The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. …”
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A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy by Hua Zhan, Changxu Jiang, Zhen Lin

“…The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues like slow computational speed and difficulties in solving large-scale models, while heuristic algorithms are prone to fall into local optima. …”
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Machine learning and transfer learning techniques for accurate brain tumor classification by Seyed Matin Malakouti, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar

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Research status and prospect of UWB radar echo signal processing technology for mine rescue by Xuezhao ZHENG, Chaoyang SUN, Yuan HUANG, Wen DING, Yang MA, Qiang LI, Haowei MOU

“…In view of the existing problems, the future development trend of UWB radar life information detection echo signal processing technology is proposed: the mine clutter model is constructed and the real-time adaptive filtering algorithm is studied. …”
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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

“…Secondly, the chaos quantum particle swarm optimization (CQPSO) algorithm is used to optimize the parameters of the generalized regression neural network, form an ideal network model based on the principle of the lowest fitness function, and better learn the fault characteristics of the ultra-high voltage DC transmission line. …”
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Integer linear programming for unsupervised training set selection in molecular machine learning by Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza, Ksenia R Briling, Jan Weinreich, Friedrich Eisenbrand, Clémence Corminboeuf

“…Altogether, this work provides a practical algorithm to improve the performance of physics-inspired ML models and offers insights into the conceptual differences with existing training set selection approaches.…”
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Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine by Shiyu Li, Hong Wan, Qun Yu, Xinyuan Wang

“…In addition, the Bayesian global optimization algorithm is used to select the optimal hyperparameters of the network in order to enhance the predictive performance of the model. …”
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Digital twin-driven management strategies for logistics transportation systems by Junfeng Li, Jianyu Wang

“…The digital model is dynamically adjusted based on prediction results, further optimizing the management strategy. …”
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Lightweight UAV Detection Method Based on IASL-YOLO by Huaiyu Yang, Bo Liang, Song Feng, Ji Jiang, Ao Fang, Chunyun Li

“…To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. …”
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Developing an Efficient Calibration System for Joint Offset of Industrial Robots by Bingtuan Gao, Yong Liu, Ning Xi, Yantao Shen

“…Therefore, there are several lines in space parameterized by robot joint offsets only and all these lines were constrained by the same point, that is, the center of the PSD surface. Consequently, an optimization model was formulated and the Levenberg-Marquardt (LM) algorithm was employed to identify the joint offsets. …”
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A Method Adjusting Consistency and Consensus for Group Decision-Making Problems with Hesitant Fuzzy Linguistic Preference Relations Based on Discrete Fuzzy Numbers by Meng Zhao, Ting Liu, Jia Su, Meng-Ying Liu

“…Besides, an illustrative example and comparative analysis are conducted through the proposed calculation process and the optimization algorithm. Finally, the analysis on the threshold values is made to help the decision-maker determine critical consensus level. …”
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Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems by Andrea Pozzi, Alessandro Incremona, Daniele Toti

“…Lithium-ion batteries play a pivotal role in enabling eco-friendly mobility, particularly in electric vehicles, but optimizing their charging process to improve battery lifespan, safety, and overall efficiency remains a significant challenge. …”
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A growth posture identification method of immature peaches in natural environments by Jidong Lv, Liangliang Niu, Ying Han, Xiaoqin Sun, Lingyun Wang, Hailong Rong, Ling Zou

“…The model was easily deployed with improved detection speed. …”
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A Non-invasive Load Recognition Approach Incorporating SENet Attention Mechanism and GA-CNN by Xin SHEN, Gang WANG, Yitao ZHAO, Zhao LUO, Zhao LI, Xiaohua YANG

“…Secondly, the U-I trajectory map of the residential load is extracted and weighted pixelated to obtain the WVI (Weighted pixelated VI) feature matrix through computation, which is applied as the feature coefficient to train the SENet-CNN model. Finally, by virtue of the genetic algorithm, the SENet-CNN model is trained and the hyperparameters of the CNN-SENet model are optimized to improve the model load recognition performance and computational efficiency. …”
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Enhancing 4G/LTE Network Path Loss Prediction with PSO-GWO Hybrid Approach by Messaoud Garah, Nabil Boukhennoufa

“…Furthermore, a hybrid optimization model, PSO-GWO, is proposed to improve prediction accuracy. …”
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Analysis of IoT-Collected Radish Growth Data Using Deep Neural Networks by Ivan P. Malashin, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…The GA-HPO approach improved model performance to a classification accuracy of 92%, showcasing its adaptability and potential as a tool for precision agriculture. …”
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Prediction of Chemical Corrosion Rate and Remaining Life of Buried Oil and Gas Pipelines in Changqing Gas Field by Xianbing Li, Rui Liu, Dong Ma, Yang Bai, Yukai Shi, Badm Anhouck

“…In this paper, the corrosion rate prediction of buried oil and gas pipelines is studied in Changqing gas field. By improving the inertial weights and learning factors of the traditional particle swarm algorithm, the parameters of the generalized regression neural network are optimized and selected, and the corrosion rate prediction model of buried pipelines is finally constructed. …”
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Perception Error-resistant Air Combat Maneuvering Decisions Based on Deep Reinforcement Learning by Chengbin TIAN, Hui LI, Xiliang CHEN, Fengguo WU

“…The model training convergence speed and maneuvering decision performance using the GPPO and RS method, which quantifies the advantage, are significantly better than basic reinforcement learning algorithms. …”
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Multi-core helper thread prefetching for irregular data intensive applications by Jian-xun ZHANG, Zhi-min GU, Xiao-han HU, Min CAI

“…Betweenness centrality algorithm was used as a case study, the multi-parameter prefetching model of helper thread and optimized instances were presented and evaluated on commercial CMP platforms Q6600 and I7, the average speedup of betweenness centrality algorithm at different input scale is 1.20 and 1.11 respectively. …”
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High-Precision Pose Measurement of Containers on the Transfer Platform of the Dual-Trolley Quayside Container Crane Based on Machine Vision by Jiaqi Wang, Mengjie He, Yujie Zhang, Zhiwei Zhang, Octavian Postolache, Chao Mi

“…An enhanced EPnP optimization algorithm incorporating lockhole coplanar constraints is proposed, establishing a 2D–3D coordinate transformation model that reduces pose-estimation errors to millimeter level (planar MAE-P = 0.024 m) and sub-angular level (MAE-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>θ</mi></semantics></math></inline-formula> = 0.11°). …”
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