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

     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

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

Background: Osteoporosis is a systemic skeletal disorder affects general health in addition to effect on salivary physical and chemical composition which lead to an adverse effect on oral health status. This study was conducted to evaluate the concentration of glycoprotein (osteonectin) in saliva and its effect on dental caries in relation to salivary flow rate and viscosity among osteoporotic women and compared to control group. Materials and Methods: The total sample composed of 60 females aged 60-65 years attending Al-Yarmook Teaching Hospital, 30 females diagnosed with osteoporosis by measuring bone density at the spine and femur with a dual-energy X-ray absorptiometry at T-score of >2.5, and 30 women without osteoporosis with T-score o

Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using

This study been conducted and applied in Alrashed Health Center/Mahmodia Sector/ kerigh Sector/Ministry of Health/Baghdad City, and conducted on samples of women who are suffering from iron deficiency (Hemoglobin%) in blood for the year 2013. Fifty women been selected (married, unmarried), their ages ranging between 19-40 years old, they been given dried grinding spinach tablet. The dose been given was 3 tablet/day/4-6 weeks, after taken the percentage of Hemoglobin and Uric acid for all studied samples before and after dried spinach tablet given. It was appeared from samples analysis primarily and statically for 50 women, that 4% upon them only suffering high Iron deficiency (8.0-9.9 mg/100ml), 38% upon them suffering from middle iron def

Abstract: Background: Drug toxicity and chemotherapeutic side effects negatively impact the quality of life of breast cancer patients. Objectives: to evaluate the efficacy of pharmaceutical Interventions (PI) on quality of life (QOL)Among chemotherapy intake breast cancer women.  Method: A pre-post interventional study was carried out at the chemotherapy ward of Alhabobi Hospital in Alnasiriyah City. Eligible patients received comprehensive pharmaceutical care and a self-compiled Breast Cancer Patients Medication Knowledge Guide pamphlet. Each patient received two sessions, the first at baseline and the second after 7, 14, or 21 days depending on the next taking dose of chemotherapy. Each session lasted for approximately 15-30 minutes. Par

Early detection of eye diseases can forestall visual deficiency and vision loss. There are several types of human eye diseases, for example, diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. Diabetic retinopathy (DR) which is brought about by diabetes causes the retinal vessels harmed and blood leakage in the retina. Retinal blood vessels have a huge job in the detection and treatment of different retinal diseases. Thus, retinal vasculature extraction is significant to help experts for the finding and treatment of systematic diseases. Accordingly, early detection and consequent treatment are fundamental for influenced patients to protect their vision. The aim of this paper is to detect blood vessels from

The aim of this research is to determine the most important and main factors that lead to Preeclampsia. It is also about finding suitable solutions to eradicate these factors and avoid them in order to prevent getting Preeclampsia. To achieve this, a case study sample of (40) patients from Medical City - Oncology Teaching Hospital was used to collect data by a questionnaire which contained (17) reasons to be investigated. The statistical package (SPSS) was used to compare the results of the data analysis through two methods (Radial Bases Function Network) and (Factorial Analysis). Important results were obtained, the two methods determined the same factors that could represent the direct reason which causes Preecla