Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. by Girmaw Abebe Tadesse, Laura Ferguson, Caleb Robinson, Shiphrah Kuria, Herbert Wanyonyi, Samuel Murage, Samuel Mburu, Rahul Dodhia, Juan M Lavista Ferres, Bistra Dilkina

“…We aim to address the existing gap in decision-makers' ability to develop and utilize malnutrition forecasting capabilities for timely interventions. …”
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Assessing urban renewal opportunities by combining 3D building information and geographic big data by Xin Zhao, Nan Xia, ManChun Li

“…However, conventional data sources often fall short in encompassing diverse urban characteristics in the evaluation process, such as urban three-dimensional (3D) building information and the intensity of human activities. To address this gap, this study integrated 3D building data and geographic data to create a comprehensive set of 28 indicators spanning four dimensions: natural environmental conditions, land use, socio-economic factors, and building conditions. …”
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ALL diagnosis: can efficiency and transparency coexist? An explainble deep learning approach by Dost Muhammad, Muhammad Salman, Ayse Keles, Malika Bendechache

“…To address the opacity inherent in Deep learning (DL) models, the framework integrates advanced XAI techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), Class Activation Mapping (CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Integrated Gradients (IG), providing transparent and explainable insights into model predictions. …”
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Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…However, SAM’s perturbation is based solely on the gradient of the standard cross-entropy loss. As the model approaches convergence, this gradient diminishes and oscillates, leading to inconsistent perturbation directions. …”
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Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting by Kittisak Maneepong, Ryota Yamanotera, Yuki Akiyama, Hiroyuki Miyazaki, Satoshi Miyazawa, Chiaki Mizutani Akiyama

“…However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. …”
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Risks and benefits of artificial intelligence deepfakes: Systematic review and comparison of public attitudes in seven European Countries by Nik Hynek, Beata Gavurova, Matus Kubak

“…This study provides an evidence-based integrated appraisal of artificial intelligence (AI)-generated deepfakes by integrating a cross-disciplinary literature synthesis with original opinion-poll evidence from seven European countries. …”
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Diversity, functionality, and stability: shaping ecosystem multifunctionality in the successional sequences of alpine meadows and alpine steppes on the Qinghai-Tibet Plateau by Xin Jin, Abby Deng, Yuejun Fan, Kun Ma, Yangan Zhao, Yingcheng Wang, Kaifu Zheng, Xueli Zhou, Guangxin Lu

“…However, these efforts have not thoroughly explored how different successional stages affect key ecological parameters, such as species and functional diversity, stability, and ecosystem multifunctionality, which are fundamental to ecosystem resilience and adaptability. Given this gap, we systematically investigate variations in vegetation diversity, functional diversity, and the often-overlooked dimension of community stability across the successional gradient from alpine meadows to alpine steppes. …”
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Modeling Hydrologically Mediated Hot Moments of Transient Anomalous Diffusion in Aquifers Using an Impulsive Fractional‐Derivative Equation by Yong Zhang, Xiaoting Liu, Dawei Lei, Maosheng Yin, HongGuang Sun, Zhilin Guo, Hongbin Zhan

“…To bridge this knowledge gap, we propose an innovative model termed “the impulsive, tempered fractional advection‐dispersion equation” (IT‐fADE) to simulate HM‐HMs of TAD. …”
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Development and validation of a deep learning-based pathomics signature for prognosis and chemotherapy benefits in colorectal cancer: a retrospective multicenter cohort study by Shenghan Lou, Yanming Huang, Fenqi Du, Jingmin Xue, Genshen Mo, Hao Li, Zhanjiang Yu, Yuanchun Li, Hang Wang, Yuze Huang, Haonan Xie, Wenjie Song, Xinyue Zhang, Huiying Li, Chun Lou, Peng Han, Peng Han, Peng Han

“…An interpretable multi-instance learning model was developed to construct PSCRC, with SHapley Additive exPlanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) for the improvement of model interpretability and the identification of critical histopathological features, respectively. …”
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Long-Term (2015–2024) Daily PM_2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition by Jiacheng Jiang, Jiaxin Dong, Yu Ding, Wenjia Ni, Jie Yang, Siwei Li

“…First, we utilize the machine learning algorithm XGBoost (EXtreme Gradient Boosting) to address gaps in the daily MAIAC (Multi-Angle Implementation of Atmospheric Correction) AOD (Aerosol Optical Depth), with R²/RMSE (coefficient of determination/Root Mean Square Error) of 0.67/0.2678 compared to AERONET (Aerosol Robotic Network) AOD. …”
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On-chip graphene photodetectors with a nonvolatile p–i–n homojunction by Ruijuan Tian, Yong Zhang, Yingke Ji, Chen Li, Xianghu Wu, Jianguo Wang, Shuaiwei Jia, Liang Liu, Mingwen Zhang, Yu Zhang, Qiao Zhang, Zhuang Xie, Zhengdong Luo, Duorui Gao, Yan Liu, Jianlin Zhao, Zhipei Sun, Xuetao Gan

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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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Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

“…The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.…”
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A Framework to Predict the Quality of a Video for Popularity on Social Media by Abqa Javed, Nimra Abid, Muhammad Shoaib, Muhammad Farrukh Shahzad, Fahad Sabah, Raheem Sarwar

“…Four machine learning models—random forest, stochastic gradient descent classifier (SGDC), gradient boosting, and XGBoost—were evaluated for classification. …”
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Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

“…To address these challenges, this study constructs the first-ever kidney bean leaf spot disease (KBLD) dataset, filling a significant gap in the field. Based on this dataset, a novel hybrid deep learning model framework is proposed, which integrates deep learning models (EfficientNet-B7, MobileNetV3, ResNet50, and VGG16) for feature extraction with machine learning algorithms (Logistic Regression, Random Forest, AdaBoost, and Stochastic Gradient Boosting) for classification. …”
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Multimodal consumer choice prediction using EEG signals and eye tracking by Syed Muhammad Usman, Shehzad Khalid, Aimen Tanveer, Ali Shariq Imran, Muhammad Zubair

“…Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored. To address this gap, we propose a novel multimodal approach to predict consumer choices by integrating EEG and ET data. …”
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An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures by Andrzej D. Dobrzycki, Ana M. Bernardos, José R. Casar

“…We systematically investigate multiple freezing configurations across YOLOv8 and YOLOv10 variants using four challenging datasets that represent critical infrastructure monitoring. Our methodology integrates a gradient behavior analysis (L2 norm) and visual explanations (Grad-CAM) to provide deeper insights into training dynamics under different freezing strategies. …”
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Engaging biological oscillators through second messenger pathways permits emergence of a robust gastric slow-wave during peristalsis. by Md Ashfaq Ahmed, Sharmila Venugopal, Ranu Jung

“…Understanding of the integrative role of neurotransmission and intercellular coupling in the propagation of an entrained gastric slow-wave, important for understanding motility disorders, however, remains incomplete. …”
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Advanced hybrid machine learning models with explainable AI for predicting residual friction angle in clay soils by Mawuko Luke Yaw Ankah, Shalom Adjei-Yeboah, Yao Yevenyo Ziggah, Edmund Nana Asare

“…This study explores three advanced hybrid machine learning models: Gradient Boosting Neural Network (GrowNet), Reinforcement Learning Gradient Boosting Machine (RL-GBM), and a Stacking Ensemble to predict the residual friction angle of clay soils, addressing a critical gap in current predictive methodologies. …”
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Assessing training needs and influencing factors among personnel at centers for disease control and prevention in northeast China: a cross-sectional study framed by SDT and TPB usi... by Kexin Wang, Peng Wang, Min Wei, Yanping Wang, Huan Liu, Ruiqian Zhuge, Qunkai Wang, Nan Meng, Yiran Gao, Yuxuan Wang, Lijun Gao, Jingjing Liu, Xin Zhang, Mingli Jiao, Qunhong Wu

“…Logistic regression, random forest, least absolute shrinkage and selection operator (LASSO), and extreme gradient boosting (XGBoost) were used to predict training needs and explore the impact of various multi-dimensional factors. …”
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