A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga

“…To improve the performance of the model, we incorporate feature selection techniques and optimization strategies to enhance reliability. …”
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods by Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao

“…The performance metrics used to evaluate the models including R 2 , root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to screen the optimal model. …”
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Application of Fuzzy-RBF-CNN Ensemble Model for Short-Term Load Forecasting by Mohini Yadav, Majid Jamil, Mohammad Rizwan, Richa Kapoor

“…Its results are validated while comparing with four benchmark models like multiple linear regression (MLR), support vector machine (SVM), ML-SVM, and fuzzy-RBFNN in terms of accuracy. …”
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Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint by Alexandre Vallée

“…By leveraging data from genomics, proteomics, imaging, sociodemographics, and real-world behaviors, DTs provide a computational framework to model disease progression, optimize treatments, and personalize health care interventions. …”
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Designing diverse and high-performance proteins with a large language model in the loop. by Carlos A Gomez-Uribe, Japheth Gado, Meiirbek Islamov

“…We present a protein engineering approach to directed evolution with machine learning that integrates a new semi-supervised neural network fitness prediction model, Seq2Fitness, and an innovative optimization algorithm, biphasic annealing for diverse and adaptive sequence sampling (BADASS) to design sequences. …”
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Predicting transient response using data-driven models for ball-impact simulations by Ross Pivovar, Fei Chen, Raghunath Katragadda, Vidyasagar Ananthan

“…This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. …”
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Innovative approaches in QSPR modelling using topological indices for the development of cancer treatments. by Xiaolong Shi, Saeed Kosari, Masoud Ghods, Negar Kheirkhahan

“…In addition to the linear regression model, which performed the best, two other machine learning models, namely SVR and Random Forest, were also used for further analysis and comparison of their performance in predicting the physicochemical properties of drugs, to assess the advantages and disadvantages of each model.…”
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The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency... by Mehmet Kivrak, Ugur Avci, Hakki Uzun, Cuneyt Ardic

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Exploring the effect of the triglyceride-glucose index on bone metabolism in prepubertal children, a retrospective study: insights from traditional methods and machine-learning-bas... by Shunshun Cao, Aolei Chen, Botian Song, Yangyang Hu

“…The categorical boosting (CatBoost) models selected based on optimal performance metrics were interpreted using SHapley Additive exPlanation (SHAP) analysis to identify key features affecting prediction. …”
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Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model by Jin Zhu, Shuowen Yang, Shuyan Li, Nan Zhou, Yi Shen, Jincheng Xing, Lixin Xu, Zhichao Hong, Yifei Yang

“…The CIWOABP model achieved superior validation accuracy (R² = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. …”
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An interpretable and stacking ensemble model for predicting heat and mass transfer of desiccant wheel by Mengyang Li, Liu Chen

“…Each base model uses Bayesian optimization for hyperparameter tuning. …”
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Improving breast cancer survival analysis through competition-based multidimensional modeling. by Erhan Bilal, Janusz Dutkowski, Justin Guinney, In Sock Jang, Benjamin A Logsdon, Gaurav Pandey, Benjamin A Sauerwine, Yishai Shimoni, Hans Kristian Moen Vollan, Brigham H Mecham, Oscar M Rueda, Jorg Tost, Christina Curtis, Mariano J Alvarez, Vessela N Kristensen, Samuel Aparicio, Anne-Lise Børresen-Dale, Carlos Caldas, Andrea Califano, Stephen H Friend, Trey Ideker, Eric E Schadt, Gustavo A Stolovitzky, Adam A Margolin

“…This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.…”
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Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study by Shengyu Liu, Anran Wang, Xiaolei Xiu, Ming Zhong, Sizhu Wu

“…These gaps hinder the development of optimized NER models for medical applications. …”
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Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images by Karthikeyan Shanmugam, Harikumar Rajaguru

“…Future work may explore further optimizations and hybrid models.…”
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Rockburst Prediction Based on the KPCA-APSO-SVM Model and Its Engineering Application by Yuefeng Li, Chao Wang, Jiankun Xu, Zonghong Zhou, Jianhui Xu, Jianwei Cheng

“…Based on the kernel principal component analysis (KPCA), the adaptive particle swarm optimization (APSO) algorithm, and the support vector machine (SVM), the KPCA-APSO-SVM model was established. …”
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A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors: A national cross-sectional and cohort stud... by Xingjian Xiao, Xiaohan Yi, Zumin Shi, Zongyuan Ge, Hualing Song, Hailei Zhao, Tiantian Liang, Xinming Yang, Suxian Liu, Bo Sun, Xianglong Xu

“…Conclusions We developed MyGutRisk, built on optimal machine learning models, relatively accurately predicts gastric ulcer risk in elderly adults using noninvasive factors like diet and lifestyle. …”
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急性卒中院前诊断识别研究进展(Research Progress in Prehospital Diagnosis and Identification of Acute Stroke) by 王荣，何松，岗瑞娟，王琪，唐宇杰，刘飞凤，杨杰，李刚，林亚鹏(WANG Rong, HE Song, GANG Ruijuan, WANG Qi, TANG Yujie, LIU Feifeng, YANG Jie, LI Gang, LIN Yapeng)

“…Currently, prehospital stroke prediction tools mainly include three categories: traditional scales, machine learning models, and biomarkers, each with distinct characteristics but significant limitations. …”
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A bearing fault diagnosis method based on hybrid artificial intelligence models. by Lijie Sun, Xin Tao, Yanping Lu

“…Addressing the issue of that the difficulty of incipient weak signals feature extraction influences the rolling bearing diagnosis accuracy, an efficient bearing fault diagnostic technique, a proposition is forwarded for hybrid artificial intelligence models, which integrates Improved Harris Hawks Optimization (IHHO) into the optimization of Deep Belief Networks and Extreme Learning Machines (DBN-ELM). …”
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Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network. by Yeping Shi, Yunbo Shi, Haodong Niu, Jinzhou Liu, Pengjiao Sun

“…In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. …”
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Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier by Ali Saeed Almuflih, Ilyos Abdullayev, Sergey Bakhvalov, Rustem Shichiyakh, Bibhuti Bhusan Dash, K. B. V. Brahma Rao, Kritika Bansal

“…However, the attacks were unidentified, for IDS still signifies tasks and concerns about consumers’ data privacy and safety. Anomaly-detection models are generally based on machine learning (ML) models. …”
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