Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

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

One of the most important problems that faces the concrete industry in Iraq is the deterioration due to internal sulfate attack , since it  reduces the compressive strength and increases the expansion of concrete. Consequently, the concrete structure may be damage .The effects of total and total effective sulfate contents on high strength concrete (HSC) have been studied in the present study.

The research studied the effect of sulfate content in cement , sand and gravel , as well as comparing  the total sulfate content with the total effective SO₃ content. Materials used were divided into two groups of SO₃ in cement ,three groups of SO₃ in sand ,and two groups of SO

The present investigation considers the effect of curing temperatures (30, 40, and 50˚C) and curing compound method on compressive strength development of high performance concrete, and compares the results with concrete cured at standard conditions and curing temperature (21˚C). The experimental results showed that at early ages, the rate of strength development at high curing temperature is greater than at lower curing temperature, the maximum increasing percentage in compressive strength is 10.83% at 50C˚ compared with 21C˚ in 7days curing age. However, at later ages, the strength achieved at higher curing temperature has been less, and the maximum percentage of reduction has been 5.70% at curing temperature 50C˚ compared with 21

One of the most important problems that faces the concrete industry in Iraq is the deterioration due to internal sulfate attack , since it reduces the compressive strength and increases the expansion of concrete. Consequently, the concrete structure may be damage .The effects of total and total effective sulfate contents on high strength concrete (HSC) have been studied in the present study. The research studied the effect of sulfate content in cement , sand and gravel , as well as comparing the total sulfate content with the total effective SO3 content. Materials used were divided into two groups of SO3 in cement ,three groups of SO3 in sand ,and two groups of SO3 in gravel. The results show that considering the total effective sulfate con

     We have presented the distribution of the exponentiated expanded power function (EEPF) with four parameters, where this distribution was created by the exponentiated expanded method created by the scientist Gupta to expand the exponential distribution by adding a new shape parameter to the cumulative function of the distribution, resulting in a new distribution, and this method is characterized by obtaining a distribution that belongs for the exponential family. We also obtained a function of survival rate and failure rate for this distribution, where some mathematical properties were derived, then we used the method of maximum likelihood (ML) and method least squares developed  (LSD)

In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then  these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing.

The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the