IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification by Dandan Zhang, Yafei Song, Qian Xiang, Yang Wang

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A Standardized Classification Scheme for Gastroduodenal Disorder Evaluation Using the Gastric Alimetry System: Prospective Cohort Study by Chris Varghese, Gabriel Schamberg, Emma Uren, Stefan Calder, Mikaela Law, Daphne Foong, Vincent Ho, Billy Wu, I-Hsuan Huang, Peng Du, Thomas Abell, Charlotte Daker, Christopher N. Andrews, Armen A. Gharibans, Gregory O’Grady

“…Studies have demonstrated a range of distinct pathophysiological profiles, and a classification scheme is now required. We used Gastric Alimetry spectral and symptom profiles to develop a mechanism-based test classification scheme, then assessed correlations with symptom severity, psychometrics, and quality of life. …”
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AquaYOLO: Enhancing YOLOv8 for Accurate Underwater Object Detection for Sonar Images by Yanyang Lu, Jingjing Zhang, Qinglang Chen, Chengjun Xu, Muhammad Irfan, Zhe Chen

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Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa by Davy Uwizera, Dr. Charles Ruranga, Prof. Patrick McSharry

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Evaluation of a Deep Learning Model for Automatic Detection of Schizophrenia Using EEG Signals by Swetha Padmavathi Polisetty, Radhamani Ellapparaj, Karthikeyan M P

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Prediction of Claim Fund Reserves in Insurance Companies Using the ARIMA Method by Goenawan Brotosaputro, Yohanes Setiawan Japriadi, Wiwin Windihastuty, Rivai Ahsani

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A Novel and Effective Model for Automatic Modulation Classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross by Jianzheng Lin

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Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification by Weichao Yang

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Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19 by Santiago Tello-Mijares, Francisco Flores, Fomuy Woo

“…As a result, CNN models outperformed traditional artificial intelligence classification techniques. Of these, Vgg16Net performed best, delivering top-tier classification results for COVID-19 severity assessment, with a recall rate of 95.38%, precision of 96%, accuracy of 95.84%, and area under the receiver operating characteristic (AUROC) curve of 0.9585; in addition, the Vgg16Net delivers the lowest false negative (FN) results. …”
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Advancing the Classification and Attribution Method for Alpine Wetlands: A Case Study of the Source Region of Three Rivers, Tibetan Plateau by Xiankun Zheng, Sihai Liang, Xingxing Kuang, Li Wan, Kuo Zhang

“…However, few studies have investigated classification methods and attribution analyses for alpine wetlands. …”
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Comparison of optical coherence tomography vs. fluorescein angiography-based macular neovascularization classifications in age-related macular degeneration by Gábor G. Deák, Klaudia Birner, Bianca S. Gerendas, Georgios Mylonas, Günther Weigert, Martin Michl, Irene Steiner, Ursula M. Schmidt-Erfurth

“…Abstract We aimed to compare fluorescein angiography (FA)-based classification of macular neovascularisation (MNV) with optical coherence tomography (OCT)-based classification, as well as examine differences in retinal fluid among OCT MNV types. …”
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Mapping the landscape of Hospital at home (HaH) care: a validated taxonomy for HaH care model classification by Kerstin Denecke

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Deep Learning Image Classification Rontgen Dada pada Kasus Covid-19 Menggunakan Algoritma Convolutional Neural Network by Leni Anggraini Susanti, Agus Mohamad Soleh, Bagus Sartono

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Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation? by David Dimitris Chlorogiannis, Georgios-Ioannis Verras, Vasiliki Tzelepi, Anargyros Chlorogiannis, Anastasios Apostolos, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos, Andreas Antzoulas, Michail Vailas, Dimitrios Schizas, Francesk Mulita

“…The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. …”
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A text classification method by integrating mobile inverted residual bottleneck convolution networks and capsule networks with adaptive feature channels by Tao Jin, Jiaming Liu

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Automatic Detection and Classification of Eye Diseases from Retinal Images Using Deep Learning: A Comprehensive Research on the ODIR Dataset by Asadi Srinivasulu, Clement Varaprasad Karu, G Sreenivasulu, Gayathri R

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Review of the Geological Strength Index (GSI) as an Empirical Classification and Rock Mass Property Estimation Tool: Origination, Modifications, Applications, and Limitations by Sajjad Hussian, Noor Mohammad, Zahid Ur Rehman, Naseer Muhammad Khan, Khan Shahzada, Sarfraz Ali, Muhammad Tahir, Salim Raza, Saira Sherin

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Fine-scale forest classification with multi-temporal sentinel-1/2 imagery using a temporal convolutional neural network by Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang, Juan Gu

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Breast cancers with neuroendocrine differentiation: Retrospective case studies series from a single institution based on the 2019 WHO classification by Youngkyung Jeon, Ji-Yeon Kim, Jin Seok Ahn, Young-Hyuck Im, Kyuehee Choi, Sun Young Jeong, Yeji Jung, Jaeyeon Jang, Dae-Ho Choi, Joohyun Hong, Hyo Jung Kim, Soo Youn Cho, Yeon Hee Park

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Measurement of Antibiotic Consumption: A Practical Guide to the Use of the Anatomical Therapeutic Chemical Classification and Defined Daily Dose System Methodology in Canada by James M Hutchinson, David M Patrick, Fawziah Marra, Helen Ng, William R Bowie, Laurie Heule, Mark Muscat, Dominique L Monnet

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