Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks can also be made with smart devices that connect to the Internet, which can be infected and used as botnets. They use Deep Learning (D.L.) techniques like Convolutional Neural Network (C.N.N.) and variants of Recurrent Neural Networks (R.N.N.), such as Long Short-Term Memory (L.S.T.M.), Bidirectional L.S.T.M., Stacked L.S.T.M., and the Gat G.R.U.. These techniques have been used to detect (DDoS) attacks. The Portmap.csv file from the most recent DDoS dataset, CICDDoS2019, has been used to test D.L. approaches. Before giving the data to the D.L. approaches, the data is cleaned up. The pre-processed dataset is used to train and test the D.L. approaches. In the paper, we show how the D.L. approach works with multiple models and how they compare to each other.
Purpose A diagnosis of tuberculosis (TB) of the head and neck has been a dilemma for clinicians, because the clinical and pathologic features tend to mimic different pathologies. Our study aimed to identify the demographic, clinical, and pathologic features of head and neck TB to help healthcare providers in the early detection of the disease. Materials and Methods We performed a retrospective analysis using the medical archives at the pathology laboratory. Twenty-one patients with a clinical and pathologic diagnosis of head and neck TB were identified from 2010 to 2019. Results The age distribution was broad, with 28.5% of the patients younger than 15 years old. Seven patients had oral TB, with the most common sites affected the labial ves
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Lymphoma is a cancer arising from B or T lymphocytes that are central immune system components. It is one of the three most common cancers encountered in the canine; lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic conditions and to improve decision-making around treating and what treatment type to use. This study aimed to evaluate a potential novel biomarker related to iron metabolism,
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Background: Patient satisfaction is of increasing importance and widely recognized as an important indicator of quality of the medical care. There was no homogeneous definition of patient satisfaction, since satisfaction concerns different aspects of care or settings, as well as care given by various professions.
Objective: The objective of this study is to assess the patients’ level of satisfaction with diabetes care and to identify the underlying factors influencing it.
Methods: This cross-sectional study had been conducted in the Specialized Center for Diabetes and Endocrinology in Baghdad Al- Rusafa 2018. Where150 type two diabetic patients attending their follow-up
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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By reading the book (Endless Forms Most Beautiful: The New Science of Evo Devo) by Sean B. Carroll, new horizons opened up about the nature of the formation of the living organism. Although he presented the idea that the artist was influenced by the material assets of nature in his holographic art formations, the new science of Evo-Devo (Evolutionary Developmental Science) provided models worth standing on when comparing the similarity of the formation of living organisms on the one hand, and the formation of works of art with holographic organic bodies on the other. But the excitement lies in the fact that the formation of living natural organisms is often driven by subtle intelligent mechanisms that are different from the mechanisms us
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Foreign direct investment has seen increasing interest worldwide, especially in developing economies. However, statistics have shown that Yemen received fluctuating FDI inflows during the period under study. Against this background, this research seeks to determine the relationship and impact of interest rates on FDI flows. The study also found other determinants that greatly affected FDI inflows in Yemen for the period 1990-2018. Study data collected from the World Bank and International Monetary Fund databases. It also ensured that the time series were made balanced and interconnected, and then the Auto Regressive Distributed Lag method used in the analysis. The results showed that the interest rates and
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