A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping by Xu Zhang, Xin Liu, Xiang Zhang, Aminjon Gulakhmadov, Jiefeng Wu, Xihui Gu, Won-Ho Nam, Panda Rabindra Kumar, Veber Afonso Figueiredo Costa, Mahlatse Kganyago, Berhanu Keno Terfa, Wenying Du, Chao Wang, Peng Wang, Jing Yuan, Nengcheng Chen

“…Finally, a bias correction method based on convolutional neural networks with transfer learning was employed for point-to-area fusion calibration to improve data accuracy. …”
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DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

“…The architecture comprises a Graph Attention Module (GAM) to capture spatial dependencies using multi-head graph attention and graph convolution layers, and a Channel Attention Module (CAM) to amplify discriminative features and suppress irrelevant information. …”
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AI-based orchard monitoring at night: Enhancing sustainable fruit production through real-time apple detection by Kutyrev Alexey, Khort Dmitry, Smirnov Igor, Zubina Valeria

“…The study evaluates the performance of the state-of-the-art convolutional neural network model YOLO11 (You Only Look Once version 11) under artificial lighting conditions at night. …”
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

“…Despite significant progress, HAR still faces critical challenges, including handling environmental variability, ensuring model interpretability, and achieving high recognition accuracy in complex, real-world scenarios. …”
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Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method by Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

“…We propose a multiscale convolutional neural network (CNN) and a Bidirectional LSTM (BiLSTM) model called CNN-BiLSTM to extract features and classify raw EEG data. …”
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Artificial Intelligence for data modeling in triboelectric nanogenerators by Chenjia Li, Ali Matin Nazar

“…Over 20 experimental implementations are analyzed, combining AI models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks with TENGs across multiple operational modes including contact-separation, lateral sliding, and freestanding configurations. …”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively. …”
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IchthyNet: An Ensemble Method for the Classification of In Situ Marine Zooplankton Shadowgraph Images by Brittney Slocum, Bradley Penta

“…The resulting images were then cleaned, segmented into regions of interest (ROIs), and fed through three convolutional neural networks (CNNs): VGG-16, ResNet-50, and a custom model created to find more high-level features in this dataset. …”
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Deep Learning Classification of Simulated Surface EMG Signals across Maximum Voluntary Contraction Levels by Radhouane Hammach, Samia Belkacem, Noureddine Messaoudi, Raïs El’hadi Bekka

“…Unlike previous studies, which focus primarily on binary classification of fatigue and non-fatigue states, our approach employs a deep convolutional neural network for the classification of sEMG signals into ten MVC levels, where the model outputs categorical predictions, with each class representing a specific MVC level. sEMG signals were generated using a computer muscle model that we developed using MATLAB, which allows for greater control over variability, ensuring robustness and generalizability of the model. …”
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Linking European Temperature Variations to Atmospheric Circulation With a Neural Network: A Pilot Study in a Climate Model by Enora Cariou, Julien Cattiaux, Saïd Qasmi, Aurélien Ribes, Christophe Cassou, Antoine Doury

“…This exploratory work opens up promising prospects for estimating the contribution of atmospheric variability to observed temperature variations.…”
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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…The methodology integrates graph neural networks to account for spatial interdependencies between wells with recurrent and convolutional neural networks for time-series analysis. …”
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Devanagari Character Recognition: A Comprehensive Literature Review by Sandhya Arora, Latesh Malik, Sonakshi Goyal, Debotosh Bhattacharjee, Mita Nasipuri, Ondrej Krejcar

“…Challenges remain, including handwriting variability, noise, and the need for real-time performance. …”
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Zoon’s Balanitis – Update of Clinical Spectrum and Management by Vineet Relhan, Abhinav Kumar, Aneet Kaur

“…It reveals lozenge-shaped keratinocytes with siderophages, haemorrhages and variable plasma cell infiltrate in the dermis. Dermoscopy shows spermatozoa-like, convoluted vessels with structureless red orange areas. …”
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Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall by Kalpesh R. Patil, Takeshi Doi, J.V. Ratnam, Swadhin K. Behera

“…We attempted to address this issue using a deep learning model based on convolutional neural networks (CNN). An ensemble of JJAS rainfall predictions using the CNN model with a unique custom function showed high skills in predicting ISMR at a long-lead time of 12 months. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…SPD-Conv was introduced to replace the original convolutional layers to retain fine-grained information and reduce model parameters and computational costs, thereby improving the accuracy of disease detection. …”
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Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model complex spatial-temporal dependencies, especially in dynamic or subtle actions. …”
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A Graphite Ore Grade Recognition Method Based on Improved Inception-ResNet-v2 Model by Xueyu Huang, Renjie Pan, Jionghui Wang

“…With the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feature similarity, we propose an improved Inception-ResNet-v2 model for graphite ore grade recognition. …”
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Depression detection methods based on multimodal fusion of voice and text by Zhenrong Xu, Yuan Gao, Fang Wang, Longqian Zhang, Li Zhang, Junke Wang, Jie Shu

“…Traditional diagnostics often rely on subjective judgments, leading to variability and inefficiency. This study proposes a fusion model for automated depression detection, leveraging bimodal data from voice and text. …”
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Federated learning for privacy-enhanced mental health prediction with multimodal data integration by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…This study addresses these challenges by utilising a multimodal dataset comprising physiological signals (heart rate variability, sleep patterns) and behavioural data (online activity, social media interactions). …”
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Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…Furthermore, we address ongoing challenges, such as dataset variability and the generalization of models to novel chemical spaces. …”
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