Blockchain represents a new promising technology with a huge economic impact resulting from its uses in various fields such as digital currency and banking; malware represents a serious threat to users, and there are many differences in the effectiveness of antivirus software used to deal with the problem of malware. This chapter has developed a coefficient for measuring the effectiveness of antivirus software. This chapter evaluates the effectiveness of antivirus software by conducting tests on a group of protection programs using a folder containing an amount of data. These programs are applied to combat viruses contained in this folder. The study revealed that the effectiveness of antivirus software is as follows: AVG scored 0%,

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

KE Sharquie, AA Noaimi, MS Abass, American Journal of Dermatology and Venereology, 2019 - Cited by 4

The research deals with Iraq's position of the Lebanese civil war and the Efforts made by Iraq in order to stop the bleeding of this war, the research also deals with the nature of regime in Lebanon and the developments that preceded the war and the positions of the internal and external competing forces, as weu as handling the Iraqi Syrian disagreement and it's impaet on the situation of Lebanon and the war developments.
The research focused on the Iraq's position towards the externd proposed solutions to solve the Lebanese civil war.

This search reports the synthesis of some new series of Schiff base compounds for trimetheprim derivatives which known high been known as a medicinal effectiveness. Trimetheprim was condensed with several substituted aldehydes compounds.(4-dimethyl amine benzaldehyde , propanal , salicaldehyde, 2.4 dimethoxy benzaldehyde and 4- methyl benzaldehyde) to obtain Schiff base products(1a-5a) and several substituted ketones compound (4-aminoacetophenone,4-chloroacetophenone, isobutyleketone, acetylacetone and acetophenone) to obtain Schiff base products(6b-10b) in ethanol in the presence of concentrated sulphuric acid as a catalyst to yield the Schiff base. The structure of synthesized compounds has been established on the basis of their Chemical

   Lithology identification plays a crucial role in reservoir characteristics, as it directly influences petrophysical evaluations and informs decisions on permeable zone detection, hydrocarbon reserve estimation, and production optimization. This paper aims to identify lithology and minerals composition within the Mishrif Formation of the Ratawi Oilfield using well log data from five open hole logs of wells RT-2, RT-4, RT-5, RT-6, and RT-42. At this step, the logging lithology identification tasks often involve constructing a lithology identification model based on the assumption that the log data are interconnected. Lithology and minerals were identified using three empirical methods: Neutron-Density cross plots for lithology id

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of