Detection and Classification of Single Machining Error of Non-circular Gear by Dai Jianqing, Wang Shengze

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Cancer Classification Using Pattern Recognition and Computer Vision Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

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Classification of Alzheimer's disease using unsupervised diffusion component analysis by Dominique Duncan, Thomas Strohmer

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Nudibranch Suborders Classification based on Densely Connected Convolutional Networks by Timothy Christyan, Safitri Yuliana Utama, Bagus Tri Yulianto Darmawan, Faisal Dharma Adhinata

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Enhancing Software Requirements Classification with Semisupervised GAN-BERT Technique by Gregorius Airlangga

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STUDY ON DEEP LEARNING MODELS FOR THE CLASSIFICATION OF VR SICKNESS LEVELS by Haechan NA, Yoon Sang KIM

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An Adaboost-Backpropagation Neural Network for Automated Image Sentiment Classification by Jianfang Cao, Junjie Chen, Haifang Li

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Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification by Ahmad Ubai Dullah, Aditya Yoga Darmawan, Dwika Ananda Agustina Pertiwi, Jumanto Unjung

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Cross-ViT based benign and malignant classification of pulmonary nodules. by Qinfang Zhu, Liangyan Fei

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

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A Mobile Deep Learning Classification Model for Diabetic Retinopathy by Daniel Rimaru, Antonio Nehme, Musaed Alhussein, Khaled Mahbub, Khusheed Aurangzeb, Anas Khan

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

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Personalised Affective Classification Through Enhanced EEG Signal Analysis by Joseph Barrowclough, Nonso Nnamoko, Ioannis Korkontzelos

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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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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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Classification et valeurs thérapeutiques des plantes ornementales du Togo by Raoufou Radji, Kouami Kokou

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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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EBV-RELATED LYMPHOPROLIFERATIVE DISEASES: A REVIEW IN LIGHT OF NEW CLASSIFICATIONS by Pietro Tralongo, Arianna Bakacs, Luigi Maria Larocca

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