The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when diagnosing a tissue sample. Small, unnoticeable changes in pixel density may indicate the beginning of cancer or tear tissue in the early stages. These details even expert pathologists might miss. Artificial intelligence (A.I.) and D.L. revolutionized radiology by enhancing efficiency and accuracy of both interpretative and non-interpretive jobs. When you look at AI applications, you should think about how they might work. Convolutional Neural Network (C.N.N.) is a part of D.L. that can be used to diagnose knee problems. There are existing algorithms that can detect and categorize cartilage lesions, meniscus tears on M.R.I., offer an automated quantitative evaluation of healing, and forecast who is most likely to have recurring meniscus tears based on radiographs.
A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.
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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 MoreCybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a
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Metal-organic frameworks (MOFs) have emerged as revolutionary materials for developing advanced biosensors, especially for detecting reactive oxygen species (ROS) and hydrogen peroxide (H₂O₂) in biomedical applications. This comprehensive review explores the current state-of-the-art in MOF-based biosensors, covering fundamental principles, design strategies, performance features, and clinical uses. MOFs offer unique benefits, including exceptional porosity (up to 10,400 m²/g), tunable structures, biocompatibility, and natural enzyme-mimicking properties, making them ideal platforms for sensitive and selective detection of ROS and H₂O₂. Recent advances have shown significant improvements in detection capabilities, with limit
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The prevalence of gastrointestinal symptoms of COVID-19 is variable with different types of presentations. Some of them many present with manifestations mimicking surgical emergencies. Yet, the pathophysiology of acute abdomen in the context of COVID-19 remains unclear. We present a case of a previously healthy child who presented with acute appendicitis with multisystemic inflammatory syndrome. We also highlight the necessity of considering the gastrointestinal symptoms of COVID-19 infection in pediatric patients in order to avoid misdiagnosis and further complications. |
One of the most Interesting natural phenomena is clouds that have a very strong effect on the climate, weather and the earth's energy balance. Also clouds consider the key regulator for the average temperature of the plant. In this research monitoring and studying the cloud cover to know the clouds types and whether they are rainy or not rainy using visible and infrared satellite images. In order to interpret and know the types of the clouds visually without using any techniques, by comparing between the brightness and the shape of clouds in the same area for both the visible and infrared satellite images, where the differences in the contrasts of visible image are the albedo differences, while in the infrared images is the temperature d
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Background: The anterior knee pain is an important chief complaint of the patients with knee osteoarthritis due to patellofemoral pathology. The pain receptors denervation can be achieved by circumferential denervation of the patellar area by a process of electrocautery.
Objectives: The aim of current study is to assess the pain after total knee arthroplasty (TKA) by patelloplastywith and without circumferential denervation via electrocautery at a minimum follow up with 1 year separately for each patient.
Type of the study:Cross- sectional study.
Methods: Thirty five patients,with mean age of about (62.8) years, were enrolled in this pros
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Fractal geometry is receiving increase attention as a quantitative and qualitative model for natural phenomena description, which can establish an active classification technique when applied on satellite images. In this paper, a satellite image is used which was taken by Quick Bird that contains different visible classes. After pre-processing, this image passes through two stages: segmentation and classification. The segmentation carried out by hybrid two methods used to produce effective results; the two methods are Quadtree method that operated inside Horizontal-Vertical method. The hybrid method is segmented the image into two rectangular blocks, either horizontally or vertically depending on spectral uniformity crit
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