Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

“…However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.MethodsA novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. …”
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A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Emad Muteb Alharbi, Hibah Qasem Salman Alatawi, Kousalya Prabahar, Jawhara Bader Aljabri, Anand Paul

“…The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. …”
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Role of circulating MicroRNAs in prostate cancer diagnosis and risk stratification in the MCC Spain study by Inés Gómez-Acebo, Sara Valero-Dominguez, Javier Llorca, Jessica Alonso-Molero, Thalía Belmonte, Gemma Castaño-Vinyals, Ana Molina-Barceló, Rafael Marcos-Gragera, Manolis Kogevinas, Paz Rodríguez-Cundín, Juan Alguacil, Beatriz Pérez-Gómez, Marina Pollán, Trinidad Dierssen-Sotos

“…A panel of 46 candidate miRNAs was profiled, and feature selection was performed using LASSO penalization. …”
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A Few-Shot SE-Relation Net-Based Electronic Nose for Discriminating COPD by Zhuoheng Xie, Yao Tian, Pengfei Jia

“…We propose an advanced electronic nose based on SE-RelationNet for COPD diagnosis with limited breath samples. The model integrates residual blocks, BiGRU layers, and squeeze–excitation attention mechanisms to enhance feature-extraction efficiency. …”
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Key factors in predictive analysis of cardiovascular risks in public health by Ghazi I. Al Jowf, Manjur Kolhar

“…Each model’s performance was assessed using metrics like accuracy, precision, recall, F1 score and ROC AUC to determine their reliability and practical relevance. …”
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Genre of “Promise” as a Reflection of Intentional and Linguistic Organization in Virtual Pedagogical Discourse by T. G. Rabenko, E. S. Denisova

“…Lebedeva), two types of genre features of promises are identified. The first type includes invariant characteristics of the genre “promise”: a) the communicative goal of the utterance, linked to a voluntary commitment by the subject to fulfill something; b) linguistic markers of the genre — syntactic constructions containing performative “I promise,” predicates “I swear” / “commit” / “give (<my word>)” with direct objects or explanatory clauses indicating the object of the promise. …”
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Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments by Huinian Li, Nannan Li, Wenmin Wang, Chengcheng Yang, Ningxia Chen, Fuqin Deng

“…The CSGT model demonstrates exceptional performance in diagnosing crop diseases across diverse backgrounds, overcoming the limitations of current models, especially in complex settings. …”
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Motion-driven adaptive frame selection strategy for video action recognition by Hao Ding, Chen Guo, Jing Sun, Xiaoping Jiang, Hongling Shi, Jianjin Li

“…Experimental results demonstrate that our selection strategy can be integrated with state-of-the-art action recognition models, leading to improved recognition performance.…”
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Deep learning tools predict variants in disordered regions with lower sensitivity by Federica Luppino, Swantje Lenz, Chi Fung Willis Chow, Agnes Toth-Petroczy

“…This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.…”
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Accuracy of Detecting Degrees of Lameness in Individual Dairy Cattle Within a Herd Using Single and Multiple Changes in Behavior and Gait by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…Through a comparative analysis of single-parameter and multiple-parameter classification models, we quantitatively demonstrated that models using multiple characteristics significantly outperformed single-parameter models, achieving an accuracy of 84% and a Macro-F1 score of 0.81, while better accounting for individual variability. …”
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Enhancing Online Fraud Detection: Leveraging Machine Learning and Behavioral Indicators for Improved Accuracy and Real-Time Detection by Shaha Prasad, Gavekar Vidya

“…This study presents a comprehensive evaluation of machine learning (ML) models for fraud detection, emphasizing the role of behavioral indicators in enhancing model performance. …”
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Detecting network intrusions in cyber-physical systems using deep autoencoder-based dimensionality reduction approach anddeep neural networks by A. E. Ibor, D. O. Egete, A. O. Otiko, D. U. Ashishie

“…The model is trained and tested on CICIDS2017 and UNSW-NB15 datasets with a rich collection of attack patterns such as DoS, DDoS, Shellcode, and Worm attacks. …”
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Enhancing Symbol Recognition in Library Science via Advanced Technological Solutions by Eleonora Bernasconi, Stefano Ferilli

“…The methodological pipeline incorporates advanced image segmentation techniques to isolate symbols from complex manuscripts, followed by data augmentation to enhance model resilience. The system is supported by a high-performance computing framework to manage large datasets efficiently, thereby facilitating more precise identification and analysis. …”
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Proposing Smart System for Detecting and Monitoring Vehicle Using Multiobject Multicamera Tracking by Phat Nguyen Huu, Bang Nguyen Anh, Quang Tran Minh

“…Furthermore, our system uses a ResNet backbone model for feature extraction of objects within each camera’s frame. …”
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Evaluation of Carboxymethyl Cellulose as an Additive for Selective Protein Removal from Wine by Stephan Sommer

“…This study was designed to evaluate powdered carboxymethyl cellulose (CMC) and a liquid formulation in model wine using bovine serum albumin (BSA) and egg white as model proteins. …”
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Design and Analysis of Low Pole Variable Flux Reluctance Machine by Hilmi Gurleyen

“…This paper presents a comprehensive analytical model that reveals the detailed harmonic structure of flux linkage in the armature windings of a 6-slot/4-pole variable flux reluctance machine (VFRM). …”
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Slim-sugarcane: a lightweight and high-precision method for sugarcane node detection and edge deployment in natural environments by Lijiao Wei, Lijiao Wei, Shuo Wang, Xinwei Liang, Dongjie Du, Xinyi Huang, Ming Li, Yuangang Hua, Weihua Huang, Zhenhui Zheng, Zhenhui Zheng

“…However, existing detection methods often suffer from large model sizes and suboptimal performance, limiting their applicability on resource-constrained edge devices. …”
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Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? by Liping Wang, Huanjun Liu, Xiang Wang, Xiaofeng Xu, Liyuan He, Chong Luo, Yong Li, Xinle Zhang, Deqiang Zang, Shufeng Zheng, Xiaodan Mei

“…Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R² > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. …”
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Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network by Dengke WANG, Longhang WANG, Yaguang QIN, Le WEI, Tanggen CAO, Wenrui LI, Lu LI, Xu CHEN, Yuling XIA

“…Firstly, the VGG16 module is used as the backbone feature extraction network to enhance the model’s generalization ability and prevent the initialization of model parameters from being too random. …”
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LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning by Tofayet Sultan, Mohammad Sayem Chowdhury, Nusrat Jahan, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, Dunren Che

“…A comprehensive dataset comprising 5491 images across four distinct disease categories was employed for the training, validation, and testing of the model. The experimental results showcased outstanding performance, achieving 98% accuracy, 99% precision, 98% recall, and a 98% F1‐score. …”
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