Multi-scale extreme climate disaster prediction model integrated with ConvLSTM: taking rainstorm and flood disaster as an example by Lei He, Yunhao He, Yongqiang Xia, Yuxia Li, Bin Liu, Siqi Zhang, Cunjie Zhang

“…As one of the most influential and harmful disasters in extreme climate events, rainstorm and flood threaten people’s lives, property and social stability seriously. …”
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On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations by Tingting Ye, Zhonghui Tan, Weihua Ai, Shuo Ma, Xianbin Zhao, Shensen Hu, Chao Liu, Jianping Guo

“…Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. …”
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Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales by Haobo Fu, Ruizhuo Wang, Bingxu Zhai, Yuanzhuo Li, Pengyuan Li, Rui Zhang, Haoyuan He, Siyang Liao

“…Then, a multi-time-scale prediction model based on a convolutional neural network and a bi-directional long and short-term memory network is constructed to realize the active early warning of the power system in the face of grid congestion events. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. …”
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Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward by Sudeep Tanwar, Qasim Bhatia, Pruthvi Patel, Aparna Kumari, Pradeep Kumar Singh, Wei-Chiang Hong

“…In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. …”
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Tree semantic segmentation from aerial image time series by Venkatesh Ramesh, Arthur Ouaknine, David Rolnick

“…We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. …”
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Learning Spectral–Spatial-Former Deep Prior for Hyperspectral Image Superresolution by Zeinab Dehghan, Jingxiang Yang, Abdolraheem Khader, Jian Fang, Liang Xiao

“…To overcome this, we integrated our model into a deep convolutional neural network enhanced by a Transformer module for regularization. …”
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A Novel Federated Learning Framework for Sustainable and Efficient Breast Cancer Classification System (FL-L₂CNN-BCDet) by Nesma Abd El-Mawla, Mohamed A. Berbar, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

“…AI is now being used in a variety of applications to diagnose Breast Cancer (BC). However, most of the recent research has focused on centralized learning, which can pose privacy risks. …”
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Development of Bimodal Emotion Recognition System Based on Skin Temperature and Heart Rate Variability Using Hybrid Neural Networks by Sayat Orynbassar, Duygun Erol Barkana, Evan Yershov, Madiyar Nurgaliyev, Ahmet Saymbetov, Batyrbek Zholamanov, Gulbakhar Dosymbetova, Ainur Kapparova, Nursultan Koshkarbay, Nurzhigit Kuttybay, Askhat Bolatbek, Kymbat Kopbay, Dinara Almen

“…Most studies indicate that bimodal emotion recognition systems are more objective and accurate. …”
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An adaptive deep learning approach based on InBNFus and CNNDen-GRU networks for breast cancer and maternal fetal classification using ultrasound images by Mamuna Fatima, Muhammad Attique Khan, Anwar M. Mirza, Jungpil Shin, Areej Alasiry, Mehrez Marzougui, Jaehyuk Cha, Byoungchol Chang

“…Abstract Convolutional Neural Networks (CNNs), a sophisticated deep learning technique, have proven highly effective in identifying and classifying abnormalities related to various diseases. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. …”
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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…This study evaluates the performance of three algorithms: Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) network. …”
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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

“…Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. …”
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A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas by Xiaoyong Tan, Min Deng, Kaiqi Chen, Yan Shi, Bingbing Zhao, Qinghao Liu

“…We redefine the neighborhood structure and introduce lightweight convolutional neural networks to capture the complex spatio-temporal interaction in neighborhood effects. …”
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Urban Public Space Safety Perception and the Influence of the Built Environment from a Female Perspective: Combining Street View Data and Deep Learning by Shudi Chen, Sainan Lin, Yao Yao, Xingang Zhou

“…This study, using Wuhan as a case study, proposes a method for ranking street safety perceptions for women by combining RankNet with Gist features. Fully Convolutional Network-8s (FCN-8s) was employed to extract built environment features, while Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) were used to explore the relationship between these features and women’s safety perceptions. …”
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Optimizing battery health monitoring in electric vehicles using interpretable CART–GX model by Mohnish Karthikeyan B, Anirudh N, Navaneetha Krishnan S, Christopher Columbus C, Aravind C. K

“…The proposed model combines Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), attention mechanisms, residual connections, and transformers to extract spatial and temporal features, prioritize critical information, and model long-range dependencies. …”
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Large-scale-aware data augmentation for reduced-order models of high-dimensional flows by Philipp Teutsch, Mohammad Sharifi Ghazijahani, Florian Heyder, Christian Cierpka, Jörg Schumacher, Patrick Mäder

“…Convolutional autoencoders have proven to be an adequate tool to perform reduced-order modeling for high-dimensional nonlinear dynamical systems. …”
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Machine Learning to Recognise ACL Tears: A Systematic Review by Julius Michael Wolfgart, Ulf Krister Hofmann, Maximilian Praster, Marina Danalache, Filippo Migliorini, Martina Feierabend

“…Deep learning algorithms in the form of convolutional neural networks (CNNs) were most frequently used. …”
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Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

“…<b>Objectives:</b> Lung cancer is one of the most prevalent cancers worldwide. Accurately determining lung cancer subtypes and identifying high-risk patients are helpful for individualized treatment and follow-up. …”
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Quantum Classical Algorithm for the Study of Phase Transitions in the Hubbard Model via Dynamical Mean-Field Theory by Anshumitra Baul, Herbert Fotso, Hanna Terletska, Ka-Ming Tam, Juana Moreno

“…Modeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. …”
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