Research on Scheduling Algorithm of Agricultural Machinery Cooperative Operation Based on Particle Swarm Neural Network by Wei Li

“…The outer layer of the algorithm uses the improved particle swarm algorithm IPSO module, the inner layer uses the simplex algorithm SIM module, and the optimal solution of the MINLP problem is obtained through the iterative update of the inner and outer modules. …”
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Multi-Objective Optimization for Green BTS Site Selection in Telecommunication Networks Using NSGA-II and MOPSO by Salar Babaei, Mehran Khalaj, Mehdi Keramatpour, Ramin Enayati

“…Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. …”
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A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application by Chunyi Wang, Qiancheng Yan, Xiaolan Qiu, Yitong Luo, Lingxiao Peng, Zhe Zhang

“…Further, the Cramér–Rao lower bound of this algorithm is derived to theoretically illustrate how it improves imaging accuracy. …”
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Estimation of the Ultimate Bearing Capacity of the Rocks via Utilization of the AI-Based Frameworks by Bianca Damico, Matteo Conti

“…The approach adopted here is new and solves the problem using KNN combined with two modern nature-inspired optimization frameworks, namely the Honey Badger Algorithm (HBA) and Equilibrium Slime Mould Algorithm (ESMA). …”
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Data-Driven Cooperative Localization Algorithm for Deep-Sea Landing Vehicles Under Track Slippage by Zhenzhuo Wei, Wei Guo, Yanjun Lan, Ben Liu, Yu Sun, Sen Gao

“…In this study, a data-driven cooperative localization algorithm with a velocity prediction model is proposed to improve the positioning accuracy of DSLV under track slippage. …”
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Fusion of multi-scale and context for small target detection algorithm of unmanned aerial vehicle rescue by LIU Yuan, ZHAO Jing, JIANG Guoping, XU Fengyu, LU Ningyun

“…Finally, balance L1 loss was used to optimize the loss function of the baseline algorithm and enhance the stability of the model during the process of detection. …”
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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

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Location privacy protection method based on lightweight K-anonymity incremental nearest neighbor algorithm by Saite CHEN, Weihai LI, Yuanzhi YAO, Nenghai YU

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Efficient Optical Spatial First-Order Differentiator Based on Graphene-Based Metalines and Evolutionary Algorithms by Tian Zhang, Jia'nan Xie, Yihang Dan, Shuai Yu, Xu Han, Jian Dai, Kun Xu

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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators by Xiaowei Huang, Xuhua Shi, Peiyao Wang, Hongzan Xu, Xiaojun Tang, Gaoran Zhang

“…Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. …”
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A review on multi-fidelity hyperparameter optimization in machine learning by Jonghyeon Won, Hyun-Suk Lee, Jang-Won Lee

“…Tuning hyperparameters effectively is crucial for improving the performance of machine learning models. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. …”
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Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

“…The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (R_P = 0.9190–0.9385, RMSEP = 0.3927–0.5743%, and RPD = 3.0424–3.2509). …”
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Optimized window functions for spectral analysis based on digital filters by R.V. Petrosian

“…The article addresses a relevant issue of improving the accuracy of spectral analysis in computerized systems by optimizing window functions used in the Discrete Fourier Transform (DFT). …”
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Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment by JianWun Lai

“…The ICS is superior to standard WWTCS by a vital error boundary, minimizing energy consumption by 17% and boosting chemical-based consumption optimization by 24%. With an average removal rate of 94.23% for Chemical Oxygen Demand (COD) compared to 88.76% for standard systems, the findings from experiments exhibited significant performance improvements.…”
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Efficient Approach for Brain Tumor Detection and Classification Using Fuzzy Thresholding and Deep Learning Algorithms by Nashaat M. Hussain Hassan, Wadii Boulila

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BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm by Qing Wang, Ning Yan, Yasen Qin, Xuedong Zhang, Xu Li

“…The experimental results demonstrated that the improved BED-YOLO model achieved significant performance improvements compared to the original model. …”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm by Shaotong Pei, Weiqi Wang, Chenlong Hu, Keyu Li, Haichao Sun, Mianxiao Wu, Bo Lan

“…To solve the above problems, this paper optimizes the small target and complex environment problems in the low‐value defect recognition of insulator infrared images, and proposes the STCE‐YOLO algorithm: based on YOLOv8, the deformable large kernel attention is used to improve the detection ability of small targets; then the cross‐modal contextual feature module is applied to Integrate the features of different scales to reduce the computation of the model. …”
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