Intelligent Teaching Recommendation Model for Practical Discussion Course of Higher Education Based on Naive Bayes Machine Learning and Improved <i>k</i>-NN Data Mining Algorithm by Xiao Zhou, Ling Guo, Rui Li, Ling Liu, Juan Pan

“…Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved <i>k</i>-NN data mining algorithm. …”
Get full text

Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory pa... by Jing Zhang, Cheng Zhang, Xi Zhang, Wuchen Yang, Jingya Liu, Yang Gou, Xingqin Huang, Maoshan Chen, Dezhi Huang, Shengwang Wu, Shuiqing Liu, Xiangui Peng

“…Multiple machine learning (ML) models were constructed and optimised by using the active learning (AL) algorithm. …”
Get full text

Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database by De Su, Jie Zheng, Yue-kai Shao, Jun-ya Liu, Xin-xin Liu, Kun Yu, Bang-hai Feng, Hong Mei, Song Qin

“…Conclusion This study demonstrates the potential of machine learning models, particularly ensemble learning models based on soft voting mechanisms, in predicting in-hospital mortality risk among heart failure patients in the ICU. …”
Get full text

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models by Imran Said, Vasit Sagan, Kyle T. Peterson, Haireti Alifu, Abuduwanli Maiwulanjiang, Abby Stylianou, Omar Al Akkad, Supria Sarkar, Noor Al Shakarji

“…Convolutional neural networks (CNNs) with attention mechanisms were proposed along with traditional machine learning models based on feature engineering including Random Forest (RF) and Support Vector Machine (SVM) regression for comparative analysis. …”
Get full text

The Impact of Wave Prediction Uncertainty on the Control of a Multi-Axis Wave Energy Converter by Carrie Hall, Yueqi Wu, Igor Rizaev, Wanan Sheng, Robert Dorrell, George Aggidis

“…The uncertainty in these predictions and the model could degrade the WEC’s power output. This work examines the impact of uncertainty on the control of a WEC system that leverages machine learning to predict wave forces over the upcoming time horizon. …”
Get full text

Predicting the Compressive Strength of High-Performance Concrete utilizing Radial Basis Function Model integrating with Metaheuristic Algorithms by LiWei Hu

“…The artificial neural network (ANN) model is the subset of ML, which the experimental tasks can replace. …”
Get full text

A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

Get full text

Optimizing Euclidean Distance Computation by Rustam Mussabayev

Get full text

Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm by Youle Song, Yuting Duan, Tong Rao

“…Therefore, this study proposes an improved support vector machine model, combined with grey wolf optimization algorithm, aimed at improving the accuracy and efficiency of power equipment fault diagnosis. …”
Get full text

A tool for automatic parallelization of affine programs for systems with shared and distributed memory by Sh. G. Magomedov, A. S. Lebedev

Get full text

Modeling Multimedia Ad Exposure: The Role of Low-Level Audiovisual Features by Urban Burnik, Andrej Kosir, Gregor Strle

Get full text

Data-Driven Hybrid CNN-LSTM Neural Networks for Predicting Drying Kinetics of Green Tomatoes (Physalis ixocarpa) Integrating Mathematical Models by Carolina Livier Recio-Colmenares, Roxana Berenice Recio-Colmenares, Robin Fernando Conchas-Cedano, Isaac Pilatowsky-Figueroa, Cesar Alejandro Garcia Garcia

Get full text

Characterizing Duodenal Immune Microenvironment in Functional Dyspepsia: An AutoML-Driven Diagnostic Framework by Zhang X, Fan X, Hu X, Qian Z, Li J, Wu W, Chen L, Wu S, Ma L, Yang C, Zhang T, Su X, Wei W

“…We utilized the AutoGluon framework to automate the construction and optimization of a model based on hub genes, generating a high-performance diagnostic model. …”
Get full text

Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health by Yiyang Liu, Aokun Chen, Hwayoung Cho, Khairul A. Siddiqi, Robert L. Cook, Mattia Prosperi

“…Contextual-level SDoH were linked to EHR/claim data. Various machine learning (ML) methods were tested, and Shapley Additive Explanations (SHAP) values were used to interpret the model. …”
Get full text

An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. …”
Get full text

Risk prediction and clinical utility analysis of postoperative pancreatic fistula: a comparative study of multivariable logistic regression and random forest models by Kaixuan Zhang, Kunlun Chen

“…Comparing the performance of traditional statistical methods and machine learning models provides insight into the optimal approach for CR-POPF prediction.MethodsClinical data from patients undergoing PD were collected. …”
Get full text

Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

“…Our approach introduces a Quantile Uniform transformation to reduce feature skewness, a multi-layered feature selection method to enhance discriminative power, an individual performance of deep learning–traditional machine learning and a hybrid models (ensemble models) for robust detection. …”
Get full text

Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis by Yulin Zhang, Jian Zhou, Jialu Li, Biao He, Danial Jahed Armaghani, Shuai Huang

“…To address these limitations, this research proposes three innovative hybrid models that integrate metaheuristic optimization algorithms with support vector machine (SVM): multi-verse optimizer-SVM (MVO-SVM), salp swarm algorithm-SVM (SSA-SVM), and Harris’s Hawk optimization-SVM (HHO-SVM). …”
Get full text

Text classification using SVD, BERT, and GRU optimized by improved Seagull optimization (ISO) algorithm by Yuanyuan Chen, Nan Sun, Yuanbang Li, Rong Peng, Abbas Habibi

“…The outcomes highlight the model’s reliability and strength, exceeding all baseline models, namely GRU, BiGRU, BiLSTM, KNN, LSTM, and CNN. …”
Get full text

Understanding the Role of Diversity in Ensemble-Based AutoML Methods for Classification Tasks by Salomey Osei, Andres R. Masegosa, Antonio D. Masegosa

“…Ensemble-based Automated Machine Learning (AutoML) methods have gained prominence for their ability to combine diverse machine learning models, achieving superior generalization performance. …”
Get full text