An Intelligent Medical Imaging Approach for Various Blood Structure Classifications by Madallah Alruwaili

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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fathima Fajila, Yuhanis Yusof

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IMA Commission on New Minerals, Nomenclature and Classification (CNMNC) – Newsletter 83 by F. Bosi, F. Bosi, F. Hatert, F. Hatert, M. Pasero, M. Pasero, S. J. Mills, S. J. Mills

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On Internet Traffic Classification: A Two-Phased Machine Learning Approach by Taimur Bakhshi, Bogdan Ghita

“…The classifier specificity factor which accounted for differentiating content specific from supplementary flows ranged between 98.37% and 99.57%. Furthermore, the computational performance and accuracy of the proposed methodology in comparison with similar machine learning techniques lead us to recommend its extension to other applications in achieving highly granular real-time traffic classification.…”
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Microplastic in the Soil Environment – Classification and Sources in Relation to Research Conducted in Poland by Monika Kisiel, Agnieszka Poniatowska, Anita Kaliszewicz

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Posterior Shoulder Instability in Tennis Players: Aetiology, Classification, Assessment and Management by Lyn Watson, Gregory Hoy, Timothy Wood, Tania Pizzari, Simon Balster, Shane Barwood, Sarah Ann Warby

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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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Elevated Circulating Adipocyte-Fatty Acid Binding Protein Levels Predict Incident Ischemic Cardiovascular Events in a Longitudinal and Prospective AMI Aging Study by Zhao X, Zhao H, Chen R, Zhou J, Li N, Li J, Yan S, Liu C, Zhou P, Chen Y, Song L, Yan H

“…Initially, participants were stratified into three groups according to the tertile levels of FABP4, followed by further categorization based on various lipid profiles and specific inflammatory markers.Results: On follow-up (median 751 days, maximum 1506 days), a total of 158 ischemic events were recorded. 1) In multivariable models meticulously adjusted for age, gender, traditional coronary heart disease factors, Killip classification, and discharge medications, the association of elevated levels of FABP4 (Tertile 3 HR 1.618 [1.061 to 2.468], p=0.026), augmented concentrations of PTX3 (Tertile 3 HR 1.811 [1.211 to 2.710], p=0.004), or LL-37 (Tertile 3 HR 0.651 [0.433 to 0.981], p=0.040) with ischemic risk was markedly intensified. 2) Multivariate HRs associated with 1 standard deviation (SD) (mg/dL) increase in the FABP4 parameters were as follows in different subgroups. 1-SD difference in FABP4 was associated with a 23%, 23%, 21 and 29% increase in ischemic events over after fully adjusted the confounding risk factors among male, patients with hyperlipidemia, hypertension and diabetes respectively. 3) The Kaplan-Meier curve demonstrated significant differences between the tertiles of FABP4 index levels among all enrolled participants (p=0.0180).Conclusion: This study reinforces the utility of FABP4 for enhancing risk stratification specifically among older patients diagnosed with ST-elevation myocardial infarction.Keywords: FABP4, lipid profile, PTX3, LL-37, ischemic vascular events…”
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Erratum to “Chronic active lesions in multiple sclerosis: classification, terminology, and clinical significance”

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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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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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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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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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Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks by Juncheng Guo, Yuyan Wu, Lizhi Chen, Guanghua Ge, Yadong Tang, Wenlong Wang

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