A State-of-the-Art Survey on Advanced Electromagnetic Design: A Machine-Learning Perspective by Masoud Salmani Arani, Reza Shahidi, Lihong Zhang

“…Limitations of conventional optimization methods are discussed, while the need for novel machine learning techniques capable of handling multiple objectives and large design spaces is highlighted. …”
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Mixed Time/Event-Triggered Model Predictive Tracking Control for Networked Mobile Robots by Huixin Liu, Yonghua Lai, Hongsong Lian, Guobin Wang, Dongsheng Zheng

“…The method integrates time-triggered and event-triggered mechanisms, where the time-triggered module improves the performance of the MPC, and the event-triggered module reduces the resource consumption without sacrificing the control performance. The MPC algorithm is developed based on the auxiliary optimization problem (OP), which is constrained by linear matrix inequalities. …”
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A Software Defined Network of Video Surveillance System Based on Enhanced Routing Algorithms by Mustafa I. Salman et al.

“…POX and Pyretic SDN controllers are used such that POX controller is ‎responsible for the network monitoring, while Pyretic controller is responsible for the ‎routing algorithm and path selection. …”
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Enhancing Daylight and Energy Efficiency in Hot Climate Regions with a Perforated Shading System Using a Hybrid Approach Considering Different Case Studies by Basma Gaber, Changhong Zhan, Xueying Han, Mohamed Omar, Guanghao Li

“…A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. …”
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Study on intelligent height adjustment control method of shearer based on coal-rock interface recognition by Songyong LIU, Cheng CHENG, Hongzhuang WU, Yuming CUI, Deyuan MENG, Lei SI

“…Considering the specific requirements of coal mining process and equipment constraints, the cutting trajectory is optimized using a constrained optimization algorithm with the maximum recovery rate as the objective. …”
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Learning Rate Tuner with Relative Adaptation (LRT-RA): Road to Sustainable Computing by Saptarshi Biswas, Sumagna Dey, Subhrapratim Nath

“…The proposed method achieves 84.96% accuracy on the CIFAR-10 dataset while reducing the power usage to 0.07 kWh, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> emissions to 0.05, and both <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><msub><mi>O</mi><mi>x</mi></msub></mrow></semantics></math></inline-formula> emissions to 0.00003 pounds, during the whole training and testing process.…”
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Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models by Nikos Themelis, George Nikolaidis, Vasilios Zagkas

“…The power prediction models are data-driven based on machine learning algorithms. The process includes feature engineering, filtering, and data smoothing, while an evaluation of regression algorithms of the decision tree family is performed. …”
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Matching design for structural parameters of the dual-friction pair in a piston pin by Jikun Chen, Jilin Lei, Yang Liu, Chengxi Wang, Wei Deng, Dongjun Sun

“…The influence of bearing structural parameters on the lubrication performance of the piston pin bearing system was analyzed by integrating experimental design with the Pareto multi-objective optimization algorithm. Subsequently, an optimized structural parameter design was explored to achieve a balanced lubrication performance within the piston pin bearing system. …”
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Predicting forest carbon storage and identifying hotspot in the Loess Plateau under future climate change–supporting China's dual carbon strategy by Haihong Qiu, Hairong Han, Xiaoqin Cheng, Fengfeng Kang

“…In this study, we employed a k-fold random forest (K-RF) algorithm to classify forest types across the Loess Plateau and developed a spatial optimization simulation method for multi-climate scenario projections. …”
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A Novel Metaheuristic-Based Methodology for Attack Detection in Wireless Communication Networks by Walaa N. Ismail

“…Furthermore, the metaheuristic Cat Swarm Optimization (CSO) algorithm is employed to fine-tune the hyperparameters of both the CSO-2D-CNN and the attention-based XGBoost classifier. …”
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A multi-objective fuzzy model based on enhanced artificial fish Swarm for multiple RNA sequences alignment by Medhat A. Tawfeek, Ibrahim Alrashdi, Madallah Alruwaili, Gamal Farouk Elhady

“…Traditional cross-alignment methods have significant challenges, particularly in optimizing multiple objectives during RNA sequencing. …”
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A novel multivariate decomposition-based hybrid model for interpretable multi-step-ahead daily reference evapotranspiration forecasting by Ali Matoog Obaid Lebawi, Mahnoosh Moghaddasi, Mehdi Mohammadi Ghaleni, Mansour Moradi

“…Initially, significant input predictor lags were established through cross-correlation, and the War Strategy Optimization (WSO) algorithm was used for optimal Feature Selection (FS) and determining Multivariate Variational Mode Decomposition (OMVMD) parameters. …”
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Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO₂ Emissions Trade-Offs by Yuzhuo Zhang, Jiale Peng, Zi Wang, Meng Xi, Jinlong Liu, Lei Xu

“…Subsequently, a MOO framework minimized unit cost and CO₂ emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Furthermore, we systematically compare three hyperparameter optimization algorithms, including the sparrow search algorithm (SSA), particle swarm optimization (PSO), and Bayesian optimization (BO). …”
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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…The particle swarm optimization (PSO) algorithm is employed to optimize ELM parameters, enhancing generalization and model convergence. …”
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CFD-Guided Design of Non-Uniform Flow Channels in PEMFCs for Waste Heat Utilization in District Heating Networks by Dai Cui, Dong Liu, Peng Yu, Jiayi Li, Zhi Zhou, Meishan Zhang, Qun Chen, Fang Yuan

“…These findings validate the algorithm’s efficacy in resolving coupled transport constraints and underscore the necessity of multi-component optimization for advancing PEMFC design.…”
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A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis by Abbas Alili, Varun Nalam, Minhan Li, Ming Liu, Jing Feng, Jennie Si, He Huang

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Advancing named entity recognition in interprofessional collaboration and education by Rui Zhang, Yifeng Shan, MengZhe Zhen

“…Each agent contributes dynamically to the collaborative process, adapting its knowledge, skills, and resources to optimize global utility while minimizing conflicts and enhancing synergy. …”
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Probing the Pitfalls: Understanding SVD’s Shortcomings in Language Model Compression by Сергей Александрович Плетенев

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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir, Aiman Erbad, Bechir Hamdaoui, Samir Brahim Belhaouari, Mohsen Guizani, Thang X. Vu

“…Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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