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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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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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings by Chuanxiang Ren, Chunxu Chai, Changchang Yin, Haowei Ji, Xuezhen Cheng, Ge Gao, Heng Zhang

“…Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. …”
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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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Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…Due to the limitations, deep learning (DL) models have been explored to improve DD by analyzing complex neurological, behavioral, and visual data. …”
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Enhancing DPM Techniques in Outdoor Industrial WSN Applications by Kacem Halim, Glaoui Mohamed, Gharsallah Ali

“…In this context, a microcontroller dynamic power management (MDPM) algorithm is proposed to improve DPM scheme. This algorithm is deployed on a measurement circuit able to calculate the consumption during the different low power modes in real environments conditions and then selects the better one. …”
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Designing a Sustainable Supply Chain Network for Perishable Products Integrating Internet of Things and Mixed Fleets by Lihong Pan, Xialian Li, Miyuan Shan

“…A case study based on real-world data demonstrates the model’s effectiveness. Sensitivity analysis indicates that balancing the emphasis on quality and delivery reliability leads to improved cost and service performance. …”
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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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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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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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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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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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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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Microservice Deployment Based on Multiple Controllers for User Response Time Reduction in Edge-Native Computing by Zhaoyang Wang, Jinqi Zhu, Jia Guo, Yang Liu

“…Finally, extensive simulation experiments were conducted to validate the effectiveness of the proposed algorithm. The experimental results demonstrate that, compared with other algorithms, our algorithm significantly improves user response time, optimizes resource utilization, and reduces the total cost.…”
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Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID by Afef Badis, Mohamed Nejib Mansouri, Mohamed Habib Boujmil

“…Cascade control is one of the most efficient systems for improving the performance of the conventional single-loop control, especially in the case of disturbances. …”
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High precision water quality retrieval in Dianchi Lake using Gaofen 5 data and machine learning methods by Yuewen Feng, Jun Zhang, Sanjie Guo, Yunbai Zhang, Zhongwei Zhang

“…The Back Propagation Nondominated Sorting Genetic Algorithm-II (BP-NGA) model consistently yielded positive results for most WQI. (2) Water quality in Dianchi varied significantly by region and season. (3) It was recommended to build wetlands and ecological parks on the southwest side of Dianchi and improve sewage interception pipelines on the northeast side to lessen the risk of eutrophication by reducing the inflow of nitrogen and phosphorus.…”
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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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Research on Unbalanced Electromagnetic Force Under Static Eccentricity of the Wheel Hub Motor Based on BP Neural Network by Xiangpeng Meng, Yunquan Zhang, Renkai Ding, Wei Liu, Ruochen Wang

“…A BP neural network optimization algorithm can reduce the time of electromagnetic force model simulation for 10 s from 1 h to about 50 s, which greatly improves the calculation efficiency of the electromagnetic force on the basis of ensuring the accuracy of the model, thus providing an unbalanced electromagnetic force model that is more suitable for the dynamic simulation of wheel hub direct-drive electric vehicles, which effectively solves the problem that the traditional electromagnetic force is difficult to couple with the vehicle dynamics model and lays a better foundation for subsequent research on the vertical vibration effect of wheel hub direct-drive electric vehicles.…”
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An integrated IKOA-CNN-BiGRU-Attention framework with SHAP explainability for high-precision debris flow hazard prediction in the Nujiang river basin, China. by Hao Yang, Tianlong Wang, Nikita Igorevich Fomin, Shuoting Xiao, Liang Liu

“…This study proposes an explainable deep learning framework, the Improved Kepler Optimization Algorithm-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention (IKOA-CNN-BiGRU-Attention) model, for precise debris flow hazard prediction in the Yunnan section of the Nujiang River Basin, China. …”
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