private banks practice profit management motivated by reducing the taxable base by increasing the provision for loan losses by relying on the LLP it model, which consists of a main independent variable (net profit before tax) and independent sub-variables (bank size, total debts to total equity, loans granted to total obligations) under the name of the variables governing the banking business. (Colmgrove-Smirnov) was used to test the normal distribution of data for all banks during the period 2017-2020, and then find the correlation between the main independent variable sub and the dependent variable by means of the correlation coefficient person, and then using the multiple
... Show MoreIntrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification
... Show MoreEarly and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,
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