Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by using different tools and techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation. Lastly, the paper concludes with a concise discussion and provides a direction toward the upcoming trend of more advanced research studies on brain image segmentation and Tumor detection.
Background: Chronic periodontitis is a bacterial infection that result in bone destruction associated with the increasing level of salivary tumor necrosis alpha and interleukin6 that affect Mother-infant bonding status. The aim of the present study was to assess the relationship between the Mother-infant bonding status in mothers with chronic periodontitis in relation to Salivary Tumor necrosis factor alpha and Salivary Interleukin6. Materials and Methods: The selected sample consisted of mothers with chronic periodontitis compared with mothers with healthy periodontium in postpartum period, their age ranged between 30-40 years. Both groups were subjected to postpartum Bonding Questionnaire (PBQ). Periodontal health status was assessed f
... Show MoreChronic inflammation can induce proliferative events and posttranslational DNA modifications in prostate tissue through oxidative stress. The present study was designed to evaluate the changes in serum levels of TNF-α, malomdialdehyde (MDA) and total antioxidant status (TAS) patients with different stages of malignant prostatic cancer (PCa) and benign prostatic hyperplasia (BPH). One hundred males (age range of 58-72 years) with different stages of malignant PCa were recruited from the Radiotherapy and Nuclear Medicine Teaching Hospital in Baghdad during the period from September 2010 to April 2011. The patients were categorized according to the 4 disease stages (I, II, III, and IV); 25 patients with benign prostatic hyperplasia (BPH)
... Show MoreTheoretical and experimental investigations have been carried out on developing laminar
combined free and forced convection heat transfer in a vertical concentric annulus with uniformly
heated outer cylinder (constant heat flux) and adiabatic inner cylinder for both aiding and opposing
flows. The theoretical investigation involved a mathematical modeling and numerical solution for
two dimensional, symmetric, simultaneously developing laminar air flows was achieved. The
governing equations of motion (continuity, momentum and energy) are solved by using implicit
finite difference method and the Gauss elimination technique. The theoretical work covers heat flux
range from (200 to 1500) W/m2, Re range from 400 to 2000 an
The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr
... Show MoreIn this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete
... Show MoreSummary:
This research revolves around the probing of those whom Ibn Hajar said, "He has a vision", its significance, and the ruling on the connection and transmission to it. The number of narrators reached fifty-one (51) narrators, among whom it was said, “He has a vision, whether it is definite or possibly. Some of them had a vision and companionship.”They are eleven (11) narrators, And among them were those who had visions and had no company, and their number was twenty-one (21) narrators, and among them were those who had no vision and nor company, and their number is nineteen (19) narrators.
As a result , whoever said about him “has a vision” and has companions, his hadith is connected, even i
Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on
... Show MoreWith the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative
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