A recurrent multimodal sparse transformer framework for gastrointestinal disease classification by V. Sharmila, S. Geetha

“…However, existing diagnostic frameworks often face limitations due to modality imbalance, feature redundancy, and cross-modal inconsistencies, particularly when dealing with heterogeneous data such as medical text and endoscopic images. To bridge these gaps, this study proposes a novel recurrent multimodal principal gradient K-proximal sparse transformer (RMP-GKPS-transformer) framework for comprehensive GI disease classification. …”
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Identification of Priority Areas for Ecological Restoration at a Small Watershed Scale: A Case Study in Dali Prefecture of Yunnan Province in China by Qiyuan Zhou, Qiuping Zhu, Yu Feng, Jinman Wang

“…A spatial analysis revealed an east-to-west gradient in ecosystem service distribution, where eastern regions consistently exhibited lower values compared to central and western areas. …”
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Exploring drought hazard, vulnerability, and related impacts on agriculture in Brandenburg by F. Brill, P. H. L. Alencar, H. Zhang, F. Boeing, F. Boeing, S. Hüttel, S. Hüttel, T. Lakes, T. Lakes

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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…This study aims to bridge the existing research gap by employing a multi-factor approach using machine learning algorithms including Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to map and analyze the subsidence susceptibility of the Isfahan–Borkhar region. …”
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Explainable Self-Supervised Dynamic Neuroimaging Using Time Reversal by Zafar Iqbal, Md. Mahfuzur Rahman, Usman Mahmood, Qasim Zia, Zening Fu, Vince D. Calhoun, Sergey Plis

“…Saliency maps were generated using Integrated Gradients (IG) to provide post hoc explanations for pretraining, while Earth Mover’s Distance (EMD) quantified the temporal dynamics of salient features in the downstream tasks. …”
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Cross-survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis by Zhijian Luo, Shaohua Zhang, Jianzhen Chen, Zhu Chen, Liping Fu, Hubing Xiao, Wei Du, Chenggang Shu

“…We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the Wasserstein generative adversarial network with gradient penalty to convert low-quality observational images into high-quality counterparts. …”
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What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants by Radwan Qasrawi, Radwan Qasrawi, Abir Ajab, Leila Cheikh Ismail, Leila Cheikh Ismail, Ayesha Al Dhaheri, Sharifa Alblooshi, Razan Abu Ghoush, Stephanny Vicuna Polo, Malak Amro, Suliman Thwib, Ghada Issa, Haleama Al Sabbah

“…This study addresses that gap by applying machine learning to a wide range of behavioral, dietary, and demographic data. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Moreover, bridging the gap from in silico promise to real‑world impact demands large‑scale validation studies and methods that can accommodate heterogeneous evidence, ensuring AI‑driven innovations deliver equitable global health outcomes and reinforce pandemic preparedness.…”
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UAV-based phenotyping identifies net assimilation rate as a diagnostic trait for synergistic enhancement of rice yield and grain quality by Weiyuan Hong, Xiangqian Feng, Ziqiu Li, Jinhua Qin, Huaxing Wu, Yunbo Zhang, Guang Chu, Chunmei Xu, Kai Yu, Yuanhui Liu, Danying Wang, Song Chen

“…Achieving rice yield-quality synergy, which is critical for breeding and agronomic practice, is hindered by dynamic regulatory gaps due to methodological constraints, while high-throughput unmanned aerial vehicle (UAV) phenotyping can enable breakthroughs by decoding dynamic traits at scale. …”
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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Stud... by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

“…ResultsIn the internal testing cohort, 7 models (K-nearest neighbors, naïve Bayes, decision tree, random forest, extreme gradient boosting, gradient-boosting decision tree, and CatBoost) were evaluated. …”
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First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions by Min Chul Choi, Wooil Yang, Young-Woo Son, Se Young Park

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Diversity of anopheline species and malaria transmission dynamics in high-altitude areas of western Cameroon by Belinda Claire Kiam, Aline Gaelle Bouopda-Tuedom, Jean Arthur Mbida Mbida, Ibrahima Ibrahima, Samuel J. White, Pacome V. K. Tchuenkam, Zachary R. Popkin-Hall, Mariama Mbouh, Charlène Tina Nanssong V., Luc Abate, Clément Janvier Onguene, Brigitte Tumamo Fotso, Jacob M. Sadler, Jonathan B. Parr, Jessica T. Lin, Jonathan J. Juliano, Innocent Mbulli Ali, Rhoel R. Dinglasan, Sandrine Eveline Nsango

“…Several entomological studies have been conducted to describe malaria transmission in different eco-epidemiological settings in Cameroon; however, data gaps persist, particularly in the highland areas. …”
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Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…Focusing on Fuzhou, a national pilot city for child-friendly initiatives, the research addresses a critical gap in urban planning literature: The lack of empirical evidence linking micro-scale street design to the emotional dynamics of children and their caregivers. …”
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Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries by Mequannent Sharew Melaku, Lamrot Yohannes, Berhanu Sharew, Mintesnot Hawaz Derseh, Eliyas Addisu Taye

“…Digital health solutions, especially mobile-based platforms, can also bridge information gaps. Integrating counseling into routine healthcare, training providers, and expanding mass media campaigns can enhance awareness. …”
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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
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Technical note: Reconstructing missing surface aerosol elemental carbon data in long-term series with ensemble learning by Q. Meng, Y. Zhang, S. Zhong, J. Fang, L. Tang, Y. Rao, M. Zhou, J. Qiu, X. Xu, J.-E. Petit, O. Favez, X. Ge

“…The model used readily accessible ground observation air pollutant data as proxies for EC-related tracers, along with meteorological parameters, to enhance prediction accuracy. It integrated outputs from Gradient Boosting Regression Trees, eXtreme Gradient Boosting, and random forest models, combining them through ridge regression to produce robust predictions. …”
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Applications of Machine Learning Algorithms in Geriatrics by Adrian Stancu, Cosmina-Mihaela Rosca, Emilian Marian Iovanovici

“…The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. …”
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Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models by Mohammad Ennab, Hamid Mcheick

“…This work establishes PLI as a robust tool for bridging gaps in AI model transparency and clinical usability. …”
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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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A Comprehensive Review of Cryptographic Techniques in Federated Learning for Secure Data Sharing and Applications by Anik Sen, Swee-Huay Heng, Shing-Chiang Tan

“…Despite its promise, FL faces significant security challenges, such as gradient inversion, model poisoning, and privacy leakage, which involve strong cryptographic techniques. …”
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