Scenario-Based Association Rule Mining in Veterinary Services Using FP-Growth: Differentiating Clinical and Customer-Driven Patterns by Rafi Dio, Aulia Agung Dermawan, Dwila Sempi Yusiani, Rifaldi Herikson, Andikha Andikha, Dwi Ely Kurniawan, Adyk Marga Raharja

“…The comparative analysis demonstrates the utility of scenario-based association rule mining for both operational optimization and customer engagement. While the findings provide actionable insights for clinic management, further validation with practitioners and implementation in multi-clinic settings are recommended to confirm real-world applicability and enhance generalizability.…”
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MAB-RSP: Data pricing based on Stackelberg game in MCS by Yongjiao Sun, Xueyan Ma, Anrui Han

“…Experiments demonstrate that the algorithm improves revenue while ensuring balanced benefits for all participants.…”
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An Approach to Finding a Robust Deep Learning Model by Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev

“…This approach, supplemented with a model selection algorithm designed as a meta-algorithm, is versatile and applicable to any machine learning model, provided that it is appropriate for the task at hand. …”
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HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks by Luca Marchetti, Rosario Lombardo, Corrado Priami

“…HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. …”
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Joint Adaptive Modulation and Power Control Scheme for Energy Efficient FSO-based Non-Terrestrial Networks by Thang V. Nguyen, Hien T. T. Pham, Ngoc T. Dang

“…To cope with these difficulties, we propose to apply a joint adaptive modulation and power control (JAMPC) scheme to FSO-based NTN. Our proposed JAMPC algorithm aims to enhance energy efficiency while guaranteeing the targeted outage probability, bit-error rate, and the required data rate. …”
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Research on secure transport strategy of mobile edge computing based on deep reinforcement learning by WANG Yijun, LI Jiaxin, YAN Zhiying, LYU Jingying, QIAN Zhihong

“…Firstly, the mobile edge computing system based on unmanned aerial vehicle (UAV) was studied, which was composed of<italic> I</italic> user devices, <italic>M</italic> legal UAV (L-UAV) and <italic>N</italic> eavesdropping UAV (E-UAV). Secondly, while ensuring the unloading of L-UAV within a specified period, the multi-agent depth deterministic policy gradient (Attention-MADDPG) algorithm with the addition of attention mechanism was adopted to solve and optimize the problem with the aim of maximizing the safety unloading efficiency of the communication system. …”
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Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents by Lida Barba, Nibaldo Rodríguez, Cecilia Montt

“…The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. …”
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IRSnet: An Implicit Residual Solver and Its Unfolding Neural Network With 0.003M Parameters for Total Variation Models by Yuanhao Gong

“…Then, we generalize this algorithm to a compact implicit neural network that has only 0.003M parameters. …”
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Prediction after a Horizon of Predictability: Nonpredictable Points and Partial Multistep Prediction for Chaotic Time Series by Vasilii A. Gromov, Philip S. Baranov

“…The resulting strategy demonstrates accurate results for both benchmark and real-world time series, with the number of predicted steps exceeding that of any other published algorithm.…”
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Multimodal Control by Variable-Structure Neural Network Modeling for Coagulant Dosing in Water Purification Process by Jun Zhang, Da-Yong Luo

“…This method rectifies coagulant dosage effectively while keeping valid coagulation performance. By the way, the MM-P-VSRVNN algorithm can decrease computation time and avoid overfitting learning ability. …”
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Edge Cloud Resource Scheduling with Deep Reinforcement Learning by Y. Feng, M. Li, J. Li, Y. Yu

“…Designing optimal scheduling algorithms for task allocation in edge cloud clusters presents significant challenges due to the constantly changing workloads and service requests in edge cloud data center environments. …”
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MultS-ORB: Multistage Oriented FAST and Rotated BRIEF by Shaojie Zhang, Yinghui Wang, Jiaxing Ma, Jinlong Yang, Liangyi Huang, Xiaojuan Ning

“…The proposed method preserves all the advantages of the traditional ORB algorithm while significantly improving feature matching accuracy under illumination-induced blurring. …”
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Cooperative Patrol Control of Multiple Unmanned Surface Vehicles for Global Coverage by Yuan Liu, Xirui Xu, Guoxing Li, Lingyun Lu, Yunfan Gu, Yuna Xiao, Wenfang Sun

“…Simulation results demonstrate that the algorithm achieves 99.75% coverage in a 1 km × 1 km task area, reducing completion time by 23% and 74% compared to anti-flocking and partition scanning algorithms, respectively, while maintaining collision rates between agents (CRBAA) and obstacles (CRBAO) below 0.15% and 0.5%. …”
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An adaptive continuous threshold wavelet denoising method for LiDAR echo signal by Dezhi Zheng, Tianchi Qu, Chun Hu, Shijia Lu, Zhongxiang Li, Guanyu Yang, Xiaojun Yang

“…The adaptive threshold is dynamically adjusted according to the wavelet decomposition level, and the continuous threshold function ensures continuity with lower constant error, thus optimizing the denoising process. Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error (RMSE) compared with other algorithms. …”
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Closely Spaced Multi-Target Association and Localization Using BR and AOA Measurements in Distributed MIMO Radar Systems by Zehua Yu, Ziyang Jin, Ting Sun, Jinshan Ding, Jun Li, Qinghua Guo

“…The message-passing algorithm dynamically approximates the complex local functions involved in the problem, delivering excellent performance while maintaining low complexity. …”
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ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion by Hanyu Song, Xinyue Huang, Ziqiang Wang, Jianwei Hu, Huasheng Zhang, Hui Yang

“…Experimental results demonstrate that ADAM-DETR achieves 94.76% mAP@50 on the RiDDET-5 dataset, representing a 3.25% improvement over the baseline, and 83.32% mAP@50 on the public Kamatis dataset with a 2.19% enhancement, validating its cross-domain generalization capability. The algorithm requires only 42.8G FLOPs with 14.3M parameters, achieving an optimal balance between accuracy and efficiency, providing an effective technical solution for disease monitoring in smart agriculture.…”
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Rethinking Exploration and Experience Exploitation in Value-Based Multi-Agent Reinforcement Learning by Anatolii Borzilov, Alexey Skrynnik, Aleksandr Panov

“…We aim to optimize a deep MARL algorithm with minimal modifications to the well-known QMIX approach. …”
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Sensitive and specific peak detection for SELDI-TOF mass spectrometry using a wavelet/neural-network based approach. by Vincent A Emanuele, Gitika Panicker, Brian M Gurbaxani, Jin-Mann S Lin, Elizabeth R Unger

“…The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development). …”
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Investigating Motion Data Selections Based on Patient-Specific Respiration Pattern at External Surrogates Radiotherapy Using Cyberknife Synchrony Respiratory Tracking System by Seyed Amirreza Dastyar, Ahmad Esmaili Torshabi

“…As example, for a patient with high motion variability while the number of critical data points recognized by our algorithm is significant, the targeting error with and without utilizing these data points are 4.4 mm and 6.6 mm, respectively. …”
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IMU-aided adaptive mesh-grid based video motion deblurring by Ahmet Arslan, Gokhan Koray Gultekin, Afsar Saranli

“…Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. …”
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