An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

“…In addition, the study area's hydro-chemical facies were examined, and machine learning models’ hyperparameters such as Random Forest (RF), Borda Scoring Algorithm (BSA), Decision Tree (DT), Multilayer Perception (MLP), and Naïve Bayes (NB), were executed before, to training and testing the samples of surface water. …”
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A novel canopy water indicator for UAV imaging to monitor winter wheat water status by Meiyan Shu, Zhenghang Ge, Yang Li, Jibo Yue, Wei Guo, Yuanyuan Fu, Ping Dong, Hongbo Qiao, Xiaohe Gu

“…To develop robust estimation models, four machine learning algorithms were implemented across individual and combined growth stages, and their performance was validated using independent ground-measured datasets that were not used during the training process. …”
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Forecasting Insurance Company Commitments with Long Short-Term Memory Models by Negar Tehraniyazdi, Reza Vaezi, Saeed Setayeshi, Iman Raeesi Vanani

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Integrating Machine Learning and Deep Learning for Predicting Non-Surgical Root Canal Treatment Outcomes Using Two-Dimensional Periapical Radiographs by Catalina Bennasar, Antonio Nadal-Martínez, Sebastiana Arroyo, Yolanda Gonzalez-Cid, Ángel Arturo López-González, Pedro Juan Tárraga

“…<b>Methods</b>: The DL model was trained and validated using leave-one-out cross-validation (LOOCV). …”
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Screening and Validation of Potential Biomarkers of Immune Cells in Childhood Asthma Patients via Mendelian Randomization and Machine Learning by Zhang Y, Hai Y, Song B, Xu J, Cao L, Yasen R, Xu W, Zhang J, Hu J

“…LASSO logistic regression and SVM algorithms were used to identify key genes associated with childhood asthma. …”
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Establishment of prognostic risk model related to disulfidptosis and immune infiltration in hepatocellular carcinoma by Zhe Xu, Chong Pang, Xundi Xu

“…Methods: The TCGA-LIHC cohort was utilized as the training set to identify molecular subtypes associated with disulfidptosis and to perform immune infiltration analysis. …”
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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions by Chao Cao, Mengli Li, Chunyu Wang, Lei Xu, Quan Zou, Yansu Wang, Wu Han

“…Although some computational models have been developed to identify these associations, they fail to capture the deep collaborative features between circRNA and miRNA interactions and do not guide the training of feature extraction networks based on these high-order relationships, leading to poor prediction performance. …”
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Unsupervised Feature Selection via a Dual-Graph Autoencoder with <inline-formula><math display="inline"><semantics><mrow><msub><mi mathvariant="bold-script">l</mi><mrow><mn mathvar... by Zhichao Song, Meiling Chen, Liang Xie, Xi Fang

“…This paper proposes a novel unsupervised feature selection algorithm based on a dual-graph autoencoder (DGA), which combines the powerful data reconstruction capability of autoencoders with the structural preservation strengths of graph regularization. …”
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Identification of Energy Metabolism-Related Subtypes and Diagnostic Biomarkers for Osteoarthritis by Integrating Bioinformatics and Machine Learning by Xu S, Ye J, Cai X

“…Abnormal energy metabolism is closely associated with the pathological mechanisms of OA. This study aims to identify key genes related to energy metabolism that are closely linked to the treatment and diagnosis of OA.Methods: The transcriptomic data for OA were collected from the Gene Expression Omnibus (GEO), with GSE51588 and GSE63359 serving as the training and validation datasets, respectively. …”
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Latent environment allocation of microbial community data. by Koichi Higashi, Shinya Suzuki, Shin Kurosawa, Hiroshi Mori, Ken Kurokawa

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Integrating Machine Learning for Early Mortality Prediction in Lung Adenosquamous Carcinoma: A Web-Based Prognostic Model by Min Liang MD, PhD, Xiaocai Li MD, Shangyu Xie MD, Xiaoying Huang MD, Shifan Tan MD

“…This study aimed to quantify the 90-day mortality rate in patients with ASC, identify associated features, and develop a predictive machine learning model. …”
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Construction of a prediction model for sarcopenic obesity based on machine learning by Mengru Xu, Mengru Xu, Jia Liu, Jia Liu, Song Hu, Song Hu, Tongxiao Luan, Tongxiao Luan, Yuting Duan, Yuting Duan, Aohua Wang, Aohua Wang, Ziwei Cui, Ziwei Cui, Jing Zhou, Yongjun Mao, Yongjun Mao

“…BackgroundIn the context of the rapidly aging global population, sarcopenic obesity (SO) in older adults is associated with significantly higher rates of disability and mortality. …”
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A novel and efficient personalized stress detection technique using a deep learning model by Ulligaddala Srinivasarao, Gopisetty Rathnamma, M. Satish Kumar, Lakshmipathi Anantha, Rakesh Kumar Donthi, T. Jhansi Rani

“…Several limitations occurred, such as higher training time, high time consumption, and limited features utilized to train the model. …”
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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions by Qingchun Guo, Zhenfang He, Shanshan Li, Xinzhou Li, Jingjing Meng, Zhanfang Hou, Jiazhen Liu, Yongjin Chen

“…When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage. …”
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Accurate size-based protein localization from cryo-ET tomograms by Weisheng Jin, Ye Zhou, Alberto Bartesaghi

“…We compare the performance of our approach against a commonly used algorithm based on deep learning, crYOLO, and show that our method: i) has higher detection accuracy, ii) does not require user input for labeling or time-consuming training, and iii) runs efficiently on non-specialized CPU hardware. …”
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VG-CGARN: Video Generation Using Convolutional Generative Adversarial and Recurrent Networks by Fatemeh Sobhani Manesh, Amin Nazari, Muharram Mansoorizadeh, MirHossein Dezfoulian

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Class‐specific data augmentation for plant stress classification by Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian

“…We fine‐tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. …”
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Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics by Mulan Pan, Lu Lu, Xingyu Mu, Xingyu Mu, Guanqiao Jin

“…The K-means clustering algorithm was utilized to segment the tumor into five distinct habitat subregions based on imaging features. …”
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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

“…Three machine‐learning algorithms were used to develop radiomic signatures. …”
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A bayesian network model for neurocognitive disorders digital screening in Chinese population: development and validation study by Yifan Yu, Shuaijie Zhang, Hongkai Li, Fuzhong Xue

“…Participants were assigned to a training set or a validation set based on their geographic locations. …”
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