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

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 study concerns the isolation of oil degraded bacterial samples from oil polluted soil in Al-Dora refinery/ Baghdad – Iraq. Soil samples (15) were on mineral salt agar medium (MSM) used to screen the oil degrading bacteria by forming clear zones around the colonies. To confirm the degradation of oil by these bacteria, the isolates were inoculated in mineral salt broth, 15 isolates of Pseudomonas spp. was detected from which two isolates identified as P. aeruginosa by morphological, physical and biochemical characteristics that confirmed by using Vitick identification system. Growth was estimated in terms of whole cell by measuring optical density at 620 nm and free extract protein was estimated by protein measurement with Folin phe

     Human cytomegalovirus (HCMV) infects a wide range of human cells, resulting in both benign and malignant tumors. In the last few decades, proteins and/or nucleic acids of the virus were found to be often highly expressed in in patients with basal cell hyperplasia and prostatic neoplasia.

This research aimed to unravel the rate of HCMV infections among prostatic tissue subsets from Iraqi patients with adenocarcinoma and benign hyperplasia.

One hundred, formalin-fixed and paraffin embedded prostatic tissues were obtained from 40 tissue samples collected from different grades of prostate carcinoma; 40 from benign prostatic hyperplasia and 20 from apparently healthy prostatic tissues. These tissue spe

In this work laser detection and tracking system (LDTS) is designed and implemented using a fuzzy logic controller (FLC). A 5 mW He-Ne laser system and an array of nine PN photodiodes are used in the detection system. The FLC is simulated using MATLAB package and the result is stored in a lock up table to use it in the real time operation of the system. The results give a good system response in the target detection and tracking in the real time operation.

The Internet of Things (IoT) has become a hot area of research in recent years due to the significant advancements in the semiconductor industry, wireless communication technologies, and the realization of its ability in numerous applications such as smart homes, health care, control systems, and military. Furthermore, IoT devices inefficient security has led to an increase cybersecurity risks such as IoT botnets, which have become a serious threat. To counter this threat there is a need to develop a model for detecting IoT botnets.

This paper's contribution is to formulate the IoT botnet detection problem and introduce multiple linear regression (MLR) for modelling IoT botnet features with discriminating capability and alleviatin

Karbala province was one of the most important areas in Iraq and considered an
economic resource of vegetation such as trees of fruits, sieve and other vegetation.
This research aimed to utilize change detection for investigating the current
vegetation cover at last three decay. The main objectives of this research are collect
a group of studied area (Karbala province) satellite images in sequence time for
the same area, these image captured by Landsat (TM 1995, ETM+ 2005 and
Landsat 8 OLI (Operational Land Imager) 2015. Preprocessing such as atmosphere
correction and rectification has been done. Mosaic model between the parts of
studied area was performing. Gap filling consider being very important step has
be