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Leveraging Machine Learning in Modeling andOptimization of Laser Hardening of Aluminum Alloys
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The current study focuses on utilizing machine learning (ML) models in optimizing and predicting laser-induced surface cold hardening in aluminum alloy 6061-O using a pulsed fiber laser. Three input features were utilized: power density (Pd), frequency (f), and pulse overlap (OV), with surface hardness as the matching aim. A set of ML models, including XGBoost and Adaptive Ensemble, was employed. The dataset was preprocessed for normalization and outlier handling, hyperparameter tuning, and assessment through the grid search and cross-validation, and model performance was evaluated using the coefficient of determination (R²) and mean square error (MSE) metrics. The Linear regression and Random Forest regression models were excluded due to their weakness in performance, exhibiting noticeable enhancement in performance of the ensemble. After excluding weak models, the results of XGBoost and Adaptive Ensemble models demonstrated great results of R2/MSE of 0.970/0.774 and 0.949/1.319, respectively. The XGBoost model occupied the first order, followed by the Adaptive Ensemble model, in improving the predictive accuracy to around 96% compared to other models.

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Publication Date
Mon Nov 24 2025
Journal Name
2025 13th International Conference On Control, Mechatronics And Automation (iccma)
Modeling Twisting and Coiling Actuators with Regression-Based Learning: Application to Neck Rehabilitation
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Twisting and coiling actuators (TCAs) are lightweight artificial muscles that can produce large linear contractions while lifting heavy loads with low power. A TCA consists of two or more strings connected to a rotational motor and a load. When activated, the strings twist and coil, causing contraction. This study presents a data-driven framework for modeling TCA behavior using experimental data. Polynomial regression, Support Vector Regression (SVR), and symbolic regression were applied using the motor angle and payload weight as inputs. The models were evaluated under various loads and implemented in a neck-rehabilitation prototype. SVR showed the highest accuracy (RMSE: 6.17 mm upward, 4.98 mm downward); however, it lacks a closed-form e

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Publication Date
Fri Jan 21 2022
Journal Name
Environmental Science And Pollution Research
Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States
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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet atta</p> ... Show More
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Publication Date
Sun Jun 21 2020
Journal Name
Baghdad Science Journal
Structural and Thermal Unusual Properties in Invar Behavior of Ni-Mn Alloys
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The Invar effect in 3D transition metal such as Ni and Mn, were prepared on a series composition of binary Ni1-xMnx system with x=0.3, 0.5, 0.8 by using powder metallurgy technique. In this work, the characterization of structural and thermal properties have been investigated experimentally by X-ray diffraction, thermal expansion coefficient and vibrating sample magnetometer (VSM) techniques. The results show that anonymously negative thermal expansion coefficient are changeable in the structure. The results were explained due to the instability relation between magnetic spins with lattice distortion on some of ferromagnetic metals.    

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Publication Date
Sun Jun 06 2010
Journal Name
Baghdad Science Journal
Effect of Hardening to drought Tolerance on the Moisture Content of Sunflower Plant. III. Moisture Percentage in Whole Plant
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The study was conducted during spring seasons of 2000 and 2001.The aim was to study the changes in the moisture content of sunflower plants during growth stages under hardening conditions to drought tolerance .Agricultural practices were made according to recommendation. Asplit-split plots design was used with three replications. The main plots included irrigation treatments:irrigation to100%(full irrigation),75and50%of available water. The sub plots were the cultivars Euroflor and Flame.The sub-sub plots represented four seed soaking treatments: Control (unsoaked), soaking in water ,Paclobutrazol solution(250ppm),and Pix solution(500ppm). The soaking continued for 24 hours then seeds were dried at room temperature until they regained t

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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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Publication Date
Sat Jan 01 2011
Journal Name
Advances In Condensed Matter Physics
Compositional Dependence of Structural Properties of Prepared Alloys and Films
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Results of a study of alloys and films with various Pb content have been reported and discussed. Films of of thickness 1.5 μm have been deposited on glass substrates by flash thermal evaporation method at room temperature, under vacuum at constant deposition rate. These films were annealed under vacuum around 10−6Torr at different temperatures up to 523 K. The composition of the elements in alloys was determined by standard surfaces techniques such as atomic absorption spectroscopy (AAS) and X-ray fluorescence (XRF), and the results were found of high accuracy and in very good agreement with the theoretical values. The structure for alloys and films is determined by using X-ray

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Publication Date
Fri Jan 01 2021
Journal Name
Artificial Intelligence For Covid-19
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
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Publication Date
Sat Dec 25 2021
Journal Name
International Journal Of Drug Delivery Technology
Study of Main Factors Affecting the Leakage of Aluminum into the Food Wrapped in Aluminum Foil During Cooking
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Different cooking conditions were examined for aluminum content in food cooked while wrapped with aluminum foil. The influence of each anticipated factor (the acidity of the cooking medium, type of acids normally used in cuisines namely acetic and tartaric acids, various cooking temperatures, influence of the presence of sodium chloride salt, the effect of cooking oil, and the length of time of cooking) was studied thoroughly as a function of aluminum degraded out of the aluminum foils to the medium. The experimental samples were digested with nitric acid upon fulfillment of examining each factor separately before quantifying aluminum with the sensitive technique of atomic absorption spectroscopy. The outcomes of the study have shown that t

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Publication Date
Fri Jul 30 2021
Journal Name
Iraqi Journal For Electrical And Electronic Engineering
EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm
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The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi

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