Wound imaging software and digital platform to assist review of surgical wounds using patient smartphones: The development and evaluation of artificial intelligence (WISDOM AI stud... by Melissa Rochon, Judith Tanner, James Jurkiewicz, Jacqueline Beckhelling, Akuha Aondoakaa, Keith Wilson, Luxmi Dhoonmoon, Max Underwood, Lara Mason, Roy Harris, Karen Cariaga

“…Secondary objectives focused on specificity, positive predictive value (PPV), and negative predictive value (NPV) for various wound features.…”
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Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks by Aya M. A. Heikal, Shady H. E. Abdel Aleem, Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz

“…Results demonstrate significant improvements in system reliability, cost efficiency, and flexible operation, validating the effectiveness of ANN-based AL to optimize EHs management and ensure sustainable operation complexities. The AL algorithm enhances the ANN model’s predictive ability, resulting in a 57.9% decrease in operating costs and a 0.010682 loss of energy supply probability (LESP) value. …”
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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…., bioink viscosity, nozzle size, printing time, printing pressure, and cell concentration), and develop algorithms capable of immediate cellular droplet size prediction. …”
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Enhanced estimation of reference evapotranspiration using hybrid deep learning models and remote sensing variables by Tze Ying Fong, Yuk Feng Huang, Ren Jie Chin, Chai Hoon Koo

“…They managed to improve the accuracy of the prediction in most of the cases, with the highest R2 = 0.805 and the lowest prediction errors, MAE = 0.265 mm/day, RMSE = 0.343 mm/day and NRMSE = 0.096. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

“…The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. …”
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Effective Dose Estimation in Computed Tomography by Machine Learning by Matteo Ferrante, Paolo De Marco, Osvaldo Rampado, Laura Gianusso, Daniela Origgi

“…E calculated by DTS was chosen as the target value for prediction. Different machine learning algorithms were selected, optimizing parameters to achieve the best performance for each algorithm. …”
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Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients by Farnaz Khodami, Amanda S. Mahoney, James L. Coyle, Ervin Sejdic

“…By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…Effectiveness of models is validated with eight deep learning algorithms trained for two forecasting horizons. Results demonstrate a 93.74% improvement on an average in convergence compared to conventional mini-batch sizes, achieving one among top three accuracy across various prediction horizons and user categories. …”
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How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors. by Rui Wang, Tamar Schlick

“…Here we introduce a computational topology-based approach with unsupervised machine-learning algorithms to estimate the database size and content of RNA-like graph topologies. …”
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Artificial intelligence and chordoma: A scoping review of the current landscape and future directions by Eddie Guo, Rafael D. Sanguinetti, Lyndon Boone, Jiawen Deng, Husain Shakil, Mehul Gupta

“…Common algorithms used included convolutional neural networks, support vector machines, random forests, and clustering algorithms. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

“…Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). …”
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Adaptive Optimization of Quantized Perturbation Coefficients for Fiber Nonlinearity Compensation by M. Malekiha, D. V. Plant

“…In this paper, we propose and experimentally verify the adoption of a decision-directed least mean square algorithm for optimization and complexity reduction of PB-NLC equalizer. …”
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ABCD microstates: a new word in EEG mathematical analysis by A. A. Ivanov

“…Here, a fairly new approach to the mathematical processing of EEG signals is overviewed that allows not only to diagnose brain functional state, but also to predict the treatment outcomes in critically ill patients in intensive care units. …”
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基于粗糙集与神经网络的齿轮箱故障诊断 by 刘慧玲, 潘宏侠, 王爱玉

“…A method of combining the rough sets and neural network based on the condition attributes discretization and reduct ion algorithm is proposed to fault diagnosis. …”
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Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening. by Michael Phillips, Thomas L Bauer, Renee N Cataneo, Cassie Lebauer, Mayur Mundada, Harvey I Pass, Naren Ramakrishna, William N Rom, Eric Vallières

“…The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). …”
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Remote Management of Heart Failure in Patients with Implantable Devices by Luca Santini, Francesco Adamo, Karim Mahfouz, Carlo Colaiaco, Ilaria Finamora, Carmine De Lucia, Nicola Danisi, Stefania Gentile, Claudia Sorrentino, Maria Grazia Romano, Luca Sangiovanni, Alessio Nardini, Fabrizio Ammirati

“…<b>Results</b>: Precise multi-parameter algorithms, available for ICD and CRT-D patients, have been created, which also use artificial intelligence and are able to predict a new heart failure event more than 30 days in advance. …”
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Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice by Yuke Ren, Yuke Ren, Shuangxing Li, Di Zhang, Yongtian Zhao, Yanwei Yang, Guitao Huo, Xiaobing Zhou, Xingchao Geng, Zhi Lin, Zhe Qu

“…Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.ResultsThe Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. …”
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Machine learning study on magnetic structure of rare earth based magnetic materials by Dan Liu, Jiahe Song, Zhixin Liu, Jine Zhang, Weiqiang Chen, Yinong Yin, Jianfeng Xi, Xinqi Zheng, Jiazheng Hao, Tongyun Zhao, Fengxia Hu, Jirong Sun, Baogen Shen

“…In this work, 11 machine learning algorithms were trained to predict the material magnetic structure. …”
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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…While various global-scale SSN regression and machine learning algorithms based on SSN-environment variable relationships have been developed, the prediction accuracy and spatiotemporal resolution of their applications continue to face limitations. …”
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Serum Lactate Dehydrogenase Levels Reflect the Lung Injury Extension in COVID‐19 Patients at Hospital Admission by Maria Sofia Bertilacchi, Giulia Vannucci, Rebecca Piccarducci, Lorenzo Germelli, Chiara Giacomelli, Chiara Romei, Brian Bartholmai, Greta Barbieri, Claudia Martini, Michela Baccini

“…Results High serum lactate dehydrogenase (LDH) values appeared to be predictive of high CALIPER ILD percentages at hospital admission in both retrospective and prospective datasets, even if the predictive performance of the algorithm was not optimal. …”
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