Classification of English loanwords in Business Spanish by Olga S. Beletskaya

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Classification of the risk in the new financing framework of the Deposit Guarantee Systems in Europe: K-Means Cluster Analysis and Soft Computing by Pilar Gómez, Antonio Partal, Macarena Espinilla

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Convolution neural network for diabetic retinopathy classification with select enhancment algorithm by Mohammed Sheet, Sinan Salim

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Convolution neural network for diabetic retinopathy classification with select enhancment algorithm by Mohammed Sheet, Sinan Salim

“…The IDRID achieved a 93.6% classification accuracy, 92.74% precision, 94.05% sensitivity, 98% specificity, and 93.05% F1-score. …”
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Classification of shallow water seabed profile based on Landsat 8 imagery and in-situ data. Case study in Gili Matra Island Lombok, Indonesia by Ayustina Ratih, Aulia Zahra, Mustakin Haji, Alam Fiyesti, Amron Amron, Yuwono Doddy, Ahmad Triguardi, Prayogo Aldo, Sari Fitra

“…Methods of this research are satellite mage pre-processing, image classification, field survey, image classification, and accuracy assesment . Therefore, 6 classification of shallow water seabed profile, there are rubble (R), seagrass mixed sand (MIX -SG/SD), coral reefs mixed rubble (MIX-C/RB), rubble mixed dead coral (MIX-RB/DC), sand mixed rubble (MIX-SD/RB), and sand mixed seagrass (MIX-SD/SG), respectevely. …”
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Classification of ransomware using different types of neural networks by Houria Madani, Noura Ouerdi, Ahmed Boumesaoud, Abdelmalek Azizi

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Arrhythmia classification detection based on multiple electrocardiograms databases. by Meng Qi, Hongxiang Shao, Nianfeng Shi, Guoqiang Wang, Yifei Lv

“…Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.…”
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Predicting heart disease using machine learning algorithms by A. Berdaly, Z. Abdiakhmetova

“…As a result, various methods of analyzing disease factors were proposed, aimed at reducing differences in the practice of doctors and reducing medical costs and errors. In this study, 6 classification learning algorithms were used, including machine learning methods such as classification Tree, Close neighborhood method, Naive Bayes, random forest tree, and busting methods. …”
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Classification of acetic acid bacteria and their acid resistant mechanism by Xiaoman Qiu, Yao Zhang, Housheng Hong

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Biologically Plausible Class Discrimination Based Recurrent Neural Network Training for Motor Pattern Generation by Parami Wijesinghe, Chamika Liyanagedera, Kaushik Roy

“…The method is evaluated on the TI-46 speech data corpus, and we have achieved 98.6% classification accuracy on the TI-46 digit corpus.…”
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Classification of distal radius fractures in children: good inter- and intraobserver reliability, which improves with clinical experience by Randsborg Per-Henrik, Sivertsen Einar A

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Learning features from irrelevant domains through deep neural network by Pengcheng Wen, Yuhan Zhang, Guihua Wen

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Classification of Region’s Municipalities by Structure and Level of Incomes and Consumer Spending by Vladislav Yakovlevich Fokin

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Classification of early age facial growth pattern and identification of the genetic basis in two Korean populations by Mi-Yeon Cha, Yu-Jin Hong, Ja-Eun Choi, Tae-Song Kwon, Ig-Jae Kim, Kyung-Won Hong

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Classification with 2-D convolutional neural networks for breast cancer diagnosis by Anuraganand Sharma, Dinesh Kumar

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Testing The Performance of Random Forest and Support Vector Machine Algorithms for Predicting Cyclist Casualty Severity by Nurten Akgün

“…After training the algorithm, the testing results showed that the Random Forest algorithm predicted the outcomes with 88.6% classification accuracy. On the other hand, Support Vector Machine algorithm predicted the testing values with 84.73% classification accuracy. …”
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Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning by Harri Kaartinen, Hannu Hyyppä, Markus Holopainen, Antero Kukko, Eliisa Lotsari, Anttoni Jaakkola, Mikko Vastaranta, Matti Vaaja, Ninni Saarinen, Petteri Alho

“…This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. …”
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A practical guide to (successfully) collect and process images through online surveys by Patricia A. Iglesias, Carlos Ochoa, Melanie Revilla

“…This paper aims to guide researchers inexperienced in image analysis by presenting the main steps involved in the process of using images as a new data source: 1) operationalization, 2) definition of the labels, 3) choice of the most suitable classification method(s), 4) collection, 5) enhancement, and 6) classification of the images, 7) verification of the classification outcomes, and 8) data analysis. …”
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Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism. by Hyuk Jin Yun, Kichang Kwak, Jong-Min Lee, Alzheimer’s Disease Neuroimaging Initiative

“…Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. …”
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Classification of marine microdebris: A review and case study on fish from the Great Barrier Reef, Australia by Frederieke J. Kroon, Cherie E. Motti, Lene H. Jensen, Kathryn L. E. Berry

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