Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2 by Xingyu Zhang, Hairong Wang, Guan Yu, Wenbin Zhang

“…This study aimed to predict hospital admissions by integrating both structured clinical data and unstructured text data using machine learning models. Methods Data were obtained from the 2021 National Hospital Ambulatory Medical Care Survey—Emergency Department (NHAMCS-ED), including adult patients aged 18 years and older. …”
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Machine Learning for Accurate Office Room Occupancy Detection Using Multi-Sensor Data by Yusuf Ibrahim, Umar Yusuf Bagaye, Abubakar Ibrahim Muhammad

“…Also, hyperparameter optimization was performed for selected models with a view to improving classification accuracy. …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…The fairness metric suggests models perform well in terms of equality. There is a sizeable risk of false negatives (predicting longer intervals than optimal), which requires attention.…”
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Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting by Mustapha Ez-zaiym, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, Vasile Palade

“…This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. …”
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Advancements in one-dimensional protein structure prediction using machine learning and deep learning by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…This review highlights the evolution of predictive methodologies, from early machine learning models to sophisticated deep learning frameworks that integrate sequence embeddings and pretrained language models. …”
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Study on Predicting Blueberry Hardness from Images for Adjusting Mechanical Gripper Force by Hao Yin, Wenxin Li, Han Wang, Yuhuan Li, Jiang Liu, Baogang Li

“…The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. …”
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Combining semiparametric and machine learning approaches for short-term prediction of satellite clock bias by Lihong Jin, Wanzhuo Zhao, Xiong Pan, Qingsong Ai, Mao Cai, Xiaoli Ruan

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Life’s Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions by Jianxin Xi, Yuguo Chen, Chen Jie, Jason Chi Shing Law, Zhongqi Fan, Guoyue Lv

“…Restricted cubic spline (RCS) analysis was conducted to explore dose-response relationships, and Kaplan-Meier survival curves were utilized to examine differences in survival outcomes. Machine learning (ML) approaches were employed to construct predictive models, with the optimal model further interpreted using SHapley Additive exPlanations (SHAP). …”
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A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments by Adi Shuchami, Teddy Lazebnik, Shai Ashkenazi, Avner Herman Cohen, Yael Reichenberg, Vered Shkalim Zemer

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Gravitational search algorithm‐extreme learning machine for COVID‐19 active cases forecasting by Boyu Huang, Youyi Song, Zhihan Cui, Haowen Dou, Dazhi Jiang, Teng Zhou, Jing Qin

“…The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. …”
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Advancements in artificial intelligence and machine learning for poultry farming: Applications, challenges, and future prospects by Muhammad Halim Natsir, Wayan Firdaus Mahmudy, Mochamad Tono, Yuli Frita Nuningtyas

“…Rapid advancements in Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) have transformed modern poultry farming by enabling precision management and real-time decision-making. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…We performed comprehensive data cleaning and preprocessing, then applied tenfold cross-validation with randomized search to the training set to identify optimal hyperparameters for various machine learning models. …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“… BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA). …”
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Air pollution and prostate cancer: Unraveling the connection through network toxicology and machine learning by Yuqi Li, Tao Zhou, Zhiyu Liu, Xinyao Zhu, Qilong Wu, Chunyang Meng, Qingfu Deng

“…GO and KEGG functional enrichment analyses revealed that these targets are primarily involved in regulating biological processes such as apoptosis, carcinogenesis, and cell proliferation. Based on machine learning algorithm selection, the combination of RSF and Lasso regression was identified as the optimal predictive model, which highlighted five key genes associated with air pollutants and PCa. …”
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Innovative Design and Optimization of High-Quality Peanut Digging-Inverter by Haiyang Shen, Man Gu, Hongguang Yang, Jie Ling, Feng Wu, Fengwei Gu, Liang Pan, Zhaoyang Yu, Zhichao Hu

“…Based on a two-stage peanut harvesting model, the operating principles of a high-quality peanut digging-inverter are elaborated upon, and the design of key machine components is discussed. …”
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Self-attention Deep Field-embedded Factorization Machine for Click-through Rate Prediction by Guangli LI, Yiyuan YE, Guangxin XU, Hongbin ZHANG, Guangting WU, Jingqin LYU

“…As a result, the performance of the models is heavily restricted. To alleviate these issues, a novel CTR model named self-attention deep field-embedded factorization machine (Self-AtDFEFM) is proposed. …”
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Analysis and experiment of entanglement relationship between straw and rotating rollers of the grass curtain machine by Dong Wang, Dian Wang, Hongrui Liu

“…In order to clarify the entanglement relationship between the grass curtain machine’s rotating rollers and the straw, and to optimize the structure of the rotating roller to reduce entanglement. …”
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Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes by Carlos Toro, Walter Díaz, Gonzalo Reyes, Miguel Peña, Nicolás Caselli, Carla Taramasco, Pablo Ormeño-Arriagada, Eduardo Balladares

“…A one-dimensional convolutional neural network (1D-CNN) was developed and trained on experimentally acquired spectral data, achieving a balanced accuracy score of 99.0% in a test set. The optimized deep learning model outperformed conventional machine learning methods, highlighting the effectiveness of deep learning for spectral analysis in high-temperature environments. …”
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