Application of the Mean-shift Segmentation Parameters Estimator (MSPE) to VHSR satellite images: Tetuan-Morocco by O. Benarchid, N. Raissouni, J.A. Sobrino, A. El Ayyan

“…<p>Image segmentation is considered as crucial step dealing with Object-Based Image Analysis (OBIA) and different segmentation results could be achieved by combining possible parameters values. Optimal parameters selection is usually carried out on the basis of visual interpretation; therefore, defining optimal combinations is a challenging task. …”
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A distributed energy-efficient opportunistic routing accompanied by timeslot allocation in wireless sensor networks by Man Gun Ri, Ye Song Han, Jin Pak

“…In our new algorithm, clustering and routing tree construction is performed while introducing the optimal relay transmission distance achieved by using opportunistic routing principle, and at the same time, interference-free wake up time is scheduled, which may optimize energy consumption and decrease the number of various control messages as possible to prolong the network lifetime. …”
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Implementation of VR application in cultural and tourism integration under the background of smart city and IoT by Wei Huang

“…In terms of system performance, the optimized virtual roaming system maintained stable memory and CPU usage while reducing GPU load by over 20 % and increasing the FPS from 30 fps to 44.6 fps. …”
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UAV Robust Strategy Control Based on MAS by Jian Han, Changhong Wang, Guoxing Yi

“…After simulation, the MAS has been proved to be able to promote the success ratio and raise the robustness, while realizing feasibility of coordinated flight and optimality of cooperative mission.…”
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Probabilistic alignment of multiple networks by Teresa Lázaro, Roger Guimerà, Marta Sales-Pardo

“…Our approach opens the door to a whole new family of network alignment algorithms, and to their application to problems for which existing methods are perhaps inappropriate.…”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…The dataset was split into 80% training and 20% testing data (hold-out test) to ensure objective evaluation. Hyperparameter optimization was performed using Bayesian optimization, while model evaluation employed stratified K-fold cross-validation to prevent overfitting. …”
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Research on the Stability of Express Delivery Supply Chain Based on Risk Propagation Model ------ Case Studies of SF Express and JD Logistics by Fan Yunhui

“…Based on the research results, optimization strategies such as dynamic path optimization algorithms and blockchain-based information isolation mechanisms are proposed, providing theoretical tools and practical references for risk prevention and control in the express delivery industry.…”
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Advances in Numerical Modeling for Heat Transfer and Thermal Management: A Review of Computational Approaches and Environmental Impacts by Łukasz Łach, Dmytro Svyetlichnyy

“…Sustainable computing practices, including energy-efficient algorithms and renewable-powered data centers, offer potential solutions. …”
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Interval-Aware Scheduling of Surveillance Drones: Exact and Heuristic Approaches by Kaito Mori, Hiroki Nishikawa, Hiroyuki Tomiyama

“…In comparison to the Greedy method, the Multi-stage method achieved an average improvement of 37.8% in total flight time, while its algorithm runtime deteriorated by a maximum of 1.26 seconds. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. …”
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Building a composition-microstructure-performance model for C–V–Cr–Mo wear-resistant steel via the thermodynamic calculations and machine learning synergy by Shuaiwu Tong, Shuaijun Zhang, Chong Chen, Tao Jiang, Peng Li, Shizhong Wei

“…Furthermore, feature importance analysis, conducted using the Random Forest algorithm, revealed significant differences in the factors affecting sliding friction and abrasive wear. …”
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The Long-Term Surface Deformation Monitoring and Prediction of Hutubi Gas Storage Reservoir in Xinjiang Based on InSAR and the GWO-VMD-GRU Model by Wang Huang, Wei Liao, Jie Li, Xuejun Qiao, Sulitan Yusan, Abudutayier Yasen, Xinlu Li, Shijie Zhang

“…The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. …”
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Development and Validation of a Discrete Element Simulation Model for Pressing Holes in Sowing Substrates by Hongmei Xia, Chuheng Deng, Teng Yang, Runxin Huang, Jianhua Ou, Lingjin Dong, Dewen Tao, Long Qi

“…A neural network model for predicting the angle of repose was constructed, and a genetic algorithm was applied to optimize the significant contact mechanical parameters. …”
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An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…Statistical <i>t</i>-tests confirm the superiority of DRL-NSABC over other algorithms, while achieving a variance of 0.00014. Moreover, DRL-NSABC demonstrates the fastest convergence, reaching near-optimal accuracy within the first 100 epochs. …”
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Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
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Subcortical Brain Segmentation Based on a Novel Discriminative Dictionary Learning Method and Sparse Coding by Xiang Li, Ying Wei, Yunlong Zhou, Bin Hong

“…For the shared dictionary, a regularization term is used to minimize the between-class scatter of corresponding shared coefficients so that they can learn shared image patterns. The optimization algorithms are developed to solve the problems in the learning step. …”
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Causal Unit Selection using Tractable Arithmetic Circuits by Haiying Huang, Adnan Darwiche

“…While early studies focused mainly on bounding a specific class of counterfactual objective functions using data, more recent work allows one to find optimal units exactly by reducing the causal objective to a classical objective on a meta-model, and then applying a variant of the classical Variable Elimination (VE) algorithm to the meta-model---assuming a fully specified causal model is available. …”
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Adaptive Security Solutions for NOMA Networks: The Role of DDPG and RIS-Equipped UAVs by Syed Zain Ul Abideen, Abdul Wahid, Mian Muhammad Kamal

“…Our main objective is to prevent eavesdroppers from accessing the network while preserving seamless communication for authorized users. …”
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Modeling the Dynamic Evolution of the Vehicular Ad Hoc Networks under the City Scenario by Lili Zhang, Yueheng Li, Guoping Tan, Rongbo Zhu, Hao Chen

“…While the network evolution structure is the key to design the optimal data forwarding strategy, hence, in this paper, we will analyze the dynamic evolution of topology characteristics based on the real data collected by the GPS devices installed on vehicles and then model the network evolution by the dynamic topology characteristics. …”
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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. …”
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