Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

“…The optimal model was selected based on AUC-ROC and further optimized via hyperparameter tuning. …”
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A Rice-Mapping Method with Integrated Automatic Generation of Training Samples and Random Forest Classification Using Google Earth Engine by Yuqing Fan, Debao Yuan, Liuya Zhang, Maochen Zhao, Renxu Yang

“…The spatial distribution characteristics of rice cultivation extracted by the LR algorithm were accurate and the performance was optimal. …”
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Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: For Better Performance and User Connectivity by Najwan M. Swadi, Firas A. Sabir, Hamed S. Al-Raweshidy

“…This paper concludes that optimized cell site deployment combined with association and clustering algorithms can significantly enhance 5G network performance, particularly in dense urban environments. …”
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A Novel Underwater Wireless Optical Communication Optical Receiver Decision Unit Strategy Based on a Convolutional Neural Network by Intesar F. El Ramley, Nada M. Bedaiwi, Yas Al-Hadeethi, Abeer Z. Barasheed, Saleha Al-Zhrani, Mingguang Chen

“…Our new CNN algorithm utilises the eye diagram (ED) image processing technique. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. …”
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Early Forest Fire Detection With UAV Image Fusion: A Novel Deep Learning Method Using Visible and Infrared Sensors by Kunlong Niu, Chongyang Wang, Jianhui Xu, Jianrong Liang, Xia Zhou, Kaixiang Wen, Minjian Lu, Chuanxun Yang

“…The main contributions include: 1) the creation of a dataset containing 2752 synchronized visible and infrared image pairs to overcome existing dataset limitations; 2) the application of deep learning techniques to enhance image registration and fusion, incorporating an improved algorithm that increases automation; and 3) the development of the Forest Fire Detection Model—Fusion (FFDM-F) model, based on YOLOv5s and fused images, designed to accurately detect small fires at their early stages. …”
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Torque capability enhancement of commercial interior permanent magnet motors using t-shaped notching and merged barrier rotor topology by H.N. Lin, P. Seangwong, N. Fernando, A. Siritaratiwat, P. Khunkitti

“…A multi-objective genetic algorithm with sensitivity-based constraints is employed to optimizes key design parameters, and the electromagnetic performance is evaluated using 2D finite element analysis under no-load and on-load conditions. …”
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A novel seismic inversion method based on multiple attributes and machine learning for hydrocarbon reservoir prediction in Bohai Bay Basin, Eastern China by Zongbin Liu, Jianmin Zhu, Bo Tian, Rui Zhang, Yongheng Fu, Yuan Liu, Lixin Wang

“…GA helps CNNs to get an optimal solution in a fast speed. The results reveal that the model's sand thickness predictions closely match the actual measurements at wells, with a new horizontal well's alignment with the predicted output reaching an accuracy of 85.1%. …”
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Design of and Experiment with a Dual-Arm Apple Harvesting Robot System by Wenlei Huang, Zhonghua Miao, Tao Wu, Zhengwei Guo, Wenkai Han, Tao Li

“…Finally, to improve collaboration efficiency, a multi-arm task planning method based on a genetic algorithm is used to optimize the target harvesting sequence for each arm. …”
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Evaluation of MODIS and VIIRS BRDF Parameter Differences and Their Impacts on the Derived Indices by Chenxia Wang, Ziti Jiao, Yaowei Feng, Jing Guo, Zhilong Li, Ge Gao, Zheyou Tan, Fangwen Yang, Sizhe Chen, Xin Dong

“…This study reveals the need in optimizing the Clumping Index (CI)-NDHD algorithm to produce VIIRS CI product and highlights the importance of considering BRDF product quality flags for users in their specific applications. …”
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Swin‐YOLOX for autonomous and accurate drone visual landing by Rongbin Chen, Ying Xu, Mohamad Sabri bin Sinal, Dongsheng Zhong, Xinru Li, Bo Li, Yadong Guo, Qingjia Luo

“…And finally, the RBN data batch normalization method is used to improve the performance of the model in extracting effective features from the data. …”
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A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder by Hoyeon Lee, Jaehong Yu

“…In modern industrial systems, establishing the optimal maintenance policy for rotating machinery is essential to improve productivity and prevent catastrophic accidents. …”
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Intelligent Trust Evaluation Method for Underwater Sensor Networks Based on Fuzzy Clustering and Dynamic Weight Allocation by Zhaohui WANG, Guangjie HAN, Jiaxin DU, Chuan LIN, Lei WANG

“…Underwater sensor networks (USN) play a crucial role in marine environmental monitoring and other fields while facing significant security challenges. Trust models can effectively defend against insider attacks and ensure network reliability. …”
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Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. …”
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SwinFishNet: A Swin Transformer-based approach for automatic fish species classification using transfer learning. by Ebru Ergün

“…Images were preprocessed by resizing to 224 [Formula: see text] 224 pixels, normalizing, and converting to tensor format for compatibility with deep learning models. Transfer learning was applied using the ST, which was fine-tuned on these datasets and optimized with the AdamW algorithm. …”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…This optimization of the signal enhancement methodology significantly improved the fault diagnosis accuracy. …”
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Detecting Botrytis Cinerea Control Efficacy via Deep Learning by Wenlong Yi, Xunsheng Zhang, Shiming Dai, Sergey Kuzmin, Igor Gerasimov, Xiangping Cheng

“…Experimental results show that the validation loss of this method reaches 0.007, with a mean absolute error of 0.0148, outperforming other comparative models. This study enriches the theory of gray mold control and provides information technology for optimizing and selecting its inhibitors.…”
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A state-of-the-art review of soft computing-based monitoring and control in the machining of hard alloys by Lokesh Kumar, Ashish Goyal, Vimal Kumar Pathak, Abhijit Bhowmik

“…These innovations are supported by suitable modeling and metaheuristic techniques, including adaptive neuro-fuzzy inference systems (ANFIS), simulated annealing, teaching-learning-based optimization (TLBO), the finite element method (FEM), and the non-dominated sorting genetic algorithm II (NSGA-II). …”
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DrugBERT: a BERT-based approach integrating LDA topic embedding and efficacy-aware mechanism for predicting anti-tumor drug efficacy by Weiwei Zhu, Xiaodong Jiang, Lei Zhang, Peng Zhou, Xinping Xie, Hongqiang Wang

“…Furthermore, when validated on an independent dataset of 266 bowel cancer patients, the model achieved a 3% improvement in AUC over previous methods, signifying its robust generalization capability. …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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