Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiographyResearch in context by Jiesuck Park, Jiyeon Kim, Jaeik Jeon, Yeonyee E. Yoon, Yeonggul Jang, Hyunseok Jeong, Youngtaek Hong, Seung-Ah Lee, Hong-Mi Choi, In-Chang Hwang, Goo-Yeong Cho, Hyuk-Jae Chang

“…Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. …”
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Role of Virtual iMRI in Glioblastoma Surgery: Advantages, Limitations, and Correlation with iCT and Brain Shift by Erica Grasso, Francesco Certo, Mario Ganau, Giulio Bonomo, Giuseppa Fiumanò, Giovanni Buscema, Andrea Maugeri, Antonella Agodi, Giuseppe M. V. Barbagallo

“…<b>Objective</b>: To evaluate the safety and reliability of the EIF algorithm (Virtual iMRI Cranial 4.5, Brainlab AG, Munich Germany, for the identification of residual tumour in glioblastoma surgery. …”
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Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics by Hayam Alamro, Mohammed Maray, Jawhara Aljabri, Saad Alahmari, Monir Abdullah, Jehad Saad Alqurni, Faiz Abdullah Alotaibi, Abdelmoneim Ali Mohamed

“…The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. …”
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Development and Validation of a Nomogram for Predicting Long-Term Net Adverse Clinical Events in High Bleeding Risk Patients Undergoing Percutaneous Coronary Intervention by Junyan Zhang, Zhongxiu Chen, Ran Liu, Yuxiao Li, Hongsen Zhao, Yanning Li, Minggang Zhou, Hua Wang, Chen Li, Li Rao, Yong He

“…The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to select variables in the training set. …”
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Retinal optical coherence tomography intensity spatial correlation features as new biomarkers for confirmed Alzheimer’s disease by Zi Jin, Xinmin Wang, Ying Lang, Yufeng Song, Huangxiong Zhan, Wuge Shama, Yingying Shen, Guihua Zeng, Faying Zhou, Hongjian Gao, Shuling Ye, Yanjiang Wang, Fan Lu, Meixiao Shen

“…Except retinal thickness, we developed the grey level co-occurrence matrix algorithm to extract retinal OCT intensity spatial correlation features (OCT-ISCF), including angular second matrix (ASM), correlation (COR), and homogeneity (HOM), one-way analysis of variance was used to compare the differences in retinal parameters among the groups, and to analyze the correlation with brain Aβ plaques and cognitive scores. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm. Results For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. …”
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Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach by Victor Chaumeau, Sunisa Sawasdichai, Thu Zar Ma Ma Moe Min, Thithiwarada Kularbkeeree, Naw Jaruwan, Naw Gloria, Naw Yu Lee, Muesuwa Trackoolchengkaew, Monticha Phanaphadungtham, Patcharamai Rongthong, Aritsara Inta, Wanitda Watthanaworawit, François Nosten

“…A simulation experiment was carried out by querying the reference database with the spectra of the test panel to evaluate the performance of species identification with MALDI–TOF MS at varying thresholds of the cross-correlation index for the algorithm to output an identification result and with varying numbers of technical replicates for the tested specimens, considering PCR identification results as the reference. …”
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Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topso... by Calogero Schillaci, Simone Scarpa, Felipe Yunta, Aldo Lipani, Fernando Visconti, Gábor Szatmári, Kitti Balog, Triven Koganti, Mogens Greve, Giulia Bondi, Georgios Kargas, Paraskevi Londra, Fuat Kaya, Giuseppe Lo Papa, Panos Panagos, Luca Montanarella, Arwyn Jones

“…In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. The final model resulted in an R2 of 0.302 with an RMSE of 0.265 dS m−1 for soil samples not used for model calibration. …”
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Characteristics and outcomes of patients with LAM receiving sirolimus in France based on real-life data by Vincent Cottin, Vincent Cottin, Antoine Cases, Virginie Bourdin, Martine Reynaud-Gaubert, Sandrine Hirschi, Mallorie Kerjouan, Rémi Diesler, Rémi Diesler, Brieux Chardès, Stéphane Fiévez, Nada Assi, Aurélie Schmidt, Hélène Denis, Lidwine Wémeau-Stervinou, Yurdagul Uzunhan

“…In the absence of a specific LAM code in the system, an algorithm was developed to identify patients treated for possible LAM exclusion of other sirolimus indications (transplantation, graft-versus-host disease), or probable LAM (among possible LAM, patients hospitalized for pneumothorax, pleural drainage, pleurisy, ascites, chronic respiratory failure, lung transplantation, or angiomyolipoma). …”
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Unveiling and validating biomarkers related to the IL-10 family in chronic sinusitis with nasal polyps: insights from transcriptomics and single-cell RNA sequencing analysis by Xinghong Liu, Yi Peng, Ling Guo, Weilan Xiong, Weijiang Liao, Jiangang Fan

“…Biomarker selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the Boruta algorithm. Further refinement of biomarkers was carried out using receiver operating characteristic (ROC) analysis, with genes demonstrating an area under the curve (AUC) greater than 0.7 being considered significant. …”
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Implementation of Compensation-Sensitive Shot-Style-Feedback of Aiming Point Training Data at the Swiss Shooting Federation by Dino Tartaruga, Ralf Kredel

“…The analysis is performed according to the methods proposed in our study, including the use of a validated movement phase segmentation algorithm of aiming point trajectory data and the identification of compensation-sensitive shot styles based on this data. …”
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EpCare: Prototipe Sistem Detektor Pre-Iktal Pasien Epilepsi Berbasis Fitur CSI dari Sinyal EKG 1 Kanal Menggunakan AD8232 by Diah P. Wulandari, Arief Kurniawan, Alvin Nathanael, Santi W. Purnami, Anda L. Juniani, Wardah R. Islamiyah

“…Patient’s heart electricity is measured using 3 electrodes which are connected to AD8232 to represent 1 channel signal. Pan-Tompkins algorithm is implemented to obtain RR intervals of ECG signals. …”
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Comparison of IMO vifa24plus(1-2) and Humphrey Field Analyzer 24-2 by Takagi Y, Asano R, Yamashita K, Sakai Y, Yokoyama S, Ichikawa K, Ichikawa K

“…On the same day, the HFA 24– 2 test using the Swedish Interactive Threshold Algorithm Standard and the IMO vifa 24plus(1– 2) test using the Ambient Interactive Zippy Estimated strategy were performed. …”
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Prospects for the Use of Quasi-Mersen Numbers in the Design of Parallel-Serial Processors by Aruzhan Kadyrzhan, Kaisarali Kadyrzhan, Akhat Bakirov, Ibragim Suleimenov

“…It is shown that a serial-parallel processor, comparable in bit capacity to a 16-bit binary processor, can be implemented based on an algorithm built on the residue number system, a distinctive feature of which is the use of the first four quasi-Mersenne numbers, i.e., prime numbers representable as <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>p</mi><mi>k</mi></msub><mo>=</mo><msup><mn>2</mn><mi>k</mi></msup><mo>+</mo><mn>1</mn><mo>,</mo><mo> </mo><mi>k</mi><mo>=</mo><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>,</mo><mn>4</mn></mrow></semantics></math></inline-formula>. …”
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Pembentukan Daftar Stopword Menggunakan Term Based Random Sampling Pada Analisis Sentimen Dengan Metode Naïve Bayes (Studi Kasus: Kuliah Daring Di Masa Pandemi) by Raditya Rinandyaswara, Yuita Arum Sari, Muhammad Tanzil Furqon

“…This study uses a stopword list formed by the Term Based Random Sampling algorithm. In Term Based Random Sampling, there are 3 parameters, namely Y for the number of random word retrieval repetitions, X for the lowest number of weights in Y repetitions, and L as the percentage of the number of stopwords you want to use. …”
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VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction by Omer Nacar, Mohamed Abdelkader, Lahouari Ghouti, Kahled Gabr, Abdulrahman Al-Batati, Anis Koubaa

“…Quantitatively, the proposed GRU-based prediction algorithm demonstrates superior performance, achieving a mean square error (MSE) ranging from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study by Dong J, Duan M, Liu X, Li H, Zhang Y, Zhang T, Fu C, Yu J, Hu W, Peng S

“…We aimed to establish an interpretable machine learning model for predicting distant metastasis in RCC patients.Methods: We involved a population-based cohort of 121433 patients (mean age = 63 years; 63.58% men) diagnosed with RCC between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The lightGBM algorithm was used to develop prediction model and assessed by the area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, and specificity. …”
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Analisis Dampak Kabut Asap dari Kebakaran Hutan dan Lahan dengan Pendekatan Text Mining by Zuliar Efendi, Imas Sukaesih Sitanggang, Lailan Syaufina

“…The method used is a text mining technique that uses a clustering algorithm. The data used are tweet data related to haze in Riau Province in the range 11-17 September 2019 as well as hotspot data and Sentinel-2 imagery. …”
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Non-Luminal Disease Score for Everolimus in Patients with&nbsp;Hormone Receptor&#x2011;positive and Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: A Mult... by Tan Y, Jiang H, Tian X, Ma F, Wang J, Zhang P, Xu B, Fan Y, Zhao W

“…Yujing Tan,1,&ast; Hanfang Jiang,2,&ast; Xinzhu Tian,1,&ast; Fei Ma,1 Jiayu Wang,1 Pin Zhang,1 Binghe Xu,1 Ying Fan,1 Weihong Zhao3 1Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021, People’s Republic of China; 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Breast Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People’s Republic of China; 3Department of Medical Oncology, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ying Fan; Weihong Zhao, Email fanying@cicams.ac.cn; zhaowh0818@163.comPurpose: This study aims to explore the role of the non-luminal disease score (NOLUS) for everolimus in patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC).Methods: NOLUS has previously been established as an algorithm: NOLUS (0– 100) = − 0.45 × ER(%) − 0.28 × PR(%) + 0.27 × Ki67(%) + 73. …”
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Analisis Sentimen Berbasis Aspek Pada Ulasan Pelanggan Restoran Menggunakan Algoritma Support Vector Machine (Studi Kasus: Depot Bamara) by Muhammad Fariz Firdaus, Dian Eka Ratnawati, Nanang Yudi Setiawan

“…Terms found in the reviews were weighted using the frequency inverse document frequency (TF-IDF) weighting technique and classified using the support vector machine (SVM) algorithm. The classification model was tested with k-fold cross validation, resulting in an average accuracy of 92%, indicating good performance. …”
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