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

This paper reports a fiber Bragg grating (FBG) as a biosensor. The FBGs were etched using a chemical agent,namely,hydrofluoric acid (HF). This implies the removal of some part of the cladding layer. Consequently, the evanescent field propagating out of the core will be closer to the environment and become more sensitive to the change in the surrounding. The proposed FBG sensor was utilized to detect toxic heavy metal ions aqueous medium namely, copper ions (Cu2+). Two FBG sensors were etched with 20 and 40 μm diameters and fabricated. The sensors were studied towards Cu2+ with different concentrations using wavelength shift as a result of the interaction between the evanescent field and copper ions. The FBG sensors showed

     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

     Credit card fraud has become an increasing problem due to the growing reliance on electronic payment systems and technological advances that have improved fraud techniques. Numerous financial institutions are looking for the best ways to leverage technological advancements to provide better services to their end users, and researchers used various protection methods to provide security and privacy for credit cards. Therefore, it is necessary to identify the challenges and the proposed solutions to address them.  This review provides an overview of the most recent research on the detection of fraudulent credit card transactions to protect those transactions from tampering or improper use, which includes imbalance classes, c

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

The use of antibiotics (AB) in surgery focused in either treating established infection or to prevent suspected post-operative infection. Inappropriate use of antibiotic for treatment of patients with common infections is a major problem worldwide, with great implications with regards to cost of treatment and development of resistance to the antimicrobial agent. Moreover, antibiotics may often be dispensed without a clear clinical indication. This study was conducted to estimate the medication errors in using antibiotic for surgery patients which may effect their wound healing. A 260 patients with clean-contaminated and contaminated surgery were included from two teaching hospitals, 160 patient from Medical city hospital and 100 from Al-

Background:Parkinson’disease(PD) is a neurodegenerative disorder of the central nervous system characterized by resting tremor, bradykinesia, cogwheel rigidity, and impairment of postural reflexes; the frequency of PD increases with aging.Clinically Parkinson's disease characterized by two groups of symptoms: motor and non-motor symptoms.Non-motor symptoms can be categorized as autonomic, cognitive/psychiatric (may include depression, dementia, anxiety, hallucinations), sensory and rapid eye movements (REM) sleep behavior disorder (RBD).

Objectives:The objectives of this study are to find out the frequency of the non-motor symptoms of idiopathic Parkinson disease in a group of patients in Baghd

The use of antibiotics (AB) in surgery focused in either treating established infection or to prevent suspected post-operative infection. Inappropriate use of antibiotic for treatment of patients with common infections is a major problem worldwide, with great implications with regards to cost of treatment and development of resistance to the antimicrobial agent. Moreover, antibiotics may often be dispensed without a clear clinical indication. This study was conducted to estimate the medication errors in using antibiotic for surgery patients which may effect their wound healing. A 260 patients with clean-contaminated and contaminated surgery were included from two teaching hospitals, 160 patient from Medical city hospital and 100 fro