Construction of the risk predition model of mild cognitive impairment in hospitalized elder patients by WU Ruikai, MA Long, ZHOU Xiaohui, HAN Zhengfeng

“…The area under curve (AUC) of receiver operating characteristic (ROC) was adopted to compared the performance of three sets of prediction models.Results A total of 992 hospitalized elderly patients were included, and the detection rate of MCI was 21.17%. The analysis results of multivariate Logistic regression model, decision tree model and neural network model all showed that age, cerebrovascular disease and education level were the main influencing factors of MCI, and the multiple Logistic regression model and neural network model also showed that the score of daily living ability below 60 was also the influencing factors of MCI. …”
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Feasibility of Little Cherry/X-Disease Detection in <i>Prunus avium</i> Using Field Asymmetric Ion Mobility Spectrometry by Gajanan S. Kothawade, Lav R. Khot, Abhilash K. Chandel, Cody Molnar, Scott J. Harper, Alice A. Wright

“…FAIMS spectra for LCD/X-disease symptomatic samples differed from those for asymptomatic samples in other Prunus species, such as peach and nectarines. …”
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Enhancing automatic early arteriosclerosis prediction: an explainable machine learning evidence by Eka Miranda, Suko Adiarto

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Preoperative detection of papillary and medullary thyroid cancer metastases in the lymph nodes of the neck lateral cellular tissue using a molecular classifier by S. E. Titov, G. A Katanyan, T. L. Poloz, L. G. Izmaylova, О. А. Zentsova, L. G. Dryaeva, V. V. Anishchenko

“…The decision tree-based classifier was used to discriminate between benign and malignant samples.Results. …”
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Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection by Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li, Kangshun Li

“…The MEO-BIT* component delivers efficient global path planning through (1) a multithreaded batch sampling mechanism for rapid state-space exploration, (2) heuristic-driven search accelerated by KD-tree spatial indexing for optimized path discovery, and (3) an energy-aware cost function balancing path length and steering effort for enhanced endurance. …”
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Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections by S.M. Rowe, E. Zhang, S.M. Godden, A.K. Vasquez, D.V. Nydam

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Establishment and application of a one-step multiplex real-time PCR assay for detection of A, B, and C subtypes of avian metapneumovirus by Mingjun Su, Jiongze Cheng, Xiangwen Xu, Yijia Liu, Yulin Zhao, Yutao Wang, Xiaoxu Du, Jiale Ying, Junfang Yan, Huihua Zheng, Changyong Cheng, Jing Sun

“…Of these, the aMPV types A (aMPV-A), B (aMPV-B) and C (aMPV-C) are frequently detected in Chinese waterfowl and live poultry markets. …”
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NONPOLIO ENTEROVIRUSES WHICH CAUSED THE RISE OF ENTEROVIRUS INFECTION ON SOME TERRITORIES OF RUSSIA IN 2016 by N. I. Romanenkova, M. A. Bichurina, L. N. Golitsyna, N. R. Rozaeva, O. I. Kanaeva, I. V. Cherkasskaya, L. P. Kirillova, A. Yu. Bataeva, A. S. Baryshnikova, N. A. Novikova

“…Materials and methods: We investigated 2138 samples from the patients with enterovirus infection. …”
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Enterovirus infection morbidity and peculiarities of nonpolio enteroviruses circulation on some territories of Russia in 2017 by N. I. Romanenkova, L. N. Golitsyna, M. A. Bichurina, N. R. Rozaeva, O. I. Kanaeva, V. V. Zverev, D. V. Sozonov, I. V. Cherkasskaya, L. P. Kirillova, M. V. Ermakova, L. S. Kamynina, M. B. Petukhova, A. B. Gritsay, N. A. Novikova

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Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG by Thais de Andrade Silva, Gabriel Fernandes Souza dos Santos, Adilson Ribeiro Prado, Daniel Cruz Cavalieri, Arnaldo Gomes Leal Junior, Flávio Garcia Pereira, Camilo A. R. Díaz, Marco Cesar Cunegundes Guimarães, Servio Túlio Alves Cassini, Jairo Pinto de Oliveira

“…This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. …”
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Phylogenomic Analyses Reveal Species Relationships and Phylogenetic Incongruence with New Member Detected in <i>Allium</i> Subgenus <i>Cyathophora</i> by Kun Chen, Zi-Jun Tang, Yuan Wang, Jin-Bo Tan, Song-Dong Zhou, Xing-Jin He, Deng-Feng Xie

“…Despite the robust phylogenies inferred, extensive phylogenetic conflicts were detected not only among gene trees but also between SCGs and plastid-derived species trees. …”
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Estimation of Mango Fruit Production Using Image Analysis and Machine Learning Algorithms by Liliana Arcila-Diaz, Heber I. Mejia-Cabrera, Juan Arcila-Diaz

“…Furthermore, this study has developed a web application with a user interface that facilitates the uploading of images from mango trees considered samples. The YOLO-trained model detects the fruits of each tree in the representative sample and uses extrapolation techniques to estimate the total number of fruits across the entire population of mango trees.…”
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Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters by S. Aishwarya, P. C. Siddalingaswamy, Krishnaraj Chadaga

“…These impacts underscore the need for early detection of smoking status to enable timely intervention. …”
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A Barcoded ITS Primer-Based Nanopore Sequencing Protocol for Detection of <i>Alternaria</i> Species and Other Fungal Pathogens in Diverse Plant Hosts by Vladimer Baramidze, Luca Sella, Tamar Japaridze, Nino Dzotsenidze, Daviti Lamazoshvili, Nino Abashidze, Maka Basilidze, Giorgi Tomashvili

“…Sequencing was performed on infected grapevine, mandarin orange, thuja, and maple tree samples. In total, we analyzed 38 samples using qPCR; 8 tested positive for <i>Alternaria</i>, which were sequenced using a newly developed protocol. …”
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Occurrence of Tomato Brown Rugose Fruit Virus in Lorestan Province Tomato Greenhouses: A First Report by Leila Baharvand, Samira Pakbaz, Farshad Rakhshandehroo, Forough Sanjarian, Ehsan Hasanvand

“…This report represents the first documented ToBRFV detection instance in Lorestan Province tomato greenhouses.…”
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Intrusion Detection in IoT and IIoT: Comparing Lightweight Machine Learning Techniques Using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset Datasets by Shereen Ismail, Salah Dandan, Ala'a Qushou

“…The security of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems has been significantly enhanced through the integration of effective intrusion detection systems (IDSs). Machine learning (ML) has emerged as a highly efficient approach for designing cyber-attack detection systems to improve the security. …”
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Development of an AI-Based Image Analysis Model for Verifying Partial Defects in Nuclear Fuel Assemblies by Seulah Kim, Dayun Park, Hyung-Joo Choi, Chulhee Min, Jaejoon Ahn

“…SENet in particular demonstrates high performance with fewer samples, indicating its defect detection effectiveness in scenarios with minimal data. …”
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Machine learning-based estimation of crude oil-nitrogen interfacial tension by Safia Obaidur Rab, Subhash Chandra, Abhinav Kumar, Pinank Patel, Mohammed Al-Farouni, Soumya V. Menon, Bandar R. Alsehli, Mamata Chahar, Manmeet Singh, Mahmood Kiani

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Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems by V. V. Ligi-Goryaev, G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, E. N. Dzhakhnaeva

“…Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. …”
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A hierarchical statistical model for estimating population properties of quantitative genes by Samuel S Wu, Chang-Xing Ma, Rongling Wu, George Casella

“…A simulation study is presented to evaluate finite sample properties of the model. Conclusions A hierarchical model was derived for detecting major genes affecting a quantitative trait based on progeny tests of outcrossing species. …”
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