A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics by Nitin Rai, Harsh Pathak, Maria Villamil Mahecha, Dennis R. Buckmaster, Yanbo Huang, Paul Overby, Xin Sun

“…Additionally, to perform a comparative test with neural network-based deep learning (DL) architectures, a convolutional neural network-1-dimensional (CNN-1D_Canola) model was developed to train data points and predict canola yield. …”
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Wild ActionFormer: Enhancing wildlife action recognition for 11 endangered species in Wolong by Dan Liu, Qianqian Sun, Jin Hou, Bochuan Zheng, Jindong Zhang, Desheng Li, Tomas Norton, Jifeng Ning

“…This study focuses on 11 endangered wild animal species videos in the Wolong Nature Reserve and develops a video self-supervised learning-based animal action recognition network—Wild ActionFormer, to achieve automated analysis of wild animal action classes. …”
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Does ESG Predict Business Failure in Brazil? An Application of Machine Learning Techniques by Mehwish Kaleem, Hassan Raza, Sumaira Ashraf, António Martins Almeida, Luiz Pinto Machado

“…The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. …”
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Regionalization Analysis of Environmental Drivers of CONUS Grazing Land Biomass by Jisung Geba Chang, Feng Gao, Martha C. Anderson, Richard Cirone, Haoteng Zhao

“…This study focuses on regionalizing the analysis of grazing lands in the conterminous United States by combining earth observation data on Google Earth Engine, with machine learning techniques. …”
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BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data by Md Hasanul Ferdaus, Rizvee Hassan Prito, Ahmed Abdal Shafi Rasel, Masud Ahmed, Md. Jahid Hassan Saykot, Shanjida Sultan Shanta, Sonali Akter, Ankan Chandra Das, Mohammad Manzurul Islam, Mahamudul Hasan, Md Sawkat Ali

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Analysis of Flexibility, Strength, and Speed Using Dynamometer and Accelerometer Tests in Swimming Sports by Sungkowo, Tandiyo Rahayu, Bambang Widjanarko, Taufiq Arif Setyanto, Dewangga Yudhistira

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Bibliometric analysis on the STEM/STEAM approach in the training of natural sciences teachers in Colombia by María Del Carmen Suárez Millán, Juan Pablo Betancourt Arango

“…Objective To conduct a bibliometric analysis of STEM/STEAM teacher training in Colombia by analyzing scientific output, research impact, collaboration networks, and thematic trends using data from Scopus and Web of Science. …”
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Automated orthodontic diagnosis via self-supervised learning and multi-attribute classification using lateral cephalograms by Qiao Chang, Yuxing Bai, Shaofeng Wang, Fan Wang, Shuang Liang, Xianju Xie

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STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data by Wei Zhang, Weiming Zeng, Hongyu Chen, Jie Liu, Hongjie Yan, Kaile Zhang, Ran Tao, Wai Ting Siok, Nizhuan Wang

“…STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. …”
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PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION by Eghbal Motaei, Seyed Mehdi Tabatabai, Tarek Ganat, Ahmad Khanifar, Sulaiman Dzaiy, Timur Chis

“…Additionally, models based on physics-driven features exhibited less sensitivity to data splits for learning and testing, proving more reliable with better performance metrics compared to those using original features.…”
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CT-Free Attenuation Correction in Paediatric Long Axial Field-of-View Positron Emission Tomography Using Synthetic CT from Emission Data by Maria Elkjær Montgomery, Flemming Littrup Andersen, René Mathiasen, Lise Borgwardt, Kim Francis Andersen, Claes Nøhr Ladefoged

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Earth Observation Data to Support Environmental Justice: Linking Non‐Permitted Poultry Operations to Social Vulnerability Indices by Mirela G. Tulbure, Júlio Caineta, Brooke Cox, Stephen V. Stehman, Ayse Ercumen, Rebecca Witter, Ryan Emanuel, Dana E. Powell, Kemp Burdette, Sherri White‐Williamson, Shea Tuberty

“…Using literature‐derived heuristics, this study refined the locations of poultry CAFOs derived based on Earth Observation (EO) data and deep learning, reducing the overestimation of poultry CAFO density by 54% after heuristic adjustments. …”
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Integration of Sentinel-1 and Sentinel-2 for temporal identification of aquacultural ponds by Vaishnavi Joshi, Dipanwita Haldar, Subhadip Dey

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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…This research introduces an AI-driven approach to improve smart parking using deep learning and IIoT-based monitoring for real-time analysis. …”
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The combined effects of motor imagery and motor practice are influenced by differences in working memory function: Examination of brain, spinal cord, and muscle performance data by Yuki Fukumoto, Hiroki Bizen, Marina Todo, Naoki Yoshida, Toshiaki Suzuki

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Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms by Carlos Roberto López Paredes, Cesar García, Kennedy C. Onyelowe, Kennedy C. Onyelowe, Maria Gabriela Zuniga Rodriguez, Tammineni Gnananandarao, Alexis Ivan Andrade Valle, Nancy Velasco, Greys Carolina Herrera Morales

“…In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. …”
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Deep multi-view feature fusion with data augmentation for improved diabetic retinopathy classification by Boualleg Yaakoub, Daouadi Kheir Eddine, Guehairia Oussama, Djeddi Chawki, Cheddad Abbas, Siddiqi Imran, Bouderah Brahim

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Short-Term Electric Load Forecasting for an Industrial Plant Using Machine Learning-Based Algorithms by Oğuzhan Timur, Halil Yaşar Üstünel

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Physical Property Prediction and Simulation Analysis of Hydrogen‐Doped Natural Gas Pipeline by Lianghui Guo, He Zhang, Ran Liu, Keke Zhi, Xinzhe Li

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Application of the RFA-XGBoost model in predicting potential complaint users in mobile network by ZHANG Peng, GAO Yuan

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