Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. …”
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An Evaluation Method for University Classroom Education Quality under Machine Vision and Single-Valued Neutrosophic Hesitant Fuzzy Set Environment by Rui Wang, Mingjie Li, Fangwei Zhang, Yiying Pan, Zongao Zhang

“…With the advancement of artificial intelligence, machine vision offers a novel approach to university teaching quality evaluation (TQE). …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. …”
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Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer by Yi Tian, Wen-ya Zhao, Yi-ru Liu, Wen-wen Song, Qiao-xin Lin, Yan-na Gong, Yi-ting Deng, Dian-na Gu, Ling Tian

“…Herein, we would apply machine learning methods to specifically analyze the issue of biomarker applicability across different age groups in NSCLC. …”
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Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm by Yushuai Yuan, Li Yang, Rui Gao, Cheng Chen, Min Li, Jun Tang, Xiaoyi Lv, Ziwei Yan

“…The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. …”
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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. …”
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Multi-Factor Carbon Emissions Prediction in Coal-Fired Power Plants: A Machine Learning Approach for Carbon Footprint Management by Xiaopan Liu, Haonan Yu, Hanzi Liu, Zhiqiang Sun

“…However, the unclear boundary definition and incomplete data types often lead to insufficient accuracy in model calculations and predictive performance. Herein, we developed machine learning models to predict carbon emissions in a 1000 MW coal-fired power plant. …”
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Edge–Cloud Intelligence for Sustainable Wind Turbine Blade Transportation: Machine-Vision-Driven Safety Monitoring in Renewable Energy Systems by Yajun Wang, Xiaodan Wang, Yihai Wang, Shibiao Fang

“…To address these challenges, this study proposes an intelligent safety monitoring framework that combines machine vision with edge–cloud collaboration. The framework employs an optimized YOLOv7-Tiny model. …”
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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis by Walid Abdelfattah, Munthar Kadhim Abosaoda, Krunal Vaghela, Gowrishankar J, Prabhat Kumar Sahu, Kamred Udham Singh, R. Sivaranjani, Rohit Chauhan, Siya Singla, Samim Sherzod

“…This study employs a comprehensive data-driven approach to predict biochar yield using machine learning algorithms. The dataset, comprising 14 chemical, physical, and reaction parameters collected from reputable studies, was processed using outlier detection via the Monte Carlo Outlier Detection (MCOD) algorithm and hyperparameter tuning to optimize model performance. …”
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Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis by Mosab Kaseem, Talitha Tara Thanaa, Krishna Kumar Yadav, Hagar H. Hassan, Arash Fattah-alhosseini

“…The results emphasize the critical importance of cation selection and illustrate the potential of integrating experimental design with machine learning for optimizing LDH-based protective coatings on Mg alloys.…”
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Mechanical Structure Design and Motion Simulation Analysis of a Lower Limb Exoskeleton Rehabilitation Robot Based on Human–Machine Integration by Chenglong Zhao, Zhen Liu, Yuefa Ou, Liucun Zhu

“…Population aging is an inevitable trend in contemporary society, and the application of technologies such as human–machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. …”
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Machine learning−derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patientsCentral MessagePerspective by Edward G. Soltesz, MD, MPH, Randi J. Parks, PhD, Elise M. Jortberg, MS, Eugene H. Blackstone, MD

“…In total, 68 preoperative clinical variables were considered in machine-learning algorithms trained and optimized using scikit-learn software. …”
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Machine learning discrimination and prediction of different quality grades of sauce-flavor baijiu based on biomarker and key flavor compounds screening by Shuai Li, Tao Li, Yueran Han, Pei Yan, Guohui Li, Tingting Ren, Ming Yan, Jun Lu, Shuyi Qiu

“…Additionally, reducing sugar content in Jiupei significantly impacted base Baijiu quality. Finally, 11 machine learning classification models and 9 prediction models were evaluated, leading to the selection of the optimal model for accurate quality grade classification and prediction. …”
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Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR by Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das, Prince Jain, Ghanashyam Sahoo, Haipeng Liu

“…<b>Methods:</b> This research presents an innovative cancer classification technique that combines fast minimum redundancy-maximum relevance-based feature selection with Binary Portia Spider Optimization Algorithm to optimize features. The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. …”
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BA-ELM Gear Fault Diagnosis Method based on Energy Feature of Wavelet Packet Optimal Node by Qin Bo, Liu Yongliang, Wang Jianguo, Qin Yan, Yang Yunzhong

“…In order to solve the problems that gear fault classification model has weak generalization ability,poor accuracy causing by the fault features of gear is difficult to extract and extreme learning machine input weights and threshold of hidden layer nodes randomly selected,a BA- ELM gear fault diagnosis method is puts forward based on energy feature of wavelet packet optimal nodes.First,the gear vibration signals are decomposed by using wavelet packet in this method,the optimal nodes is selected by using the correlation coefficient between each node decomposition signals and original signal,and the energy feature is calculated.Second,the bat algorithm is used to optimize the extreme learning machine input weights and threshold of hidden layer node and the gear fault classification model of BA-ELM is established.Finally,the energy entropy feature vectors of the optimal wavelet packet nodes as the model input is used to identify the different fault states of gear.The experimental results show that,comparing with SVM and ELM fault classification method,the BA-ELM gear fault diagnosis method based on energy feature of wavelet packet optimal nodes has higher classification accuracy and better generalization ability.…”
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Autonomous Detection of Mineral Phases in a Rock Sample Using a Space-prototype LIMS Instrument and Unsupervised Machine Learning by Salome Gruchola, Peter Keresztes Schmidt, Marek Tulej, Andreas Riedo, Klaus Mezger, Peter Wurz

“…In situ mineralogical and chemical analyses of rock samples using a space-prototype laser ablation ionization mass spectrometer along with unsupervised machine learning are powerful tools for the study of surface samples on planetary bodies. …”
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Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB₁ in Corn Silage by Daqian Wan, Haiqing Tian, Lina Guo, Kai Zhao, Yang Yu, Xinglu Zheng, Haijun Li, Jianying Sun

“…Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). Five machine learning models were constructed: Light Gradient Boosting Machine (LightGBM), XGBoost, Support Vector Regression (SVR), RF, and K-Nearest Neighbor (KNN). …”
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Medical Image Segmentation Algorithm Based on Optimized Convolutional Neural Network-Adaptive Dropout Depth Calculation by Feng-Ping An, Jun-e Liu

“…In addition, an optimized convolutional neural network model is established. …”
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Vis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning by Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Francesca Masciola, Daniela Scutaru, Roberto Ciccoritti

“…Regarding the Vis/NIR spectrophotometer dataset, good predictive performances were achieved for TSS (R² = 0.855) and DM (R² = 0.857), while the performance for TA was unsatisfactory (R² = 0.681). In contrast, the optimal predictive ability was found for models of the HSI dataset (TSS: R² = 0.904; DM: R² = 0.918, TA: R² = 0.811), as confirmed by external validation. …”
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Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning by Xietian Xia, Shaohua Lei, Hui Lu, Zenghui Xu, Xiang Li, Xing Chen, Niancheng Hong, Jie Xu, Kun Shi, Jiacong Huang

“…The particle absorption coefficient of pigments (<i>a</i>_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of <i>a</i>_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. …”
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