A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…The Histogram-Based Gradient Boosting algorithm, optimized with Bayesian techniques and trained with data balanced through Random Oversampling, achieved a balanced accuracy of 0.63 on a test set with real-world class distribution. …”
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Classification of Some Fruits using Image Processing and Machine Learning by Dilara Gerdan Koç, Mustafa Vatandaş

“…On the other hand, the size and color values read in fruits with the image processing algorithm were evaluated using predictive techniques used in data mining. …”
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Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach by Lukas Zbinden, Samuel Erb, Damiano Catucci, Lars Doorenbos, Leona Hulbert, Annalisa Berzigotti, Michael Brönimann, Lukas Ebner, Andreas Christe, Verena Carola Obmann, Raphael Sznitman, Adrian Thomas Huber

“…Relevance statement The proposed automated, sequence-adaptive algorithm for total and segmental analysis of liver mpMRI may be co-registered to any combination of parametric sequences, enabling comprehensive quantitative analysis of liver mpMRI without sequence-specific training. …”
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A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences by Christopher Schnur, Payman Goodarzi, Yannick Robin, Julian Schauer, Andreas Schütze

“…This allows the user to exclude specific groups in the training to validate and test the algorithm. Using this approach, algorithms can be evaluated for their robustness and the effect on the model caused by distribution shifts, allowing their generalization capabilities to be studied under realistic conditions.…”
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Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American populat... by Qian Li, Yuan Tian, Wei Peng, Shangcheng Yan, Weiran Yang, Zhuan Du, Ming Cheng, Renwei Chen, Qiankun Shao, Mengchao Sheng, Yongyou Wu

“…The predictive value of these models was validated and evaluated through receiver operating characteristic curves, precision-recall (PR) curves, calibration curves, decision curve analysis and accuracy metrics.Results Among the ML algorithms, the ANN outperformed others, achieving the highest accuracy (0.722; 95% CI: 0.692 to 0.751), precision (0.732; 95% CI: 0.694 to 0.776), F1 score (0.733; 95% CI: 0.695 to 0.773), specificity (0.728; 95% CI: 0.684 to 0.770) and area under the PR curve (0.781; 95% CI: 0.740 to 0.821) in the external validation results. …”
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Real-Time Fire Object Detection System Using Machine Learning by Akuthota Venkata Bhargavi., Syed Khadar Basha., Ramineni Dhanush., Guduru Vikram.

“…This set of scenarios under various fire conditions, environmental conditions, and backgrounds was curated for training a CNN. In terms of evaluating the model’s robustness in real applications across various settings, the metrics were defined by accuracy, precision, recall, and F1 scores. …”
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Machine learning differentiation of rheumatoid arthritis-Sjögren’s syndrome overlap from Sjögren’s syndrome with polyarthritis by Minzhi Gan, Yong Peng, Ying Ying, Keyue Zhang, Yong Chen

“…ObjectiveThis study aimed to evaluate the utility of machine learning algorithms in differentiating rheumatoid arthritis-Sjögren’s syndrome overlap (RA-SS) from Sjögren’s syndrome with polyarthritis (SS-PA), and to identify key factors influencing diagnostic differentiation.MethodsThis retrospective analysis included 106 RA-SS and 135 SS-PA patients randomized 7:3 into training and validation sets. …”
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YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera by Iyad Almadani, Mohammed Abuhussein, Aaron L. Robinson

“…We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. …”
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Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis by Iyad Almadani, Aaron L. Robinson, Mohammed Abuhussein

“…Leveraging the advantages of deep learning, we train a model on these annotated images, enabling segmentation using the cutting-edge YOLOv9 algorithm. …”
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Dataset for estimating reinforcement, width and penetration of the weld bead in the GMAW process using thermographic informationfigshare by Bruno Mota de Souza, Guillermo Alvarez Bestard, Sadek C. Absi Alfaro

“…Data from four experiments are available, offering extensive material for training, validation, and further exploration.…”
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A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer by Rongrong Bian, Feng Zhao, Bo Peng, Jin Zhang, Qixing Mao, Lin Wang, Qiang Chen

“…In the discovery phase, two algorithms, least absolute shrinkage and selector operation and support vector machine‐recursive feature elimination, were used to identify candidate genes. …”
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Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification by Keming Mao, Renjie Tang, Xinqi Wang, Weiyi Zhang, Haoxiang Wu

“…The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representation of lung nodule image. Finally, softmax algorithm is employed for lung nodule type classification, which can assemble the whole training process as an end-to-end mode. …”
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IMPROVING PHARMACEUTICAL COUNSELLING OF PHARMACIES VISITORS CHOOSING THE METHODS OF CONSERVATIVE TREATMENT OF VARICOSE DISEASE OF THE LOWER LIMBS (WITHOUT ULCERS AND INFLAMMATION) by N. N. CHESNOKOVA, S. V. KONONOVA, S. V. PETROVA

“…Research was conducted among 500 pharmaceutical specialists of Nizhny Novgorod Oblast, working in retail pharmaceutical organizations, during which they evaluated their professional competence in the field of pharmaceutical and basic medical knowledge. …”
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Hyperspectral data of understory elements in boreal forests: In situ and laboratory measurementsMendeley DataMendeley Data by Audrey Mercier, Susanna Karlqvist, Aarne Hovi, Miina Rautiainen

“…These data support the analysis of vegetation characteristics, training of classification algorithms and improvement of forest radiative transfer models, and could be used to evaluate the potential of hyperspectral data to discriminate the understory elements of boreal forest.…”
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Spatial-temporal attention for video-based assessment of intraoperative surgical skill by Bohua Wan, Michael Peven, Gregory Hager, Shameema Sikder, S. Swaroop Vedula

“…Algorithms to classify surgical video into expert or novice categories provide a summative assessment of skill, which is useful for evaluating surgeons at discrete time points in their training or certification of surgeons. …”
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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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Predicting grade II-IV bone marrow suppression in patients with cervical cancer based on radiomics and dosiomics by Yanchun Tang, Yanchun Tang, Yaru Pang, Jingyi Tang, Jingyi Tang, Xinchen Sun, Xinchen Sun, Peipei Wang, Jinkai Li

“…The patients were randomly divided into training set and test set in an 8:2 ratio. The radiomic features and dosiomic features were extracted from the pelvic bone marrow (PBM) of planning CT images and radiotherapy planning documents, and the least absolute shrinkage and selection operator (LASSO) algorithm was employed to identify the best predictive characteristics. …”
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Effectiveness of an electronic clinical decision support system in improving the management of childhood illness in primary care in rural Nigeria: an observational study by Marek Kwiatkowski, Rodolfo Rossi, Torsten Schmitz, Fenella Beynon, Capucine Musard, Marco Landi, Daniel Ishaya, Jeremiah Zira, Muazu Muazu, Camille Renner, Edwin Emmanuel, Solomon Gideon Bulus

“…Objectives To evaluate the impact of ALgorithm for the MANAgement of CHildhood illness (‘ALMANACH’), a digital clinical decision support system (CDSS) based on the Integrated Management of Childhood Illness, on health and quality of care outcomes for sick children attending primary healthcare (PHC) facilities.Design Observational study, comparing outcomes of children attending facilities implementing ALMANACH with control facilities not yet implementing ALMANACH.Setting PHC facilities in Adamawa State, North-Eastern Nigeria.Participants Children 2–59 months presenting with an acute illness. …”
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Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer by Yun Zhu, Shuni Zhang, Wei Wei, Li Yang, Lingling Wang, Ying Wang, Ye Fan, Haitao Sun, Zongyu Xie

“…The intratumoral radiomics model (IRM), 2-mm, 4-mm, 6-mm, 8-mm peritumoral radiomics model (PRM), the combined intra- and the optimal peritumoral radiomics model (CIPRM) were constructed based on five machine learning algorithms, and then the radiomics scores (Rad-score) were obtained. …”
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Multi‐sequence MRI‐based clinical‐radiomics models for the preoperative prediction of microsatellite instability‐high status in endometrial cancer by Zhuang Li, Yi Su, Yongbin Cui, Yong Yin, Zhenjiang Li

“…The performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). …”
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