Chronic myelogenous leukemia (CML) is a myeloproliferative neoplasm arises from Bcr-Abl gene translocation (called Ph chromosome) in hematopoietic stem cells (HSCs). This genetic abnormality results in constitutive activation of tyrosine kinase and subsequent uncontrol growth and multiplication of granulocytes. The cornerstone in treatment of CML are tyrosine kinase inhibitors, of which imatinib is the most effectively used. JAK2V617F mutation is an acquired single nucleotide polymorphism (SNP) occurs in JAK2 gene and is associated with many hematological malignancy other than CML. It was thought that the two genetic abnormalities (Bcr-Abl and JAK2V617F) occur mutually; however, growing body of evidences suggested the reverse. This study aimed to investigate the prevalence of JAK2V617 mutation associated with serum levels of alkaline phophatase (ALK) and lactate dehydrogenase (LDH) in Ph+ CML Iraqi patients treated with imatinib. A total of 43 Ph+ CML patients (24 males and 18 females, age range 16-80 years) who attend Iraqi National Center of Hematology for Research and Treatment/Baghdad were enrolled in this study. Each patient has been received at least six month therapy with imatinib. A consent form involving age, gender, height, weight, smoking status, residency and first family relative history of leukemia was obtained from each patient. Besides, blood samples were collected, from which the granulocytes were separated and then DNA was extracted using a ready kit. Two assays were used for detection of JAK2V617F mutation; real time polymerase chain reaction (qPCR) using specific primers and probe, and allele specific PCR (AS-PCR) using specific primers. Total white blood corpuscles (WBC) as well as serum levels of ALP and LDH were measured. qPCR assay revealed 5 patients out of 43 (11.62%) were heterozygous for the muatant allele of JAK2V617F mutation (genotype GT). The concentration of this allele ranged from 0.01% to 0.12%. None of blood sample gave positive result for AS-PCR assay. From the all risk factors, only gender had significant association with the incidence of JAK2V617F mutation (p= 0.034, OR= 0.5, 95%CI= 0.364-0.687). Average total WBC count, and serum levels of ALP and LDH were higher in JAK2V617F-positive patients (9042±1512.55, 146.05±8.028 IU/L and 204±10.85 IU/L respectively) than that of JAK2V617F-negative patients (6039±1772.239, 64.45±40.15 IU/L and 178.33±13.693 IU/L respectively) with significant differences. These results indicate that JAK2V617F mutation can occur simultaneously with Ph chromosome in CML patients, and qPCR is a highly sensitive method for the detection of this mutation. Furthermore, serum activity of APL can be used as an indicator for the presence of JAK2V617F mutation in CML patients.
Computer vision is an emerging area with a huge number of applications. Identification of the fingertip is one of the major parts of those areas. Augmented reality and virtual reality are the most recent technological advancements that use fingertip identification. The interaction between computers and humans can be performed easily by this technique. Virtual reality, robotics, smart gaming are the main application domains of these fingertip detection techniques. Gesture recognition is one of the most fascinating fields of fingertip detection. Gestures are the easiest and productive methods of communication with regard to collaboration with the computer. This analysis examines the different studies done in the field of
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In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant features from
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In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant
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In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.
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Recent research has shown that a Deoxyribonucleic Acid (DNA) has ability to be used to discover diseases in human body as its function can be used for an intrusion-detection system (IDS) to detect attacks against computer system and networks traffics. Three main factor influenced the accuracy of IDS based on DNA sequence, which is DNA encoding method, STR keys and classification method to classify the correctness of proposed method. The pioneer idea on attempt a DNA sequence for intrusion detection system is using a normal signature sequence with alignment threshold value, later used DNA encoding based cryptography, however the detection rate result is very low. Since the network traffic consists of 41 attributes, therefore we proposed the
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
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لا يزال المهتمون بلعبة كرة السلة يبحثون عن إيجاد الوسائل الأكثر أهمية وصولاً إلى ما تطمح إليه الدول لتحقيق افضل المستويات في نواحي اللعبة كافة من خلال التغلب على المعوقات التي تحول دون تقدمها إلى الأمام بالدراسة والبحث. ومن هذا المنطلق انصب البحث في ضرورة معالجة القصور الناتج عن عدم وجود المستويات المعيارية ذات العلاقة باختبارات قدرات اللاعبين وعلى وفق مراكز اللعب ولا سيما المهارية الهجومية، ومما شكل ذلك ضع
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