Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…In these diversified test scenarios, the improved TD3 algorithm exhibits remarkable adaptability and stability. …”
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Integrated Chassis Control of Active Front Steering and Yaw Stability Control Based on Improved Inverse Nyquist Array Method by Bing Zhu, Yizhou Chen, Jian Zhao

“…An integrated chassis control (ICC) system with active front steering (AFS) and yaw stability control (YSC) is introduced in this paper. The proposed ICC algorithm uses the improved Inverse Nyquist Array (INA) method based on a 2-degree-of-freedom (DOF) planar vehicle reference model to decouple the plant dynamics under different frequency bands, and the change of velocity and cornering stiffness were considered to calculate the analytical solution in the precompensator design so that the INA based algorithm runs well and fast on the nonlinear vehicle system. …”
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A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm by Tianli Wang, Yi Zhang, Haiyan Liu, Fei Li, Dayong Guo, Ning Cao, Yubin Zhang

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An improved beluga whale optimizer-Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images. by Anilkumar P, Venugopal P

“…With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). …”
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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…Therefore, this paper proposes a Model Knowledge Embedded Graph Neural Network (MKEGNN) scheduling algorithm. …”
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A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity by Liwen Guo, Zhihong Kang, Shuaiwei Ding, Xuehao Yuan, Haitong Yang, Meng Zhang, Shuoliang Wang

“…Given that more and more oil reservoirs are reaching the high water cut stage during water flooding, the construction of an advanced algorithmic model for identifying inter-well connectivity is crucial to improve oil recovery and extend the oilfield service life cycle. …”
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Exploring the Capacity of Large Language Models to Assess the Chronic Pain Experience: Algorithm Development and Validation by Jacopo Amidei, Rubén Nieto, Andreas Kaltenbrunner, Jose Gregorio Ferreira De Sá, Mayte Serrat, Klara Albajes

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An Effective Hybrid Metaheuristic Approach Based on the Genetic Algorithm by Olympia Roeva, Dafina Zoteva, Gergana Roeva, Maya Ignatova, Velislava Lyubenova

“…The evolutionary approach (EA) is applied to form the initial population of the GA, thus improving the algorithm’s performance, especially its convergence speed. …”
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Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Sultan Ahmad, Alhasan A. Alharbi, Mohammed Alarfaj, Mohammed Alarfaj, Mukta Dhopeshwarkar, Theyazn H. H. Aldhyani

“…The EEG data classification by applying ML and DL framework to improve the accuracy of seizure detection. The EEG dataset consisted of 102 patients (55 seizure and 47 non-seizure cases), and the data underwent comprehensive preprocessing, including noise removal, frequency band extraction, and data balancing using SMOTE to address class imbalance. …”
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A multi-algorithm prognostic model combining inflammatory indices and surgical features in distal cholangiocarcinoma by Yi Yin, Yi Yin, Luyuan Bai, Xinyue Mu, Xinyue Mu, Shan Zhang, Panpan Zhai, Panpan Zhai

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Burning rate research of NEPE propellant containing CL-20 on the base of improved Canny edge detection and PSO-BP by Cai Wenxiang, Yu Xintong, Wang Zhixiang

“…Furthermore, the Particle Swarm Optimization algorithm -Back Propagation neural network model (PSO-BP) is developed for predicting the burning rate, which is compared with the traditional BP algorithm. …”
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Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review by Shaina Mackin, Vincent J. Major, Rumi Chunara, Remle Newton-Dame

“…Early bias mitigation literature has focused on addressing bias in the preparation and development phases of the algorithm life cycle (pre- and in-processing). Post-processing methods, applied at the point of implementation, are less computationally intensive and do not require re-building or training the model, allowing lower-resourced health systems to improve bias in off-the-shelf binary classification models, which are increasingly common within electronic medical records. …”
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Prediction of Telkomsel 4G LTE Card Sales using The K-Nearest Neighbor Algorithm by Alfiana Fontes Martins, Yasinta Oktaviana Legu Rema, Debora Chrisinta, Alejandro Jr. V. Matute, Krisantus Jumarto Tey Seran

“…Accurate sales prediction is a critical challenge in business decision-making, as factors such as data imbalance, outliers, and overfitting may compromise the reliability of predictive models. This study aims to develop a precise model for predicting card sales using the K-Nearest Neighbor (KNN) algorithm and to offer recommendations for improving prediction quality by addressing issues related to data imbalance and overfitting. …”
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Optimization method of task uninstallation in mobile edge computing environment combining improved deep Q-learning and transmission learning by Lihong Zhao, Shuqin Wang, Xiaomei Ding

“…This method utilizes the DQN algorithm to construct a model that can handle complex states and action spaces. …”
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Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network by Yaoguo Xie, Chengang Gao, Puzhe Wang, Lei Zhou, Chuanjie Zhang, Xianqiang Qu

“…To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. …”
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A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field by Bo Ma, Yi Ji, Liyong Fang

“…To address these challenges, this paper presents a novel hybrid obstacle avoidance algorithm—deflected simulated annealing–adaptive artificial potential field (DSA-AAPF)—which combines an improved simulated annealing mechanism with an enhanced APF model. …”
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Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine by Wenjie Guo, Jie Liu, Jun Ma, Zheng Lan

“…Further, an optimized genetic algorithm is deployed to optimize model parameters in ILSSVM by integrating the adaptive genetic algorithm and simulated annealing to improve load forecasting accuracy. …”
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Design and Research on a Reed Field Obstacle Detection and Safety Warning System Based on Improved YOLOv8n by Yuanyuan Zhang, Zhongqiu Mu, Kunpeng Tian, Bing Zhang, Jicheng Huang

“…This study proposes a lightweight model for farmland obstacle detection by improving the YOLOv8n object detection algorithm. …”
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Assimilation of L-band interferometric synthetic aperture radar (InSAR) snow depth retrievals for improved snowpack quantification by P. Shrestha, A. P. Barros

“…In this study, we employ an interferometric synthetic aperture radar (InSAR) technique to quantify snow depth change using modeled snow density and assimilate the referenced and calibrated retrievals into the Multilayer Snow Hydrology Model (MSHM). …”
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Renewable energy forecasting using optimized quantum temporal model based on Ninja optimization algorithm by Mona Ahmed Yassen, El-Sayed M. El-kenawy, Mohamed Gamal Abdel-Fattah, Islam Ismael, Hossam El.Deen Salah Mostafa

“…Experimental implementation revealed that incorporating the Ninja Optimization Algorithm with the QTM model delivered the best R2 performance at 95.15% with an exceptional RMSE value of 0.00003, thus establishing its ability to optimize renewable energy forecasting accuracy.…”
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