Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
... Show MoreInterleukin-6 (IL-6) is a proinflammatory cytokine implicated in the immunopathogenesis of tuberculosis (TB). TB is recognized worldwide as an important public health issue. To study the relationship between the age of patients with pulmonary TB and serum IL-6 levels, from the other hand, the severity of this disease with IL-6 levels. This study included 30 patients (16 female and 14 male) with pulmonary TB and 10 healthy persons (5 female and 5 male) as control group for comparison. An ELISA assay was used to quantify IL-6 in the sera. The results showed a significant increase of IL-6 levels with increase of age of patients, in (23-38) year old patients the IL-6 levels (median= 17.9 pg/ml, range 12.3-29.1), while in (50-70) year old patien
... Show MoreThe plethora of the emerged radio frequency applications makes the frequency spectrum crowded by many applications and hence the ability to detect specific application’s frequency without distortion is a difficult task to achieve.
The goal is to achieve a method to mitigate the highest interferer power in the frequency spectrum in order to eliminate the distortion.
This paper presents the application of the proposed tunable 6th-order notch filter on Ultra-Wideband (UWB) Complementary Metal-Oxide-Semiconductor (CMOS) Low Noise
Ground-based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holl
... Show MoreAbstract The Object of the study aims to identify the effectiveness of using the 7E’s learning cycle to learn movement chains on uneven bars, for this purpose, we used the method SPSS. On a sample composed (20) students on collage of physical education at the university of Baghdad Chosen as two groups experimental and control group (10) student for each group, and for data collection, we used SPSS After collecting the results and having treated them statistically, we conclude the use 7E’s learning cycle has achieved remarkable positive progress, but it has diverged between to methods, On this basis, the study recommended the necessity of applying 7E’s learning cycle strategy in learning the movement chain on uneven bar
... Show MorePurpose: To evaluate the effect of intravitreal Aflibercept injection on wet AMD both functionally and anatomically after loading doses. The secondary aim is to evaluate the effect of risk factors including (gender, age, smoking, hypertension, and diabetes meatus) on the patient’s response. Study Design: Interventional case series. Place and Duration of Study: Al-Haitham Eyes Teaching Hospital in Baghdad, Iraq, from November 2021 and September 2022. Methods: Fifty eyes of 47 patients with treatment naïve wet AMD were selected through convenient sampling. Data were collected for age, gender, smoking, and chronic disease. Clinical examination, best corrected visual acuity (BCVA), optical coherence tomography angiography
... Show MoreThe Gender study is consider one of the concepts which the Postmodernism reached
after the end of Modernism, where the first one has limited the criticism study choices before
the second after closed many doors of subjects which was enriched by researches.
It is pretty clear that the root of this concept belongs to the Linguistics which provided
the Criticism with a countable reasons of it is growth.
The attention in the study of gender in Feminine Literature and Criticism increased in
Arabic studies since the early years of twenty one century, so this research is presented to be
an introduction to this subject which could pave the way to more studies.
In addition to the Gender studies this research deals with ano
Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.
Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are
... Show MoreSupport vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa
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