Detection of exoplanets: exploiting each property of light by Rouan, Daniel, Lagrange, Anne-Marie

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FedEach: Federated Learning with Evaluator-Based Incentive Mechanism for Human Activity Recognition by Hyun Woo Lim, Sean Yonathan Tanjung, Ignatius Iwan, Bernardo Nugroho Yahya, Seok-Lyong Lee

“…After this aggregation, the server calculates each client’s contribution, prioritizing each client’s contribution to ensure the fair recognition of high-quality updates and penalizing malicious clients based on their contributions. …”
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Techniques in detect selective leaching in metal components by Maritza Rodríguez Gual, Bruno Lopes Vieira, Rennan Souza da Silva

“…In this paper, we will describe the main techniques that are used in detect selective leaching in metals and the advantages and disadvantages of each. …”
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In-Company Adaptation of Employees and Employers to Each Other by V. V. Mortikov

“…To analyze the potential of changing work-place characteristics the data of Risstat selective study was used. Factors showing specific features of adaptation of employees and employers to each other were identified and analyzed. …”
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Automatic Selection of Machine Learning Models for Armed People Identification by Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin

“…In this work, we propose to create selectors for deciding which APD to use in each video frame (APD4F) to improve the detection results. …”
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Fraud Detection on Crowdfunding Platforms Using Multiple Feature Selection Methods by Seunghun Lee, Heemin Park, Hyun-Chul Kim

“…We also employ Shapley Additive ExPlanations (SHAP) to interpret the fraud detection model, explaining the importance of each feature.…”
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Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals by Rosanna Ferrara, Martino Giaquinto, Gennaro Percannella, Leonardo Rundo, Alessia Saggese

“…These channels are automatically selected using a data-driven mechanism that identifies the most informative scalp regions based on each patient’s unique seizure patterns. …”
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Large changes in detected selection signatures after a selection limit in mice bred for voluntary wheel-running behavior. by David A Hillis, Liran Yadgary, George M Weinstock, Fernando Pardo-Manuel de Villena, Daniel Pomp, Theodore Garland

“…Previously, we reported multiple regions of differentiation between the HR and C lines, based on whole-genome sequence data for 10 mice from each line at generation 61, which was >31 generations after selection limits had been reached in all HR lines. …”
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Quantifying the impact of surgical teams on each stage of the operating room process by Adam Meyers, Mertcan Daysalilar, Arman Dagal, Arman Dagal, Michael Wang, Onur Kutlu, Mehmet Akcin, Mehmet Akcin

“…The OR process was segmented into eight stages that quantify eight process times, e.g., procedure duration and procedure start time delay. Model selection was performed to identify the key factors in each stage and to quantify variability.ResultsProcedure type accounted for the most variability in three process times and for 44.2% and 45.5% of variability, respectively, in procedure duration and OR time (defined as the total time the patient spent in the OR). …”
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Individual yeast cells signal at different levels but each with good precision by Steven S. Andrews, Roger Brent

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Manifestation of Virtuous Life in Each of the Six Dimensions of Elementary Educational Content by Sabah Abdi, Rashid Ahmadifar

“…The statistical population included the content of the social studies curriculum for elementary education in Iran for the academic year 1402-1403, encompassing four textbooks, all of which were selected as the sample through census sampling. The research instrument was a checklist designed based on the opinions of 13 experts in each of the six educational dimensions. …”
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NSLS: A Neighbor Similarity and Label Selection-Based Algorithm for Community Detection by Shihu Liu, Hui Chen, Shuang Li, Xiyang Yang

“…Aiming at this issue, this paper proposes a community detection algorithm based on neighbor similarity and label selection (NSLS). …”
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Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset by Ines Aulia Latifah, Fauzi Adi Rafrastara, Jevan Bintoro, Wildanil Ghozi, Waleed Mahgoub Osman

“…This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. …”
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Graph-contrast ransomware detection (GCRD) with advanced feature selection and deep learning by Suneeta Satpathy, Pratik Kumar Swain

“…To overcome the limitations of conventional detection strategies, this study proposes the Graph-Contrast Ransomware Detection (GCRD) model comprising Graph-Based Feature Selection (GFS), Contrastive Learning (CLR), and Transformer-Based Classification (FT-Transformer + MLP). …”
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Specificity and Selectivity of Raman Spectroscopy for the Detection of Dose‐Dependent Heavy Metal Toxicities by Isaac D. Juárez, Nicholas Shepard, Cole Sebok, Sudip Biswas, Endang Septiningsih, Dmitry Kurouski

“…We found these changes were specific to each HM, reflecting the activation of distinct stress‐response mechanisms. …”
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Detection of LUAD-Associated Genes Using Wasserstein Distance in Multiomics Feature Selection by Shaofei Zhao, Siming Huang, Lingli Yang, Weiyu Zhou, Kexuan Li, Shige Wang

“…Unlike traditional approaches that rely on simple statistical tests or dataset splitting for validation, we adopt a method-based validation strategy, selecting top-ranked features from each method and identifying consistently selected genes across all three. …”
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The unwell patient with advanced chronic liver disease: when to use each score? by Oliver Moore, Wai-See Ma, Scott Read, Jacob George, Golo Ahlenstiel

“…We will also review future areas of research for each score and current gaps in the literature. Main body The Child–Pugh score is the pre-eminent prediction score for liver disease that was developed through empiric selection of relevant variables. …”
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Improving Image Spam Detection Using a New Image Texture Features Selection by Azam Shekari Shahrak, Seyed javad Mirabedini, Nasser Mikaeilvand, Seyed Hamid Haj Seyed Javadi

“…The steps mentioned have a great impact on the performance of the categories and improve the accuracy of detection. In the classification stage, the most widely used machine learning algorithms are used to detect image spams, and after obtaining the results of each category, the output of the algorithms used on spam and valid images is examined and compared. …”
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Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element by Francisco Maciá Pérez, Jose Vicente Berna Martienz, Alberto Fernández Oliva, Miguel Abreu Ortega

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Analysis of the Microbial Community Structure of <i>Ixodes persulcatus</i> at Each Developmental Stage by Yonghong Liu, Xiaonan Dong, Lianyang Sun, Hao Cui, Jiamei Kang, Nan Bu, Yishuai Zhang, Zehao Qi, Zixuan Li, Zilong Zhang, Li Zhao

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