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Attention-Deficit Hyperactivity Disorder Prediction by Artificial Intelligence Techniques
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Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained were 96.5% and 93.47%, respectively, before applying balancing to the data. In addition, 98.59% and 97.18%, respectively, after applying the balancing technique The extreme gradient boosting (XGBoost) technique had been applied to selecting the important features and the Pearson correlation for finding the correlation between features.

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Publication Date
Thu Mar 18 2010
Journal Name
Spe Projects, Facilities & Construction
Correlating Optimum Stage Pressure for Sequential Separator Systems
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Summary<p>A study to find the optimum separators pressures of separation stations has been performed. Stage separation of oil and gas is accomplished with a series of separators operating at sequentially reduced pressures. Liquid is discharged from a higher-pressure separator into the lower-pressure separator. The set of working separator pressures that yields maximum recovery of liquid hydrocarbon from the well fluid is the optimum set of pressures, which is the target of this work.</p><p>A computer model is used to find the optimum separator pressures. The model employs the Peng-Robinson equation of state (Peng and Robinson 1976) for volatile oil. The application of t</p> ... Show More
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Publication Date
Sat Oct 04 2025
Journal Name
Mesopotamian Journal Of Computer Science
Enhanced IOT Cyber-Attack Detection Using Grey Wolf Optimized Feature Selection and Adaptive SMOTE
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The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

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Publication Date
Thu Apr 25 2019
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
The Correlation between the Dimensions of Organizational Silence and the F Function Life an Exploratory Study of the Views of a Sample of Workers in the Health Center / Sulaykh: The Correlation between the Dimensions of Organizational Silence and the F Function Life an Exploratory Study of the Views of a Sample of Workers in the Health Center / Sulaykh
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The research aims to test the effect of the behavioral factors (intangible) represented by an explanatory variable represented by organizational silence and a responsive variable of quality of Function life. The problem was the negative effects of the organizational silence on the morale of the employees and consequently their performance and the quality of function life. To collect the data and information needed to measure the two variables of research conducted in the health center / Sulaikh by taking a sample of (40) employees to test the hypotheses of research through the survey of their views, using statistical tools non parametric using the program. The most important recommendations were the establishment of training workshops fo

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Publication Date
Mon Feb 04 2019
Journal Name
Iraqi Journal Of Physics
A study of Fermi hole and partial Fermi hole for Li- and C-Atoms in different states 1s 2p, 1s 3p, 1s 3d and 2s 2p
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The electron correlation effect for inter-shell have been analysed in terms of Fermi hole and partial Fermi hole for Li-atom in the excited states (1s2 3p) and (1s2 3d) using Hartree-Fock approximation (HF). Fermi hole Δf(r12) and partial Fermi hole Δg(r12 ,r1) were determined in position space. Each plot of the physical properties in this work is normalized to unity. The calculation was performed using Mathcad 14 program.

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Publication Date
Mon May 06 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

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Publication Date
Tue Apr 01 2025
Journal Name
Mesopotamian Journal Of Cybersecurity
The Impact of Feature Importance on Spoofing Attack Detection in IoT Environment
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The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr

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Publication Date
Mon Dec 10 2018
Journal Name
Day 1 Mon, December 10, 2018
Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis
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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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Publication Date
Mon Dec 10 2018
Journal Name
Day 1 Mon, December 10, 2018
Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis
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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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Publication Date
Tue Jun 01 2021
Journal Name
Journal Of Engineering
Numerical Study for HAWT Wake shape with different Angles of Attack
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Increasing world demand for renewable energy resources as wind energy was one of the goals behind research optimization of energy production from wind farms. Wake is one of the important phenomena in this field. This paper focuses on understanding the effect of angle of attack (α) on wake characteristics behind single horizontal axis wind turbines (HAWT). This was done by design three rotors different from each other in value of α used in the rotor design process. Values of α were (4.8˚,9.5˚,19˚). The numerical simulations were conducted using Ansys Workbench 19- Fluent code; the used turbulence model was (k-ω SST). The results showed that best value for extracted wind energy was at α=19˚, spread distance of wak

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Publication Date
Mon Mar 23 2020
Journal Name
Baghdad Science Journal
Compact MIMO Slots Antenna Design with Different Bands and High Isolation for 5G Smartphone Applications
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 In this paper, two elements of the multi-input multi-output (MIMO) antenna had been used to study the five (3.1-3.55GHz and 3.7-4.2GHz), (3.4-4.7 GHz), (3.4-3.8GHz) and (3.6-4.2GHz) 5G bands of smartphone applications that is to be introduced to the respective US, Korea, (Europe and China) and Japan markets. With a proposed dimension of 26 × 46 × 0.8 mm3, the medium-structured and small-sized MIMO antenna was not only found to have demonstrated a high degree of isolation and efficiency, it had also exhibited a lower level of envelope correlation coefficient and return loss, which are well-suited for the 5G bands application. From the fabrication of an inexpensive FR4 substrate with a 0.8 mm thickness level, a loss tang

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