Discrimination of Chinese prickly ash origin place using electronic nose system and feature extraction with support vector boosting machine by Junbo Lian, Peng Wu, Wenhui Han, Yaping Xie, Yue Zheng, Yuxuan Xu, Xinlin Li, Guofeng Hou, Chengxiang Yong, Qi Lv, Qiansheng Ye, Guohua Hui

“…These novel techniques were coupled with a support vector boosting machine for origin place classification. The hyperparameters of the model were optimized using the Harris Hawk optimization algorithm. …”
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Optimization and prediction of corporate credit rating through advanced feature selection based on AI and deep learning by Jumanah Ahmed Darwish

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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

“…Abstract This study explores diabetic foot (DF), a severe complication in diabetes, by combining deep learning (DL) and machine learning (ML) to develop a multi-model prediction tool. …”
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Comprehensive Feature-Driven PCOS Predictor: A Reinforcement Learning-Based Binary Equilibrium Optimization Approach by S. Reka, T. Suriya Praba, Krishna Kumar Manchala, Anna Venkateswarlu

“…In these situations, a (ML) Machine Learning-based PCOS prediction model aids in the diagnostic procedure, addresses time constraints and potential inaccuracies. …”
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Optimizing Stroke Risk Prediction: A Primary Dataset‐Driven Ensemble Classifier With Explainable Artificial Intelligence by Md. Maruf Hossain, Md. Mahfuz Ahmed, Md. Rakibul Hasan Rakib, Mohammad Osama Zia, Rakib Hasan, Md. Rakibul Islam, Md. Shohidul Islam, Md Shahariar Alam, Md. Khairul Islam

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Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study by Anjana Eledath Kolasseri, Venkataramana B.

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Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results by Zhechuan Jin, Jiale Dong, Jian Yang, Chengxiang Li, Zequan Li, Zhaofei Ye, Yuyu Li, Ping Li, Yulin Li, Zhili Ji

“…Conclusions: This study employed machine learning algorithms to develop a model capable of identifying MMP risk based on initial preoperative laboratory test results. …”
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Physics-Informed Neural Networks for the Reynolds Equation with Transient Cavitation Modeling by Faras Brumand-Poor, Florian Barlog, Nils Plückhahn, Matteo Thebelt, Niklas Bauer, Katharina Schmitz

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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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Self-organizing maps to evaluate optimal strategies for balancing binary class distributions: a methodological approach by Alberto Nogales, Diego Guadalupe, Álvaro J. García-Tejedor

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Towards sustainable construction: estimating compressive strength of waste foundry sand-blended green concrete using a hybrid machine learning approach by Hoang Nhat-Duc, Nguyen Quoc-Lam

“…This dataset is used to train and verify a hybrid machine learning method, which is an integration of Light Gradient Boosting Machine (LightGBM) and Biogeography-Based Optimization (BBO). …”
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Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing by Monique S. McClain, Dhrubajit Chowdhury, Omar Eldaghar, Kris Villez

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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence by Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad

“…To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. …”
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A review of physics-informed and data-driven approaches for manufacturing process optimization in polymer matrix composites by Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko

“…Machine learning approaches that integrate physical laws with data-driven models are transforming process optimization and quality assurance in polymer matrix composite manufacturing. …”
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Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

“…Results Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. …”
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Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study by Yi Liu, Yehua Xu, Lixia Guo, Zhongbin Chen, Xueqin Xia, Feng Chen, Li Tang, Hua Jiang, Caixia Xie

“…This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24 h of intensive care unit (ICU) admission, culminating in a web-based malnutrition prediction tool for clinical decision support. …”
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Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction by Qi Fei, Guisheng Yin, Zhian Sun

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The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network by Qi Yang, Dequan Zhang, Chongxin Liu, Le Xu, Shaobo Li, Xiaochun Zheng, Li Chen

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Intelligent text similarity assessment using Roberta with integrated chaotic perturbation optimization techniques by Esraa Hassan, Amira Samy Talaat, M. A. Elsabagh

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Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer’s disease diagnosis by Qian Zhang, Jinhua Sheng, Rougang Zhou, Qiao Zhang, Binbing Wang, Rong Zhang

“…In this paper, an enhanced Particle Swarm Optimization (PSO) algorithm, which integrates opposition-based Latin squares sampling initialization (OL) with dynamic inertia weights and learning factors (D), termed OLDPSO, is proposed to improve feature selection and classification within a Support Vector Machine (SVM) model for AD diagnosis using magnetic resonance imaging (MRI) data. …”
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