Investigation of Hysteresis Phenomena and Compensation in Piezoelectric Stacks for Active Rotor by Xiancheng Gu, Weidong Yang, Linghua Dong, Jinlong Zhou

“…Subsequently, the Bouc–Wen model is adopted to establish the hysteresis model of the piezoelectric actuator, with its parameters identified through the particle swarm optimization (PSO) algorithm. …”
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The outcome prediction method of football matches by the quantum neural network based on deep learning by Yang Sun, Hongyang Chu

“…During the model training phase, gradient descent is used to optimize weight parameters, and quantum algorithms are integrated to continuously adjust network weights to minimize prediction errors. …”
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Construction of a Prediction Model for Sleep Quality in Embryo Repeated Implantation Failure Patients Undergoing Assisted Reproductive Technology Based on Machine Learning: A Singl... by Zhao Y, Xu C, Qin N, Bai L, Wang X, Wang K

“…Yanjun Zhao,1,* Chenying Xu,1,* Ningxin Qin,2,* Lina Bai,1 Xuelu Wang,1 Ke Wang2 1Operating Room, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China; 2Reproductive Medicine Center, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuelu Wang, Email wangxuelu@51mch.com Ke Wang, Email wangkeyfy@126.comObjective: Constructing a predictive model for sleep quality in embryo Repeated Implantation Failure(RIF) patients using multiple machine learning algorithms, verifying its performance, and selecting the optimal model.Methods: Retrospective collection of clinical data from RIF patients who underwent assisted reproductive technology at the Reproductive Medicine Center of Tongji University Affiliated Obstetrics and Gynecology Hospital from January 2022 to June 2022, divided into a training set and a validation set in an 8:2 ratio. …”
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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

“…The grid search for random forest algorithms achieved a score of 79% when optimally tuned; however, the training accuracy was 99%. …”
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Application of Fuzzy Logic Sliding Mode Control Approach With PID Structure for Electro-Hydraulic Actuator Tracking System by Muhamad Fadli Ghani, Ahmad Athif Mohd Faudzi, Rozaimi Ghazali, Shahrol Mohamaddan

“…The proposed control strategy was designed with the switching function modification based on an FL approach in the conventional SMC algorithm. Due to the difficulty of concurrent hybrid design, a particle swarm optimization (PSO) algorithm was employed to determine the optimal control variables value. …”
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SPECIAL CHARACTERISTICS OF AERODYNAMIC PROPERTIES OF UNMANNED AIRCRAFT WITH THE HIGH-ASPECT-RATIO WING by O. Е. Lukyanov, A. V. Ostrovoy, M. A. Mensez Soto, Y. A. Klimov, V. G. Shakhov

“…The technique is based on numerical multidisciplinary mathematical modeling using the discrete vortex method and the topology optimization algorithm based on the variable density body model. …”
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Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study by Mengting Gu, Wenjie Zou, Huilin Chen, Ruilin He, Xingyu Zhao, Ningyang Jia, Wanmin Liu, Peijun Wang

“…Compared with the two models aforementioned, the Radiology MLP model demonstrated a 33.4%-131.3% improvement in NRI and a 9.3%-50% improvement in IDI, showing better discrimination, calibration and clinical usefulness in three sets, which was selected as the optimal predictive model. …”
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Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…At the same time, the ReLU transfer function ensures robustness, mainly when dealing with noisy or incomplete image segments. Feature vector optimization is performed using a chaotic particle swarm algorithm, enhancing the model’s overall accuracy, reliability, and ease of implementation. …”
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Analytical Solution and Analysis of Aerodynamic Noise Induced by the Turbulent Flow Interaction of a Plate with Double-Wavelength Bionic Serration Leading Edges by Chenye Tian, Xiaomin Liu, Lei Wang, Yuefei Li, Yandong Wu

“…In this study, in order to reduce the aerodynamic noise of a flat plate operating in a steady uniform flow, double-wavelength leading-edge serrations based on Ayton’s analytical model are optimized by the meta-heuristic optimization algorithm. …”
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Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation by Zhipeng Wang, Soon Xin Ng, Mohammed EI-Hajjar

“…The proposed region segmentation algorithm and cumulative reward model have been tested in different DRL techniques, where we show that the cumulative reward model can improve the training efficiency of deep neural networks by 30.8% and the region segmentation algorithm enables deep Q-network agent to avoid 99% of local optimal traps and assists deep deterministic policy gradient agent to avoid 92% of local optimal traps.…”
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Power capacity enhancement of hydropower plant through the penetration of solar and wind energy by Ahmad Shah Irshad, Ahmad Shah Amin, Amir Mohammad Ilham, M.H. Elkholy, Said Elias, Tomonobu Senjyu

“…A multi-objective genetic algorithm was employed to optimize this integration, addressing objectives such as maximizing power output, improving energy efficiency, and minimizing environmental impact. …”
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Distribution Network Topology Identification Based on Finite Key Nodes and Wasserstein Distance by Yao ZHAO, Yongjiang CHEN, Kunhua JI, Yun WANG

“…Firstly, the finite key nodes can be used to identify the topology when the subspace perturbation model is used to prove the topology change of the distribution network, and the concept of influence degree is introduced through the entropy method based hybrid K-Shell algorithm and the importance of nodes is obtained by the influence degree and the electrical distance between the nodes, thus determining the key nodes in the distribution network topology. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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Spatial pattern and heterogeneity of green view index in mountainous cities: a case study of Yuzhong district, Chongqing, China by Aiping Gou, Xuyuan Wang, Jiangbo Wang, Chenjie Wang, Guanzheng Tan

“…This study investigates the spatial heterogeneity of the GVI and its influencing factors in Yuzhong District, Chongqing, a mountainous city in China. Deep learning algorithms were employed to calculate the green visibility of street view images, and Geographic Weighted Regression (GWR) and the Optimal Parameter-Based Geodetector (OPGD) were utilized to analyze the relationships between GVI and factors such as road physical attributes, the Normalized Difference Vegetation Index (NDVI), and topographic features. …”
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Enhancing remote patient monitoring with AI-driven IoMT and cloud computing technologies by Vijay Kumar Damera, Ramesh Cheripelli, Narsaiah Putta, G. Sirisha, Deepthi Kalavala

“…The proposed TL-SAM framework effectively extracts spatial and spectral features from patient health data, optimizing classification accuracy. Furthermore, the model employs an Improved Wild Horse Optimization with Levy Flight Algorithm (IWHOLFA) for hyperparameter tuning, enhancing its predictive performance. …”
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FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence by Koffka Khan

“…Empirically, FedEmerge improves global model accuracy and convergence speed on highly skewed non-IID benchmarks, and it reduces performance disparities among clients compared to FedAvg. …”
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Digital Intelligence Pathology Platform and Its Service Pattern by Xiaohong Chen, Liu Liu, Yajua Niu, Xiaoliang Liu, Xiaohai Li, Jianhua Zhou, Junpu Wang

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Laser-induced Breakdown Spectroscopy Based on Pre-classification Strategy for Quantitative Analysis of Rock Samples by Weiheng KONG, Lingwei ZENG, Yu RAO, Sha CHEN, Xu WANG, Yanting YANG, Yixiang DUAN, Qingwen FAN

“…Different element quantitative models were constructed for each rock type. The kNN algorithm was selected using cross-validation to determine the optimal k value, and the key punishment parameter C and RBF width parameter γ of the SVM algorithm were determined using a grid search method. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…A genetic algorithm-optimized backpropagation neural network was then applied to assess the effectiveness of the fused remote sensing data and PROSAIL-5D simulation in improving FMC retrieval accuracy. …”
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Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net by Kun Wang, Yong Han, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang

“…Conclusion: The deep learning DCU-Net algorithm model can improve the performance of tumor CT image segmentation, and the reconstructed fine model can better reflect the actual situation of individual tumors; the MR system constructed based on this model enhances clinicians’ understanding of tumor morphology and spatial relationships. …”
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