Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. …”
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INTEGRATING CNN AND DICTIONARY MECHANISMS FOR EFFECTIVE LOOP CLOSURE DETECTION by Ayda Mohammed Sharif, Sadegh Abdollah Aminifar

“…The proposed CNN architecture, featuring a single convolutional layer with 32 filters, achieves 98% classification accuracy on the Greenhouse Scene Dataset-a structured agricultural environment. …”
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Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

“…First, the convolutional layers of the CNN were designed as multi-branch parallel layers to extract richer features. …”
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From Social to Academic: Associations and Predictions Between Different Types of Peer Relationships and Academic Performance Among College Students by Jiadong Tian, Jiali Lin, Dagang Li

“…Subsequently, we used Random Forests and Neural Networks as baseline methods, and introduced Graph Convolutional Network and Dynamic Graph Convolutional Network algorithms, on top of a graph network model based on social characteristics, to predict students’ academic performances. …”
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STGATN: A novel spatiotemporal graph attention network for predicting pollutant concentrations at multiple stations. by Huazhen Xu, Wei Song, Lanmei Qian, Xiangxiang Mei, Guojian Zou

“…Both the encoder and decoder incorporate a spatiotemporal embedding mechanism, a spatiotemporal graph attention block, a gated temporal convolutional network, and a fusion gate. Specifically, the spatiotemporal graph attention module is designed to use temporal and graph attention networks to extract dynamic spatiotemporal correlations. …”
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Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios by Shi Lei, He Yi, Jeffrey S. Sarmiento

“…In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. …”
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Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses by Hossein Gholizadeh, T. Prabhakar Clement, Christopher T. Green, Geoffrey R. Tick, Alain M. Plattner, Yong Zhang

“…The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. …”
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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

“…The proposed model efficiently models global dependencies while maintaining stable training dynamics and efficient inference. Additionally, we introduce a novel Spatio-Context (SC) module utilizing 3D convolutions without pooling to enhance feature representations and reduce information loss commonly associated with pooling operations. …”
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Machine learning-based model for behavioural analysis in rodents applied to the forced swim test by Andrea Della Valle, Sara De Carlo, Gregorio Sonsini, Sebastiano Pilati, Andrea Perali, Massimo Ubaldi, Roberto Ciccocioppo

“…To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. …”
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Deep Learning‐Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording by Shengjie Yang, Jiaqi Xue, Ziqi Li, Shiqing Zhang, Zhang Zhang, Zhifeng Huang, Ken Kin Lam Yung, King Wai Chiu Lai

“…Abstract The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole‐cell recordings. …”
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Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions by Ehtisham Lodhi, Nadia Dahmani, Syed Muhammad Salman Bukhari, Sujan Gyawali, Sanjog Thapa, Lin Qiu, Muhammad Hamza Zafar, Naureen Akhtar

“…The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…This approach not only provides a remarkably valuable perspective for policy makers, investors, and trading firms in the gold market, but also deals with the shortcomings of a single model in the face of complex market dynamics, and lays the foundation for the development of even more powerful forecasting models in the future.…”
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Attention-Module-Guided Time-Lapse Leakage Plume Imaging Driven by LeakInv-CUNet GPR Inversion Framework by Honghua Wang, Shan Wang, Fei Zhou, Yi Lei, Bin Zhang

“…To enhance network training, extensive GPR datasets are generated by augmenting simulated data and experimentally measured data, accounting for variations in injection orientation, plume dynamics, and subsurface media properties. By leveraging the dual advantages of the Convolutional Block Attention Module (CBAM) and U-Net architecture, the developed LeakInv-CUNet framework effectively extracts subtle leakage-induced response features, enabling refined imaging of leakage plumes and their orientations. …”
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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…Dynamic oxygen uptake (VO₂) reflects moment-to-moment changes in oxygen consumption during exercise and underpins training design, performance enhancement, and clinical decision-making. …”
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A Patch-Wise Mechanism for Enhancing Sparse Radar Echo Extrapolation in Precipitation Nowcasting by Yueting Wang, Hou Jiang, Tang Liu, Ling Yao, Chenghu Zhou

“…Furthermore, multiscale convolutions tailored to the patch scale are employed to expand the receptive field, and a convolutional block attention module is introduced to capture the spatiotemporal dynamics of sparse echoes and intense rainfall. …”
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Reliable Indoor Fire Detection Using Attention-Based 3D CNNs: A Fire Safety Engineering Perspective by Mostafa M. E. H. Ali, Maryam Ghodrat

“…Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). …”
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Estimating chlorophyll content in tea leaves using spectral reflectance and deep learning methods by Yuta Tsuchiya, Yuhei Hirono, Rei Sonobe

“…This study highlights the potential of combining hyperspectral sensing with advanced representation learning to non–destructively monitor chlorophyll dynamics in tea cultivation, supporting more sustainable and data–driven agricultural practices.…”
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Cross Attentive Multi-Cue Fusion for Skeleton-Based Sign Language Recognition by Ogulcan Ozdemir, Inci M. Baytas, Lale Akarun

“…Yet, the fusion process is challenged by changing spatial and temporal dynamics across articulators. To address this, we propose a multi-cue cross-attention framework that enables interactions between hand and upper body cues during fusion. …”
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Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods by Sisi Li, Sheng Hu, Lin Wang, Fanyu Zhang, Ninglian Wang, Songbai Wu, Xingang Wang, Zongda Jiang

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Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG by Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang

“…Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. …”
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