Evaluation of stroke sequelae and rehabilitation effect on brain tumor by neuroimaging technique: A comparative study. by Xueliang Guo, Lin Sun

“…The development of the 3D CNN model utilizes the ADAM optimization algorithm to facilitate the training process. …”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…This study proposes a stroke risk classification model using ensemble learning that combines Random Forest and XGBoost algorithms. …”
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Unmet needs in the clinical management of chronic hepatitis B infection by Peter D. Block, Joseph K. Lim

“…This includes efforts to optimize delivery of perinatal HBV care, improve HBV-related hepatocellular carcinoma risk stratification models, and clarify the role of finite therapy in the HBV treatment algorithm. …”
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Communication between nodes in backscatter-assisted wireless powered communication network by Jie TAO, Haijiang GE, Minyuan WU, Qike SHAO, Kaikai CHI

“…At present, there is almost no design on information transmission between the nodes in wireless powered communication network.The high-throughput information transmission between the nodes in backscatter assisted wireless powered communication network was studied.A scheme was proposed which consisted of backscattering/energy transmission phase and active communication phase.The maximum throughput was modeled as a convex optimization problem.Then, based on the relationship between the total backscatter communication duration and the threshold of node energy capture duration, the problem was decomposed into several sub problems that only need to optimize two backscatter durations.An efficient two-level golden section search algorithm was designed to solve the sub problems, and the optimal solution of the original problem was obtained from the optimal solutions of the sub problems.Simulation results show that, compared with pure active communication scheme and pure backscatter communication scheme, the proposed scheme can effectively improve the throughput.…”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…To tackle the challenge of designing an improved diabetes classification algorithm that is more accurate, random oversampling and hyper‐tuning parameter techniques have been used in this study. …”
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Design and Implementation of Local Threshold Segmentation Based on FPGA by Shangshang Gao, Yuanyuan Wang, Zhaofeng Chen, Feng Zhou, Rugang Wang, Naihong Guo

“…Finally, the design algorithm is verified by ModelSim simulation software and QT5 software. …”
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Dispatching strategy of shared bicycles for morning peak tidal travel based on spatial distribution by Xiangyu Lei, Jiaqi Yang

“…The study identifies the spatial distribution characteristics of the tidal phenomenon by analyzing the riding order and electronic fence data and combining them with the KD-Tree algorithm; subsequently, a demand prediction model based on the XGBoost algorithm and a hierarchical scheduling model based on the greedy algorithm are constructed to dynamically optimize the spatial distribution of the bicycle resources with the demand fluctuation as the guide. …”
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ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion by Hanyu Song, Xinyue Huang, Ziqiang Wang, Jianwei Hu, Huasheng Zhang, Hui Yang

“…The algorithm innovatively designs three core modules: the AdaptiveVision Network (AVN) backbone for enhanced feature extraction, the Dual-Domain Enhanced Transformer (DDET) module for spatiotemporal-frequency domain collaboration, and the Adaptive Multi-scale Feature Model (AMFM) for improved feature fusion. …”
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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

“…Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm‐extreme learning machine (PSO‐ELM). …”
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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. …”
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Linkage and Connectivity Control in Wireless Sensor Network: A New Mechanism by Pouya Derakhshan-Barjoei, Ahmad Yousofi

“…The outcomes of the proposed algorithm on the selected model show 56% improvement in the remaining battery charge. …”
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A Minimal Path-Based Method for Computing Multistate Network Reliability by Xiu-Zhen Xu, Yi-Feng Niu, Can He

“…To advance the solution efficiency of d-MPs, an improved model is developed by redefining capacity constraints of network components and minimal paths (MPs). …”
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PTCDet: advanced UAV imagery target detection by Jia Su, Yichang Qin, Ze Jia, Yanli Hou

“…The weighted perceptive field augmentation (WPFA) module is designed to improve the contextual awareness and feature representation of the model, optimizing detection accuracy for small objects. …”
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Classification of Complex Power Quality Disturbances Based on Lissajous Trajectory and Lightweight DenseNet by Xi Zhang, Jianyong Zheng, Fei Mei, Huiyu Miao

“…The experimental results demonstrate that, compared with current mainstream PQD classification methods, the proposed algorithm not only achieves superior disturbance classification accuracy and noise robustness but also significantly improves response speed in PQD classification tasks through its concise visualization conversion process and lightweight model design.…”
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Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights by Muhammad Hassan, Khabat Khosravi, Travis J. Esau, Gurjit S. Randhawa, Aitazaz A. Farooque, Seyyed Ebrahim Hashemi Garmdareh, Yulin Hu, Nauman Yaqoob, Asad T. Jappa

“…This study introduces innovative ensemble learning models that integrate the randomizable filter classifier (RFC), regression by discretization (RBD), and attribute-selected classifier (ASC) with the random forest (RF) algorithm, resulting in hybrid models (RFC-RF, RBD-RF, and ASC-RF). …”
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Real-Time Recognition of Loading Cycles’ Process Based on Electric Mining Shovel Monitoring by Bonan Wang, Yun Duan, Wei Xu

“…Moreover, the dynamic time warping (DTW) algorithm was used to detect and classify the preliminary recognition results by optimizing its distance threshold parameters, reducing the error rate of the model. …”
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A New Hybrid Reinforcement Learning with Artificial Potential Field Method for UAV Target Search by Fang Jin, Zhihao Ye, Mengxue Li, Han Xiao, Weiliang Zeng, Long Wen

“…Specifically, the SAC-APF hybrid framework achieves a 161% improvement in success rate compared to the baseline SAC model, increasing from 0.282 to 0.736 in obstacle scenarios.…”
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Two-stage resilience enhancement method for integrated electricity-gas systems through linepack and mobile compressors by Chao Qin, Yongxue Wang, Shuaihu Ye

“…The proposed method is reformulated into a mixed-integer second-order cone programming model using second-order cone relaxation. The progressive hedging algorithm is employed to further improve the solution efficiency. …”
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Berth Allocation and Quay Crane Assignment Considering the Uncertain Maintenance Requirements by Siwei Li, Liying Song

“…To address this novel problem, we propose a proactive-reactive method that incorporates a reliability-based model into the Swarm Optimization with Differential Evolution (SWO-DE) algorithm. …”
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Integrated Cloud-Twin Synchronization for Supply Chain 5.0 by Divya Sasi Latha, Tartat Mokkhamakkul

“…This study introduces a cloud-based twin optimization model for Supply Chain 5.0, validated through genetic algorithm (GA) simulations. …”
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