Generative Elastic Networks (GENs) and Application on Classification of Single-Lead Electrocardiogram by Nan Xiao, Kun Zhao, Hao Zhang

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Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification by Mhd Saeed Sharif, Maysam Abbod, Abbes Amira, Habib Zaidi

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Conserved pattern-based classification of human odorant receptor multigene family by Sang Eun Ryu, Jisub Bae, Tammy Shim, Won-Cheol Kim, Kwangsu Kim, Cheil Moon

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Comparison of Four Intrapartum Cardiotocography Classifications for Predicting Neonatal Acidemia at Birth by Nika Troha, Katja Razem, Ursa Luzovec, Miha Lucovnik

“…To compare diagnostic values of four intrapartum cardiotocography (CTG) classifications in predicting neonatal acidemia at birth. …”
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Enhancement of convolutional neural network for urban environment parking space classification by S. Rahman, M. Ramli, F. Arnia, R. Muharar, M. Ikhwan, S. Munzir

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The JADES Transient Survey: Discovery and Classification of Supernovae in the JADES Deep Field by Christa DeCoursey, Eiichi Egami, Justin D. R. Pierel, Fengwu Sun, Armin Rest, David A. Coulter, Michael Engesser, Matthew R. Siebert, Kevin N. Hainline, Benjamin D. Johnson, Andrew J. Bunker, Phillip A. Cargile, Stephane Charlot, Wenlei Chen, Mirko Curti, Shea DeFour-Remy, Daniel J. Eisenstein, Ori D. Fox, Suvi Gezari, Sebastian Gomez, Jacob Jencson, Bhavin A. Joshi, Sanvi Khairnar, Jianwei Lyu, Roberto Maiolino, Takashi J. Moriya, Robert M. Quimby, George H. Rieke, Marcia J. Rieke, Brant Robertson, Melissa Shahbandeh, Louis-Gregory Strolger, Sandro Tacchella, Qinan Wang, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Yossef Zenati

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Erratum to “Chronic active lesions in multiple sclerosis: classification, terminology, and clinical significance”

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Improved packet classification algorithm based on multidimensional space dynamic division and RFC by Xiaoyu CHEN, Yueming LU

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Description and Classification of Training Drills, Based on Biomechanical and Physiological Load, in Elite Basketball by Carlos Sosa, Enrique Alonso-Pérez-Chao, Carlos Ribas, Xavier Schelling, Alberto Lorenzo

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Prediction of Disease Stage by Machine Learning Classification Methods for Covid-19 Patients by Melis Merve Doğançay, özlem Ege Oruç, Melike Şırlancı Tüysüzoğlu, Zeynep Altın

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COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms by Nasiba M. Abdulkareem, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dathar A. Hasan

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An efficient hybrid model of CNNs and different kernels of SVM for brain tumor classification by Mohammed Bourennane, Hilal Naimi

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Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

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A new dimensionality reduction technique based on the Wavelet Transform for cancer classification by Lisardo Fernández, Mariano Pérez, Juan M. Orduña, José M. Alcaraz

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Liver fibrosis stage classification in stacked microvascular images based on deep learning by Daisuke Miura, Hiromi Suenaga, Rino Hiwatashi, Shingo Mabu

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MSCPNet: A Multi-Scale Convolutional Pooling Network for Maize Disease Classification by Mehdhar S. A. M. Al-Gaashani, Reem Alkanhel, Muthana Ali Salem Ali, Mohammed Saleh Ali Muthanna, Ahmed Aziz, Ammar Muthanna

“…Extensive evaluations on the maize leaf disease classification task yielded outstanding results, with the proposed MSCPNet achieving an accuracy of 97.44%, precision of 96.76%, recall of 97.37%, F1-score of 97.04%, and an MCC of 0.9653, with a model size of 998,084 parameters and 315,258,752 FLOPs. …”
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Optimizing the performance of a server-based classification for a large business document flow by O. A. Slavin

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Multiple Scaling Based EfficientNet Modelling for Liver Tumor Classification on CT Images by Bilga Jacob, R. S. Vinod Kumar, S. S. Kumar

“…Recent, deep Convolutional Neural Network (CNN) research has produced amazing improvements in image segmentation and classification. The same issue of diagnosing liver nodules in computed tomography (CT) scans is addressed in this research by introducing a novel Computer-Aided Detection (CAD) system that makes use of an Efficient Network (EfficientNet) image classification algorithm. …”
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NF-GAT: A Node Feature-Based Graph Attention Network for ASD Classification by Shuaiqi Liu, Beibei Liang, Siqi Wang, Bing Li, Lidong Pan, Shui-Hua Wang

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Evaluation of antibiotic consumption using WHO -antimicrobial consumption tool and AWaRe classification by Julie Birdie Wahlang, Reuben P. Syiem, Chayna Sarkar, Nari M. Lyngdoh, Iadarilang Tiewsoh, Dhriti K. Brahma, Aitilutmon Surong, Arky J Langstieh

“…The age group between 40–49 years (21.68%) was the most common for those receiving antimicrobial treatment. Surgical prophylaxis (37.34%) emerged as the most common indication. Among the different antimicrobials studied, ceftriaxone 1 gm (108.5 DDD) emerged as the most prescribed antimicrobial. …”
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