Retrieval Augmented Therapy Suggestion for Molecular Tumor Boards: Algorithmic Development and Validation Study by Eliza Berman, Holly Sundberg Malek, Michael Bitzer, Nisar Malek, Carsten Eickhoff

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Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm by Zhifang Bi, Fumin Ma, Jiaxiong Guan, Jie Wu, Juxia Li, Fuzhong Li, Yanwen Li, Zhanli Liu

“…Furthermore, the segmented leaf and disease regions were further optimized using the DRL-watershed algorithm to distinguish overlapping leaf regions. …”
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Optimization of a high gain observer for feedback linearization control by Nadia Bounouara, Mouna Ghanai, Kheireddine Chafaa

“…The main contribution of this approach is to improve the value of the high gain that corresponds to the minimum value of the cost function by using some metaheuristic algorithms. …”
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Indoor fire and smoke detection based on optimized YOLOv5. by Md Shafak Shahriar Sozol, M Rubaiyat Hossain Mondal, Achmad Husni Thamrin

“…It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. …”
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Classification and spectrum optimization method of grease based on infrared spectrum by Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li

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DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network by Peitao Cheng, Xuanjiao Lei, Haoran Chen, Xiumei Wang

“…Thirdly, the localization loss utilizes SIoU loss to further optimize the accuracy of the bounding box. Our method achieves a mAP value of 46.0% on the MS-COCO dataset, which is a 2% mAP improvement compared to the baseline YOLOv5-m, while bringing a 19.3% reduction in parameter count and a 12.9% decrease in GFLOPs. …”
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Operation Optimization of Multiphase Pollutant Treatment Considering Carbon Emissions by Guo Yu, Dongjie Zhang, Hailong Deng, Chao Jiang, Quanling Zhang

“…Aiming to improve the treatment of multiphase pollutants, this study builds a three-objective optimization model by considering the optimization of treatment effect, energy consumption, and an innovatively constructed optimization objective of direct and indirect carbon emissions. …”
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Deep Learning-Based Feature Matching Algorithm for Multi-Beam and Side-Scan Images by Yu Fu, Xiaowen Luo, Xiaoming Qin, Hongyang Wan, Jiaxin Cui, Zepeng Huang

“…It also overcomes challenges, such as large nonlinear differences, significant geometric distortions, and high matching difficulty between the MBES and side-scan images, significantly improving the optimized image matching results. The matching error RMSE has been reduced to within six pixels, enabling the accurate matching of multi-beam and side-scan images.…”
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Network slicing resource allocation strategy based on joint optimization by Zaijian WANG, Huimin GU

“…To improve network resource utilization that was decreased by different applications with different requirements in 5G networks, a network slicing resource allocation strategy based on joint optimization was proposed, which was utilized to maximize both network resource utilization and network revenue by comprehensively considering in tra-slice and inter-slice resource schedule.Firstly, the user’s average satisfaction function was defined in the inter-slicing resource allocation problem.Furthermore, in terms of the number of users, slicing schedule delay and priority, a proportional fair resource allocation algorithm based on quality of service (QoS) was proposed, which was employed to achieve the best tradeoff between fairness and the users’ requirements among slices.Secondly, after two functions (service degradation and resource migration) were introduced in the inter-slice resource schedule problem, two price models were established for internal access users and external access users respectively, where congestion and non-congestion conditions were analyzed.According to the proposed price models, a Stackelberg game between the base station and users was constructed, and a global search algorithm with low complexity was leveraged to obtain the best response of the game, where the best tradeoff between the base station revenue and user utility was obtained.Simulation results show that the proposed strategy can effectively improve resource utilization and network revenue while reducing network congestion.Therefore, it can better realize fairness in resource allocation.…”
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MACHINE LEARNING FOR CONCRETE SUSTAINABILITY IMPROVEMENT: SMART FLEET MANAGEMENT by Coralia TANASUICA (ZOTIC), Mihai Daniel ROMAN

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Cooperative trajectory optimization for multiple connected vehicles at an unsignalized intersection by Hao Yang, Xianyang Li, Duoyang Qiu, Xiaomeng Zhu

“…Through numerical simulation analysis of intersection scenarios including crossroad and roundabout, the results demonstrate that the proposed algorithm can achieve optimal trajectories for multi-vehicle cooperative motion while ensuring safe vehicle operation, which improves the efficiency of future intelligent traffic networks.…”
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Enhancing mass vaccination programs with queueing theory and spatial optimization by Sherrie Xie, Maria Rieders, Srisa Changolkar, Bhaswar B. Bhattacharya, Elvis W. Diaz, Michael Z. Levy, Michael Z. Levy, Ricardo Castillo-Neyra, Ricardo Castillo-Neyra

“…Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.MethodsWe developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. …”
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Optimization of emergency logistics for urban flooding with consideration of rainfall effects by Peiwen Zhang, Chenxing Zhang, Pan Zhang, Xuxian Yan, Huawei Yang

“…This study proposes a two-stage optimization framework for emergency logistics. First, a supply distribution model is developed by integrating resource scarcity indices and disaster severity indices, optimized through a simulated annealing algorithm. …”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). …”
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Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks by Hu Cao, Lingling Ma, Guoying Liu, Zhijian Liu, Hang Dong

“…Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. …”
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Lightweight highland barley detection based on improved YOLOv5 by Minghui Cai, Hui Deng, Jianwei Cai, Weipeng Guo, Zhipeng Hu, Dongzheng Yu, Houxi Zhang

“…The results show that the improved YOLOv5 model has a significant improvement in detection performance. …”
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Novel Design and Optimization of Porous Titanium Structure for Mandibular Reconstruction by Renshun Liu, Yuxiong Su, Weifa Yang, Xiaobing Dang, Chunyu Zhang, Ruxu Du, Yong Zhong

“…The design and optimization technique of the porous structure presented in this paper can be used to control peak stress, improve porosity, and fabricate a lightweight scaffold, which provides a potential solution for mandibular reconstruction.…”
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Dynamic optimization of intersatellite link assignment based on reinforcement learning by Weiwu Ren, Jialin Zhu, Hui Qi, Ligang Cong, Xiaoqiang Di

“…Different from the swarm intelligence method in principle, this algorithm models the combinatorial optimization problem of links as the optimal sequence decision problem of a series of link selection actions. …”
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Object Detection Method of Inland Vessel Based on Improved YOLO by Yaoqi Wang, Jiasheng Song, Yichun Wang, Rongjie Wang, Hongyu Chen

“…Finally, GSConv and VovGSCSP in Slim-Neck are introduced into the Neck network to optimize the network architecture, reduce part of the model complexity, and further improve the performance of the model. …”
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Robot Task-Constrained Optimization and Adaptation with Probabilistic Movement Primitives by Guanwen Ding, Xizhe Zang, Xuehe Zhang, Changle Li, Yanhe Zhu, Jie Zhao

“…We separate ProMPs as Gaussians at each timestep and minimize Kullback–Leibler (KL) divergence, with a gradient ascent–descent algorithm performed to obtain optimized ProMPs. Given optimized ProMPs, we outline a unified robot movement adaptation method for extending from a single obstacle to multiple obstacles. …”
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