Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…This study not only verifies the effectiveness of combining multisource data and advanced algorithms but also provides a scientific basis for the precision management and decision-making of rice cultivation.…”
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Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data by Abdelaziz Mojahid, Driss EL Ouai, Khalid EL Amraoui, Khalil EL-Hami, Hamou Aitbenamer, Jochem Verrelst, Pier Matteo Barone

“…Therefore, automated approaches using machine learning algorithms for identifying subsurface anomalies have recently emerged, providing promising pathways for real-time cavity detection. …”
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MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images by Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong Chen

“…Due to complex backgrounds, significant scale variations of targets, and dense distributions of small objects in Unmanned Aerial Vehicle (UAV) aerial images, traditional object detection algorithms face challenges in adapting to such scenarios. …”
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Assessment of the potential for carbon sink enhancement in the overlapping ecological project areas of China by Xiaojuan Xu, Fusheng Jiao, Dayi Lin, Jie Qiu, Changxin Zou, Kun Zhang

“…Using the ensemble empirical mode decomposition method and machine learning algorithms (enhanced boosted regression trees), the aims of this study to elucidate the stability of carbon sinks and their driving mechanisms in areas where ecological projects overlap and to predict the potential enhancement in carbon sinks under varying climate and human activity scenarios. …”
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Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…By integrating these algorithms, the model achieves more balanced outputs than standalone approaches. …”
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Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions by Muhammad Liaquat Raza, Muhammad Liaquat Raza, Muhammad Liaquat Raza, Syed Tawassul Hassan, Subia Jamil, Noorulain Hyder, Kinza Batool, Sajidah Walji, Muhammad Khizar Abbas

“…Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy and predicting disease progression.MethodThis narrative review synthesizes current literature on deep learning applications in Alzheimer’s disease diagnosis using multimodal neuroimaging. …”
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Computer Vision-Based Monitoring of Bridge Structural Vibration During Incremental Launching Construction by Hong Shi, Min Zhang, Tao Jin, Xiufeng Shi, Jian Zhang, Yixiang Xu, Xinyi Guo, Xiaoye Cai, Weibing Peng

“…The method utilizes high-definition cameras to capture dynamic images of bridges and incorporates advanced image processing algorithms to automatically identify and track the vibration characteristics of bridge structures, achieving low energy consumption, low cost, and high efficiency in monitoring. …”
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Adult Hope Scale: validation in older adults by Kate Labrosciano, Jillian Dorrian, Braam Lowies, Roslyn Russell, Kurt Lushington

“…Dimensionality was tested by (i) bifactor modelling (one-factor, two-factor and a bifactor model with a general factor, Hope, and two specific factors, Agency and Pathways) and (ii) exploratory graph analysis (which uses community detection algorithms to cluster variables into factors). Cross-gender invariance was also tested. …”
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ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements by Hezha H. MohammedKhan, Cascha Van Wanrooij, Eric O. Postma, Çiçek Güven, Marleen Balvert, Heersh Raof Saeed, Chenar Omer Ali Al Jaf

“…To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…The dataset of this study comprises 301 records collected from eight monitoring stations along the Kinta River, encompassing 31 pollution indicators, including hydrological, chemical, physical, and microbiological parameters. Six algorithms used include decision tree, logistic regression, random forest, support vector machine, AdaBoost, and XGBoost. …”
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Optimized Landing Site Selection at the Lunar South Pole: A Convolutional Neural Network Approach by Yongjiu Feng, Haoteng Li, Xiaohua Tong, Pengshuo Li, Rong Wang, Shurui Chen, Mengrong Xi, Jingbo Sun, Yuhao Wang, Huaiyu He, Chao Wang, Xiong Xu, Huan Xie, Yanmin Jin, Sicong Liu

“…Although intelligent algorithms have been increasingly investigated for this purpose, the application of deep learning techniques in landing site selection remains unexplored. …”
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Comprehensive analysis of regulatory B Cell related genes in prognosis and therapeutic response in lung adenocarcinoma by Liangyu Zhang, Liangyu Zhang, Jianshen Zeng, Jianshen Zeng, Xun Zhang, Xun Zhang, Menglong Zhang, Menglong Zhang, Yilin Lin, Yilin Lin, Fancai Lai, Fancai Lai

“…Differentially expressed genes between the two clusters were then used to construct the BREGI using 32 algorithms, including traditional regression, machine learning, deep learning, and 274 different combinations. …”
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Effects of respiratory viruses on febrile neutropenia attacks in children by Bilge Aldemir-Kocabaş, Ergin Çiftçi, Gülsan Yavuz, Zümrüt Uysal, Elif İnce, İştar Dolapçı, Zeynep Ceren Karahan, Esra Pekpak, Adem Karbuz, Erdal İnce

“…Identifying viral agents may help to constitute individualized infection-management algorithms in these patients. …”
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Research on Super-Resolution Reconstruction of Coarse Aggregate Particle Images for Earth–Rock Dam Construction Based on Real-ESRGAN by Shuangping Li, Lin Gao, Bin Zhang, Zuqiang Liu, Xin Zhang, Linjie Guan, Junxing Zheng

“…Experimental results show that Real-ESRGAN outperforms other traditional super-resolution algorithms in terms of edge clarity, detail recovery, and the preservation of morphological features of particle images, particularly under low-resolution conditions, with significant improvement in image reconstruction. …”
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques by Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch

“…Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. …”
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STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…Then, a relapse predictive risk-scoring model was established using the least absolute shrinkage and selection operator regression algorithms. The scores based on the expression of ATRX and STING significantly predict the recurrence for glioma patients, which further predict the survival for specific subgroups, characterized with high expression of RAD51 and wild-type TERT. …”
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ggClusterNet 2: An R package for microbial co‐occurrence networks and associated indicator correlation patterns by Tao Wen, Yong‐Xin Liu, Lanlan Liu, Guoqing Niu, Zhexu Ding, Xinyang Teng, Jie Ma, Ying Liu, Shengdie Yang, Penghao Xie, Tianjiao Zhang, Lei Wang, Zhanyuan Lu, Qirong Shen, Jun Yuan

“…To address emerging challenges, including multi‐factor experimental designs, multi‐treatment conditions, and multi‐omics data, we present a comprehensive upgrade with four key components: (1) A microbial co‐occurrence network pipeline integrating network computation (Pearson/Spearman/SparCC correlations), visualization, topological characterization of network and node properties, multi‐network comparison with statistical testing, network stability (robustness) analysis, and module identification and analysis; (2) Network mining functions for multi‐factor, multi‐treatment, and spatiotemporal‐scale analysis, including Facet.Network() and module.compare.m.ts(); (3) Transkingdom network construction using microbiota, multi‐omics, and other relevant data, with diverse visualization layouts such as MatCorPlot2() and cor_link3(); and (4) Transkingdom and multi‐omics network analysis, including corBionetwork.st() and visualization algorithms tailored for complex network exploration, including model_maptree2(), model_Gephi.3(), and cir.squ(). …”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. …”
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Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports by Sophia Wesely, Ella Hofer, Robin Curth, Shyam Paryani, Nicole Mills, Olaf Ueberschär, Julia Westermayr

“…Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. …”
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Cadmium accumulation in wheat grain: Accumulation models and soil thresholds for safe production by Lu Lin, Xiaopeng Zhao, Yumeng Li, Jingbo Ling, Jinghua Ren, Qilin Liao, Dongmei Zhou, Xueyuan Gu

“…Furthermore, the Extreme Random Tree model (RMSE ​= ​0.221, MAE ​= ​0.165) outperformed the other seven machine learning algorithms. The thresholds for both soil total Cd and bioavailable Cd for safe wheat production were further back-calculated according to the permissible value of Cd in wheat grain, which demonstrated enhanced protection accuracy compared to the current soil quality standard. …”
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