Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction by Lise M. Bjerre, Cayden Peixoto, Rawan Alkurd, Robert Talarico, Rami Abielmona

“…Conclusions: Conventional multivariate regression-based models are better than some and worse than other machine learning algorithms to provide good predictive accuracy in a moderate dataset with a reasonable number of features. …”
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Prediction models for sleep quality among frontline medical personnel during the COVID-19 pandemic: cross-sectional study based on internet new media by Shangbin Huang, Qingquan Chen, Qingquan Chen, Shengxun Qiu, Rongrong Dai, Ling Yao, Jiajing Zhuang, Zhijie Wu, Yifu Zeng, Jimin Fan, Yixiang Zhang

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Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value... by Jie Zhou

“…We developed Support Vector Machine (SVM) and Logistic Regression (LR) models, with the LR model demonstrating a more robust predictive capability. …”
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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Fractal/multifractal models are powerful tools that have been widely used to detect geochemical anomalies. …”
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Machine learning approaches for imputing missing meteorological data in Senegal by Mory Toure, Nana Ama Browne Klutse, Mamadou Adama Sarr, Md Abul Ehsan Bhuiyan, Annine Duclaire Kenne, Wassila Mamadou Thiaw, Daouda Badiane, Amadou Thierno Gaye, Ousmane Ndiaye, Cheikh Mbow

“…Furthermore, the exclusion of satellite or reanalysis inputs may constrain model generalizability.Ultimately, this study reinforces the role of advanced machine learning methods in improving climate data completeness and reliability in Africa. …”
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Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model by Shaofan Liu, Tianbao Xie, Yanxin Li, Siyu Liu

“…Then, the CNN-LSTM module directly processes raw multivariate time-series data, combining the capability of CNN in extracting local spatial patterns with the LSTM strength in modeling temporal dependencies, enabling accurate identification of battery faults. …”
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Experimental investigation and predictive modelling of CO2 laser processing of wood-plastic composites (WPCs) by Sharizal Ahmad Sobri, Teoh Ping Chow, Tan Koon Tatt, Mohd Hisham Nordin, Andi Hermawan, Mohd Natashah Norizan

“…This study evaluates the effects of feed rate and gas pressure during CO2 laser machining on cut depth, heat-affected zone (HAZ), cutting profile, and surface quality of 18 mm-thick WPC samples (60% wood, 40% recycled HDPE). …”
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Monitoring and Prediction of the Real-Time Transient Thermal Mechanical Behaviors of a Motorized Spindle Tool by Tria Mariz Arief, Wei-Zhu Lin, Jui-Pin Hung, Muhamad Aditya Royandi, Yu-Jhang Chen

“…The spindle is a critical component that significantly influences the performance of machine tools. In motorized spindles, heat generation from both the bearings and built-in motor leads to thermal deformation of structural components, which, in turn, affects machining accuracy. …”
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Large dataset on Fourier transform near infrared (FT-NIR) spectroscopy of green and roasted specialty coffee: Preprocessed infrared spectra and sensory scores for machine learning-... by Andrés F. Bahamón-Monje, Ever M. Morales-Angulo, Gentil A. Collazos-Escobar, Nelson Gutiérrez-Guzmán

“…This approach was designed to establish a robust dataset for calibrating machine learning-based predictive models of coffee cup quality. …”
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Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients by Roqia Saleem Awad Maabreh, Malik Bader Alazzam, Ahmed S. AlGhamdi

“…We used the Cox statistical method in conjunction with other statistical methods and tests to find the best possible dataset with which to train our model, despite its ease of multivariate analysis.…”
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Identifying ovarian cancer with machine learning DNA methylation pattern analysis by Jesus Gonzalez Bosquet, Vincent M. Wagner, Douglas Russo, Henry D. Reyes, Andreea M. Newtson, David P. Bender, Michael J. Goodheart

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Stock Price Pattern Prediction Based on Complex Network and Machine Learning by Hongduo Cao, Tiantian Lin, Ying Li, Hanyu Zhang

“…The results show that the optimal models corresponding to the two algorithms can be found through cross-validation and search methods, respectively. …”
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Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery by Zhao-Kui Li, Hong-Li Li, Xue-Wei Gong, Heng-Fang Wang, Guang-You Hao

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Racial and socioeconomic disparities in long term survival after surgery and radiation for spinal cord hemangioblastoma by Abdul Karim Ghaith, Xinlan Yang, Joshua Weinberg, Shaan Bhandarkar, Taha Khalilullah, Abdel-Hameed Al-Mistarehi, Khaled Zaitoun, Meghana Bhimreddy, Arjun K. Menta, Antony Fuleihan, Kamel Chebaro, Jawad Khalifeh, Andrew Grossbach, Nicholas Theodore, Daniel Lubelski

“…Additionally, we explore the utility of machine learning–based survival models to improve individualized risk prediction and to identify key clinical and sociodemographic determinants of long-term survival. …”
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Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection by Qiyi Chen, Yulin Wang, Yixiao Zhang, Fangyu Liu, Kejie Shao, Hao Lai, Chunsheng Wang, Qiang Ji

“…Utilizing data from 925 ATAAD patients undergoing EAR, we employed multivariable logistic regression and machine learning techniques, respectively, to develop nomograms for in-hospital mortality. …”
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The nursing process and total health cost variability: an analysis using machine learning by Maria Consuelo Company-Sancho, Victor M. González-Chordá, Maria Isabel Orts-Cortés

“…A bivariate analysis and a multiple regression were performed for the multivariate analysis. To improve prediction accuracy and account for non-linear relationships, the analysis was completed using two machine learning models. …”
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Machine learning-based ultrasound radiomics for predicting risk of recurrence in breast cancer by Wei Fan, Hao Cui, Xiaoxue Liu, Xudong Zhang, Xinran Fang, Junjia Wang, Zihao Qin, Xiuhua Yang, Jiawei Tian, Lei Zhang

“…Subsequently, radiomics models were constructed with eight machine learning algorithms. …”
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The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review by Wilhelm Grzesiak, Daniel Zaborski, Marcin Pluciński, Magdalena Jędrzejczak-Silicka, Renata Pilarczyk, Piotr Sablik

“…A description of ML methods (linear and logistic regression, classification and regression trees, chi-squared automatic interaction detection, random forest, AdaBoost, support vector machines, k-nearest neighbors, naive Bayes classifier, multivariate adaptive regression splines, artificial neural networks, including deep neural networks and convolutional neural networks, as well as Gaussian mixture models and cluster analysis), with some examples of their application in various aspects of dairy cattle breeding and husbandry, is provided. …”
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Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…However, traditional machine learning methods and standalone deep learning architectures struggle to balance spatiotemporal feature extraction, computational efficiency, and deployment constraints for high-frequency multivariate metering data. …”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Additionally, we use meteorological data from a set of sensors installed at a meteorological station located in Belém to train multivariate statistical and machine learning (ML) models to predict precipitation. …”
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