Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

“…Prognostic features were selected through univariate logistic regression and Lasso analyses. Predictive modeling was performed using advanced machine learning algorithms, including XGBoost, Multilayer Perceptron, K-Nearest Neighbor, and Random Forest. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…The study evaluates various precision techniques, including micro-irrigation systems, variable rate technology (VRT), and predictive modeling, along with their implementation in diverse agricultural settings. …”
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Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system. by Robert W Mathes, Ramona Lall, Alison Levin-Rector, Jessica Sell, Marc Paladini, Kevin J Konty, Don Olson, Don Weiss

“…We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. …”
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Future Outdoor Safety Monitoring: Integrating Human Activity Recognition with the Internet of Physical–Virtual Things by Yu Chen, Jia Li, Erik Blasch, Qian Qu

“…Advanced HAR–IoPVT algorithms and predictive analytics would identify potential hazards, enabling timely interventions and reducing accidents. …”
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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…Immune cell infiltration patterns were quantified via single-sample gene set enrichment analysis (ssGSEA). A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313).ResultsA total of 271 DEGs were identified, primarily enriched in multiple biological pathways. …”
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Exploring Gender Differences in Fatwa through Machine Learning by Emad Mohamed, Raheem Sarwar

“…Our methodology involves comprehensive experimental analysis, utilizing advanced Arabic text preprocessing alongside machine learning algorithms. We concentrate on two primary objectives: predicting the gender of the questioner and forecasting the popularity of answers. …”
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Interrelationships Between Inflammatory Score, Delayed Cerebral Ischemia and Unfavorable Outcome in Patients with aSAH: A Four-Way Decomposition by Zhang P, Zhu H, Li X, Qian Y, Zhu Y, Zhang W, Yan Z, Ni H, Lin Z, Lin X, Li Z, Zhuge Q, Zeng B

“…Adding the inflammatory score to the predictive model improved the AUC from 0.772 to 0.822, with an NRI of 5.3% and IDI of 6.9%.Conclusion: The inflammatory score was significantly associated with DCI and poor outcome in patients with aSAH. …”
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Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik, Hossein Bonakdari

“…The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Additionally, the PICRUSt2 algorithm was used to predict the microbial functions. …”
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Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens by Tae-Yeong Kwak, Chan Ho Lee, Won Young Park, Ja Yoon Ku, Chang Wook Jeong, Eu Chang Hwang, Seock Hwan Choi, Joonyoung Cho, Hyeyoon Chang, Kyung Hwan Kim, Byeong Jin Kang, Sun Woo Kim, Hong Koo Ha

“…We also evaluated their prognostic performance in predicting biochemical progression-free survival (BPFS). …”
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Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer by Qiang Luo, Qiang Luo, Tingting Jiang, Tingting Jiang, Dacheng Xie, Xiaojia Li, Xiaojia Li, Keping Xie, Keping Xie

“…This study aimed to identify prognostic biomarkers, develop a predictive model, and uncover novel targets for personalized therapy.MethodsInnate immune cell barrier-related genes were curated from KEGG, ImmPort, MSigDB, and InnateDB. …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…Immediate action is necessary for timely diagnosis of the ailment before irreversible damage is done.MethodsThe work aims to evaluate some of the traditional and prominent machine learning algorithms, namely, Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Gaussian Naïve Bayes, Decision Tree, Random Forest, AdaBoost, Extreme Gradient Boosting, and Light GBM for diagnosing and predicting chronic liver disease. …”
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Interpretable machine learning approaches to assess the compressive strength of metakaolin blended sustainable cement mortar by Naseer Muhammad Khan, Liqiang Ma, Waleed Bin Inqiad, Muhammad Saud Khan, Imtiaz Iqbal, Muhammad Zaka Emad, Saad S. Alarifi

“…Thus, this study was conducted to develop reliable empirical prediction models to assess CS of MK-based mortar from its mixture proportion using machine learning algorithms like gene expression programming (GEP), extreme gradient boosting (XGB), multi expression programming (MEP), bagging regressor (BR), and AdaBoost etc. …”
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Neuro-fuzzy inference system and white shark optimization of coagulation-flocculation of aquaculture wastewater treatment by A.F. Mohamed, H. Rezk

“…The R-squared values for training and testing are 1.0 and 0.82, respectively, and adaptive neuro-fuzzy inference system reduced the root mean square error from 6.8 with analysis of variance to 1.135 with adaptive neuro-fuzzy inference system achieving an 83.5 percent reduction. The R-squared value for prediction rose 7.2 percent from 0.765 via analysis of variance to 0.82 via adaptive neuro-fuzzy inference system.CONCLUSION:  Adaptive neuro-fuzzy inference system succseded to present an accurate model of  wastewater treatment process  and white shark optimizer defined accutrelly the values of chitosan dose, pontential of hydrogen, and settling time to reduce turbidity and salinity. …”
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Research progress on theory and simulation of supercritical CO2 pipeline transportation by Meng LI, Chaofei NIE, Xin OUYANG, Zhenning FAN, Haining LIANG, Jianwu LIU, Zhigang XU, Yan ZHANG

“…Conventional simulation techniques for oil and gas pipelines often involve approximate treatments in model selection and solving methods, making it challenging to ensure prediction accuracy for supercritical CO2 pipelines. …”
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A systematic review of machine learning approaches in cochlear implant outcomes by Anu Prasad Sreenivasan Nair, Srikanta K. Mishra, Pedro Andres Alba Diaz

“…Imaging-based studies demonstrated high predictive accuracy for language and speech perception outcomes. …”
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Explainable artificial intelligence for sustainable urban water systems engineering by Shofia Saghya Infant, Sundaram Vickram, A Saravanan, C M Mathan Muthu, Devarajan Yuarajan

“…Presenting cutting-edge approaches in XAI (such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and counterfactual analysis), this review defines the evolution of explainability approaches specifically for hydrological modelling, demand prediction, and leak detection. As an example, the SHAP values have quantified the impact of meteorological variables on urban runoff models, resulting in a 15 % increase in the prediction accuracy. …”
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Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks by Hanjin Kim, Young-Jin Kim, Won-Tae Kim

“…The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. …”
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Artificial intelligence - finite element method - hybrids for efficient nonlinear analysis of concrete structures by Michael A. Kraus, Rafael Bischof, Walter Kaufmann, Karel Thoma

“…We successfully calibrated and evaluated k-nearest-neighbour, LGBM and ResNet algorithms and report their predictive capabilities. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

“…The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. …”
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