Research on Atlantic surface pCO2 reconstruction based on machine learning by Jiaming Liu, Jie Wang, Xun Wang, Yixuan Zhou, Runbin Hu, Haiyang Zhang

“…Furthermore, the XGBoost model demonstrated strong applicability in regions with numerous outliers, maintaining a reconstruction accuracy of ≥95 %. (3) Stability test results reveal that the XGBoost model exhibits low sensitivity to uncertainties in all input variables. This indicates that the model can accommodate environmental data errors induced by abrupt changes in marine environments. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…The method builds upon YOLOv10 and integrates Gated Convolution (gConv) into the C2f module, forming a novel C2f-gConv structure that effectively reduces model parameters and computational complexity. …”
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Maturity Classification and Quality Determination of Cherry Using VNIR Hyperspectral Images and Comprehensive Chemometrics by Yuzhen Wei, Siyi Yao, Feiyue Wu, Qiangguo Yu

“…To improve the imaging performance, two spectral pretreatment methods (wavelet transform, standard normal variable transformation and detrend), three feature selection methods (successive projection algorithm, genetic algorithm, and shuffled frog leaping algorithm), and four regression modeling methods (principal components regression, partial least squares regression, least square-support vector regression, convolutional neural network) were employed and compared. …”
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Enhancing hand-drawn diagram recognition through the integration of machine learning and deep learning techniques by Vanita Agrawal, MVV Prasad Kantipudi, Jayant Jagtap

“…Because human-made graphics are inherently complicated and variable, hand-drawn diagram recognition is a challenging task. …”
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Dementia Classification Based on Magnetic Resonance Scans Comparing Traditional and Modern Machine Learning Models’ Quintessence by Andreea POPOVICIU, Diogen BABUC, Todor IVAŞCU

“…The tested models include a few-layers Convolutional Neural Network (CNN), a Residual Neural Network (ResNet), a Vision Transformer (ViT), an Autoencoder, and the Random Forest actions, and their training was conducted in Google Colab on an Intel Xeon CPU with 2 vCPUs and above 10 GB RAM. …”
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Intelligent Hybrid SHM-NDT Approach for Structural Assessment of Metal Components by Romaine Byfield, Ahmed Shabaka, Milton Molina Vargas, Ibrahim Tansel

“…As a case study, a 6-foot-long parallel flange I-beam, representing bridge truss elements, was subjected to variable bending loads to simulate operational conditions. …”
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A Temporal–Spatial–Spectral Fusion Framework for Coastal Wetland Mapping on Time-Series Remote Sensing Imagery by Xiang Li, Shenfu Zhang, Qiang Liu, Liang Chen, Gang Yang, Rui Zhao, Weiwei Sun, Feng Shao, Xiangchao Meng

“…A hybrid transformer-convolution module enhances fine-grained feature extraction. …”
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DCP-YOLOv7x: improved pest detection method for low-quality cotton image by Yukun Ma, Yajun Wei, Minsheng Ma, Zhilong Ning, Minghui Qiao, Uchechukwu Awada

“…In addition, the model detection head part is replaced with a DyHead (Dynamic Head) structure, which dynamically fuses the features at different scales by introducing dynamic convolution and multi-head attention mechanism to enhance the model's ability to cope with the problem of target morphology and location variability.ResultsThe model was fine-tuned and tested on the Exdark and Dk-CottonInsect datasets. …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…This study builds predictive models by using a palmd database comprised of the large datasets of palm oil tree health indicators, environmental factors and historical disease outbreaks to identify early signs of disease with high accuracy.To analyze both structured as well as unstructured data multiple machine learning algorithms were used such as Random Forest, Support Vector Machines, Convolution Neural Networks. Environmental variables like temperatures, humidity and soil conditions; as well as features of the leaves, including their texture and shape were given as input features to the trained models. …”
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Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions by Fhulufhelo Walter Mugware, Caston Sigauke, Thakhani Ravele

“…This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. …”
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Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles by Hamza Shahab Awan, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan

“…This research explores the application of supervised algorithms to predict DM using a variety of datasets such as clinical features, genetic markers, and lifestyle variables. This study proposes novel approaches and evaluates prediction models with classic machine learning algorithms and cutting-edge deep learning architecture. …”
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Predicting photodegradation rate constants of water pollutants on TiO2 using graph neural network and combined experimental-graph features by Mahia V. Solout, Jahan B. Ghasemi

“…In addition to experimental variables such as solution pH and temperature, the molecular structure of the contaminant significantly affects the reaction efficiency. …”
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A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution. by Sheng Miao, Guoqing Ni, Guangze Kong, Xiuhe Yuan, Chao Liu, Xiang Shen, Weijun Gao

“…This study explores the application of Three-Dimensional Convolutional Neural Networks (3DCNN) in spatial interpolation to evaluate soil pollution. …”
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Integration of Hyperspectral Imaging and Deep Learning for Discrimination of Fumigated Lilies and Prediction of Quality Indicator Contents by Pengfei Zhang, Youyou Wang, Binbin Yan, Xiufu Wang, Zihua Zhang, Sheng Wang, Jian Yang

“…To address this, we applied a deep learning model combined with hyperspectral imaging for the rapid prediction of nutrient quality. The CLSTM (convolutional neural network–long short-term memory) model, utilizing variable combination population analysis (VCPA) for wavelength selection, effectively differentiated sulfur fumigation patterns in lilies. …”
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Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer by Muhammad Shoaib Saleem, Javed Rashid, Sajjad Ahmad, Ali M. Al‐Shaery, Saad Althobaiti, Muhammad Faheem

“…However, it encounters notable challenges, such as variable demand, restricted data availability, the integration of various datasets, and the necessity for precise long‐term projections. …”
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Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN by Xinxin Huang, Jialin Liu, Feng Yang, Xu Qiao, Liang Gao, Tingyang Fu, Jianshe Zhao

“…In urban road detection using Ground Penetrating Radar (GPR), challenges arise from complex and variable road structures and diversified detection environments. …”
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A Relative Attitude Detection Method for Unmanned Aerial Vehicles Based on You Only Look Once Framework by Jingting Qiu, Feifan Yu, Fengrui Xu, Xinmin Chen, Jiqiang Wang

“…Variable environments and constrained edge devices pose the significantly challenging task of directly recognizing the relative attitude of unmanned aerial vehicles (UAVs). …”
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Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images by Qilin Jin, Qingbang Han, Jianhua Qian, Liujia Sun, Kao Ge, Jiayu Xia

“…Drainage pipeline construction projects are vulnerable to a range of defects, such as branch concealed joints, variable diameter, two pipe mouth significances, foreign object insertion, pipeline rupture, and pipeline end disconnection, generated during long-term service in a complex environment. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…The independent treatment variables were two climatic factors and five human activity factors affecting vegetation changes in East Africa. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. …”
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