Sugarcane leaf disease classification using deep neural network approach by Saravanan Srinivasan, S. M. Prabin, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Suresh Kulandaivelu, Mohd Asif Shah

“…Results EfficientNet-B7 and DenseNet201 achieved the highest classification accuracy rates of 99.79% and 99.50%, respectively, among 14 models tested. …”
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Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms by Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi

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Evaluating ChatGPT-4o for ophthalmic image interpretation: From in-context learning to code-free clinical tool generation by Joon Yul Choi, Tae Keun Yoo

“…Methods: We assessed ChatGPT-4o through three clinically relevant tasks: (1) image interpretation without prior examples, using fundus, external ocular, and facial photographs representing key ophthalmic conditions; (2) in-context learning with example-based prompts to improve classification accuracy; and (3) generation of an interactive HTML-based decision-support tool from a clinical diagnostic algorithm. …”
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A novel method for intelligent operation and maintenance of transformers using deep visual large model DETR + X and digital twin by Xuedong Zhang, Wenlei Sun, Ke Chen, Shijie Song

“…This model converts one-dimensional DGA data into three-dimensional feature images based on Gram angle fields, facilitating the transformation and fusion of heterogeneous modal information. …”
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Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China by Hongran Li, Hui Zhao, Chao Wei, Min Cao, Jian Zhang, Heng Zhang, Dongqing Yuan

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Early Prediction of Stroke Risk Using Machine Learning Approaches and Imbalanced Data by Hassan Qassim

“…The findings showed that KNN outperformed the three other models with an achieved accuracy of 90%. …”
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Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning by Rupsa Bhowmick

“…The model identified the longitude of RI and non-RI events, initial intensity latitude, extent of initial intensity, and relative humidity at 850 hPa as the most important variables in the classification decision. …”
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Convolutional Neural Network and Channel Attention Mechanism for Multiclass Brain Tumor Classification by Ali Naderi, Akbar Asgharzadeh-Bonab, Farid Ahmadi, Hashem Kalbkhani

“…The proposed model comprises three key components: (1) a fine-tuned EfficientNetB7 convolutional neural network (CNN), adapted through transfer learning by freezing the initial layers and retraining subsequent layers to optimize feature extraction from MR images; (2) a channel attention module that refines extracted feature maps, emphasizing essential features for accurate tumor detection; and (3) a fully connected classifier, optimized through grid search, to achieve precise multiclass tumor classification. …”
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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia by Pakiza Amin, Saima Gulzar Ahmad, Hikmat Ullah Khan, Ehsan Ullah Munir, Naeem Ramzan

“…Specifically, we propose three models: the alternative classification models include the Soft Decision Fusion Model, which applies soft-voting; the Stacking-Based Classifier, which is an ensemble stacking; and the Hybrid Soft Stacking Model. …”
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Fault classification in inverter based resources system using spherical coordinate system by Vivek Sahu, Pratim Kundu

“…Discrete Fourier transformation is utilized to extract fundamental component of three-phase voltages and currents. Two separate indices for fault classification, based on Spherical Coordinate System (SCS)-based representation is proposed. …”
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Hierarchical attention transformer provides assistant suggestions for orbital rejuvenation surgery by Xiang Lian, Xiang Lian, Xiang Lian, Xin Hu, Guannan Li, Siqi Wu, Yihao Liu, Ke Qin, Kai Liu, Kai Liu, Kai Liu

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Stability of the Subaxial Spine after Penetrating Trauma: Do Classification Systems Apply? by Jackson Rucker Staggers, Thomas Elliot Niemeier, William E. Neway, Steven Michael Theiss

“…Herein, we investigate the validity of trauma classification systems including the Thoracolumbar Injury Classification and Severity Score (TLICS), Subaxial Cervical Spine Injury Classification and Severity Score (SLIC), and Denis’ three-column model when applied to spinal penetrating trauma from gunshots, while secondarily evaluating stability of these injuries. …”
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Multitemporal Sentinel and GEDI data integration for overstory and understory fuel type classification by Pegah Mohammadpour, Domingos Xavier Viegas, Alcides Pereira, Emilio Chuvieco

“…This study generates a fuel type map of the overstory and understory based on the FirEUrisk hierarchical fuel classification system (FHFCS) in three steps, including overstory mapping using multispectral and radar data (Sentinel-1 and Sentinel-2), and topographic variables; shrubland and grassland height estimation using biophysical models based on precipitation and Normalized Difference Vegetation Index (NDVI); and understory mapping using spaceborne LiDAR data from the Global Ecosystem Dynamic Investigation (GEDI) and decision rules. …”
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Real Time Vehicle Classification Using Deep Learning—Smart Traffic Management by Tejasva Maurya, Saurabh Kumar, Mritunjay Rai, Abhishek Kumar Saxena, Neha Goel, Gunjan Gupta

“…The study introduces a real‐time vehicle classification model that categorizes vehicles into seven distinct classes: Bus, Car, Truck, Van or Mini‐Truck, Two‐Wheeler, Three‐Wheeler, and Special Vehicles. …”
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Smart Dairy Farming: A Mobile Application for Milk Yield Classification Tasks by Allan Hall-Solorio, Graciela Ramirez-Alonso, Alfonso Juventino Chay-Canul, Héctor A. Lee-Rangel, Einar Vargas-Bello-Pérez, David R. Lopez-Flores

“…The implemented model was based on the YOLOv11 architecture, which enables efficient object detection and classification with real-time performance. …”
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C-parameter version of robust bounded one-class support vector classification by Junyou Ye, Zhixia Yang, Yongxing Hu, Zheng Zhang

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Disulfidptosis classification of pancreatic carcinoma reveals correlation with clinical prognosis and immune profile by Jiangmin Shi, Liang Zhao, Kai Wang, Jieqiong Lin, Jianwei Shen

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Multidimensional Evaluation Framework and Classification Strategy for Low-Carbon Technologies in Office Buildings by Hongjiang Liu, Yuan Song, Yawei Du, Tao Feng, Zhihou Yang

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Active and Inactive Tuberculosis Classification Using Convolutional Neural Networks with MLP-Mixer by Beanbonyka Rim, Hyeonung Jang, Hongchang Lee, Wangsu Jeon

“…In this study, we developed a deep-learning-based binary classification model to distinguish between active and inactive tuberculosis cases. …”
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Deep learning-based approach to third molar impaction analysis with clinical classifications by Yunus Balel, Kaan Sağtaş

“…Specific labels, such as 48-Distoangular-C-III (F1: 0.633), exhibited lower F1 scores. The model demonstrated high accuracy and efficiency, addressing the limitations of manual classifications. …”
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