Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke by Yi Cao, Yi Cao, Haipeng Deng, Shaoyun Liu, Xi Zeng, Yangyang Gou, Weiting Zhang, Yixinyuan Li, Hua Yang, Min Peng

“…Among the four machine learning algorithms evaluated [XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes], the LR model demonstrated robust and consistent performance in predicting SAP among older adult patients with hemorrhagic stroke across multiple evaluation metrics. …”
Get full text

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Nan Yi, Shuangyang Mo, Yan Zhang, Qi Jiang, Yingwei Wang, Cheng Huang, Shanyu Qin, Haixing Jiang

“…These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. …”
Get full text

Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review by Yanzhao Yang, Jian Wang, Xinyu Guo, Xinyu Yang, Wei Qin

“…Experimental results demonstrate the effectiveness of these techniques in enhancing perception algorithm performance. The paper identifies challenges in simulating LiDAR data, such as accuracy discrepancies, brand adaptability, and the need for comprehensive evaluation metrics. …”
Get full text

Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance by Eslam Abdelhakim Seyam

“…The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. …”
Get full text

Model Selection Methods for Model-Bridge Simulation Calibration by Bojan Batalo, Lincon S. Souza, Keisuke Yamazaki

“…Often, manual calibration is time-consuming, error-prone, and dependent on expert knowledge. Therefore, many algorithmic approaches have been explored, from heuristic-based and Bayesian methods to search and genetic algorithms. …”
Get full text

Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning by Elmoundher Hadjaidji, Mohamed Cherif Amara Korba, Khaled Khelil

“…Performance evaluation encompasses four classification algorithms: support vector machine, Gradient Boosting (GB), k-nearest neighbors (KNN), and Naïve Bayes. …”
Get full text

Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

“…Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. …”
Get full text

Sentiment Analysis using Support Vector Machine and Random Forest by Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Bin Mohd Su'ud

“…Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. …”
Get full text

Supervised methods of machine learning for email classification: a literature survey by Muath AlShaikh, Yasser Alrajeh, Sultan Alamri, Suhib Melhem, Ahmed Abu-Khadrah

“…Supervised learning requires pre-training the model on labelled datasets, amalgamating classification, and regression learning. …”
Get full text

An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network by Tingzhong Wang, Honghao Xu, Yudong Hai, Yutian Cui, Ziyuan Chen

“…Finally, it saves the loss and accuracy data during the training process and evaluates the accuracy of the model. …”
Get full text

Application of machine learning in assessing disease activity in SLE by Feng Wang, Ting Wang, Wei Wei, Yun Wang, Renren Ouyang, Rujia Chen, Hongyan Hou, Shiji Wu, Peihong Yuan

“…Multiple machine learning algorithms were employed to construct models for assessing SLE disease activity.Results The patients were divided into two cohorts, cohort 1 used as the training set to build the machine learning models and cohort 2 for external validation. …”
Get full text

Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques by Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat

“…Through comprehensive evaluation, this research explores both supervised and unsupervised approaches, comparing their performance across key metrics like accuracy, F1-score, and recall. …”
Get full text

TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification by Carlos Sánchez-Antonio, José E. Valdez-Rodríguez, Hiram Calvo

“…Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. …”
Get full text

Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka by Amila T. Peiris, Jeevani Jayasinghe, Upaka Rathnayake

“…The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. …”
Get full text

Deep Reinforcement Learning in Multi-UAV Air Combat Maneuver Decision-Making: A Review of Key Techniques in Practice and Future Prospects by Fang Siyu, Zhang Zhenhua, Niu Jinglong, Bian Jiang, Xu Changyi, Wu Yuhu

“…Aimed at providing practical optimization suggestions or basic entry-level guidance for researchers in this field, this paper focuses on the key technologies involved in multi-UAV air combat from a practical perspective, including improvements in deep reinforcement learning algorithms, the design of efficient training methods, and the construction of multi-UAV combat environments. …”
Get full text

Identification and Validation of an Explainable Prediction Model of Sepsis in Patients With Intracerebral Hemorrhage: Multicenter Retrospective Study by Xianglin Liu, Zhihua Huang, Yizhi Guo, Yandeng Li, Jianming Zhu, Jun Wen, Yunchun Gao, Jianyi Liu

“…Nine ML algorithms were used to construct the prediction models. …”
Get full text

Soft-sensor modeling of silicon content in hot metal based on sparse robust LS-SVR and multi-objective optimization by GUO Dong-wei, ZHOU Ping

“…Last, the multi-objective evaluation index that synthesizes the modeling residue and the estimated trend was presented to compensate for the deficiency of the single root mean square error (RMSE) index. …”
Get full text

Application of artificial neural networks in the prediction of sugarcane juice Pol by Anderson P. Coelho, João V. T. Bettiol, Alexandre B. Dalri, João A. Fischer Filho, Rogério T. de Faria, Luiz F. Palaretti

“…Before calibration, the variables were normalized. The training algorithm used was backpropagation and the activation function was the sigmoid. …”
Get full text

Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…To improve model performance, hyperparameters were optimized using the bio-inspired Barnacles Mating Optimizer (BMO) algorithm. Model evaluation based on R2, root mean square error (RMSE), and mean absolute error (MAE) demonstrated that RBF-SVM outperformed the other models, achieving an R2 of 0.9537, RMSE of 3.5136, and MAE of 1.5326. …”
Get full text

A novel oversampling method based on Wasserstein CGAN for imbalanced classification by Hongfang Zhou, Heng Pan, Kangyun Zheng, Zongling Wu, Qingyu Xiang

“…Experimental results on multiple public datasets show that the proposed method achieves significant improvements in evaluation metrics such as Recall, F1_score, G-mean, and AUC.…”
Get full text