Assessing climate and land use impacts on surface water yield using remote sensing and machine learning by Amanuel Kumsa Bojer, Muluneh Woldetsadik Abshare, Fitsum Mesfin, Ayad M. Fadhil Al-Quraishi

“…Utilizing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) hydrological models, machine learning, and remote sensing techniques, this study assessed variations in water resources and their impacts on basin water yield. …”
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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

“…The suggested method shows notable benefits, according to experimental results. The fine-tuned model achieves a remarkable increase in SPBLEU from 2.16% to 71.30%, a rise in ROUGE-1 from 15.23% to 65.24%, and a notable reduction in WER from 183.16% to 68.32%. …”
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An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques by Ireneusz Czarnowski, Piotr Jędrzejowicz

“…In the paper, several data reduction techniques for machine learning from big datasets are discussed and evaluated. …”
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Genomic Selection in Alfalfa Across Multiple Ploidy Levels: A Comparative Study Using Machine Learning and Bayesian Methods by Xiaoyue Zhu, Ruixin Zhang, Tianxiang Zhang, Changhong Guo, Yongjun Shu

“…A total of 11 Bayesian and machine learning models and nine different reference genomes were used to conduct genomic selection on five traits in 385 alfalfa accessions. …”
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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

“…However, accurate SOC prediction remains a challenging task due to the complex, high-dimensional, and nonlinear nature of soil data. Recent advances in machine learning (ML) have improved SOC estimation, yet these models often suffer from overfitting and computational inefficiency when effective feature selection and hyperparameter tuning are not applied. …”
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Subsystem-Based Fault Detection in Robotics via <italic>L2</italic> Norm and Random Forest Models by Elham Abdullah Alamoudi

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A Stochastic Learning Algorithm for Machine Fault Diagnosis by Zhipeng Dong, Yucheng Liu, Jianshe Kang, Shaohui Zhang

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Effective Machine Learning Solution for State Classification and Productivity Identification: Case of Pneumatic Pressing Machine by Alexandros Kolokas, Panagiotis Mallioris, Michalis Koutsiantzis, Christos Bialas, Dimitrios Bechtsis, Evangelos Diamantis

“…Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). …”
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Machine Learning-Based Predictive Maintenance for Photovoltaic Systems by Ali Al-Humairi, Enmar Khalis, Zuhair A. Al-Hemyari, Peter Jung

“…A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. …”
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Sensing using SERS-substrate and machine learning approaches by Bakal, Victoria Alexandrovna, Gusliakova, Olga Igorevna, Prikhozhdenko, Ekaterina Sergeevna

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Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance by Leonel Patrício, Leonilde Varela, Zilda Silveira

“…This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. …”
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Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact by Supriya Raghav, Santosh Kumar, Hamid Ashraf, Poonam Khanna

“…BackgroundThis study investigated the cost-effectiveness and clinical impact of the 3E model (education, empowerment, and economy) in diabetes management using advanced machine learning techniques.MethodsWe conducted an observational longitudinal descriptive analysis involving 320 patients, who were grouped into intervention and control groups over a 24-month period.ResultsThe 3E model demonstrated significant cost reductions, with the intervention group achieving a 74.3% decrease in total costs compared to 41.8% in the control group while maintaining the same level of glycemic control. …”
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Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model by Yu Xia, Ao Shen, Tianci Che, Wenbo Liu, Jie Kang, Wei Tang

“…In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection model and to enhance its portability for deployment on additional mobile devices. …”
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Zooming into Berlin: tracking street-scale CO2 emissions based on high-resolution traffic modeling using machine learning by Max Anjos, Fred Meier

“…Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. …”
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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…Future research should conduct larger, multi-center, and prospective studies to further validate these findings.Keywords: advanced heart failure, inflammation, machine learning, one - year mortality, risk prediction model…”
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Exergy and energy-based sustainability evaluation of diesel-biodiesel-ethanol blends with emission forecasting using advanced machine learning models by Harish Venu, V. Dhana Raju, Jayashri N. Nair, Sameer Algburi, Ali E. Anqi, Ali A. Rajhi, Mohammed Kareemullah

“…The present study of thermodynamic analysis of ternary fuel with advanced Machine learning model provides valuable insights and adds significant outcomes to existing analysis. …”
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Investigation of comparative machine learning models in effluent dephenolization process onto H3PO4-anchored corn cob by Chijioke Elijah Onu, Joseph Tagbo Nwabanne, Ositadimma Chamberline Iheanocho, Paschal Enyinnaya Ohale, Chiamaka Peace Onu, Marcel Ikenna Ejimofor, Suraj Kumar Bhagat, Christian O. Asadu, Christopher C. Obi, Chidiogo Ezekwem

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Visualization of Learning Process in Feature Space by Tomohiro Inoue, Noboru Murata, Taiki Sugiura

“…In machine learning, the structure of feature space is an important factor that determines the performance of a model. …”
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Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011 by Markus J. Bonse, Timothy D. Gebhard, Felix A. Dannert, Olivier Absil, Faustine Cantalloube, Valentin Christiaens, Gabriele Cugno, Emily O. Garvin, Jean Hayoz, Markus Kasper, Elisabeth Matthews, Bernhard Schölkopf, Sascha P. Quanz

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An adaptive Home Energy Management system for prosumers in peer-to-peer trading networks with machine learning optimization by Ameni Boumaiza, Kenza Maher

“…This study introduces a machine-learning enhanced HEMS framework operating in three stages: asset scheduling, bid optimization, and real-time adjustment. …”
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