MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction by Haoran Wang, Haijun Liu, Jing Yuan, Huijun Le, Weifeng Shan, Liangchao Li

“…Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. …”
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Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation by Qianhui Zhang

“…A novel dataset was established and utilized in performance prediction using XGBoost, optimized with Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). …”
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Constrained total least squares localization using angle of arrival and time difference of arrival measurements in the presence of synchronization clock bias and sensor position er... by Ruirui Liu, Ding Wang, Jiexin Yin, Ying Wu

“…Subsequently, a set of pseudo-linear equations are constructed, based on which the constrained total least squares optimization model has been formulated for target localization and the Newton iteration is applied to obtain the source position and clock bias simultaneously. …”
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Advanced Load Balancing Based on Network Flow Approach in LTE-A Heterogeneous Network by Shucong Jia, Wenyu Li, Xiang Zhang, Yu Liu, Xinyu Gu

“…Furthermore, a novel algorithm named optimal solution-based LB (OSLB) is proposed. …”
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Adaptive distributed stochastic deep reinforcement learning control for voltage and frequency restoration in islanded AC microgrids with communication noise and delay by Nima Mahdian Dehkordi, Vahab Nekoukar

“…Distinct from prior DRL applications, our method rigorously couples Lyapunov-based stability guarantees with DRL-based gain tuning, ensuring formal stability despite stochastic disturbances and model uncertainties. This hybrid framework facilitates real-time optimization of control policies, contributing to improved resilience and robustness under realistic network conditions. …”
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A Study on Vector-Based Processing and Texture Application Techniques for 3D Object Creation and Visualization by Donghwi Kang, Jeongyeon Kim, Jongchan Lee, Haeju Lee, Jihyeok Kim, Jungwon Byun

“…In this research, data weight reduction, parallel processing, and polygon simplification algorithms were applied to optimize the 3D model generation speed. …”
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Detection of Foreign Bodies in Transmission Line Channels Based on Fusion of Swin Transformer and YOLOv5 by XUE Ang, JIANG Enyu, ZHANG Wentao, LIN Shunfu, MI Yang

“…Finally, considering the mismatch between the real frame and the predicted frame, the structural similarity intersection over union (SIoU) is introduced to optimize the boundary errors and improve the generalization ability of the model. …”
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Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation by Simone Costantini, Anna Falivene, Mattia Chiappini, Giorgia Malerba, Carla Dei, Silvia Bellazzecca, Fabio A. Storm, Giuseppe Andreoni, Emilia Ambrosini, Emilia Biffi

“…Conclusion The study displayed the effectiveness of psychophysiology-based AI models in predicting rehabilitation engagement, thus promoting their practical application for personalized care and improved clinical health outcomes.…”
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2HR-Net VSLAM: Robust visual SLAM based on dual high-reliability feature matching in dynamic environments. by Wang Yang, Huang Chao, Zhang Yi, Tan Shuyi

“…Finally, the shared matching Siamese network with a unique dual-branch feature fusion strategy and similarity optimization algorithm is proposed to enhance the accuracy of feature matching. …”
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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry by Mathilde Duque, Cécile Emeraud, Rémy A. Bonnin, Quentin Giai-Gianetto, Laurent Dortet, Alexandre Godmer

“…Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. …”
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Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method by Guanbin Gao, Yuan Li, Fei Liu, Shichang Han

“…The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter (EKF) and regularized particle filter (RPF) hybrid identification method is presented. …”
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Enhancing the Green Construction Performance Resilience in Infrastructure Projects: A Complexity Perspective by Yikun Su, Junhao Liu, Zhizhe Zheng

“…The optimization algorithm has been validated and shown to improve the GCPR level of the project. …”
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GSD-YOLO: A Lightweight Decoupled Wheat Scab Spore Detection Network Based on Yolov7-Tiny by Dongyan Zhang, Wenfeng Tao, Tao Cheng, Xingen Zhou, Gensheng Hu, Hongbo Qiao, Wei Guo, Ziheng Wang, Chunyan Gu

“…The experimental results show that the mAP of the proposed algorithm reaches 98.0%, which is 3.9 percentage points higher than that of the original model. …”
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Research on temperature and humidity compensation method of vehicle gas sensor based on SCA-PSO support vector regression by Lv Yingming, Yuan Wenping

“…For a nitrogen oxide gas sensor, based on its detection principle, temperature and humidity will have a greater impact on the chemical reaction in the detection process, so a corresponding temperature and humidity compensation method is required to improve the measurement accuracy of the sensor. In this paper, a vehicle-mounted vehicle exhaust gas detection device is designed and a sensor temperature and humidity compensation method based on Support Vector Regression (SVR), Sine Cosine Algorithm(SCA) and Particle Swarm Optimization (PSO) is proposed. …”
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Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data by Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma

“…Then, we utilize graph neural networks to perform semi-supervised learning on HIN to obtain the optimal meta-path weights. We also develop Binary Sample Graph Convolutional Neural Network (BS-GCN) and Binary Sample Graph Attention Network (BS-GAT) to improve the reliability of graph neural network models based on the characteristics of traffic event detection and design an incremental clustering algorithm based on event similarity to implement streaming social traffic event detection. …”
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Distributed Collaborative Control Strategy for Intra-regional AGC Units in Interconnected Power System with Renewable Energy by Lei ZHANG, Xiaowei MA, Manliang WANG, Li CHEN, Bingtuan GAO

“…Then, considering the frequency regulation economy, a power dynamic allocation optimization model is constructed, with the objective of minimizing the quadratic cost function of active power variation constrained by active power output and power balance. …”
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Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing by Tareq Nafea Alharby, Bader Huwaimel

“…Abstract A new computational framework based on machine learning was developed for prediction of Hansen solubility parameters in preparation of pharmaceutical cocrystals with improved properties. The models of Kernel Ridge Regression (KRR), Multi-Linear Regression (MLR), and Orthogonal Matching Pursuit (OMP) were optimized in prediction of three Hansen solubility parameters. …”
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Comparative Assessment of Several Effective Machine Learning Classification Methods for Maternal Health Risk by Md Nurul Raihen, Sultana Akter

“…Maternal risk analysis can improve prenatal care, improve mother and baby health, and optimize healthcare resources by identifying misclassified observations using machine learning algorithms such as LDA, QDA, KNN, Decision Tree, Random Forest, Bagging, and Support Vector Machine, all of which have a significant impact on maternity health risk assessment. …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…Compared with traditional machine learning and deep learning models, the improved model better captures domain-specific information, resulting in an R² increase of 0.23 and reductions in RMSE and MAE by 0.016 and 0.014, respectively. …”
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UAV-Assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis by Ruslan Zhagypar, Nour Kouzayha, Hesham ElSawy, Hayssam Dahrouj, Tareq Y. Al-Naffouri

“…While applicable to terrestrial base stations (BSs), the proposed algorithm relies on UAVs for local model aggregation thanks to their ability to enhance wireless channels with lower latency and improved coverage. …”
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