The Early Cenomanian Ahmadi carbonates succession in selected oil-wells in south Iraqi oil fields have undergone; into sequence stratigraphic analysis as new reservoir stratigraphy optimization understanding. The sequence stratigraphy context: has applied on the mentioned carbonate reservoir in selected oil-wells from West-Qurna and Majnoon oil fields, with respect to Arabian-plate (AP) chronosequence stratigraphy and chrono-markers setting.

A meso-genetic buildup has infra-structured the studied Formation based-on; smallest-set of the genetically-related high-frequency lithofacies-cycle and cycles-set modeling. A genetic sequence (meso-sequence one MS1) is described as a well-encountered buildup between the (MFS-K120 of lower Ah

Background: Patients with decompensated cirrhosis are often given therapeutic and prophylactic drugs. Polypharmacy raises both the likelihood of prescription errors and the complications associated with drugs. Clinical pharmacists are excellent at recognizing, addressing, and preventing clinically significant drug-related problems.

Objectives: Identification types of pharmacist interventions to address drug-related problems in patients with decompensated cirrhosis and assess the acceptance/implementation of these recommendations. And identify patient factors associated with accepting pharmacist recommend

Iraq faces significant economic challenges, owing in part to its reliance on oil revenue and the country's overburdened public sector. The supremacy of State-Owned Enterprises (SOEs), obstructive rules, a lack of access to finance, a shortage of skilled labor, and inadequate infrastructure all impede private sector growth. This research relied mainly on information from global development organizations, most markedly the World Bank, as well as policy documents, and it discovered a scarcity of pertinent educational writings. The following are the key findings of this research: Recent economic growth has not resulted in poverty reduction; the stretched history of war and insecurity in Iraq has hampered progress and development; the private se

      Cyberbullying is one of the biggest electronic problems that takes multiple forms of harassment using various social media. Currently, this phenomenon has become very common and is increasing, especially for young people and adolescents. Negative comments have a significant and dangerous impact on society in general and on adolescents in particular. Therefore, one of the most successful prevention methods is to detect and block harmful messages and comments. In this research, negative Arabic comments that refer to cyberbullying will be detected using a support vector machine algorithm. The term frequency-inverse document frequency vectorizer and the count vectorizer methods were used for feature extraction, and the results wer

Abstract Software-Defined Networking (commonly referred to as SDN) is a newer paradigm that develops the concept of a software-driven network by separating data and control planes. It can handle the traditional network problems. However, this excellent architecture is subjected to various security threats. One of these issues is the distributed denial of service (DDoS) attack, which is difficult to contain in this kind of software-based network. Several security solutions have been proposed recently to secure SDN against DDoS attacks. This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attack. The results have indicated that the algorithms for machine learning can be used to detect DDoS

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

A new human-based heuristic optimization method, named the Snooker-Based Optimization Algorithm (SBOA), is introduced in this study. The inspiration for this method is drawn from the traits of sales elites—those qualities every salesperson aspires to possess. Typically, salespersons strive to enhance their skills through autonomous learning or by seeking guidance from others. Furthermore, they engage in regular communication with customers to gain approval for their products or services. Building upon this concept, SBOA aims to find the optimal solution within a given search space, traversing all positions to obtain all possible values. To assesses the feasibility and effectiveness of SBOA in comparison to other algorithms, we conducte

     Sentiment Analysis is a research field that studies human opinion, sentiment, evaluation, and emotions towards entities such as products, services, organizations, events, topics, and their attributes. It is also a task of natural language processing. However, sentiment analysis research has mainly been carried out for the English language. Although the Arabic language is one of the most used languages on the Internet, only a few studies have focused on Arabic language sentiment analysis.

     In this paper, a review of the most important research works in the field of Arabic text sentiment analysis using deep learning algorithms is presented. This review illustrates the main steps used in these studies, which include

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

     Synthesis of sedimentologic, paleocurrent, and organic geochemistry data of the Lower Permian Ga’ara Formation from the Western Desert, western Iraq, shows good hydrocarbon potentiality and deposition by high sinuosity and mixed-load channels, likely by a meandering river system. The Ga’ara Formation includes kaolinitic mudstone beds of various colors and channelized quartzitic sandstone beds. Based on the lithofacies identification, five lithofacies associations have been recognized: channel-floor, point-bar, abandoned channel plug, crevasse splay, and interchannel flood basin. In addition, the paleocurrent analysis and sandstone percentage map indicate a variation of the paleoflow spatially and temporally with a general di