Nystromformer based cross-modality transformer for visible-infrared person re-identification by Ranjit Kumar Mishra, Arijit Mondal, Jimson Mathew

“…Abstract Person re-identification (Re-ID) aims to accurately match individuals across different camera views, a critical task for surveillance and security applications, often under varying conditions such as illumination, pose, and background. …”
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Explainable Predictive Model for Suicidal Ideation During COVID-19: Social Media Discourse Study by Salah Bouktif, Akib Mohi Ud Din Khanday, Ali Ouni

“…Suicidal ideation captures the new suicidal statements used during the COVID-19 pandemic that represents a different context of expressions. ObjectiveIn this study, our aim was to detect suicidal ideation by mining textual content extracted from social media by leveraging state-of-the-art natural language processing (NLP) techniques. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
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Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique by Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng

“…Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are popular deep learning architectures currently used for rapid flood simulations. …”
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Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy by Yuming Zhong, Ying Dan, Yin Cai, Jiamin Lin, Xiaoyao Huang, Omnia Mahmoud, Eric S. Hald, Akshay Kumar, Qiang Fang, Seedahmed S. Mahmoud

“…This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. …”
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Mock Observations: Morphological Analysis of Galaxies in TNG100 Simulations by Jun-Yu Gong, Weipeng Lin, Lin Tang, Yanyao Lan

“…Our findings show that the morphological characteristics of the mock-observed galaxy samples closely match those observed in the SDSS, successfully reproducing key features such as distinct parameter distributions for different types. However, some discrepancies are identified: notably, a significant lack of early-type galaxies (ETGs) in the dwarf galaxy regime ( M _* < 10 ^10 M _⊙ ) and minimal distinction between Sab/Sb and Sc/Sd/irregular galaxies in the mock-observed samples, unlike the clear differences seen in actual observations. …”
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Deep learning empowered sensor fusion boosts infant movement classification by Tomas Kulvicius, Dajie Zhang, Luise Poustka, Sven Bölte, Lennart Jahn, Sarah Flügge, Marc Kraft, Markus Zweckstetter, Karin Nielsen-Saines, Florentin Wörgötter, Peter B. Marschik

“…FMs were recorded from 51 typically developing participants. We compared three different sensor modalities (pressure, inertial, and visual sensors). …”
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Advanced deep learning techniques for automated extraction of non-debris-covered areas of glaciers in High-Mountain Asia using time-series remote sensing data by Gexia Qin, Ninglian Wang, Bo Jiang, Yuwei Wu, Yanchao Yin, Zhijie Li

“…Deep learning approaches have gained prominence for automatic glacier boundary extraction due to their localized nature of convolutional operations, potentially leading to incomplete or fragmented glacier pixel representations. …”
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Degradation prediction of PEM water electrolyzer under constant and start-stop loads based on CNN-LSTM by Boshi Xu, Wenbiao Ma, Wenyan Wu, Yang Wang, Yang Yang, Jun Li, Xun Zhu, Qiang Liao

“…However, it is difficult to establish a mechanism model incorporating all degradation categories due to their different time and spatial scales. In this paper, the data-driven method is employed to predict the electrolyzer voltage variation over time based on a convolutional neural network-long short term memory (CNN-LSTM) model. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. …”
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CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model by Shanwei Liu, Shuaipeng Wang, Wei Zhang, Tao Zhang, Mingming Xu, Muhammad Yasir, Shiqing Wei

“…Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. …”
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Beyond averaging: A transformer approach to decoding event related brain potentials by Philipp Zelger, Manuel Arnold, Sonja Rossi, Josef Seebacher, Franz Muigg, Simone Graf, Antonio Rodríguez-Sánchez

“…In the study, 29 normal-hearing participants between 18 and 30 years were presented with acoustic stimuli at five different sound levels between 65 and 95 dB and provided their subjective loudness rating, which was categorized as ”too loud” and ”not too loud”. …”
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MCIDN: Deblurring Network for Metal Corrosion Images by Jiaxiang Wang, Meng Wan, Pufen Zhang, Sijie Chang, Hao Du, Peng Shi, Hongying Yu, Dongbai Sun, Jue Wang, Yangang Wang

“…Recognizing the critical role of frequency components in image restoration, we develop a frequency channel attention module (FCAM) that selectively focuses on different frequency components of images, thereby enhancing deblurring performance. …”
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AI approaches for phenotyping Alzheimer's disease and related dementias using electronic health records by Sara Knox, Stephanie Aghamoosa, Paul M. Heider, Maxwell Cutty, Andrew Wright, Dmitry Scherbakov, Gabriel Hood, Sara A. Nolin, Jihad S. Obeid

“…RESULTS A foundational LLM derived from Llama 2 demonstrated superior performance in identifying patients with ADRD (area under the curve [AUC] = 0.9534, F1 score 0.8571) compared to both the current standard CCW algorithm (AUC = 0.8482, F1 score 0.8323, although only the AUC was statistically significantly different) and other AI‐based models. Several of the AI‐based models, including convolutional neural networks, also outperformed the CCW algorithm. …”
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Self-similar Decomposition of the Hierarchical Merger Tree of Dark Matter Halos by Wenkang Jiang, Jiaxin Han, Fuyu Dong, Feihong He

“…Using a set of cosmological simulations and identifying subhalos of different merger levels with hbt+ , we verify that the level-1 subhalo PMF is close to universal across halo mass, redshift, and cosmology. …”
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Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence by Xuezhi Zhou, Jing Dai, Yizhan Lu, Qingqing Zhao, Yong Liu, Chang Wang, Zongya Zhao, Chong Wang, Zhixian Gao, Yi Yu, Yandong Zhao, Wuteng Cao

“…Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. …”
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Clouds Detection in Polar Icy Terrains: A Deformable Attention-Based Deep Neural Network for Multispectral Polar Scene Parsing by Shaojin Dong, Cailan Gong, Yong Hu, Long Cheng, Yang Wang, Fuqiang Zheng

“…Considering these challenges, we introduce a deep convolutional neural network model called DLACD-Net. …”
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MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images by Kai Du, Yi Ma, Zhongwei Li, Rongjie Liu, Zongchen Jiang, Junfang Yang

“…The framework uses parallel branches, including a convolutional neural network and a vision transformer, to extract surrounding spatial features and central spectral features from the fused data. …”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…A comprehensive review of literature has been conducted by examining over 400 articles to focus on 72 studies including 14 reviews publications on ML and DL models about potato disease forecasting using different techniques. It highlights the need for proficient disease control by integrating image and climate data. …”
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Pollen–pistil interactions in divergent wide crosses lead to spatial and temporal pre-fertilization reproductive barrier in flax (Linum usitatissimum L.) by Vijaykumar Kailasrao Raut, Aneeta Yadav, Vikender Kaur, Mahesh Rao, Pooja Pathania, Dhammaprakash Wankhede, Mamta Singh, Gyanendra Pratap Singh

“…Self-incompatibility due to the occurrence of heterostyly is very well reported in distantly related crop wild relatives of Linum and, the mechanism of self-incompatibility between different floral morphs is also studied. However, pollen germination and tube growth responses in the interspecific crosses are rarely studied. …”
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