With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

After baking the flour, azodicarbonamide, an approved food additive, can be converted into carcinogenic semicarbazide hydrochloride (SEM) and biurea in flour products. Thus, determine SEM in commercial bread products is become mandatory and need to be performed. Therefore, two accurate, precision, simple and economics colorimetric methods have been developed for the visual detection and quantitative determination of SEM in commercial flour products. The 1st method is based on the formation of a blue-coloured product with λmax at 690 nm as a result of a reaction between the SEM and potassium ferrocyanide in an acidic medium (pH 6.0). In the 2nd method, a brownish-green colored product is formed due to the reaction between the SEM and phosph

This study aims to analyze the spectral properties of plasma produced from rice husk(Rh) using the laser breakdown spectroscopy (LIBS) method. The plasma generation process used the fundamental harmonic (1064 nm) of a Q-switched Nd:YAG laser. Yttrium aluminum garnet (YAG) is a man-made crystalline material. The laser fired pulses with a duration of 10 ns and a repetition rate of 6 Hz. Thus, the energy outputs achieved were 50–200 mJ at the wavelength of 1064 (nm). The silica content in the rice hulls was verified using an XRF measurement, which revealed the presence of silica in the rice hulls in a high percentage. Precise beam focusing was achieved by focusing the laser on the target material. This target material is placed with

In recent decades, breeding deer populations in Iraq have expanded significantly in size and distribution. Owing to their role in pathogen transmission, these deer populations pose a risk to the livestock industry. However, little is known about the parasitic infection status of the breeding deer and the surrounding environment in Iraq. Atotal of 150 deer faecal samples were collected from male and female deer of various ages from four regions of Iraq and examined microscopically for intestinal parasites. Microscopic analysis revealed the presence of seven intestinal parasite species: Entamoeba spp. (48%), Giardia duodenalis (17%), Toxocara spp. (12%), Balantidium coli(9%), Taenia spp. (9%), Strongyloides spp. (3%) and Trichostrongy

The mobile phone has become one of the most important in our days. The effects of waves from mobile base station may cause health effects on human. The aim of this work was to study the effect of radiofrequency (RF) emitted from mobile base station on the hemoglobin (Hb), packed cell (PCV), white blood cells (WBC) and liver enzymes activity including glutamic oxaloacetic transaminase (GOT), glutamic pyruvie transaminase (GPT) and Alkaline phosphatase (ALP). In this study the people divided into control group who living away from mobile base station and experimental group who living near to the mobile base station. The present result found there is no significant differences (P<0.05) in the Hb and PCV, but there was a significant increases (

Purpose: This study aimed to compare the stability and marginal bone loss of implants inserted with flapped and flapless approaches 8 weeks after surgery and 3 months after loading. Material and Methods: Thirty SLActive implants were inserted in 11 patients and early loaded with final restoration 8 weeks after healing period. The stability values determined by Osstell and the marginal bone loss measured by CBCT at the initial time (1st) and 8 weeks of the healing period (2nd) and 3 months after loading (3rd). Results: The overall survival rate was 100%. A significant increase in the 3rd implant stability value in the age of ˂ 40. A significant decrease in the 2nd implant stability value in both gender and traumatic zone with a flapless app