Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data by Ahmad Hasasneh, Nikolas Kampel, Praveen Sripad, N. Jon Shah, Jürgen Dammers

“…We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. …”
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A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods by Jaehee Jung, Heung Ki Lee, Gangman Yi

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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

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Relationship between Cognitive Learning Psychological Classification and Neural Network Design Elements by Xing Yang, Tingjun Yong, Meihua Li, Wenying Wang, Huichun Xie, Jinping Du

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Phenotypic Characterization, Evaluation, and Classification of Cassava (Manihot esculenta Crantz) Accessions in Ethiopia by Berhanu Bilate Daemo, Derbew Belew Yohannes, Tewodros Mulualem Beyene, Wosene Gebreselassie Abtew

“…In this regard, there needs to be more information on the morphological characterization and classification cassava accessions. Thus, the aim of this research was to systematically characterize, evaluate, and classify cassava accessions using qualitative characters to provide useful information for breeding program and conservation. …”
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Modeling Comparisons for some Classification Methods, Bayesian, Neural and Traditional Cluster Techniques by Jean-Pierre Lévy Mangin, Juan Antonio Moriano, Normand Bourgault

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Post-earthquake Disasters: A Systematic Review of the Classification and Evaluation of the Analysis Methods by Çağatay Takva, Asena Soyluk, Zeynep Yeşim İlerisoy

“…Analysis methods are summarized from academic data, classified, and assessed using bibliometric methods. …”
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Changing our Diagnostic Paradigm Part II: Movement System Diagnostic Classification by Paula M Ludewig, Gaura Saini, Aaron Hellem, Emily K Kahnert, S Cyrus Rezvanifar, Jonathan P Braman, Justin L Staker

“…Diagnostic classification is a foundational underpinning of providing care of the highest quality and value. …”
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Bearing Fault Classification Using Improved Antlion Optimizer and Extreme Learning Machine by Zhuanzhe Zhao, Yu Zhang, Qiang Ma, Yujian Rui, Guowen Ye, Mengxian Wang, Yongming Liu, Zhen Zhang, Neng Wei, Zhijian Tu

“…Bearing is an important part of rotating machinery, and its early fault diagnosis and accurate classification have always been difficult in engineering application. …”
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Concept and Classification of Functions of State Policy in the Field of Ensuring Economic Security of the State by D. O. Koshikov

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UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification by Zeyu Ren, Xiangyu Kong, Yudong Zhang, Shuihua Wang

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Genome-Wide Identification and Classification of MicroRNAs Derived from Repetitive Elements by Jeong-An Gim, Hong-Seok Ha, Kung Ahn, Dae-Soo Kim, Heui-Soo Kim

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Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform by Biniyam Mulugeta Abuhayi, Yohannes Agegnehu Bezabh, Aleka Melese Ayalew, Miraf Alemayehu Lakew

“…This study presents a novel method for classifying GI diseases using endoscopy videos. The proposed method involves three major phases: image processing, feature extraction, and classification. …”
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Drip Loss Assessment by Different Analytical Methods and Their Relationships with Pork Quality Classification by Robledo de Almeida Torres Filho, Henrique Pereira Cazedey, Paulo Rogério Fontes, Alcinéia de Lemos Souza Ramos, Eduardo Mendes Ramos

“…We analyzed drip loss in pork by comparing the standard bag (DL), filter-paper wetness (FPW), and EZ-DripLoss methods by weighing the meat juice container and dabbed sample after 24 h and 48 h. Samples were classified into quality categories based on pH, color, and drip loss. …”
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Application of multiparametric methods of data science for the classification of Russian subjects on the basis of subsidisation by A. V. Kuznetsova, L. R. Borisova, V. M. Khadartsev

“…The relevance of the study is justified by the importance of monitoring and forecasting the subsidisation of the Russian regions in order to identify the main criteria for classifying subjects on the basis of subsidisation. …”
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Tocilizumab modulates the activity of the classical and alternative complement pathways in rheumatoid arthritis patients by Iván Ferraz-Amaro, Iván Ferraz-Amaro, Sergio Santos-Concepción, Javier Castro-Hernández, Maria Vanesa Hernández-Hernández, Beatriz Tejera Segura, Cristina Luna, Esmeralda Delgado-Frias, Federico Díaz-González, Federico Díaz-González, Federico Díaz-González

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Anxiety classification in virtual reality using biosensors: A mini scoping review. by Deniz Mevlevioğlu, Sabin Tabirca, David Murphy

“…It is also important to research the application of the classification by conducting longitudinal studies.…”
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A Semi-Supervised Learning Approach to Quality-Based Web Service Classification by Mehdi Nozad Bonab, Jafar Tanha, Mohammad Masdari

Subjects: “…Classification…”
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Classification and Regression Trees on Aggregate Data Modeling: An Application in Acute Myocardial Infarction by C. Quantin, L. Billard, M. Touati, N. Andreu, Y. Cottin, M. Zeller, F. Afonso, G. Battaglia, D. Seck, G. Le Teuff, E. Diday

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Multimodal Emotion Recognition: Emotion Classification Through the Integration of EEG and Facial Expressions by Songul Erdem Guler, Fatma Patlar Akbulut

“…The results validate the effectiveness of this approach, demonstrating the high accuracy of the Gated Recurrent Unit (GRU) model, which achieved an average of 91.8% classification accuracy on unimodal (EEG-only) data and an average of 97.8% classification accuracy on multimodal (EEG and facial expressions) datasets in the multi-class emotion categories. …”
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