Classification of Paddy Rice Planting Area Through Feature Selection Method Using Sentinel-1/2 Time Series Images by Shiyu Zhang, Pengao Li, Yong Xie, Wen Shao, Xueru Tian

“…Therefore, this study took Liyang City as the study area, reconstructed Sentinel-2 cloud-free time series optical images, and extracted spectral features, vegetation indexes, and other features, in combination with the polarization features of the Sentinel-1 time series radar images. The optimal feature subset was selected through the feature selection method, and machine learning algorithms were optimized for paddy rice planting area classification. …”
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NoiseAugmentNet-HHO: Enhancing Histopathological Image Classification Through Noise Augmentation by Prem Purusottam Jena, Debahuti Mishra, Kaberi Das, Sashikala Mishra

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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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Early stage prediction of bone regeneration using FEA and cell differentiation algorithms with 3D-printed PLA and PCL scaffolds: modeling and application to dorsal double-plating i... by Hsuan Chih Liu, Ya-Han Chan, Shao-Fu Huang, Wei-Che Tsai, Yen Cheng, Chun-Li Lin

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Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework by Ling Li, Lidong Zhu, Weibang Li

“…Specifically, (1) an adaptive knowledge-sharing mechanism based on transfer learning is designed to balance device heterogeneity and data distribution divergence through dynamic weighting factors; (2) a bi-level reinforcement learning device selection strategy is proposed, combining meta-learning and hierarchical attention mechanisms to optimize global–local decision-making and enhance model convergence efficiency; (3) dynamic privacy budget allocation and robust aggregation algorithms are introduced to reduce communication overhead while ensuring privacy. …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…In addition, we established predictive models for lung doses utilizing the Least Absolute Shrinkage and Selection Operator, ridge, and linear regression algorithms while also evaluating dosimetric parameters for both the PTV and the organs at risk (OARs). …”
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Multi-Project Scheduling with Uncertainty and Resource Flexibility: A Narrative Review and Exploration of Future Landscapes by Marzieh Aghileh, Anabela Tereso, Filipe Alvelos, Maria Odete Monteiro Lopes

“…It categorizes solution approaches, from exact algorithms to heuristic and metaheuristic methods, while reviewing the primary instance sets and benchmarks used in the field. …”
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Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…The results validate the feasibility of DRL–based WPT control, paving the way for scalable, resilient, and self-optimizing wireless power grids on the Moon. Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.…”
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Distributed dynamic event-triggered time-varying resource management for microgrids via practical predefined-time multiagent methods by Tingting Zhou, Salah Laghrouche, Youcef Ait-Amirat

“…To address this problem, a fully distributed predefined-time (PDT) optimization algorithm is developed, incorporating a time-base generator (TBG) to guarantee convergence within a PDT, independently of initial conditions and system parameters. …”
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A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples by Jiasheng Yan, Yang Sui, Tao Dai

“…To address the aforementioned challenge, this paper proposes an innovative IFD method for SCES that combines the particle swarm optimization (PSO) algorithm and the ensemble broad learning system (EBLS). …”
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Vehicle-To-Grid (V2G) Charging and Discharging Strategies of an Integrated Supply–Demand Mechanism and User Behavior: A Recurrent Proximal Policy Optimization Approach by Chao He, Junwen Peng, Wenhui Jiang, Jiacheng Wang, Lijuan Du, Jinkui Zhang

“…This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.…”
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Enhancing Privacy in IoT Networks: A Comparative Analysis of Classification and Defense Methods by Ahmet Emre Ergun, Ozgu Can, Murat Kantarcioglu

“…As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padding strategies while maintaining network efficiency. …”
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A Study on the SLAM of Automotive Vehicles Using Bumper-Mounted Dual LiDAR by Jae-Hun Jang, Hyeong-Jun Kim, Kyung-Chang Lee

“…The proposed approach addresses these challenges by installing two LiDAR sensors at the ends of the vehicle bumper, optimizing the bumper-level FOV while minimizing interference from ground-level obstacles. …”
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Meeting Stringent QoS Requirements in AAV-Assisted Networks: Resource Allocation and AAVs Positioning by Meriem Hammami, Cirine Chaieb, Wessam Ajib, Halima Elbiaze, Roch Glitho

“…We first develop efficient greedy and metaheuristic genetic algorithms. Then, we propose an efficient centralized deep reinforcement learning solution based on the deep deterministic policy gradient (DDPG), where the agent learns optimal AAV positions and resource allocation. …”
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Combination of machine learning and data envelopment analysis to measure the efficiency of the Tax Service Office by Shofinurdin Soffan, Arif Bramantoro, Ahmad A. Alzahrani

“…The optimization of multilayer perceptrons using genetic algorithms reduced the mean squared error by about 75.75%, from 0.0144 to 0.0035. …”
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Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System by Daniel Cumbo, Marc Anthony Azzopardi

“…The architecture consists of a primary satellite that separates into multiple smaller units, which are subsequently dispersed using differential drag. An algorithm is developed to determine the required disengagement velocities and optimal timing for separation maneuvers. …”
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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

“…IntroductionParkinson’s disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.MethodsHere, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”
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Unscented transformation method for robustness verification of helicopter flight control system by LIU Changqi, LI Aijun, DUAN Guangzhan, LI Zuo

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High-Performance Deployment Operational Data Analytics of Pre-Trained Multi-Label Classification Architectures with Differential-Evolution-Based Hyperparameter Optimization (AutoDE... by Teo Prica, Aleš Zamuda

“…In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. …”
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Comparison of Control Law Synthesis Methods for the Wheeled Robot Motion by Anastasiia Tomilova, Margarita Sotnikova

“…The feedback linearization method, after reducing the nonlinear system to a linear form, allows one to construct an optimal control by solving the LQR-optimization problem, while the optimal damping method allows one to obtain only an approximate solution of the optimal control problem, reducing it to a parametric optimization problem. …”
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