Dynamic Aggregation and Augmentation for Low-Resource Machine Translation Using Federated Fine-Tuning of Pretrained Transformer Models by Emmanuel Agyei, Xiaoling Zhang, Ama Bonuah Quaye, Victor Adeyi Odeh, Joseph Roger Arhin

“…A thorough evaluation of translation quality is provided by the use of SPBLEU, ROUGE (ROUGE-1, ROUGE-2, and ROUGE-L) measures, and Word Error Rate (WER) metrics. A pretrained mT5 model is used to set baseline performance, which acts as a standard for the optimized model. …”
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Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis by XinPei Liu, SanXi Ai, RuiMing Yu, ChaoJi Zhang, Qi Miao

“…The 15 most critical features were valve replacement, pre-operative hypertension, large vegetations, NYHA class, alcoholism, age, post-operative low cardiac output syndrome, TyG index, pre-operative creatinine clearance, cardiopulmonary bypass duration, intra-operative red blood cell transfusion, intra-operative urine output, pre-operative hemoglobin levels, and timing of surgery.ConclusionCompared to standard cardiac surgery, AKI occurs more frequently and with a more complex etiology in surgeries for active infective endocarditis. Machine learning models enable early prediction of post-surgical AKI, facilitating targeted perioperative optimization and risk stratification in this distinct patient group.…”
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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

“…This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…The statistical approach combined with machine learning modeling was employed to systematically analyze and predict key performance indicators. …”
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Modeling of landslides susceptibility prediction using deep belief networks with optimized learning rate control by Qiang Liu, Namkha Norbu

“…To overcome critical issues in landslide susceptibility modeling, a multifactor landslide susceptibility prediction model based on deep belief networks (DBN) with optimized learning rate control (LRC-DBN) was introduced in this study. …”
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Machine learning applications for chloride ingress prediction in concrete: insights from recent literature by Quynh-Chau Truong, Anh-Thu Nguyen Vu

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Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures by Valerio Zippo, Elisa Robotti, Daniele Maestri, Pietro Fossati, David Valenza, Stefano Maggi, Gennaro Papallo, Masho Hilawie Belay, Simone Cerruti, Giorgio Porcu, Emilio Marengo

“…The integration of this model into the production process allows a large-scale steel industry to predict steel properties with heightened accuracy, optimizing the manufacturing process for minimal waste and improved consistency. …”
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Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine by Liuding Wang, Jingzi Shi, Lina Miao, Yifan Chen, Jingjing Wei, Min Jia, Zhiyi Gong, Ze Yang, Jian Lyu, Jian Lyu, Jian Lyu, Yunling Zhang, Xiao Liang

“…However, future high-performance TCM predictive models require more objective and larger datasets for optimization.…”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. …”
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State of Health Estimation for Lithium-Ion Batteries Using an Explainable XGBoost Model with Parameter Optimization by Zhenghao Xiao, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…Moreover, the proposed method shows superior estimation accuracy and performance compared to existing machine learning models.…”
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Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification by Jameel Saraireh, Mary Agoyi, Sofian Kassaymeh

“…This work created a binary white shark optimizer (WSO) to optimize standard ensemble learning models. …”
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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…The results show that RNN-CNN-LSTM, optimized through OPTUNA algorithms, overcomes conventional machine learning models and achieves an accuracy of 99.4% on unseen test data, supported by stable validation trends and robust predictive performance. …”
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Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model by Maofa Wang, Bingcheng Yan, Yibo Zhang, Lu Zhang, Pengcheng Wang, Jingjing Huang, Weifeng Shan, Haijun Liu, Chengcheng Wang, Yimin Wen

“…Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. …”
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Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm by K. Gopalakrishnan, R. Sivaraj, M. Vijayakumar

“…This method develops an Automated Weed Recognition and Classification using a Deep Learning Model with Lemrus Optimization (AWRC-DLMLO). The main purpose of the AWRC-DLMLO method is to effectively detect and classify weeds and crop. …”
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Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions by José Antonio Hernandez-Torres, Juan P. Torreglosa, Reyes Sanchez-Herrera, Aldo Bischi, Andrea Baccioli

“…Despite the numerous models and equations developed over time, including empirical and machine learning approaches, they often involve trade-offs between accuracy, simplicity, and computational cost. …”
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Mathematical Methods Applied to the Problem of Dairy Cow Replacements: A Scoping Review by Osvaldo Palma, Lluis M. Plà-Aragonés, Alejandro Mac Cawley, Víctor M. Albornoz

“…Future research could address the inclusion in models of diseases and animal characteristics that have not yet been considered, as well as expand the scarce use of machine learning tools and the hybridization of such models with statistical ones.…”
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Progress and perspectives on genomic selection models for crop breeding by Dongfeng Zhang, Feng Yang, Jinlong Li, Zhongqiang Liu, Yanyun Han, Qiusi Zhang, Shouhui Pan, Xiangyu Zhao, Kaiyi Wang

“…We also describe the advantages and disadvantages of traditional models and machine learning models and discuss several crucial factors that could affect prediction accuracy. …”
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Multitask allocation model of the coal foreign object sorting robot based on optimal capacity and benefit by Wu Xudong, Cao Xiangang, Ding Wentao, Wang Peng, Zhang Ye, Liu Jiahui

“…However, the existing coal gangue multi-task allocation methods cannot be used for the coal foreign object with multi kinds, complex features, and spatiotemporal randomness, and it is difficult to solve comprehensively improve coal quality, human–machine safety and environmental pollution. Firstly, this paper constructs a multi-task allocation model of coal foreign object sorting robot (CFoSR) based on optimal capacity and benefit. …”
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A Two-Stage Feature Selection Approach for Fruit Recognition Using Camera Images With Various Machine Learning Classifiers by Tri Tran Minh Huynh, Tuan Minh Le, Long Ton That, Ly Van Tran, Son Vu Truong Dao

“…In this paper, a two-stage model is proposed to recognize fruits using camera images. …”
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Computational modeling of drug-eluting balloons in peripheral artery disease: Mechanisms, optimization, and translational insights by Mohammed A. AboArab, Vassiliki T. Potsika, Dimitrios S. Pleouras, Dimitrios I. Fotiadis

“…CFD-based mass transfer models capture flow-driven drug dispersion and washout dynamics, whereas FEA links balloon mechanics, plaque morphology, and drug penetration efficiency. …”
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