Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques by Mohamed Bechir Ben Hamida, Ali B. M. Ali, Narinderjit Singh Sawaran Singh, Loghman Mostafa

“…Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. …”
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CFD-based optimization of dynamic cyclones with variable vortex length using GMDH artificial neural network by Hamed Safikhani, Somayeh Davoodabadi Farahani, Lakhbir Singh Brar, Faroogh Esmaeili

“…The final step involves optimizing the cyclone designs through the non-dominated sorting genetic algorithm (NSGA). …”
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Route Optimization of Hazardous Material Railway Transportation Based on Conditional Value-at-Risk Considering Risk Equity by Liping Liu, Shilei Sun, Shuxia Li

“…A k-shortest path-based algorithm was designed to balance extreme risk minimization with equitable risk allocation in route selection. …”
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Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass by Yongjie Ji, Fuxiang Zhang, Wangfei Zhang, Lei Zhao, Kunpeng Xu, Jianmin Shi, Guoran Huang, Qian Jing, Lu Wang, Feifei Yang

“…Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA-DSA) is developed for the detection of SIP-based distributed reflection denial-of-service (DRDoS) attacks in SIP network. …”
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Design and Analysis of an Interior Permanent Magnet Synchronous Motor for a Traction Drive Using Multiobjective Optimization by Yingying Xu, Yiguang Chen, Zhihua Fu, Mingxia Xu, Haiyu Liu, Li Cheng

“…Based on the genetic algorithm, a multiobjective optimization mechanism of the magnetic pole structure is established. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…This suggested framework leverages an attention-based convolutional neural network (CNN) and a genetic algorithm (GA) to enhance detection accuracy while optimizing the hyperparameters of the proposed model. …”
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Vision-Guided Maritime UAV Rescue System with Optimized GPS Path Planning and Dual-Target Tracking by Suli Wang, Yang Zhao, Chang Zhou, Xiaodong Ma, Zijun Jiao, Zesheng Zhou, Xiaolu Liu, Tianhai Peng, Changxing Shao

“…Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. …”
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Optimal State Estimation in Underwater Vehicle Discrete-Continuous Measurements via Augmented Hybrid Kalman Filter by Vadim Kramar, Kirill Dementiev, Aleksey Kabanov

“…State-vector augmentation is proposed to improve the estimation convergence. The authors present numerical modeling results demonstrating the algorithm’s efficiency on the example of motion parameter estimation for the autonomous underwater vehicle. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…This study highlights the importance of integrating Machine Learning and statistical analysis methods for the effective modeling and optimization of LPBF processes. The findings contribute significantly to the literature and serve as a valuable reference for future research aimed at improving LPBF process efficiency and performance.…”
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Process parameter optimization for minimizing overcut in abrasive waterjet deep hole drilling of SS 316L by J. Bharani Chandar, N. Lenin, Alagar Karthick, Pradeep Kumar Singh, M. Siva Kumar, Md Irfanul Haque Siddiqui, Intesaaf Ashraf, Saurav Dixit, Nikolai Ivanovich Vatin, Choon Kit Chan

“…Additionally, to mitigate the impact of overcut and improve the drilling quality necessary for diverse sectors such as automotive, biomedical, and oil and gas, a metaheuristic optimization method, specifically the Grasshopper Optimization Algorithm (GHO), has been utilized. …”
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Experimental and computational optimization of sheet metal forming parameters for cylindrical cups of Al1100 and SS202 by Amit Kaimkuriya, S. Balaguru

“…Integrating WOA with FEA yielded valuable insights into defect mitigation, particularly in reducing wrinkling and fractures, thereby improving product quality. This study demonstrates the effectiveness of combining advanced optimization algorithms with simulation tools, promoting sustainable manufacturing by enhancing efficiency and material utilization in deep drawing processes.…”
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Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data by Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

“…This study will use administrative data and ML modeling to improve suicide risk detection at jail booking. …”
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Parametric design and multiple objective intelligent optimization of marine nuclear turbine aerothermodynamics under variable conditions by Lei Zhang, Guobing Chen, Yu-Ang Shi, LuoTao Xie

“…By combining the blade parameterized reconstruction method with a Kriging approximation model and a multi-objective genetic algorithm (GA), we developed an optimized system for thermodynamic performance in turbines. …”
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Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components by Anna Konovalenko, Lars Magnus Hvattum

“…<i>Results:</i> Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. …”
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A study on optimization of passenger flow density of rail transit platform based on balanced train diagram by XIAO Lijun, LIANG Ye, LI Juanjuan, XIAO Xiyu, LI Denggen, TAN Junhua

“…With the development of urban rail transit, metro has become the preferred mode of public transportation for citizens. In order to improve passengers' travel experience, realize the matching of transport capacity and transport demands, and reduce the congestion in waiting areas of urban rail transit platform, in this paper, the optimization of the passenger flow density on platform was researched based on the balance indexes of running chart. …”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. …”
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A correlation-based binary particle swarm optimization method for feature selection in human activity recognition by Huaijun Wang, Ruomeng Ke, Junhuai Li, Yang An, Kan Wang, Lei Yu

“…In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. …”
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Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences by Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Ν. Kalantzopoulos, Nikolaos S. Tachos, Daniele Regge, Nikolaos Papanikolaou, Manolis Tsiknakis, Kostas Marias, ProCAncer-I Consortium, Dimitrios I. Fotiadis

“…Key Points Radiomics is a growing field that can still be optimized. Feature selection method impacts radiomics models’ performance more than ML algorithms. …”
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