Water Quality Prediction Using Artificial Intelligence Algorithms by Theyazn H. H Aldhyani, Mohammed Al-Yaari, Hasan Alkahtani, Mashael Maashi

“…For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. …”
Get full text

Estimation of emerald mineralization probability using machine learning algorithms by Daniela Neva-Rodriguez, Luis Hernán Ochoa-Gutierrez

Get full text

Prediction of high-risk pregnancy based on machine learning algorithms by Xinyu Pi, Junzhi Wang, Liangliang Chu, Guochun Zhang, Wenli Zhang

“…Six machine learning algorithms—multilayer perceptron (MLP), logistic regression (LR), decision tree (DT), random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM)—are employed to construct predictive models. …”
Get full text

Comparing text classification algorithms with n-grams for mediation prediction by Retzi Y. Lewu, Kusrini Kusrini, Ainul Yaqin

Get full text

Predicting Quail Egg Quality Using Machine Learning Algorithms by BI Yildiz, K Eskioğlu, D Özdemir, M Akşit

“…A dataset comprising 350 eggs from 18-week-old Japanese quails was analyzed using Logistic Regression, Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Random Forest, and Gradient Boosting. …”
Get full text

Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection by Tajamul Shahzad, Kashif Aman

“…To give the brief knowledge of phishing attacks and their types of the objective of this work is to investigate various AI algorithms. Through a detail literature 14 AI algorithms which are repeatedly used for detection, and these are Random Forests, Convolutional Neural Network, Naïve Bayes, K-Nearest Neighbours algorithm, Decision Trees, long short-term memory, gated recurrent unit, Artificial Neural Network, AdaBoost, Logistic Regression, Gradient Boost, Multi-layer perceptron, Recurrent Neural Network, Extreme gradient boosting, and Support Vector Machine to detect phishing attacks. …”
Get full text

A Robust Regression-Based Modeling to Predict Antiplasmodial Activity of Thiazolyl–Pyrimidine Hybrid Derivatives against <i>Plasmodium falciparum</i> by Kevin S. Umoette, Charles O. Nnadi, Wilfred O. Obonga

“…The X- and Y-matrix were split into 34 train and 9 test sets using a split ratio of 0.20. Regression models were built using scikit-learn algorithms: multiple linear regression (MLR), k-Nearest Neighbors (kNN), Support Vector Regressor (SVR), and Random Forest Regressor (RFR) to predict the pIC₅₀ of the test set. …”
Get full text

LogiTriBlend: A Novel Hybrid Stacking Approach for Enhanced Phishing Email Detection Using ML Models and Vectorization Approach by Aqsa Khalid, Maria Hanif, Abdul Hameed, Zeeshan Ashraf, Mrim M. Alnfiai, Salma M. Mohsen Alnefaie

“…The model combines multiple base learners, including Support Vector Machine (SVM), Logistic Regression, Random Forest, and XGBoost, with a Logistic Regression meta-learner. …”
Get full text

Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

Get full text

Scanning Micromirror Calibration Method Based on PSO-LSSVM Algorithm Prediction by Yan Liu, Xiang Cheng, Tingting Zhang, Yu Xu, Weijia Cai, Fengtian Han

“…This paper presents a novel scanning micromirror calibration method based on the prediction of a particle swarm optimization-least squares support vector machine (PSO-LSSVM). The objective is to establish a correspondence between the actual deflection angle of the micromirror and the output of the measurement system employing a regression algorithm, thereby enabling the prediction of the tilt angle of the micromirror. …”
Get full text

Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. …”
Get full text

Machine Learning Algorithms for Nondestructive Sensing of Moisture Content in Grain and Seed by Arthur P. LeBlanc, Samir Trabelsi, Khaled Rasheed, John A. Miller

“…Performance of this model is investigated and compared with models trained on an individual grain or seed by using different algorithms, including artificial neural network (NN), support vector regression (SVR), ElasticNet, among other algorithms. …”
Get full text

Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…The objective of this study was to compare predictive performances of four machine learning (ML) models: Support Vector Machines with Radial Basis Function Kernel (SVMR), Classification and Regression Trees (CART), Random Forest (RF) and Model Average Neural Networks (MANN) to predict live weight from morphological measurements of Norduz sheep (n=93). …”
Get full text

A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models by Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili, Mohammad Lutfi Othman, Mohammad Effendy Ya’acob, Noor Izzri Bin Abdul Wahab, Masoud Ahmadipour

“…The proposed hybrid IMGOPMFFNN model proved its superior correlation evaluations, surpassing the performance of ant lion optimizer based on random forest (ALORF) model, two stages of ANN (ALO2ANN) model, long short-term memory (LSTM), gated recurrent unit (GRU), extreme learning machine (ELM), least square support vector machine (LSSVM), and convolutional neural network (CNN) models. …”
Get full text

Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, Najmus Saqib Bin Rafi

“…Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. …”
Get full text

Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…For the machine learning models, seven algorithms, namely, extremely randomized trees (ET), multilayer perceptron (MLP), stacking random forest (SRF), Gaussian process regression (GPR), support vector machine (SVM), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms, were tested. …”
Get full text

A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

“…Five classical machine learning algorithms, such as Random Forest, Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), & Gradient Boosting, are used to classify the extracted features. …”
Get full text

Optimization of carbon peak path in Fujian Province based on sparrow search algorithm by CAI Huang, LIN Xiaoyu*, CAI Zhiling, ZHONG Yiwen, ZHONG Fenglin

“…To address this, this paper takes Fujian Province as an example, and constructs an SSA-SVR (Sparrow Search Algorithm-Support Vector Regression) model based on the analysis of Fujian′s energy consumption and carbon emission data. …”
Get full text

A novel pedestrian detection algorithm based on data fusion of face images by Jianhu Zheng, Jinshuan Peng

“…Building upon the theoretical principles used to solve small-sample statistical problems, a new hypothesis has been developed; using this concept, we integrate the conjugate orthonormalized partial least-squares regression with the revised support vector machine algorithm to undertake the solution of the facial recognition problem. …”
Get full text

Prediction models based on machine learning algorithms for COVID-19 severity risk by Hansong Zhang, Ying Wang, Yan Xie, Cuihan Wang, Yuqi Ma, Xin Jin

“…Methods Prediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. …”
Get full text