This research studies the comparison of deep neural network models and performance evaluation to predict the gold prices of time series, where the gold prices contain high fluctuations and non-linear patterns that are difficult to capture using traditional models, which makes predicting them a significant challenge. Therefore, the focus was on using deep learning models represented by (LSTM), (Bi-LSTM), (GRU) and (Bi-GRU). The results showed the superiority of the (Bi-GRU) model according to comparison criteria (MSE), (RMSE), (MAE), and (R∧2) compared to other models because it was able to understand the time patterns better by processing the data in both directions and provided superior performance, which indicates its effectiveness, eff

This narrative review provides updated insights into pharmacy education, research, and professional practice in Iraq.

Objective: The current investigation focused on Acinetobacter baumannii (A. baumanni), due to its growing significance as a hospital infection-causing pathogen and its resistance to several medications.Material and Method: Sixty-five isolates of A. baumannii were isolated from wound samples of patients admitted to different hospitals in Baghdad between January and April of 2023. Two types of methods were used in the detection of biofilm formation: the first one was Congo red agar method and the second one was microtiter plate method. Genotypic detection of various virulence factors associated with A. baumannii was performed using monoplex, multiplex, and ERIC-PCR.Result and Discussion: To use the PCR method to examine

Cybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the

The Traveling Salesman Problem is a story application of Atom Swarm Optimizations in this research. We have developed several novel techniques intended for solving TSP with PSO. Additionally, we introduced the notions to Swap Operative and Swap Chronological sequence and redefining the remaining operatives their foundation; that way, the study created unique PSO. Research prove it can produce satisfactory outcomes. The aim of this paper be there to assess the functioning of particle swarm optimization, for the going salesman issue TSP. The solution to this trouble is common to be NP-hard, it has N! permutations. The study's goal is to examine the capacity of both algorithms to solve intercontinental and other benchmark problems. Overall, th

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t

Bitcoin is a decentralized blockchain-based cryptocurrency that has taken the world by storm. Since its introduction in 2009, it has grown tremendously in terms of popularity and market cap. The idea of having a decentralized public ledger while maintaining anonymity and security attracted the attention of developers and customers alike. Special nodes in the bitcoin network, called miners, are responsible for making the network secure by using a concept called proof-of-work. A certain degree of anonymity is also maintained as no personally identifiable information of a person, like name, address, etc., is linked to the bitcoin wallet. In terms of bitcoin, a user is anonymous if different interactions of the user cannot be linked to