A BERT-based rice enhancer identification model combined with sequence-representation differential entropy interpretation by Yajing Pu, Xintong Hao, Zhaoqi Zheng, Huiyan Ma, Zhibin Lv

“…The mechanism underlying the optimization of model performance is elucidated through differential entropy analysis of feature representations. …”
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Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target by Gu-Wei Ji, Zheng-Gang Xu, Shuo-Chen Liu, Shu-Ya Cao, Chen-Yu Jiao, Ming Lu, Biao Zhang, Yue Yang, Qing Xu, Xiao-Feng Wu, Ke Wang, Yong-Xiang Xia, Xiang-Cheng Li, Xue-Hao Wang

“…We highlighted functional IRS-immune interactions within tumor epithelium, rather than stromal compartment, irrespective of geospatial locations. Machine learning pipeline identified the optimal 3-feature radiotranscriptomic signature that was well-validated by immunohistochemical assays in molecular cohort, exhibited favorable external prognostic validity with C-index over 0.64 in resection cohort, and predicted treatment response with an area under the curve of up to 0.84 in immunochemotherapy cohort. …”
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Optimization of classifier ensemble diversity by Leif E. Peterson

“… Ensemble diversity was investigated for 12 classifiers and 16 datasets using a generalization error and ambiguity decomposition of the model bias-variance relationship. Diversity was optimized using a genetic algorithm and particle swarm optimization. …”
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AI-Based Prediction of Visual Performance in Rhythmic Gymnasts Using Eye-Tracking Data and Decision Tree Models by Ricardo Bernardez-Vilaboa, F. Javier Povedano-Montero, José Ramon Trillo, Alicia Ruiz-Pomeda, Gema Martínez-Florentín, Juan E. Cedrún-Sánchez

“…These findings support the integration of machine learning in sports vision screening and suggest that predictive modeling can inform individualized training and performance optimization in visually demanding sports such as rhythmic gymnastics.…”
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Development and validation of a multiple myeloma diagnostic model based on systemic lupus erythematosus-associated genes and identification of specific genes by Yuepei Liu, Songshan Liu

“…To establish a diagnostic model for SLE-related MM, we compared 113 combinations of 12 machine learning algorithms, ultimately determining the optimal model. …”
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A Mortality Risk Prediction Model for Septic Shock in Patients Aged ≥50: Role of Norepinephrine Index and Procalcitonin by Li XL, Mi T, Liu C, Feng M

“…Future multicenter studies with larger sample sizes are necessary to validate this model’s applicability across populations. This model holds potential to optimize clinical management, enabling timely interventions such as more intensive hemodynamic support and infection control.Keywords: septic shock, mortality prediction, norepinephrine index, procalcitonin, machine learning, risk stratification…”
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Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics by Zhenze Zhang, Zhenze Zhang, Caiqing Zhao, Caiqing Zhao, Yijun Zhou, Ling Yao, Peng Liu, Ziling Fang, Nian Fang, Nian Fang

“…The cohort was split into training (75%) and internal testing (25%) sets, with hyperparameter optimization via 5-fold cross-validation. Model performance was evaluated using AUC, sensitivity, specificity, and calibration curves. …”
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A Holistic Review of the TinyML Stack for Predictive Maintenance by Emil Njor, Mohammad Amin Hasanpour, Jan Madsen, Xenofon Fafoutis

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MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics by Hudhaifa Mohammed Abdulwahab, S. Ajitha, Mufeed Ahmed Naji Saif, Belal Abdullah Hezam Murshed, Fahd A. Ghanem

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Intelligent Selection of Machining Parameters in Multipass Turnings Using Firefly Algorithm by Abderrahim Belloufi, Mekki Assas, Imane Rezgui

“…In this paper, a new optimization technique, firefly algorithm, is used for determining the machining parameters in a multipass turning operation model. …”
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Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…These findings emphasize the value of integrating well-calibrated crop models with climate projections and machine learning tools to support climate-resilient agricultural planning. …”
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Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks by Shamma Alshebli, Hyeran Mun, Deepak Puthal, Mohamed Jamal Zemerly, Luigi Martino, Ernesto Damiani, Chan Yeob Yeun

“…To tackle these issues and enhance the performance of machine learning (ML) and DL detection models, we propose novel detection models based on a generative adversarial network (GAN). …”
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A novel approach to spectral moisture interference correction for nitrogen and soil organic matter inversion in native black soils: Bayesian-optimized dynamic moisture mitigation by Jiaze Tang, Qisong Wang, Dan Liu, Junbao Li, Ruifeng Zhang, Meiyan Zhang, Jinwei Sun

“…To further assess the effectiveness of the BO-DMM method, we integrated it with different machine learning models to test soil properties and predict the TN and SOM contents. …”
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Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review by Duygu Topaloğlu, Olcay Polat

“…Bayesian optimization approaches were used to increase model coherence and diversity. …”
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Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation by Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang

“…A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. …”
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Secure and Trustworthy Open Radio Access Network (O-RAN) Optimization: A Zero-Trust and Federated Learning Framework for 6G Networks by Mohammed El-Hajj

“…We first establish a novel threat model targeting O-RAN’s optimization processes, highlighting risks such as adversarial Machine Learning (ML) attacks on resource allocation models and compromised third-party applications. …”
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Design of an Efficient Deep Learning-Based Diagnostic Model for Wind Turbine Gearboxes Using SCADA Data by Xuan-Kien Mai, Jun-Yeop Lee, Jae-In Lee, Byeong-Soo Go, Seok-Ju Lee, Minh-Chau Dinh

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Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

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The Estimation of the Remaining Useful Life of Ceramic Plates Used in Iron Ore Filtration Through a Reliability Model and Machine Learning Methods Applied to Industrial Process Var... by Robert Bento Florentino, Luiz Gustavo Lourenço Moura

“…Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

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