Development of model for human factor influence assessment on construction and road machines operation efficiency by V. E. Ovsiannikov, V. I. Vasiliev

“…The developed model for assessing the influence of the human factor on the efficiency of machine operation uses risk as an output variable, and input variables a generalized indicator of the complexity of the algorithm and the level of qualification of the machine operator.Discussion and conclusions. …”
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Human–Computer Vision Collaborative Measurement of Surgical Exposure and Length in Endonasal Endoscopic Skull Base Surgery by Chia-En Wong, Yu-Chen Kuo, Da-Wei Huang, Pei-Wen Chen, Heng-Jui Hsu, Wei-Ting Lee, Shang-Yu Hung, Jung-Shun Lee, Sheng-Fu Liang

“…The measured length and area were calibrated by training the current algorithm using EEA videos. A total of 50 EEA operative videos were analyzed, with 95.1%, 95.8%, and 96.2% accuracies in the training, test-1 and test-2 datasets, respectively. …”
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Solar Sail Transfers under Uncertainties: A Deep Reinforcement Learning Approach by Christian Bianchi, Lorenzo Niccolai, Giovanni Mengali

“…To account for these uncertainties, a proximal policy optimization algorithm is used to train an agent that learns a control policy associating any orbital state with the corresponding sail attitude, minimizing deviations from the reference trajectory. …”
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An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 Data by F. Quispe-Huaynasi, F. Roig, N. Holanda, V. Loaiza-Tacuri, Romualdo Eleutério, C. B. Pereira, S. Daflon, V. M. Placco, R. Lopes de Oliveira, F. Sestito, P. K. Humire, M. Borges Fernandes, A. Kanaan, C. Mendes de Oliveira, T. Ribeiro, W. Schoenell

“…First, using an anomaly detection technique based on an autoencoder model, we select a large sample of objects (∼19,000) whose Spectral Energy Distribution is not well reconstructed by the model after training it on a well-behaved star sample. Then, we apply the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm to the 66 color measurements from S-PLUS, complemented by information from the SIMBAD database, to identify stellar populations. …”
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Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features by Haoru Wang MD, Ling He MD, Xin Chen MD, Shuang Ding MD, Mingye Xie MD, Jinhua Cai MD

“…Correlation analysis, Least Absolute Shrinkage and Selection Operator regression, and one-way analysis of variance were used to identify radiomics features associated with bone marrow metastasis. A predictive model for bone marrow metastasis was then developed using the support vector machine algorithm based on the selected radiomics features. …”
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Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning by Shaocheng Hong, Hongqian Wang, Shixin Chan, Jiayi Zhang, Bangjie Chen, Xiaohan Ma, Xi Chen

“…Consensus clustering based on these 52 MRGs divided the integrated UC cohorts into three subtypes. Machine learning algorithms were used to identify ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (SLC6A14), and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) in the training set, and their diagnostic value was validated in independent validation sets. …”
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Integrated single cell and bulk RNA sequencing analyses reveal the impact of tryptophan metabolism on prognosis and immunotherapy in colon cancer by Yanyan Hu, Ximo Xu, Hao Zhong, Chengshen Ding, Sen zhang, Wei Qin, Enkui Zhang, Duohuo Shu, Mengqin Yu, Naijipu Abuduaini, Xiao Yang, Bo Feng, Jianwen Li

“…Abstract Tryptophan metabolism is intricately associated with the progression of colon cancer. This research endeavored to meticulously analyze tryptophan metabolic characteristics in colon cancer and forecast immunotherapy responses. …”
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Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models by Jie Liu, Xia Li, Yanting Wang, Zhenzhen Xu, Yong Lv, Yuyao He, Lu Chen, Yiqi Feng, Guoyang Liu, Yunxiao Bai, Wanli Xie, Qingping Wu

“…The included patients were randomly divided into training and validation sets at a ratio of 7:3. The features selected by the least absolute shrinkage and selection operator regression algorithm were used as the input variables of the ML and LR models. …”
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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

“…The machine learning models were developed using five different algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting model (XGBoost), and light gradient boosting machine (Light GBM).ResultsThe XGBoost model exhibited the most superior performance in predicting amikacin-associated AKI among the developed machine learning models. …”
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Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models by Zengming Wang, Lingxin Kong, Bin Li, Qingtao Zhao, Xiaopeng Zhang, Huanfen Zhao, Wenfei Xue, Wei Li, Shun Xu, Guochen Duan

“…Results Imaging histology features showed good model efficacy in both the training set (LR AUC = 0.764) and the test set (LR AUC = 0.776), and we combined the imaging histology and clinical features to jointly build a nomogram graph (AUC = 0.878), extracted the deep learning features, and built a machine learning model based on the ResNET50 algorithm, where the LR AUC = 0.918. …”
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Spatial identification of manipulable objects for a bionic hand prosthesis by Yurii LOBUR, Kostiantyn VONSEVYCH, Natalia BEZUGLA

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Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM by Fuyu Wang, Huiying Xu, Huifen Ye, Yan Li, Yibo Wang

“…In order to address challenges such as the large computational workload, tedious training process, and multiple influencing factors associated with predicting earthquake casualties, this study proposes a Support Vector Machine (SVM) model utilizing Principal Component Analysis (PCA) and Bayesian Optimization (BO). …”
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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…However, the underlying machine learning strategy used to develop and refine the EGF algorithm has not yet been detailed. Here, we present how our EGF Model—trained on procedural outcomes from 199 fully anonymized retrospective patient datasets—identifies clinically significant sources of AF and how this machine learning–driven hyperparameter optimization underlies its clinical effectiveness. …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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Development of a Model of Segmentation of the Capillaries of the Ocular Surface Based on Images from an Ophthalmological Slit Lamp Using Artificial Intelligence Tools by V. V. Neroev, A. A. Bragin, O. V. Zaytseva, E. V. Yani

“…The system of segmentation of the capillaries of the eye in the images from the ophthalmological slit lamp is based on the trained neural network Unet.Results. The main result of the study is the development of an algorithm for automatic segmentation of eye capillaries in images from an ophthalmic slit lamp. …”
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Study of machine learning techniques for outcome assessment of leptospirosis patients by Andreia Ferreira da Silva, Karla Figueiredo, Igor W. S. Falcão, Fernando A. R. Costa, Marcos César da Rocha Seruffo, Carla Cristina Guimarães de Moraes

“…Abstract Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. …”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…However, due to their nature, these networks often struggle to delineate desired structures in data that fall outside their training distribution. The goal of this study is to address the challenges associated with domain generalization in CT segmentation by introducing a novel method called BucketAugment for deep neural networks. …”
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Use of deep learning in early software bug detection by Syed Mibran Hassan Zaidi, Maria Khan, Mustafa Latif, Ali Akhtar, Sallar Khan

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An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery by Hao Zhu, Danyang Shen, Xiaojie Gan, Ding Sun

“…Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. …”
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Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy by Jing Xue, Menghan Wu, Jing Zhang, Jiayang Yang, Guannan Lv, Baojun Qu, Yanping Zhang, Xia Yan, Xia Yan, Jianbo Song, Jianbo Song

“…Logistic regression (LR), Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO) methods were utilized to select optimal imaging features, leading to a combined prediction model developed using a random forest (RF) algorithm. Model performance was assessed using the area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA), with Shapley Additive exPlanations (SHAP) values for interpretation.ResultsThe samples were split into training (70%) and validation (30%) sets. …”
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