Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…We enhance the model’s diagnostic capability through complex image preprocessing techniques, such as improved noise reduction and morphological approaches. …”
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UAV-SfM Photogrammetry for Canopy Characterization Toward Unmanned Aerial Spraying Systems Precision Pesticide Application in an Orchard by Qi Bing, Ruirui Zhang, Linhuan Zhang, Longlong Li, Liping Chen

“…Precisely adjusting pesticide application rates based on the target conditions is an effective method to improve pesticide use efficiency. In orchard spraying, the structural characteristics of the canopy are crucial for guiding the pesticide application system to adjust spraying parameters. …”
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The classification method of donkey breeds based on SNPs data and machine learning by Dekui Li, Dekui Li, Xiaolong Hu, Yongdong Peng

“…A method for accurately classifying donkey breeds has been developed by integrating single nucleotide polymorphism (SNPs) data with machine learning algorithms. The approach includes preprocessing donkey genomic sequencing data, addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE), and utilizing an improved Leave-One-Out Cross-Validation (LOOCV) for dataset partitioning. …”
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Motion Trajectory Control System for Production Line Robots Based on Variable Domain Fuzzy Control by Ling Pan

“…Experiments show that the algorithm effectively solves the contradiction between computational complexity and control accuracy, improves the system's response speed, and reduces the system's steady-state tracking error.…”
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Enhancing stochastic optimal power flow with modified cheetah optimizer for integrating renewable energy sources by Majid Saeidi, Taher Niknam, Mohsen Zare, Zulfiqar Ali Memon

“…Abstract In this paper, a modified cheetah optimizer (MCO) algorithm is presented, which has been designed to address the optimal power flow (OPF) problem in power grids that utilize renewable energy sources (RES). …”
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DCT-Based White Blood Cell Image Enhancement for Recognition Using Deep Learning by Anh Quynh Vu, Hoan Quoc Bui, Long Tuan Nguyen, Tuyen Ngoc Le

“…Experimental results for the four most common PBC_dataset_normal_DIB, Raabin-WBC, BCCD, and Munich AML Morphology datasets using state-of-art deep learning models, including VGG16, GoogLeNet, and RestNet, illustrate the effectiveness of the CSVDC algorithm. …”
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Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

“…In addition, ablation experiments show that the multi-scale feature extraction, screening and local feature coding in this paper have a significant effect on improving the algorithm’s ability to distinguish different cloud images.…”
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Hardware-in-loop implementation of an adaptive MPPT controlled PV-assisted EV charging system with vehicle-to-grid integration by Surabhi Singh, Hari Om Bansal

“…The proposed work presents the modelling and controlling a 10 kW EV charging/discharging framework integrating PV and grid. …”
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Recent advances in deep learning for protein-protein interaction: a review by Jiafu Cui, Siqi Yang, Litai Yi, Qilemuge Xi, Dezhi Yang, Yongchun Zuo

“…Moreover, we examined enhanced algorithms for dealing with data imbalances, variations, and high-dimensional feature sparsity, as well as industry challenges (including shifting protein interactions, interactions with non-model organisms, and rare or unannotated protein interactions), and offered perspectives on the future of the field. …”
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A General Framework for Sorting Large Data Sets Using Independent Subarrays of Approximately Equal Length by Shahriar Shirvani Moghaddam, Kiaksar Shirvani Moghaddam

“…The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms. …”
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Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers by Reza Kalantar, PhD, Manasi Ingle, FRCR, Romelie Rieu, BA, BmBCh, Sebastian Curcean, MD, Jessica Mary Winfield, PhD, Gigin Lin, MD, PhD, Christina Messiou, MD, MRCP, FRCR, Susan Lalondrelle, FRCR, Dow-Mu Koh, MD, FRCP, FRCR, Matthew David Blackledge, PhD

“…Results: The synthetic MRL-assisted model improved organ at risk segmentation accuracy on MRL images, with fraction-adapted contours displaying high anatomical fidelity. …”
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Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer by Zhenfeng Huang, Minghui Zhang, Nana Zhang, Mengyao Zeng, Yao Qian, Meng Zhu, Xiangyan Meng, Ming Shan, Guoqiang Zhang, Feng Liu

“…Additionally, functional analyses were conducted to investigate the underlying mechanisms involved. Prognostic risk models were developed via random forest, support vector machines and the least absolute shrinkage and selection operator algorithm. …”
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Study on Correction Methods for GPM Rainfall Rate and Radar Reflectivity Using Ground-Based Raindrop Spectrometer Data by Lin Chen, Huige Di, Dongdong Chen, Ning Chen, Qinze Chen, Dengxin Hua

“…Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. …”
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Predictive framework of vegetation resistance in channel flow by Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

“…This study introduces a machine learning-based framework for predicting vegetation flow resistance, incorporating nine ML methods, including SVM, XGBoost, and BP. To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. …”
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The artificial intelligence revolution in gastric cancer management: clinical applications by Runze Li, Jingfan Li, Yuman Wang, Xiaoyu Liu, Weichao Xu, Runxue Sun, Binqing Xue, Xinqian Zhang, Yikun Ai, Yanru Du, Jianming Jiang

“…These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. …”
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ESTIMATION OF THERMAL IMAGER MINIMUM RESOLVABLE TEMPERATURE DIFFERENCE by Ahmed M.L. Al-Mzirawi, V. G. Kolobrodov, V. I. Mykytenko

“…The paper is devoted to improving the accuracy of determining the minimum resolvable temperature difference (MRTD) of thermal imagers by means of more sophisticated models of thermal images visual perception. …”
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Adaptative Pressure Estimation and Control Architecture for Integrated Electro-Hydraulic Brake System by Zhenhai Gao, Yi Yang, Guoying Chen, Liang Yuan, Jianguang Zhou, Jie Zhang

“…Apart from this, this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode. …”
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GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity by Zhaoyang Yin, Zehua Wang, Junhua Ye, Suyin Zhou, Aijun Xu

“…Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking efficiency, we propose a pig-tracking method that is based on skeleton feature similarity, which we named GcnTrack. …”
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Predicting clozapine-induced adverse drug reaction biomarkers using machine learning by John-Paul Cooper, Pierre Chue, Arno G. Siraki, Lusine Tonoyan

“…Predicting agranulocytosis could improve patient safety. Our study aimed to develop and validate machine learning (ML) models for predicting agranulocytosis in clozapine-prescribed patients using the Canada Vigilance Adverse Reaction Online Database (n = 9395 reports). …”
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Applications of Artificial Intelligence in Vasculitides: A Systematic Review by Mahmud Omar, Reem Agbareia, Mohammad E. Naffaa, Abdulla Watad, Benjamin S. Glicksberg, Girish N. Nadkarni, Eyal Klang

“…Conclusion The current literature shows that AI algorithms can enhance vasculitis diagnosis and prediction, with deep‐ and machine‐learning models showing promise in Kawasaki disease. …”
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