Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…Abstract When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. …”
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Clinical significance of a machine learning model based on short-term changes in NT-proBNP after TAVR by Yu Mao, Mengen Zhai, Ping Jin, Gejun Zhang, Haibo Zhang, Lai Wei, Xiaoke Shang, Jian Liu, Yingqiang Guo, Xiangbin Pan, Yang Liu, Jian Yang

“…Methods: The differences in the NT-proBNP ratio between baseline, 30-day, and 6-month follow-up of patients in the internal derivation cohort (n = 1115) were recorded as D1 and D2; the difference ratio of the NT-proBNP ratio (D2/D1) was recorded as DR. …”
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Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction by Omar Abdullatif Jassim, Mohammed Jawad Abed, Zenah Hadi Saied Saied

“…Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. …”
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Interpretability-Oriented Adjustment of K-Means: A Multiple-Objective Particle Swarm Optimization Framework by Liang Chen, Leming Sun, Caiming Zhong

“…Clustering is an unsupervised machine learning technique used to partition unlabeled data into different groups. However, traditional clustering methods only provide a set of results without any explanations. …”
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Cetacean feeding modelling using machine learning: A case study of the Central-Eastern Mediterranean Sea by Carla Cherubini, Giulia Cipriano, Leonardo Saccotelli, Giovanni Dimauro, Giovanni Coppini, Roberto Carlucci, Carmelo Fanizza, Rosalia Maglietta

“…Behavioural data from April 2016 to October 2023, coupled with 20 environmental variables from Copernicus Marine Service and EMODnet-bathymetry datasets, were used to build Cetacean Feeding Models (CFMs) for the target species using Random Forest and RUSBoost algorithms. Multiple subsets of environmental predictors—physiographic, physical, inorganic, and bio-chemical—were employed to develop and evaluate ML models tailored to feeding prediction. …”
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NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…In this study, using the MS UAV NDVI map as reference, a comprehensive evaluation approach was applied where each pixel of the NDVI prediction maps produced by categorical boosting (CatBoost), light gradient boosting machine (LightGBM) and a stacking ensemble learning model obtained from the combination of both algorithms, whose performance in NDVI estimation has not been tested extensively before. …”
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Providing a Robust Dynamic Pricing Model and Comparing It with Static Pricing in Multi-level Supply Chains Using a Game Theory Approach by Sara Mehrjoo, Hanan Amoozad Mahdirji, Jalil Heidary Dahoei, Seyyed Hossein Razavi Haji Agha, Mahnaz Hosseinzadeh

“…For data analysis, a genetic algorithm, particle accumulation optimization, and MATLAB software were employed. …”
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Stain Normalization of Histopathological Images Based on Deep Learning: A Review by Chuanyun Xu, Yisha Sun, Yang Zhang, Tianqi Liu, Xiao Wang, Die Hu, Shuaiye Huang, Junjie Li, Fanghong Zhang, Gang Li

“…However, color variations caused by differences in tissue preparation and scanning devices can lead to data distribution discrepancies, adversely affecting the performance of downstream algorithms in tasks like classification, segmentation, and detection. …”
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Genomic Analysis of Reproductive Trait Divergence in Duroc and Yorkshire Pigs: A Comparison of Mixed Models and Selective Sweep Detection by Changyi Chen, Yu He, Juan Ke, Xiaoran Zhang, Junwen Fei, Boxing Sun, Hao Sun, Chunyan Bai

“…Additive and dominant genetic effects were partitioned and evaluated by using the combination of the linear mixed models (LMM) and ADDO’s algorithm (LMM + ADDO). …”
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Intensifying cropping sequences in the US Central Great Plains: an in silico analysis of a sorghum–wheat sequence by Lucia Marziotte, Ana J. P. Carcedo, Daniel Rodriguez, Laura Mayor, P. V. Vara Prasad, Ignacio A. Ciampitti, Ignacio A. Ciampitti

“…Using terciles of historical input costs for all crop sequences we calculated three cost scenarios low, intermediate, and high. A fuzzy-C means algorithm was used to classify regions based on crop sequences’ profits, resulting in four clusters.Results and discussionResults included two regions where sorghum-wheat was more profitable than the monocrops i.e., one with lower profits (S+W lower), and a second one with higher profits (S+W higher); a third cluster where wheat monocrop was most profitable (W), and lastly one cluster showing no difference between the sorghum-wheat sequence and the wheat monocrop (S+W or W). …”
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Imbalance between skeletal muscle and intermuscular fat predicts treatment failure in Crohn’s disease: an imaging biomarker for risk stratification by Ziman Xiong, Yufan Wang, Yuchen Jiang, Yaqi Shen, Zhen Li

“…Cox proportional hazards analysis identified predictors of escalation; mediation analysis evaluated inflammatory-nutritional pathways.Results Among 157 patients (penetrating: n = 42; non-penetrating: n = 115), treatment escalation rates were 64.3% (27/42) and 53.0% (61/115) respectively, without significant intergroup difference (p = 0.21). …”
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Construction and Comparison of Machine Learning-Based Risk Prediction Models for Major Adverse Cardiovascular Events in Perimenopausal Women by Chen A, Chang X, Bian X, Zhang F, Ma S, Chen X

“…In the training set, Random Forest (RF) algorithm, backpropagation neural network (BPNN) and Logistic Regression (LR) were used to construct a MACE risk prediction model for perimenopausal women, and the test set was used to verify the model. …”
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CO21 | Viscoelastic testing in inherited bleeding disorders: a cross-sectional comparison between viscoelastic coagulation monitoring (VCM) and rotational thromboelastometry (ROTE...

“…Spearman correlation (ρ) was used: (i) to assess the association between residual FVIII and VCM/ROTEM parameters in HA; (ii) to evaluate agreement between homologous VCM and ROTEM parameters in the entire cohort. …”
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Identification and experimental validation of ulcerative colitis-associated hub genes through integrated WGCNA and lysosomal autophagy analysis by Yuanpei Zhao, Yijun Li, Qingwen Xu, Lili Ding, Weiming Li, Qinghua Zou, Yichen Hu, Kaiwen Shi, Hongyuan Liu

“…Immune cell infiltration of these gene sets was evaluated using the CIBERSORT algorithm. Lysophagy-related genes set were retrieved from the GeneCards database. …”
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Impact of anthropogenic disturbance and climate on bamboo distribution in shifting cultivation landscapes of Northeast India by Muna Tamang, Subrata Nandy, Ritika Srinet, Yamini Bhat, Hitendra Padalia, Arun Jyoti Nath, Ashesh Kumar Das, R. P. Singh

“…The influence of climatic drivers on bamboo distribution was analyzed using the RF algorithm, and vapour pressure deficit was identified as the most influential factor. …”
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Rapid test for the qualitative simultaneous determination of cardiac fatty acid-binding protein and cardiac troponin I in the diagnosis of acute coronary syndrome by V. A. Kokorin, I. G. Gordeev, M. N. Arefyev, A. Ya. Goncharova, A. A. Yakovtsova

“…Further studies will clarify the place of this technique in the modern algorithm for the management of patients with ACS and evaluate the possibility of using the rapid test in predicting the course of the disease.…”
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Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study by Shu Wang, Shuang Wang, Rong Chen, Ling Luo, Qiaoli Zhang, Danli Kong, Rudai Cao, Chunwen Lin, Jialu Huang, Haibing Yu, Yuan Lin Ding

“…The accuracy, precision, recall and area under receiver operating characteristic curve (AUC) were used to evaluate the prediction effect of models, and Delong test was used to analyse the differences of AUC values of each model.Results After balancing data, the sample size increased to 8013, of which 4023 are patients with T2DM and 3990 in control group. …”
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Critical soil moisture detection and water–energy limit shift attribution using satellite-based water and carbon fluxes over China by Y. Liu, J. Xiao, X. Li, Y. Li

“…At flux sites, ET and GPP products were evaluated by eddy-covariance-based measurements; CSM values using two satellite-based methods were assessed using the soil moisture–evaporative fraction method. …”
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Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features by Xue Du, Xue Du, Chunbao Chen, Lu Yang, Yu Cui, Min Li

“…Region of interest (ROI) was outlined using the 3D Slicer software, and radiomics after feature extraction and feature screening was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression algorithms were used to construct a fusion clinical-radiomics model to visualize nomograms. …”
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Analysis of Wind–Wave Relationship in Taiwan Waters by Kai-Ho Cheng, Chih-Hsun Chang, Yi-Chung Yang, Yu-Hao Tseng, Chung-Ru Ho, Tai-Wen Hsu, Dong-Jiing Doong

“…According to the power law formula describing the relationship between wind speed and SWH, the eastern waters exhibited a larger prefactor coupled with a smaller scaling exponent, while the western waters manifested a converse parametric configuration. Through an evaluation of four machine learning algorithms, it was determined that wind speed is the most influential factor driving these regional differences, especially in the waters west of Taiwan. …”
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