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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Potential of random forest machine learning algorithm for geological mapping using PALSAR and Sentinel-2A remote sensing data: A case study of Tsagaan-uul area, southern Mongolia by Munkhsuren Badrakh, Narantsetseg Tserendash, Erdenejargal Choindonjamts, Gáspár Albert

“…Geological mapping in remote and geologically complex regions can be substantially improved by integrating remote sensing data with machine learning algorithms. This study evaluates the effectiveness of the Random Forest algorithm for geological mapping in the Tsagaan-uul area of the Khatanbulag ancient massif, Mongolia, a region characterized by limited accessibility and sparse field data. …”
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A carbon aware ant colony system for the sustainable generalized traveling salesman problem by Marina Lin, Laura P. Schaposnik

“…In an era where sustainability is becoming increasingly crucial, we introduce a novel Carbon-Aware Ant Colony System (CAACS) Algorithm that addresses the Generalized Traveling Salesman Problem while minimizing carbon emissions. …”
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Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty by Chao Chen, Weifeng Peng, Cheng Xie, Shufeng Dong, Yibo Hua

“…The model considers system constraints and employs the sparrow search algorithm (SSA) to optimize the maximum PV hosting capacity. …”
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Vertex Coloring and Eulerian and Hamiltonian Paths of Delaunay Graphs Associated with Sensor Networks by Manuel Ceballos, María Millán

“…Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. …”
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A Hybrid Evolutionary Fuzzy Ensemble Approach for Accurate Software Defect Prediction by Raghunath Dey, Jayashree Piri, Biswaranjan Acharya, Pragyan Paramita Das, Vassilis C. Gerogiannis, Andreas Kanavos

“…To address this, effective feature selection is essential but remains an NP-hard challenge best tackled with heuristic algorithms. This study introduces a binary, multi-objective starfish optimizer for optimal feature selection, balancing feature reduction and classification performance. …”
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Achieving high precision and productivity in laser machining of Ti6Al4V alloy: A comprehensive study using a n-predictor polynomial regression model and PSO algorithm by Avinash Chetry, Sandesh Sanjeev Phalke, Arup Nandy

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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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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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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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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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