Training a Minesweeper Agent Using a Convolutional Neural Network by Wenbo Wang, Chengyou Lei

“…Both a Deep Q-Network (DQN) and supervised learning methods were successfully applied to optimize the training of the game. …”
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Empowering drones in vehicular network through fog computing and blockchain technology. by Shivani Wadhwa, Divya Gupta, Shalli Rani, Maha Driss, Wadii Boulila

“…The proposed algorithm dynamically plans drone trajectories and optimizes computation offloading. Results from simulations demonstrate the effectiveness of the proposed architecture, showcasing reduced average response latency and improved throughput, particularly when accessing resources from fog nodes. …”
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Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection by Ali Nizam, Ertugrul Islamoglu, Omer Kerem Adali, Musa Aydin

“…A triplet loss-based deep learning network is designed to optimize in-class similarity and increase the distance between classes. …”
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TAQNet: Traffic-Aware Minimum-Cost Quantum Communication Network Planning by Ilora Maity, Junaid ur Rehman, Symeon Chatzinotas

“…In addition, we propose a genetic algorithm-based solution to optimally distribute the end-to-end QKD requests over the QCI. …”
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Fairness-Aware Utility Maximization for Multi-UAV-Aided Terrestrial Networks by Nishant Gupta, Satyam Agarwal, Aymen Fakhreddine

“…To address these key challenges, in this paper, we maximize the network utility by jointly optimizing the scheduling and cell association, transmit power of all base stations, and ABS deployment locations in the presence of co-channel interference. …”
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An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection by Xinya Ding, Xuan Peng, Yanguang Xue, Liang Zhang, Tianying Wang, Yunpeng Zhang

“…An intelligent adapter module that performs adaptive feature fusion, automatically weighting and combining multi-source meteorological inputs (including temperature, wind fields, and humidity data) to maximize their synergistic effects while minimizing feature conflicts; the utilized network achieves an average improvement of over 4% across various metrics. 2. …”
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SSD-YOLO: a lightweight network for rice leaf disease detection by Canlin Pan, Shen Wang, Shen Wang, Yahui Wang, Chaoyang Liu

“…We introduce the Squeeze-and-Excitation Network (SENet) attention mechanism to optimize the Bottleneck structure of YOLOv8, improving feature extraction capabilities. …”
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Seismic Random Noise Attenuation via Low-Rank Tensor Network by Taiyin Zhao, Luoxiao Ouyang, Tian Chen

“…By applying the alternating direction method of multipliers (ADMM), we solve the model and transform the iterative schemes into a DL framework, where each iteration corresponds to a network layer. The key learnable parameters, including weights and thresholds, are optimized using labeled data to enhance performance. …”
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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. …”
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Preparation Process Optimization and Its Stability of Selenium-Rice Protein Peptides Nanoparticles by Yanfang OU, Chenghua WANG

“…Under these optimal conditions, the average particle size, polydispersity index value, and Zeta potential value of the nano-selenium were 92.00±2.68 nm, 0.104±0.008, −37.20±2.52 mV, respectively. …”
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Cross-Feature Hybrid Associative Priori Network for Pulsar Candidate Screening by Wei Luo, Xiaoyao Xie, Jiatao Jiang, Linyong Zhou, Zhijun Hu

“…To enhance pulsar candidate recognition performance and improve model generalization, this paper proposes the cross-feature hybrid associative prior network (CFHAPNet). CFHAPNet incorporates a novel architecture and strategies to integrate multi-class heterogeneous feature subimages from each candidate into multi-channel data processing. …”
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Siamese Neural Networks in Unmanned Aerial Vehicle Target Tracking Process by Athraa Sabeeh Hasan Allak, Jianjun Yi, Haider M. Al-Sabbagh, Liwei Chen

“…The results showed that the improved YOLOv5 model combined with the optimized loss function had the highest average accuracy of 47.84% and a frame rate of 28.34fps, which was better than the traditional YOLOv5 model. …”
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INFORMATION TECHNOLOGY FOR RECOGNITION OF ROAD SIGNS USING A NEURAL NETWORK by Elena Yashina, Roman Artiukh, Nikolai Рan, Andrei Zelensky

“…The recognition process takes place by constructing a convolutional neural network. Features of the layers of the roller network are considered.  …”
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Graph-based reinforcement learning for software-defined networking traffic engineering by Jingwen Lu, Chaowei Tang, Wenyu Ma, Wenjuan Xing

“…However, efficient traffic management has become a core challenge due to the high costs of building and maintaining these networks. Traditional traffic engineering methods based on linear programming achieve optimal solutions but suffer from exponential computational complexity growth with network size, making them impractical for real-time applications in large-scale networks. …”
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Revival of Muslin by Phuti Karpas plant identification with convolution neural network by Redwan Ahmed Rizvee, Omar Farrok, Mahamudul Hasan, Faisal Farhan, Md Hafanul Islam, Md Khalid Hasan, Abidur Rahman, Maheen Islam, Md Sawkat Ali, Taskeed Jabid, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam

“…AlexNet yielded the highest average accuracy, while the custom baseline model, optimized for mobile deployment, provided comparable accuracy with faster inference. …”
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Enhancing Attention Network Spatiotemporal Dynamics for Motor Rehabilitation in Parkinson’s Disease by Guangying Pei, Mengxuan Hu, Jian Ouyang, Zhaohui Jin, Kexin Wang, Detao Meng, Yixuan Wang, Keke Chen, Li Wang, Li-Zhi Cao, Shintaro Funahashi, Tianyi Yan, Boyan Fang

“…The identified brain spatiotemporal neural markers were validated using machine learning models to assess the efficacy of MIRT in motor rehabilitation for PD patients, achieving an average accuracy rate of 86%. These findings suggest that MIRT may facilitate a shift in neural networks from sensory processing to higher-order cognitive control, with the dynamic reallocation of attentional resources. …”
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Radio resource and trajectory optimization for UAV assisted communication based on user route by Lei LANG, Jingning WANG, Yi WANG, Zitao ZHAO

“…Recently, research on the application of unmanned aerial vehicle (UAV) in wireless communication networks has been widely studied.Aiming at the downlink wireless transmission system of UAV assisted mobile user communication, a method of resource allocation and trajectory optimization based on user route was proposed.According to the known user route, the estimated large-scale channel state information was obtained in advance, and a joint optimization problem of communication bandwidth allocation and trajectory optimization was established to maximize the minimum average rate of users.The problem was a nonconvex optimization problem, and there was nonlinear coupling between the variables.By introducing the method of alternating optimization of auxiliary variables and separated variables, the original problem was decomposed into two approximate convex optimization subproblems which could be solved, and the successive convex approximation was used to iteratively optimize the two subproblems, and an approximate suboptimal solution of the original nonconvex problem was obtained.Simulation results show that the proposed method of resource allocation and trajectory optimization can effectively improve the average data throughput of users, and improve the efficiency of UAV assisted communication on the premise of ensuring the communication quality of all users.…”
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Radio resource and trajectory optimization for UAV assisted communication based on user route by Lei LANG, Jingning WANG, Yi WANG, Zitao ZHAO

“…Recently, research on the application of unmanned aerial vehicle (UAV) in wireless communication networks has been widely studied.Aiming at the downlink wireless transmission system of UAV assisted mobile user communication, a method of resource allocation and trajectory optimization based on user route was proposed.According to the known user route, the estimated large-scale channel state information was obtained in advance, and a joint optimization problem of communication bandwidth allocation and trajectory optimization was established to maximize the minimum average rate of users.The problem was a nonconvex optimization problem, and there was nonlinear coupling between the variables.By introducing the method of alternating optimization of auxiliary variables and separated variables, the original problem was decomposed into two approximate convex optimization subproblems which could be solved, and the successive convex approximation was used to iteratively optimize the two subproblems, and an approximate suboptimal solution of the original nonconvex problem was obtained.Simulation results show that the proposed method of resource allocation and trajectory optimization can effectively improve the average data throughput of users, and improve the efficiency of UAV assisted communication on the premise of ensuring the communication quality of all users.…”
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IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…This study explores the integration of Convolutional Neural Networks (CNNs) with Particle Swarm Optimization (PSO) to enhance traffic management in smart cities. …”
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Performance optimization of electrical equipment in high-altitude photovoltaic power stations based on PSO–MOEAD algorithm by Zhijun Xiao

“…In practical high-altitude PV scenarios, it improves voltage stability by 3% (The average voltage increases by 0.01p.u.), reduces power generation costs by 12.3%, and lowers network losses by 15%. …”
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