SAlexNet: Superimposed AlexNet using residual attention mechanism for accurate and efficient automatic primary brain tumor detection and classification by Qurat-ul-ain Chaudhary, Shahzad Ahmad Qureshi, Touseef Sadiq, Anila Usman, Ambreen Khawar, Syed Taimoor Hussain Shah, Aziz ul Rehman

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Classification of Aortic Stenosis Patients via ECG-Independent Multi-Site Measurements of Cardiac-Induced Accelerations and Angular Velocities at the Skin Level by Chiara Romano, Emanuele Maiorana, Annunziata Nusca, Simone Circhetta, Sergio Silvestri, Schena Emiliano, Gian Paolo Ussia, Carlo Massaroni

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Risk assessment and hierarchical classification of foodborne disease epidemics during major events in Beijing: an empirical study based on surveillance data analysis by Jinru JIANG, Yangbo WU, Tongyu WANG, Chao WANG, Yanlin NIU, Di WANG, Liping LI, Qian CHEN, Yao ZHAO, Xiaochen MA

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Performance of the modified 2022 ACR/EULAR giant cell arteritis classification criteria without age restriction for discriminating from Takayasu arteritis by Takahiko Sugihara, Masayoshi Harigai, Haruhito A. Uchida, Hajime Yoshifuji, Yasuhiro Maejima, Jun Ishizaki, Yoshiko Watanabe, Hiroaki Dobashi, Yoshinori Komagata, Naoto Tamura, Yoshikazu Nakaoka, Japan Research Committee of the Ministry of Health, Labour, and Welfare for Intractable Vasculitis (JPVAS)

“…The sensitivity of the modified 2022 ACR/EULAR GCA classification criteria with a score ≥ 6 was 82.0%, 68.5%, and 32.1% in all GCA, GCA with large-vessel involvement, and GCA without cranial arteritis, respectively. …”
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Kombinasi Intent Classification dan Named Entity Recognition pada Data Berbahasa Indonesia dengan Metode Dual Intent and Entity Transformer by Zahra Asma Annisa, Rizal Setya Perdana, Putra Pandu Adikara

“…The best hyperparameter combination obtained is a warm-up step of 70, early stopping patience of 15, weight decay of 0.01, NER loss weight of 0.6, and intent classification loss weight of 0.4. …”
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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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A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning by E. Sreehari, L. D. Dhinesh Babu

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TerraSAR-X SAR data for classification of ice-free areas and glacier facies on Fildes Peninsula, King George Island, Antarctica by ANDRÉ M. DE ANDRADE, ROBERTO F.M. MICHEL, KÁTIA K. DA ROSA, ULISSES F. BREMER, CARLOS ERNESTO G.R. SCHAEFER, JEFFERSON C. SIMÕES

“…This study characterized three surface zones of Collins Glacier and three other zones in ice-free areas on the Fildes Peninsula, which has an area of 29.6 km². We used TerraSAR-X satellite images from 2014 to 2016 and analyzed the influence of meteorological and environmental conditions on these surface zones. …”
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GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 by Jian Zuo, Li Zhang, Jingfeng Xiao, Bowei Chen, Bo Zhang, Yingwen Hu, M. M. Abdullah Al Mamun, Yang Wang, Kaixin Li

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Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques by Phan Thi Huong, Lam Thanh Hien, Nguyen Minh Son, Huynh Cao Tuan, Thanh Q. Nguyen

“…While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. …”
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An explainable deep learning model for diabetic foot ulcer classification using swin transformer and efficient multi-scale attention-driven network by R. Karthik, Armaano Ajay, Anshika Jhalani, Kruthik Ballari, Suganthi K

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Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3 by Abhiram Sharma, R. Parvathi

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MRI findings in patients with psychogenic non‐epileptic seizures: Prevalence, distribution, and classification of the findings. A single tertiary epilepsy center experience by Aleix Jareno‐Badenas, Mario Matute‐González, Luis Pintor, Estefanía Conde‐Blanco, Mar Carreño, Xavier Setoain, Camilo Pineda, Fernando Diego Choque‐Chávez, Tomás Fernández, Núria Bargalló, Sofía González‐Ortiz

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Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study by Eman H. Alkhammash

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BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data by Md Hasanul Ferdaus, Rizvee Hassan Prito, Ahmed Abdal Shafi Rasel, Masud Ahmed, Md. Jahid Hassan Saykot, Shanjida Sultan Shanta, Sonali Akter, Ankan Chandra Das, Mohammad Manzurul Islam, Mahamudul Hasan, Md Sawkat Ali

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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…The final predictor, PaleAle 6.0, predicts RSA in real values as well as two-state (exposure threshold of 25%) and four-state (exposure thresholds of 4%, 25%, and 50%) discrete classifications. …”
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Features and predictive value of 6-min walk test outcomes in interstitial lung disease: an observation study using wearable monitors by Jiaying Li, Xiaobing Wu, Xiaoyan Li, Miaozhen Deng, Xinyin Liang, Huiqun Wei

“…Further, the SpO2 nadir for 78.27% of the participants appeared at the end of the fourth minute. The 6-min walk distance (6MWD) had the strongest correlation with NYHA classification (r=0.82, p<0.01). …”
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THE DEVELOPMENT OF HOTEL BASE IN POLAND WITH THE USE OF EU FUNDS by Maciej Stawicki

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La fabrication de l’élève en difficulté by Christophe Roiné

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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…Compared with the latest YOLOv11n, our model achieved a 3.3% improvement in mAP, with reductions of 26.4% in parameters, 14.3% in FLOPs, and 14.6% in inference time, demonstrating comprehensive enhancements. …”
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