Fault‐tolerant quantum implementation of conventional decoder logic with enable input by Laxmidhar Biswal, Bappaditya Mondal, Hafizur Rahaman

“…On the other hand, the conventional logic circuit is no more useful in quantum computing due to much difference from quantum logic. However, quantum computer has to perform classical tasks which can be addressed by translating to its equivalent quantum algorithm. …”
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INTEGRATION OF SVM AND SMOTE-NC FOR CLASSIFICATION OF HEART FAILURE PATIENTS by Dina Tri Utari

“…SMOTE (Synthetic Minority Over-sampling Technique) and SMOTE-NC (SMOTE for Nominal and Continuous features) are variations of the original SMOTE algorithm designed to handle imbalanced datasets with continuous and nominal features. …”
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Neural network pruning based on channel attention mechanism by Jianqiang Hu, Yang Liu, Keshou Wu

“…However, most of the existing methods ignore the differences in the contributions of the output feature maps. …”
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Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed‐Cohort Study Based on 16,189 Participants by Cong Lai, Zhensheng Hu, Jintao Hu, Zhuohang Li, Lin Li, Mimi Liu, Zhikai Wu, Yi Zhou, Cheng Liu, Kewei Xu

“…Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. …”
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Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension by Zunfeng Fu, Jing Wang, Wenyi Shen, Yanqing Wu, Jiajun Zhang, Yan Liu, Chongqiang Wang, Yanlin Shen, Ye Zhu, Weifu Zhang, Chunju Lv, Lin Peng

“…A combined nomogram was developed by integrating DLR (deep learning radiomics) features with clinical-ultrasound variables, and its diagnostic performance over different thresholds was evaluated using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis (DCA). …”
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Compatibility Between OLCI Marine Remote-Sensing Reflectance from Sentinel-3A and -3B in European Waters by Frédéric Mélin, Ilaria Cazzaniga, Pietro Sciuto

“…For the atmospheric correction <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>l</mi><mn>2</mn><mi>g</mi><mi>e</mi><mi>n</mi></mrow></semantics></math></inline-formula>, validation results obtained with field data from the ocean-color component of the Aerosol Robotic Network (AERONET-OC) and uncertainty estimates appear consistent between S-3A and S-3B as well as with other missions processed with the same algorithm. Estimates of the error correlation between S-3A and S-3B <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mrow><mi>R</mi><mi>S</mi></mrow></msub></semantics></math></inline-formula>, required to evaluate their compatibility, are computed based on common matchups and indicate varying levels of correlation for the various bands and sites in the interval 0.33–0.60 between 412 and 665 nm considering matchups of all sites put together. …”
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Calculation Model of Multi-roll Straightening Process Based on Bilinear Hardening and Power Hardening by ZHU Xiaoyu, CHENG Zixing, WANG Xiaogang, HAN Peisheng

“…To evaluate the applicability of different material models, five straightening schemes were designed, corresponding to plasticity rates of 33.4%, 50.0%, 66.7%, 75.0%, and 80.0%. …”
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Analysis of the molecular nature associated with microsatellite status in colon cancer identifies clinical implications for immunotherapy by Wei Wu, Qihan Fu, Lulu Liu, Yi Zheng, Hangyu Zhang, Peng Zhao, Weijia Fang, Xuanwen Bao, Shaobing Cheng, Xiaomeng Dai, Xudong Zhu, Zhou Tong, Fanglong Liu

“…A receiver operating characteristic curve was used to evaluate the accuracy rate of neural network, reaching 100%.Conclusion Our analysis unraveled the difference in the molecular nature and genomic variance in MSI and MSS colon cancer. …”
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Body roundness index, visceral adiposity index, and metabolic score for visceral fat in predicting new-onset atrial fibrillation: a UK Biobank cohort study by Yi ZHENG, Lei LIU, Xinyu ZHENG, Tong LIU, Xiaoping LI

“…We further applied the eXtreme Gradient Boosting (XGBoost) algorithm, with the feature importance being measured to evaluate the predictive value of each adiposity index for imaging parameters. …”
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Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study by Na Wang, Yujia Chen, Jianli Lin, Yulan Lin, Haoyuan Song, Weihai Huang, Liling Shen, Fa Chen, Fengqiong Liu, Jing Wang, Yu Qiu, Bin Shi, Ling Li, Lisong Lin, Lizhen Pan, Baochang He

“…Then to establish and evaluate disease classification models capable of distinguishing OC patients from healthy controls. …”
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Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI by Seung Ha Cha, Yeo Eun Han, Na Yeon Han, Min Ju Kim, Beom Jin Park, Ki Choon Sim, Deuk Jae Sung, Seulki Yoo, Patricia Lan, Arnaud Guidon

“…DWI images were reconstructed using a vendor-provided DL algorithm (AIR^TM Recon DL; GE Healthcare)—a CNN-based algorithm that reduces noise and enhances image quality—applied here as a prototype for MUSE-DWI. …”
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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

“…Four basic ML algorithms including support vector machine (SVM), K-nearest neighbor (kNN), decision tree (DT), and naive Bayes (NB), and five deep learning models including convolutional neural network (CNN), long-short term memory (LSTM), bidirectional long short-term memory (BiLSTM), and CNN-BiLSTM were used to process the EMG signals recorded under different gaits. …”
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Deep Reinforcement Learning: A Chronological Overview and Methods by Juan Terven

“…Objective: This paper seeks to provide a comprehensive yet accessible overview of the evolution of deep RL and its leading algorithms. It aims to serve both as an introduction for newcomers to the field and as a practical guide for those seeking to select the most appropriate methods for specific problem domains. …”
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Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo. by Audêncio Victor, Diego Augusto Medeiros Santos, Eduardo Koerich Nery, Danilo Pereira Mori, Pamella Cristina de Carvalho Lucas, Denise Cammarota, Guillermo Leonardo Florez Montero, Fabiano Novaes Barcellos Filho, Ana Lúcia Frugis Yu, Telma Regina Marques Pinto Carvalhanas

“…<h4>Conclusion</h4>ML algorithms, particularly CatBoost, XGBoost, and LightGBM, proved highly effective in the differential diagnosis of meningitis, offering a valuable tool for the rapid identification of meningitis types and bacterial serogroups. …”
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Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review by Vishal Gaurav, Chander Grover, Mehul Tyagi, Suman Saurabh

“…In studies evaluating onychomycosis, AI has shown the ability to distinguish between normal nails, fungal infections, and other differentials, including nail psoriasis, with a high accuracy. …”
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Effect of tool angle in nanocutting of single crystal GaN using diamond cutter by Yongqiang WANG, Hao XIA, Zhihang HU, Shuaiyang ZHANG, Shaohui YIN

“…Post-simulation analysis utilizes sophisticated algorithms to dissect the deformation mechanisms: employed to identify, characterize, and quantify the evolution of dislocations, including their types (e.g., perfect dislocations, partial dislocations), Burgers vectors, and densities within the workpiece. …”
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Comparison of five relative radiometric normalization techniques for remote sensing monitoring by DING Li-xia, ZHOU Bin, WANG Ren-chao

“…The root-mean-square error and the dynamic range were employed in comparing and evaluating the images normalized by five methods. A change detection algorithm, i. e., image subtraction, was applied to compare the effects on change detection. …”
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Nomogram model using serum Club cell secretory protein 16 to predict prognosis and acute exacerbation in patients with idiopathic pulmonary fibrosis by Yaqiong Tian, Xuan Zhou, Mi Tian, Lijun Ren, Ruyi Zou, Hanyi Jiang, Miaomiao Xie, Mei Huang, Jingjing Ding, Yin Liu, Jingyu Chen, Min Cao, Hourong Cai

“…COX regression and LASSO algorithm were used to screen featured characteristics. …”
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Correspondence between thalamic injury-induced changes in resting-state fMRI of monkeys and their sensorimotor behaviors and neural activities by Anirban Sengupta, Pai-Feng Yang, Jamie L. Reed, Arabinda Mishra, Feng Wang, Isaac V Manzanera Esteve, Zhangyan Yang, Li Min Chen, John C. Gore

“…Finally, end-point histological evaluations were performed on brain tissue slices to quantify the VPL damage. …”
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Formative research to optimize pre-eclampsia risk-screening and prevention (PEARLS): study protocol by Nicole Minckas, Alim Swarray-Deen, Sue Fawcus, Rosa Chemwey Ndiema, Annie McDougall, Jennifer Scott, Samuel Antwi Oppong, Ayesha Osman, Alfred Onyango Osoti, Katherine Eddy, Mushi Matjila, George Nyakundi Gwako, Joshua P. Vogel, A. Metin A. Gülmezoglu, Adanna Uloaku Nwameme, Meghan A. Bohren, the PEARLS Trial collaborative group

“…In the formative phase for the “Preventing pre-eclampsia: Evaluating AspiRin Low-dose regimens following risk Screening” (PEARLS) trial, we aim to validate and implement a pre-eclampsia risk-screening algorithm, and validate an artificial intelligence (AI) ultrasound for gestational age estimation. …”
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