Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…MTEPN is built upon the CSPDarkNet network, which is employed to extract fundamental features from traffic scene images. …”
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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Finally, a multi algorithm fusion learning model based on stacking technology is proposed to address the problem of poor generalization ability of single algorithm models in prediction; Furthermore, radial basis function network (RBF) was used as a two-level meta learner to fuse the above models, and particle swarm optimization (PSO) was used to optimize RBF parameters to reduce generalization error. …”
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Object detection with afordable robustness for UAV aerial imagery: model and providing method by Viacheslav Moskalenko, Artem Korobov, Yuriy Moskalenko

“…Combination of ideas and methods for dynamic neural networks, and methods for robustness and resilience optimization for neural networks. …”
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Developing a Drowsiness Detection System for Safe Driving Using YOLOv9 by Fernando Candra Yulianto, Wiwit Agus Triyanto, Syafiul Muzid

“…The model performance was evaluated based on precision, recall, F1 score, and mean average precision (mAP) metrics. The best model was optimized using several optimization techniques to determine the most optimal results. …”
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Integrated Planning for Shared Electric Vehicle System Considering Carbon Emission Reduction by Xiaohui Sun, Yumei Mi, Askar Ahtam, Zhi Zuo

“…By applying these models to the Chicago Sketch network and using a genetic algorithm to solve the models, it is concluded that the optimal outlet location solution considering carbon emission reduction will increase the outlet construction cost and user travel time cost. …”
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Text Detection Method With Emphasis on Text Component Importance and Lightweight Design by Lanlan Yin, Qiming Wu

“…In conclusion, the research contributes to the advancement of text detection and provides a reference for the development of lightweight networks and the optimization of performance in other computer vision tasks.…”
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Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing by Akinbode A. Olawole, Fambirai Takawira, Chabalala S. Chabalala

“…Multi-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment’s energy consumption. …”
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Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game by Xiu Ji, Mingge Li, Zheyu Yue, Haifeng Zhang, Yizhu Wang

“…For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. …”
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SMANet: A Model Combining SincNet, Multi-Branch Spatial—Temporal CNN, and Attention Mechanism for Motor Imagery BCI by Danjie Wang, Qingguo Wei

“…We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. …”
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Investigation of Microbial Community Characteristics in Pepper Rhizosphere at Seedling Stage Under Varied Soil Conditions by Yuyu ZHANG, Zhixiong CHEN, Fang CHEN, Jinqiang YAN, Junyu WU, Qingyuan XI, Shumei GE

“…The bacteria-bacteria network structure was more complex, with a positive correlation percentage of about 49.9%.…”
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Device-Driven Service Allocation in Mobile Edge Computing with Location Prediction by Qian Zeng, Xiaobo Li, Yixuan Chen, Minghao Yang, Xingbang Liu, Yuetian Liu, Shiwei Xiu

“…The results show that, compared to the baseline methods, our approach achieves improvements of 2.56%, 5.29%, and 2.16% in terms of the average user connection to edge servers, average service deployment cost, and average service allocation execution time, respectively, demonstrating the superiority and feasibility of the proposed methods.…”
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An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning by Joseph Berman, I-Chieh Lee, Jie Yin, He Huang

“…The RL-based framework performed model optimization in under one second on average in cases that took SA over 13 minutes and yielded comparable kinematics estimations both offline and online. …”
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Study on Predicting Blueberry Hardness from Images for Adjusting Mechanical Gripper Force by Hao Yin, Wenxin Li, Han Wang, Yuhuan Li, Jiang Liu, Baogang Li

“…The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. …”
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A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis by Basma Jumaa Saleh, Zaid Omar, Muhammad Amir As’ari, Vikrant Bhateja, Lila Iznita Izhar

“…To address this, we propose an efficient, modified radiomics feature processing method that integrates an optimal aerial perspective (OAP) parameter-based intensity dark channel prior (IDCP) with a 50-layer residual deep neural network (ResNet50 DNN) for autolesion segmentation (ALS-IOAP-DNN). …”
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Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines by Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen, Li Zhang, Chuyang Wang

“…The framework first employs an adaptive Kalman filter to denoise raw data and remove outliers, followed by Bayesian multiple imputation that constructs posterior distributions using normal linear correlations between historical and operational data, generating optimized imputed values through arithmetic averaging. …”
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AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm by CHEN Xiufeng, WANG Chengxin, WU Yuechen, GU Kexin

“…To improve the detection rate of small target vehicles，an optimization of the YOLOv5s algorithm network structure was established，which added a small target detection layer and spliced the shallow feature map with the deep feature map in the detection. …”
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Real-time classroom student behavior detection based on improved YOLOv8s by Xiaojing Sheng, Suqiang Li, Sixian Chan

“…This design enhances the network’s sensitivity to feature information by incorporating convolution kernels of varying sizes. …”
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Fault Diagnosis and Data Reconstruction of Temperature Sensors for Wind Turbine Stator Winding by Ling Zhou, Qiancheng Zhao, Jinhui Zeng, Anfeng Zhu, Xiafei Long, Ling He

“…The method combines supervisory control and data acquisition (SCADA) with an improved firefly sparrow search algorithm (FISSA)–optimized deep belief network (DBN). The input parameters of the model are determined via Pearson’s correlation coefficient method, whereas the exponentially weighted moving average (EWMA) is utilized to detect faults. …”
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Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults by Zhenhua Niu

“…The memory usage test findings denoted that the average memory usage of the research model was less than 46%, which was better than the control model.DiscussionThe research promotes innovation and optimization of mechanical fault diagnosis technology, improves the accuracy and timeliness of fault diagnosis analysis models, and is of great significance for ensuring production safety, reducing maintenance costs, and improving enterprise economic benefits.…”
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MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency.…”
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