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Journal articles on the topic 'Extreme Gradient Boosting Classifier'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Abdualjabar, Rana Dhia’a, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1610. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles

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Abdu-Aljabar, Rana Dhiaa, and Osama A. Awad. "Parallel extreme gradient boosting classifier for lung cancer detection." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1610–17. https://doi.org/10.11591/ijeecs.v24.i3.pp1610-1617.

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Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles

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Shakya, Achala, Mantosh Biswas, and Mahesh Pal. "Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients." Remote Sensing 15, no. 1 (2023): 274. http://dx.doi.org/10.3390/rs15010274.

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This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset. For the fusion of SAR and optical images, Sentinel 1A and Sentinel 2B data covering Central State Farm in Hissar (India) was used. The major agricultural crops grown in this area include paddy, maize, cotton, and pulses during kharif (summer) and wheat, sugarcane, mustard, gram, and peas during rabi (winter) seasons. The gradient method using a Sobel op

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Parente, Daniel J. "PolyBoost: An enhanced genomic variant classifier using extreme gradient boosting." PROTEOMICS – Clinical Applications 15, no. 2-3 (2021): 1900124. http://dx.doi.org/10.1002/prca.201900124.

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Setyarini, Dela Ananda, Agnes Ayu Maharani Dyah Gayatri, Christian Sri Kusuma Aditya, and Didih Rizki Chandranegara. "Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 2 (2024): 477–90. http://dx.doi.org/10.30812/matrik.v23i2.3555.

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A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used

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Pratiwi, Nor Kumalasari Caecar, Hilal Tayara, and Kil To Chong. "An Ensemble Classifiers for Improved Prediction of Native–Non-Native Protein–Protein Interaction." International Journal of Molecular Sciences 25, no. 11 (2024): 5957. http://dx.doi.org/10.3390/ijms25115957.

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In this study, we present an innovative approach to improve the prediction of protein–protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulati

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Panigrahi, Millee, Dayal Kumar Behera, and Krishna Chandra Patra. "Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 884–91. https://doi.org/10.11591/ijeecs.v25.i2.pp884-891.

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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked

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Mukhanova, Ayagoz, Madiyar Baitemirov, Azamat Amirov, et al. "Forecasting creditworthiness in credit scoring using machine learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5534. http://dx.doi.org/10.11591/ijece.v14i5.pp5534-5542.

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This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each met

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Panigrahi, Millee, Dayal Kumar Behera, and Krishna Chandra Patra. "Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 884. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp884-891.

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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for

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Iqbal, Saqib, Azhar Imran, and Muhammad Adnan. "Breast Tumor Detection using Machine Learning Boosting Classifiers." Journal of Computing & Biomedical Informatics 4, no. 01 (2022): 118–31. http://dx.doi.org/10.56979/401/2022/64.

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Breast cancer is the frequently found in women and the second greatest reason of death worldwide. As breast cancer is detected early, the ratio of survival rate increases because better therapy may be provided. ML algorithms are very vital in the early diagnosis of breast cancer. In this study, we purposed a Novel method that increases the accuracy and performance using these three different classifiers: Gradient Boost (GB), Ada Boost (ABC), and Extreme Gradient Boost (XGB). On the Public dataset WBC, we evaluated and compared the classifiers’ performance and accuracy. Because the chance of ex

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Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

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Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network

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Emima, A., and D. I. George Amalarethinam. "Integrative Ensemble Learning Algorithm for Predicting Students’ Performance." Indian Journal Of Science And Technology 18, no. 1 (2025): 72–84. https://doi.org/10.17485/ijst/v18i1.3718.

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Objectives: To create a stable student performance prediction model utilizing ensemble learning methods. Methods: The study uses boosting techniques such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as simple classifiers, which are then combined into a composite classifier to improve predictive accuracy. During the training phase, a 5-level hyperparameter optimization for the basic classifiers is performed using ETLBO Optimization IELA's distinguishing feature is its Stacking ensemble method, which functions as an ensemble technique, combinin

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A, Emima, and I. George Amalarethinam D. "Integrative Ensemble Learning Algorithm for Predicting Students' Performance." Indian Journal of Science and Technology 18, no. 1 (2025): 72–84. https://doi.org/10.17485/IJST/v18i1.3718.

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Abstract <strong>Objectives:</strong> To create a stable student performance prediction model utilizing ensemble learning methods. <strong>Methods:</strong> The study uses boosting techniques such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as simple classifiers, which are then combined into a composite classifier to improve predictive accuracy. During the training phase, a 5-level hyperparameter optimization for the basic classifiers is performed using ETLBO Optimization IELA's distinguishing feature is its Stacking ensemble

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A. Alharbi, Lubna. "Heart Disease Prediction of Cleveland Clinic Patients using Advanced Machine Learning Algorithms." Journal of Advanced Research Design 126, no. 1 (2025): 1–14. https://doi.org/10.37934/ard.126.1.114.

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Globally, cardiovascular diseases (CVDs) constitute the primary cause of morbidity and mortality worldwide. Early diagnosis of those at risk of CVDs may lower the number of avoidable fatalities. It has been shown that machine learning (ML) is helpful in anticipating cardiac issues. Adoption of a prediction system that can detect cardiac diseases before they deteriorate would offer people worldwide enormous hope and help in decision-making. ML has become a popular technique for generating predictions from enormous real-world datasets. It has also been discovered that many ML classifiers contain

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Costa, Viviane, Mateus Silva Rocha, Glaucia Amorim Faria, Silvio Fernando Alves Xavier Junior, Tiago Almeida de Oliveira, and Ana Patricia Bastos Peixoto. "Boosting algorithms for prediction in agriculture: An application of Feature importance and Feature Selection." Sigmae 13, no. 4 (2024): 339–48. https://doi.org/10.29327/2520355.13.4-31.

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The Agriculture sector has created and collected large amounts of data. It can be gathered, stored, and analyzed to assist in decision making generating competitive value, and the use of Machine Learning techniques has been very effective for this market. In this work, a Machine Learning study was carried out using supervised classification models based on boosting to predict disease in a crop, thus identifying the model with the best areas under curve metrics. Light Gradient Boosting Machine, CatBoost Classifier, Extreme Gradient, Gradient Boosting Classifier, Adaboost models were used to qua

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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection

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Saleem, Muniba, Waqar Aslam, Muhammad Ikram Ullah Lali, Hafiz Tayyab Rauf, and Emad Abouel Nasr. "Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis." Diagnostics 13, no. 22 (2023): 3441. http://dx.doi.org/10.3390/diagnostics13223441.

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Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the e

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Octavianto, Noer Rachmat, and Antoni Wibowo. "Stacking classifier method for prediction of human body performance." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1832. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp1832-1839.

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<p>A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification base

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Octavianto, Noer Rachmat, and Antoni Wibowo. "Stacking classifier method for prediction of human body performance." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1832–39. https://doi.org/10.11591/ijeecs.v34.i3.pp1832-1839.

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A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based o

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Fudhlatina, Dina, and Fikri Budiman. "Peningkatan Akurasi Prediksi Curah Hujan menggunakan Gradient Boosting dan CatBoost dengan Pendekatan Voting Classifier." Edumatic: Jurnal Pendidikan Informatika 9, no. 1 (2025): 51–59. https://doi.org/10.29408/edumatic.v9i1.28988.

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Accurate rainfall prediction is essential for agriculture, disaster mitigation, and water resource management, especially in the face of climate change impacts. This research aims to improve the accuracy of rainfall prediction using gradient boosting and CatBoost with a voting classifier approach. The data used in this study amounted to 1,461 based on weather data from BMKG Semarang City (2020-2023). The data was analyzed using the Gradient Boosting and CatBoost algorithms with a voting classifier framework. The input features include temperature (Tn, Tx, Tavg), humidity (RH_avg), rainfall (RR

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Noor, Hamzah Alkurdy, K. Aljobouri Hadeel, and Kassim Wadi Zainab. "Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3440–48. https://doi.org/10.11591/ijece.v13i3.pp3440-3448.

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This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classi

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Abdualgalil, Bilal, Sajimon Abraham, and Waleed M. Ismael. "Early Diagnosis for Dengue Disease Prediction Using Efficient Machine Learning Techniques Based on Clinical Data." Journal of Robotics and Control (JRC) 3, no. 3 (2022): 257–68. http://dx.doi.org/10.18196/jrc.v3i3.14387.

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Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT. Five different efficient machine learning models, including K-Nearest Neighbor (

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Yusuf Bayu Wicaksono and Christina Juliane. "Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 3 (2023): 646–51. http://dx.doi.org/10.29207/resti.v7i3.4492.

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By 2022 it is estimated that 29 billion devices have been connected to the internet so that cybercrime will become a major threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system (Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has infected a user's device. This study uses the opposite approach, which is to predict the potential for malware in

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Fadare, Oluwaseun Gbenga, Adebayo Olusola Adetunmbi, Oghenerukevwe Eloho Oyinloye, and Stephen Alaba Mogaji. "Towards Optimization of Malware Detection using Chi-square Feature Selection on Ensemble Classifiers." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 4 (2021): 254–62. https://doi.org/10.35940/ijeat.D2359.0410421.

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The multiplication of malware variations is probably the greatest problem in PC security and the protection of information in form of source code against unauthorized access is a central issue in computer security. In recent times, machine learning has been extensively researched for malware detection and ensemble technique has been established to be highly effective in terms of detection accuracy. This paper proposes a framework that combines combining the exploit of both Chi-square as the feature selection method and eight ensemble learning classifiers on five base learners- K-Nearest Neighb

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Ahmad, Ahmad. "Heart failure detection using deep learning and Gradient Boosting classifier." El-Cezeri Fen ve Mühendislik Dergisi 12, no. 1 (2025): 1–8. https://doi.org/10.31202/ecjse.1476222.

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Heart failure (HF) is marked by a diminished capacity of the heart to effectively pump blood. Traditionally, the electrocardiogram (ECG) has served as a non-invasive diagnostic tool, gauging the heart's electrical activity and rhythm. Recent advancements have leveraged machine learning (ML) and deep learning (DL) techniques to automate the identification and classification of HF types from ECG data. This study introduces a novel deep learning architecture, blending the efficacy of a convolutional neural network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGBoost) layer for

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Anwar, Muchamad Taufiq, and Denny Rianditha Arief Permana. "Perbandingan Performa Model Data Mining untuk Prediksi Dropout Mahasiwa." Jurnal Teknologi dan Manajemen 19, no. 2 (2021): 33–40. http://dx.doi.org/10.52330/jtm.v19i2.34.

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Penentuan teknik/model data mining yang tepat pada sebuah kasus sangat penting untuk mendapatkan model yang baik (tingkat akurat tinggi dan kesesuaiannya dengan masalah yang dipecahkan). Penelitian ini bertujuan untuk membandingkan performa teknik data mining untuk diterapkan pada kasus prediksi dropout mahasiswa. Perbandingan performa dilakukan menggunakan library PyCaret pada Python untuk melakukan pemodelan menggunakan 14 model / teknik data mining yaitu: Extreme Gradient Boosting, Ada Boost Classifier, Light Gradient Boosting Machine, Random Forest Classifier, Gradient Boosting Classifier,

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Sungur, Mustafa, Aykut Aykaç, Mehmet Erhan Aydin, Ozer Celik, and Coskun Kaya. "Machine Learning-Based Prediction of Prostate Biopsy Necessity Using PSA, MRI, and Hematologic Parameters." Journal of Clinical Medicine 14, no. 1 (2024): 183. https://doi.org/10.3390/jcm14010183.

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Background: To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. Methods: The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected. Patients with benign prostate pathology were classified as Group 1, and those with prostate cancer (PCa) were classified as Group 2. The following ML algorithms were used to create the ML mode

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Asfaw, Temesgen. "Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia." Scientific Temper 14, no. 03 (2023): 618–24. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.08.

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The number of service providers is increasing rapidly in every business. These days, there is plenty of options for customers in the banking sector when choosing where to put their money. As a result, customer churn and engagement have become one of the top issues for most of the banks. In this paper, a method to predict customer churn in a Bank using machine learning techniques, which is a branch of artificial intelligence, is proposed. The research promotes the exploration of the likelihood of churn by analyzing customer behavior. random forest (RF), logistic regression (LR), gradient boosti

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Gbenga*, Fadare Oluwaseun, Prof Adetunmbi Adebayo Olusola, Dr (Mrs) Oyinloye Oghenerukevwe Eloho, and Dr Mogaji Stephen Alaba. "Towards Optimization of Malware Detection using Chi-square Feature Selection on Ensemble Classifiers." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 254–62. http://dx.doi.org/10.35940/ijeat.d2359.0410421.

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The multiplication of malware variations is probably the greatest problem in PC security and the protection of information in form of source code against unauthorized access is a central issue in computer security. In recent times, machine learning has been extensively researched for malware detection and ensemble technique has been established to be highly effective in terms of detection accuracy. This paper proposes a framework that combines combining the exploit of both Chi-square as the feature selection method and eight ensemble learning classifiers on five base learners- K-Nearest Neighb

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Jiang, Lin. "Breast Cancer Prediction Feature Selection Using ML Algorithms." Journal of Physics: Conference Series 2547, no. 1 (2023): 012021. http://dx.doi.org/10.1088/1742-6596/2547/1/012021.

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Abstract Breast cancer is a disease that breast epithelial cells uncontrolled hyperplasia under the effects of several different carcinogens. The early symptoms of breast cancer are breast lesions, nipple discharge and axillary fossa lymphadenectasis. Breast cancer can directly threat the life of patient by casing various organ lesions and metastasis of cancer cells in the late stage of suffering from this disease. The method of classifying tumors into malignant or benign is the main challenge that needed to be dealt with to treat patient in a correct way. This paper shows the different classi

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Rahman, Senjuti, Mehedi Hasan, Ajay Krishno Sarkar, and Fayez Khan. "Classification of Parkinson’s Disease using Speech Signal with Machine Learning and Deep Learning Approaches." European Journal of Electrical Engineering and Computer Science 7, no. 2 (2023): 20–27. http://dx.doi.org/10.24018/ejece.2023.7.2.488.

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Parkinson's disease (PD) is a chronic neurological condition that is growing in prevalence and manifests both motor and non-motor symptoms. Most PD patients have trouble speaking, writing, and walking during the early stages of the disease. Analysis of speech problems has been effective in identifying Parkinson's disease. According to studies, 90% of Parkinson's disease patients experience speech problems. Even though there is no known cure for Parkinson's disease, using the right medication at an early stage can greatly reduce the symptoms. One of the key categorization issues for the diagnos

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Sapdi, Rohmat Mulyana, Dian Sa'adillah Maylawati, Diena Rauda Ramdania, Ichsan Budiman, Muhammad Insan Al-Amin, and Mi'raj Fuadi. "Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 652. http://dx.doi.org/10.62527/joiv.8.2.1823.

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This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history.

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Sharma, Sandeep, Saruchi Saruchi, Avneesh Narwal, et al. "Machine Learning Algorithm for Detecting and Predicting Chronic Kidney Disease." Biomedical and Pharmacology Journal 18, no. 2 (2025): 1235–50. https://doi.org/10.13005/bpj/3165.

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Chronic kidney disease is a progressive condition that often remains undiagnosed until its later stages due to the absence of noticeable symptoms. Early detection is essential for timely intervention and treatment. Whereas other research has mostly centered on the detection of kidney disease in later stages, this research contributes to the field by combining predictive modeling in order to ascertain disease progression in earlier phases. Through the use of both multi-classification and binary classification methods, this research improves the knowledge of chronic kidney disease progression, e

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Alanazi, Abed, and Abdu Gumaei. "A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection." Applied Sciences 13, no. 18 (2023): 10260. http://dx.doi.org/10.3390/app131810260.

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Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified base

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Shao, Chen, and Yue zhong yi Sun. "Shilling attack detection for collaborative recommender systems: a gradient boosting method." Mathematical Biosciences and Engineering 19, no. 7 (2022): 7248–71. http://dx.doi.org/10.3934/mbe.2022342.

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<abstract> <p>Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods

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Duc-Duong, Nguyen, Le Minh-Thuy, and Cung Thanh-Long. "Improving intrusion detection in SCADA systems using stacking ensemble of tree-based models." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 119–27. https://doi.org/10.11591/eei.v11i1.3334.

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This paper introduces a stacking ensemble model, which combines three single models, to improve intrusion detection in supervisory control and data acquisition (SCADA) systems. The first layer of the proposed model is the combination of random forest, light boosting gradient machine, and eXtreme gradient boosting models. We use an multilayer perceptron (MLP) network as a meta-classifier of the model. The proposed model is optimized and tested on an international dataset (gas pipeline dataset). The tested results show an accuracy of 99.72% with the f1-score of 99.72% for binary classification t

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Jafarzadeh, Hamid, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, and Saeid Homayouni. "Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation." Remote Sensing 13, no. 21 (2021): 4405. http://dx.doi.org/10.3390/rs13214405.

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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this

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Baddam, Sowmya Sri Reddy. "Heart Stroke Prediction Using Bagging and Boosting Classifiers." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2300–2304. http://dx.doi.org/10.22214/ijraset.2022.44297.

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Abstract: Forecast of coronary illness is one of the superb regions where AI can yield an extreme benefit. Electrocardiographic (ECG) measures and AI for ECG highlights can be applied to foresee the Heart Stroke by utilizing a dataset made out of ECG highlights. Electrocardiogram (ECG) is one of the significant biomedical signs. Rather than utilizing general arrangement procedures whose precision goes from restricted to acceptable, this undertaking points on investigating outfit grouping. The sole point is to research the changes to the exactness with the utilization of gathering characterizat

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Alkurdy, Noor Hamzah, Hadeel K. Aljobouri, and Zainab Kassim Wadi. "Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3440. http://dx.doi.org/10.11591/ijece.v13i3.pp3440-3448.

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This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classi

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Ahamad, Ghulab Nabi, Shafiullah, Hira Fatima, et al. "Influence of Optimal Hyperparameters on the Performance of Machine Learning Algorithms for Predicting Heart Disease." Processes 11, no. 3 (2023): 734. http://dx.doi.org/10.3390/pr11030734.

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One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparamet

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Sugiharti, Endang, Riza Arifudin, Dian Tri Wiyanti, and Arief Broto Susilo. "Integration of convolutional neural network and extreme gradient boosting for breast cancer detection." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 803–13. http://dx.doi.org/10.11591/eei.v11i2.3562.

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With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBo

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Endang, Sugiharti, Arifudin Riza, Tri Wiyanti Dian, and Broto Susilo Arief. "Integration of convolutional neural network and extreme gradient boosting for breast cancer detection." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 803–13. https://doi.org/10.11591/eei.v11i2.3562.

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With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of convolutional neural network (CNN) and extreme gradient boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBo

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Bakasa, Wilson, and Serestina Viriri. "VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction." Journal of Imaging 9, no. 7 (2023): 138. http://dx.doi.org/10.3390/jimaging9070138.

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The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly improved by an early and accurate diagnosis. Several studies have created automated methods to forecast PDAC development utilising various medical imaging modalities. These papers give a general overview of the classification, segmentation, or grading of many cancer types utilising conventional machine learning techniques and hand-engineered characteristics, including pancreatic cancer. This study uses cutting-edge deep learning techniques to identify PDAC utilising computerised tomography (CT) medical imaging mo

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Vasantha, Sandhya Venu Chellapilla V. K. N. S. N. Moorthy Preeti S. Patil Navnath D. Kale Chetan Vikram Andhare Mukesh Kumar Tripathi. "Analyzing electroencephalogram signals with machine learning to comprehend online learning media." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1876–85. https://doi.org/10.11591/ijeecs.v35.i3.pp1876-1885.

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In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines le

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Alqahtani, Gumaei, Mathkour, and Maher Ben Ismail. "A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks." Sensors 19, no. 20 (2019): 4383. http://dx.doi.org/10.3390/s19204383.

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An Intrusion detection system is an essential security tool for protecting services and infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few works of machine learning have been proposed for intrusion detection in wireless sensor networks and that have achieved reasonable results. However, these works still need to be more accurate and efficient against imbalanced data problems in network traffic. In this paper, we proposed a new model to detect intrusion attacks based on a genetic algorithm and an extreme gradient boosting (XGBoot) classifier, called GXGBoost

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Sun, Congcong, Hui Tian, Chin-Chen Chang, et al. "Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting." Electronics 9, no. 3 (2020): 522. http://dx.doi.org/10.3390/electronics9030522.

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Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical

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Matyukira, Charles, and Paidamwoyo Mhangara. "Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis." Remote Sensing 15, no. 23 (2023): 5520. http://dx.doi.org/10.3390/rs15235520.

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Land use and land cover change constitute a significant driver of land degradation worldwide, and machine-learning algorithms are providing new opportunities for effectively classifying land use and land cover changes over time. The aims of this study are threefold: Firstly, we aim to compare the accuracies of the parametric classifier Naïve Bayes with the non-parametric classifier Extreme Gradient Boosting Random Forest algorithm on the 2020 LULC dataset. Secondly, we quantify land use and land cover changes in the Cradle of Humankind from 1990 to 2020 using the Extreme Gradient Boosting Rand

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Ghanim, Abdulnoor A. J., Ahmad Shaf, Tariq Ali, et al. "An Improved Flood Susceptibility Assessment in Jeddah, Saudi Arabia, Using Advanced Machine Learning Techniques." Water 15, no. 14 (2023): 2511. http://dx.doi.org/10.3390/w15142511.

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The city of Jeddah experienced a severe flood in 2020, resulting in loss of life and damage to property. In such scenarios, a flood forecasting model can play a crucial role in predicting flood events and minimizing their impact on communities. The proposed study aims to evaluate the performance of machine learning algorithms in predicting floods and non-flood regions, including Gradient Boosting, Extreme Gradient Boosting, AdaBoosting Gradient, Random Forest, and the Light Gradient Boosting Machine, using the dataset from Jeddah City, Saudi Arabia. This study identified fourteen continuous pa

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Liu, Yushuang, Shuping Jin, Lili Song, Yu Han, and Bin Yu. "Prediction of protein ubiquitination sites via multi-view features based on eXtreme gradient boosting classifier." Journal of Molecular Graphics and Modelling 107 (September 2021): 107962. http://dx.doi.org/10.1016/j.jmgm.2021.107962.

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Nalasari, Lista Tri, Syaiful Anam, and Nur Shofianah. "Liver Cirrhosis Classification using Extreme Gradient Boosting Classifier and Harris Hawk Optimization as Hyperparameter Tuning." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 2 (2025): 508–19. https://doi.org/10.35882/jeeemi.v7i2.730.

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This study proposes an early diagnosis model based on Machine Learning for liver cirrhosis classification using the Hepatitis C dataset, which is the leading cause of cirrhosis, from UCI ML. The classification is performed using the XGBoost algorithm because it provides high accuracy and time efficiency based on previous studies. However, these advantages depend on the combination of its hyperparameters set. XGBoost has a large number of hyperparameters, which can be time-consuming for researchers to manually configure. Therefore, this study proposes combining XGBoost with the Harris Hawks Opt

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