Deep Reinforcement Learning for Optimal Replenishment in Stochastic Assembly Systems by Lativa Sid Ahmed Abdellahi, Zeinebou Zoubeir, Yahya Mohamed, Ahmedou Haouba, Sidi Hmetty

“…The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. …”
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Optimization of Geometric Characteristics of Cycloidal Profiles of Gerotor Hydraulic Machines by S. O. Kireev, A. R. Lebedev, M. V. Korchagina

“…The basic research method was modeling the gerotor machine profile using Mathcad computer mathematics system. …”
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Research on shale TOC prediction method based on improved BP neural network by Chaorong Wu, Kaixing Huang, Zhengtao Sun, Yizhen Li, Yong Li, Yuexiang Hao, Zhengxing Sun, Ziqi Wang

“…This paper studies a method for predicting shale TOC content using a BP neural network optimized by an improved cuckoo search algorithm. First, for the Longmaxi Formation shale, through logging sensitivity analysis, seven logging parameters sensitive to TOC content were determined: DEN, AC, RT, U, K, GR, and CNL. …”
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YOLO-SRSA: An Improved YOLOv7 Network for the Abnormal Detection of Power Equipment by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…This paper proposes a YOLO-SRSA-based anomaly detection algorithm. For data enhancement, geometric and color transformations and rain-fog simulations are applied to preprocess the dataset, improving the model’s robustness in outdoor complex weather. …”
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A Fast and Efficient Beamforming Algorithm Imitating Plant Growth Gene for Phased Array Antenna by Jinling Zhang, Chen Han, Zhanqi Zheng, Xiongzhi Zhu, Xiaohui Li

“…This algorithm converts the complex optimization process of array antenna excitation by the classical optimization algorithm into a simple process of fine-tuning the gain at any angle on the beam to make it grow and approach the target pattern. …”
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DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…The approach integrates a Decision Tree (DT) algorithm for rapid initial classification of Primary User (PU) activity, followed by a Hill Climbing (HC) optimization algorithm that fine-tunes the detection based on a fitness function. …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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To the analysis of methods and mechanisms of predictive modeling of onboard equipment reliability when solving problems of aircraft maintenance workload planning by B. I. Ogunvoul, V. D. Budaev, D. O. Sizikov, N. V. Gorbakon, A. V. Vlasova

“…Finally, an efficient maintenance plan that takes into account the predicted failures has been developed using an optimization algorithm. Validation of the model’s predictive capabilities and optimization of the maintenance strategy are performed by comparing with archived data on previously performed work. …”
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Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning by Charilaos Latinopoulos, Efstathios Zavvos, Dimitrios Kaklis, Veerle Leemen, Aristides Halatsis

“…This study explores two model-free Deep Reinforcement Learning (DRL) algorithms: (i) a Double Deep Q Network (DDQN) and (ii) a Deep Deterministic Policy Gradient (DDPG). …”
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Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

“…The improved whale optimization algorithm iWOA-based SVR (iWOA-SVR) model exhibited the strongest predictive capability among the five optimal wavelengths-based models, achieving comparable accuracy to the full-range spectra SVR for all gluten parameters (R_P = 0.9190–0.9385, RMSEP = 0.3927–0.5743%, and RPD = 3.0424–3.2509). …”
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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators by Xiaowei Huang, Xuhua Shi, Peiyao Wang, Hongzan Xu, Xiaojun Tang, Gaoran Zhang

“…Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. …”
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Breast Cancer Classification with Various Optimized Deep Learning Methods by Mustafa Güler, Gamze Sart, Ömer Algorabi, Ayse Nur Adıguzel Tuylu, Yusuf Sait Türkan

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Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person by Mustufa Haider Abidi

“…Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). …”
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A Heuristic Optical Flow Scheduling Algorithm for Low-Delay Vehicular Visible Light Communication by Zhengying Cai, Shumeng Lei, Jingyi Li, Chen Yu, Junyu Liu, Guoqiang Gong

“…In response to this problem, we propose a heuristic optical flow scheduling algorithm. First, the optical flow scheduling problem of VVLC is built as a multi-objective optimization model considering the makespan, delay, schedulable ratio, and bandwidth utilization with non-conflict constraints. …”
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Optimization of transportation efficiency through fuzzy logic and LINGO software by Zahoor Ahmad Ganie, Zahid Gulzar Khaki, Tanveer Ahmad Tarray, Gazala Salam, Eid Sadun Alotaibi

“…This study significantly contributes to the development of optimization algorithms and transportation systems with a solid basis for accurate, privacy-preserving data collection and efficient decision-making.…”
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Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN by Shouhua JIANG, Yiwu WANG

“…With the continuous development of network technology, the network topology distributed network control mode based on Fat-Tree gradually reveals its limitations.Software-defined data center network (SDCN) technology, as an improved technology of Fat-Tree network topology, has attracted more and more researchers’ attention.Firstly, an edge computing architecture in SDCN and a task offloading model based on the three-layer service architecture of the mobile edge computing (MEC) platform were built, combined with the actual application scenarios of the MEC platform.Through the same strategy experience playback and entropy regularization, the traditional deep Q-leaning network (DQN) algorithm was improved, and the task offloading strategy of MEC platform was optimized.An improved DQN algorithm based on same strategy empirical playback and entropy regularization (RSS2E-DQN) was compared with three other algorithms in load balancing, energy consumption, delay and network usage.It is verified that the improved algorithm has better performance in the above four aspects.…”
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Predicting Days on Market to Optimize Real Estate Sales Strategy by Mauro Castelli, Maria Dobreva, Roberto Henriques, Leonardo Vanneschi

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Quantifying and optimizing single-molecule switching nanoscopy at high speeds. by Yu Lin, Jane J Long, Fang Huang, Whitney C Duim, Stefanie Kirschbaum, Yongdeng Zhang, Lena K Schroeder, Aleksander A Rebane, Mary Grace M Velasco, Alejandro Virrueta, Daniel W Moonan, Junyi Jiao, Sandy Y Hernandez, Yongli Zhang, Joerg Bewersdorf

“…Recent advances in this technique have greatly accelerated the data acquisition speed and improved the temporal resolution of super-resolution imaging. …”
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A cooperative dynamic target search approach for multi-UAV systems utilizing the MAPPO algorithm by Peiyan Zhang, Guodong Li

“…In response to these issues, this study proposes an improved multi-agent proximal policy optimization algorithm (AS-MAPPO). …”
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A Novel Loss Minimization Algorithm for the 3-Port Converter in a Multi-Subgrid Microgrid by Rajarshi Basu, Mahesh Kumar Mishra

“…The algorithm employs an optimization function, “fmincon”, that uses a trust region method based on the interior point technique to minimize the objective function. …”
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