Machine learning algorithms for predicting low birth weight in Ethiopia by Wondesen Teshome Bekele

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Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks by Prabu Ravindran, Adriana Costa, Richard Soares, Alex C. Wiedenhoeft

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Classification models for assessing coronary artery disease instances using clinical and biometric data: an explainable man-in-the-loop approach by Agorastos-Dimitrios Samaras, Serafeim Moustakidis, Ioannis D. Apostolopoulos, Nikolaos Papandrianos, Elpiniki Papageorgiou

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Influence of abnormal potassium levels on mortality among hospitalized heart failure patients in the US: data from National Inpatient Sample by Sijan Basnet, Rashmi Dhital, Biswaraj Tharu, Sushil Ghimire, Dilli Ram Poudel, Anthony Donato

“…The inclusion criteria used to identify patients was those with a diagnosis of heart failure as per ICD-9 classification. Other demographic factors considered in data collection included income, and cardiac risk factors. …”
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Prediction, classification and diagnosis of spur gear conditions using artificial neural network and acoustic emission by Ali, Yasir Hassan

“…The findings showed that use of specific film thickness has resulted in the FFBP network being able to provide 99.9% classification accuracy, while regression and multiple regression models attained 73.3 % and 81.2% classification accuracy respectively. …”
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Estimates of changes of structural parameters of forest ecosystems in decoding high resolution satellite images by Yuri F. Rozhkov, Maria Yu. Kondakova

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Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes by Nanako Kanno, Shingo Kato, Moriya Ohkuma, Motomu Matsui, Wataru Iwasaki, Shinsuke Shigeto

“…Our RF classifier achieved a 98.8 ± 1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. …”
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A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue by Yuan hong Zhong, Shun Zhang, Rongbu He, Jingyi Zhang, Zhaokun Zhou, Xinyu Cheng, Guan Huang, Jing Zhang

“…Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.…”
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Deep Learning for Multi-Tissue Cancer Classification of Gene Expressions (GeneXNet) by Tarek Khorshed, Mohamed N. Moustafa, Ahmed Rafea

“…Our model achieves 98.9% classification accuracy on human samples representing 33 different cancer tumor types across 26 organ sites. …”
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Classification of nonnegative solutions to static Schrödinger–Hartree–Maxwell system involving the fractional Laplacian by Yunting Li, Yaqiong Liu, Yunhui Yi

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Classification of stable solutions for non-homogeneous higher-order elliptic PDEs by Abdellaziz Harrabi, Belgacem Rahal, Mohamed Karim Hamdani

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Classification and automatic scoring of arousal intensity during sleep stages using machine learning by Hyewon Han, Min Jae Seong, Janghun Hyeon, Eunyeon Joo, Junhyoung Oh

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Plant disease detection and classification techniques: a comparative study of the performances by Wubetu Barud Demilie

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Construction and Building Automation : From Concepts to Implementation / by Raphael, Benny, author 650288

Genetic Links to Episodic Movement Disorders: Current Insights by Garg D, Mohammad S, Shukla A, Sharma S

“…Broadly, these comprise paroxysmal dyskinesias (paroxysmal kinesigenic and non-kinesigenic dyskinesia [PKD/PNKD], paroxysmal exercise-induced dyskinesias [PED]) and episodic ataxias (EA) types 1– 9. Classification of paroxysmal dyskinesias has traditionally been clinical. …”
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Psychological Disorders of Patients With Allergic Rhinitis in Chengdu, China: Exploratory Research by Heyin Huang, Yichen Wang, Lanzhi Zhang, Qinxiu Zhang, Xiaojuan Wu, Hengsheng He

“…MethodsThe Symptom Checklist 90 (SCL-90) was used to group and score the mental state of 827 strictly screened patients with AR according to 9 classification criteria. The scores were then compared within groups. …”
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Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures by Ching‐Kai Lin, Jerry Chang, Ching‐Chun Huang, Yueh‐Feng Wen, Chao‐Chi Ho, Yun‐Chien Cheng

“…Results Malignant cells were successfully classified by ResNet101 with 98.8% classification accuracy, 98.8% sensitivity, and 98.8% specificity in patch‐based classification; 95.5% classification accuracy in image‐based classification; and 92.9% classification accuracy in patient‐based classification. …”
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Food safety from consumer perspective: health safety by Jozef Golian, Ľudmila Nagyová, Alexandra Andocsová, Peter Zajác, Jozef Palkovič

“…Respondents answered to 12 factual, and 9 classification questions, which were consequently analyzed using the Friedman test, Nemenyi test and Chi-Square test of Independence. …”
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ANALISIS METODE SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PENGGUNAAN LAHAN BERBASIS PENUTUP LAHAN PADA CITRA ALOS AVNIR-2 by , Khikmanto Supribadi, S.T, , Dr. Nurul Khakhim, M.Si.

“…Data are the mean texture filter used is the data of each band as well as a composite of all the bands with processing window 3x3, 5x5, 7x7, 9x9. Classification scheme used is a land use classification scheme according BPN 2012 with modifications adapted to the conditions on the ground. …”

Perbandingan Teknik Klasifikasi Dalam Data Mining Untuk Bank Direct Marketing by Irvi Oktanisa, Ahmad Afif Supianto

“…In this paper, we present the comparison of  9 classification techniques performed to classify customer response on the dataset of Bank Direct Marketing. …”
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