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

“…By introducing Channel Attention Mechanism (CAM), the model assigns different weights to auxiliary variables, improving the prediction accuracy of soil hydrocarbon content. …”
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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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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…The remote sensing data were collected under variable sky conditions (clear to overcast) throughout a 1-month growing-season period, and field data collected by United States Department of Agriculture (USDA) Forest Service, Forest Inventory and Analysis program (FIA). …”
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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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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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Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method by Chengcheng Wang, Zhi Yan, Qifeng Li, Zhaopeng Zhu, Chengkai Zhang

“…However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. …”
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Multimode Fiber Specklegram Sensor for Multi-Position Loads Recognition Using Traversal Occlusion by Bohao Shen, Jianzhi Li, Zhe Ji

“…Our study introduces a construction method for a multi-variable, multi-class, one-shot specklegram dataset, significantly enhancing the sample diversity for more perturbation positions and intensities in an MMF-distributed sensor recognition model. …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The approach assists in predicting key values of the dependent variable associated with defect occurrence, enabling foundries to enhance product quality, reduce waste, and augment overall production process efficiency. …”
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Dual-Stream Enhanced Deep Network for Transmission Near-Infrared Dorsal Hand Vein Age Estimation with Attention Mechanisms by Zhenghua Shu, Zhihua Xie, Xiaowei Zou

“…Simultaneously, another deep residual network is leveraged to strengthen the representation ability on subtle dorsal vein textures. Moreover, variable activation functions and pooling layers are integrated into the respective streams to enhance the nonlinearity modeling of the dual-stream model. …”
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How to Handle Data Imbalance and Feature Selection Problems in CNN-Based Stock Price Forecasting by Zinnet Duygu Aksehir, Erdal Kilic

“…Different image-based input variable sets were created using technical indicators, gold, and oil price data to feed the CNN model. …”
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Machine learning–enhanced screening funnel for clinical trials in Alzheimer's disease by Scott Gladstein, Liuqing Yang, Dustin Wooten, Xin Huang, Robert Comley, Qi Guo, the Alzheimer's Disease Neuroimaging Initiative

“…Abstract INTRODUCTION Alzheimer's disease (AD) clinical trials with therapeutic interventions require hundreds of subjects to be studied over many months/years due to variable and slow disease progression. This article presents a novel screening paradigm integrating disease progression models to improve trial efficiency by identifying appropriate candidates for early phase clinical studies. …”
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Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…Various networks were trained and tested as we considered different variations of each method regarding input variable configurations and dataset filtering. Ultimately, the results indicated that the proposed approach was the best in performance. …”
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Detecting shallow subsurface anomalies with airborne and spaceborne remote sensing: A review by Adam M. Morley, Tamsin A. Mather, David M. Pyle, J-Michael Kendall

“…To close, we take a brief look at future research opportunities with very high resolution (VHR) datasets, multi-branch convolutional neural networks (CNNs) and active remote sensing in variable potential fields.…”
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Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…CR performs recalls on old data using images stored in a memory buffer with variable frequency and recall length to ensure efficient learning. …”
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Mapping herbaceous wetlands using combined phenological and hydrological features from time-series Sentinel-1/2 imagery by Zhaolong Yang, Xiaodong Na

“…The results showed the following. (1) The proposed method was stable and scalable and resulted in OAs of 92.69%, 89.18%, and 88.61% and kappa coefficients of 0.91, 0.87, and 0.86 in 2019, 2020, and 2021, respectively. (2) The crucial phenological periods to distinguish between herbaceous marshes and meadows were June, July, and August, and the optimal CVHIs corresponded to the phenological stages of the wetlands vegetation. (3) The optimal feature variables and its derivation time were selected from the CHVIs based on the TempCNN algorithm, which mitigated the impacts of seasonal variability of vegetation and hydrological conditions on the classification accuracies.…”
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