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18 papers · ranked by Valyu relevance

01
PPubMed20 May 2022
An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things
Deepti Rani, Nasib Singh Gill, Preeti Gulia, Jyotir Moy Chatterjee
Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and…
89relevance
02
PPubMed23 Dec 2021
Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine
Ebenezer Owusu, Prince Boakye-Sekyerehene, Justice Kwame Appati, Julius Yaw Ludu
'Julius Yaw Ludu'] Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the…
88relevance
03
PPubMed18 Oct 2021
A new framework based on features modeling and ensemble learning to predict query performance A New Framework based on Features Modeling and Ensemble Learning to Predict Query Performance
Mohamed Zaghloul, Mofreh Salem, Amr Ali-Eldin, Usman Qamar
A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance…
87relevance
04
PPubMed3 Aug 2023
The Experimentalist’s Guide to Machine Learning for Small Molecule Design
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…
87relevance
05
PPubMed27 Apr 2022
A boosting first-hitting-time model for survival analysis in high-dimensional settings
Riccardo De Bin, Vegard Grødem Stikbakke
In this paper we propose a boosting algorithm to extend the applicability of a first hitting time model to high-dimensional frameworks. Based on an underlying stochastic process, first hitting time models do not require the proportional hazards assumption, hardly verifiable in the high-dimensional context, and…
86relevance
06
PPubMed5 Apr 2025
Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process
Clara Bertinelli Salucci, Azzeddine Bakdi, Ingrid Kristine Glad, Bo Henry Lindqvist + 2 more
'Bo Henry Lindqvist' 'Erik Vanem' 'Riccardo De Bin'] In the context of time-to-event analysis, First hitting time methods consider the event occurrence as the ending point of some evolving process. The characteristics of the process are of great relevance for the analysis, which makes this class of models interesting…
85relevance
07
PPubMed14 Feb 2025
Boosting-Based Machine Learning Applications in Polymer Science: A Review
Ivan Malashin, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub + 2 more
'Aleksei Borodulin' 'Qingsheng Wang'] The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest in machine learning (ML) methods to aid in data analysis, material design, and predictive modeling. Among the various ML approaches, boosting methods…
83relevance
08
PPubMed23 Nov 2025
Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods
Jiming Liu, Dongjin Xu
Lithology is a key parameter in reservoir fine description and evaluation. It is difficult to directly identify reservoir lithology using a single logging curve or conventional cross-plot method due to the mud-gravel mixing in complex reservoirs. The accurate identification of conglomerate reservoir lithology has…
83relevance
09
PPubMed12 Dec 2022
A Proposed Framework for Early Prediction of Schistosomiasis
Zain Ali, Muhammad Faisal Hayat, Kamran Shaukat, Talha Mahboob Alam + 8 more
'Ibrahim A. Hameed' 'Suhuai Luo' 'Shakila Basheer' 'Manel Ayadi' 'Amel Ksibi' 'Dilbag Singh' 'Vijay Kumar' 'Dinesh Kumar'] Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water…
82relevance
10
PPubMed29 Jan 2023
Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data
Mahesh Thyluru Ramakrishna, Vinoth Kumar Venkatesan, Ivan Izonin, Myroslav Havryliuk + 2 more
'Myroslav Havryliuk' 'Chandrasekhar Rohith Bhat' 'Gholamreza Anbarjafari'] Today’s world faces a serious public health problem with cancer. One type of cancer that begins in the breast and spreads to other body areas is breast cancer (BC). Breast cancer is one of the most prevalent cancers that claim the lives of…
82relevance
11
PPubMed18 Apr 2022
AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease
T. R. Mahesh, V. Dhilip Kumar, V. Vinoth Kumar, Junaid Asghar + 3 more
As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating…
81relevance
12
PPubMed20 Mar 2026
Forecasting auditor’s going concern opinion using with hybrid robust machine learning model Forecasting auditor’s going concern opinion using with hybrid robust machine learning model
Uğur Ejder, Alpaslan Yaşar, Aamna AlShehhi
The importance of forecasting company bankruptcies makes the auditor’s reporting of the going concern opinion (GCO) a focal point for interested parties. Therefore, researchers have recently turned to predicting GCO using various machine learning (ML) methods. The aim of this research is to propose a novel hybrid model…
80relevance
13
PPubMed29 May 2022
A Model for Predicting Cervical Cancer Using Machine Learning Algorithms
Naif Al Mudawi, Abdulwahab Alazeb, Ayman El-baz, Guruprasad A. Giridharan + 3 more
A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming…
80relevance
14
PPubMed14 Jul 2022
Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm
Jie Zhang, Fang Wang
Gestational diabetes mellitus (GDM) is one of the risk factors for fetal dysplasia and maternal pregnancy difficulties. Therefore, the prediction of the risk of GDM in advance has become a big demand for millions of families. Therefore, machine learning technology is adopted to study GDM prediction. Firstly, the data…
80relevance
15
PPubMed10 Nov 2025
Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data
Hagar F. Gouda, Fatma D. M. Abdallah
Ensemble machine learning (ML) algorithms, such as bagging and boosting, are powerful decision-support tools that enhance disease prediction and risk management in the veterinary field. Lumpy Skin Disease (LSD) poses a significant threat to livestock health and results in substantial economic losses. This study aims to…
79relevance
16
PPubMed14 Mar 2025
Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR
Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das + 4 more
'Manmath Nath Das' 'Prince Jain' 'Ghanashyam Sahoo' 'Haipeng Liu' 'Mohammad Rahimi-Gorji'] Objective: The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an…
78relevance
17
PPubMed23 Dec 2024
Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease
Placida Orochi Orlunwo, Friday Eleonu Onuodu
Alzheimer’s disease (AD) is a progressive neurological condition characterized by a loss in cognitive functions, with no disease-modifying medication now available. It is crucial for early detection and treatment of Alzheimer’s disease before clinical manifestation. The stage between cognitively healthy older persons…
72relevance
18
PPubMed11 Oct 2023
Early warning of telecom enterprise customer churn based on ensemble learning Early warning of customer churn based on ensemble learning
Yancong Zhou, Wenyue Chen, Xiaochen Sun, Dandan Yang + 1 more
'Anwar P. P. Abdul Majeed'] Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization…
70relevance

Results for Boosting algorithm

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

A new framework based on features modeling and ensemble learning to predict query performance A New Framework based on Features Modeling and Ensemble Learning to Predict Query Performance

The Experimentalist’s Guide to Machine Learning for Small Molecule Design

A boosting first-hitting-time model for survival analysis in high-dimensional settings

Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process

Boosting-Based Machine Learning Applications in Polymer Science: A Review

Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods

A Proposed Framework for Early Prediction of Schistosomiasis

Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Forecasting auditor’s going concern opinion using with hybrid robust machine learning model Forecasting auditor’s going concern opinion using with hybrid robust machine learning model

A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm

Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data

Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR

Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease

Early warning of telecom enterprise customer churn based on ensemble learning Early warning of customer churn based on ensemble learning

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

A new framework based on features modeling and ensemble learning to predict query performance A New Framework based on Features Modeling and Ensemble Learning to Predict Query Performance

The Experimentalist’s Guide to Machine Learning for Small Molecule Design

A boosting first-hitting-time model for survival analysis in high-dimensional settings

Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process

Boosting-Based Machine Learning Applications in Polymer Science: A Review

Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods

A Proposed Framework for Early Prediction of Schistosomiasis

Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data

AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease

Forecasting auditor’s going concern opinion using with hybrid robust machine learning model Forecasting auditor’s going concern opinion using with hybrid robust machine learning model

A Model for Predicting Cervical Cancer Using Machine Learning Algorithms

Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm

Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data

Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR

Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease

Early warning of telecom enterprise customer churn based on ensemble learning Early warning of customer churn based on ensemble learning