Enhanced Stability and Performance of Islanded DC Microgrid Systems Using Optimized Fractional Order Controller and Advanced Energy Management by Md. Wahidujjaman, Tasnim Ul Bari, Md. Shafiul Alam, Md. Rashidul Islam, Md. Alamgir Hossain, Md. Arafat Hossain, Md. Rafiqul Islam Sheikh

“…To address these issues, this study proposes the use of an optimized fractional order PI (FOPI) controller and an efficient energy management algorithm. …”
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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes by Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao

“…Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. …”
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Two-layer asynchronous distributed optimal voltage control for VSC-HVDC-connected large-scale wind farm clusters by Fuyan Liu, Feifan Shen, Pan Hu, HeSong Cui, Sheng Huang, Ji Zhang, Xia Ma, Kun He

“…The results showed that the proposed scheme could improve the computation efficiency without affecting the original problem’s optimality, while also exhibiting enhanced robustness and environmental adaptability.…”
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Structural Parameter Design of Magnetic Pulse Welding Coil for Dissimilar Metal Joints: Numerical Simulation, Parameter Optimization, and Experiments by Yangfan Qin, Changhui Ji, Hao Jiang, Yuefan Jiang, Junjia Cui, Guangyao Li

“…The optimal Latin hypercube sampling technique (OLHS), Kriging approximate model, and the Non-Linear Programming by Quadratic Lagrangian (NLPQL) algorithm were employed in the optimization procedure, based on the finite element model built in LS-DYNA. …”
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Optimizing the Migratory Environment of Wildebeests in the Maasai Mara Reserve with a New Ecological Corridor and Customized Buffer Zone Model by Xinrui Fan, Kuok Ho Daniel Tang, Shoushuo Liu, Yang Liu, Charles Ken Smith

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Optimizing Automatic Voltage Control Collaborative Responses in Chain-Structured Cascade Hydroelectric Power Plants Using Sensitivity Analysis by Li Zhang, Jie Yang, Jun Wang, Lening Wang, Haiming Niu, Xiaobing Liu, Simon X. Yang, Kun Yang

“…Subsequently, a regional-voltage-coordinated regulation model is developed using sensitivity analysis, followed by the establishment of a mathematical model, solution algorithm, and operational procedure for multi-station AVC-coordinated response optimization. …”
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Enhanced thyroid nodule detection and diagnosis: a mobile-optimized DeepLabV3+ approach for clinical deployments by Changan Yang, Muhammad Awais Ashraf, Mudassar Riaz, Pascal Umwanzavugaye, Kavimbi Chipusu, Hongyuan Huang, Yueqin Xu

“…Preprocessing steps, including noise reduction and contrast optimization, were applied to enhance image clarity. …”
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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. …”
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Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron by Yacine Karmi, Haithem Boumediri, Omar Reffas, Yazid Chetbani, Sabbah Ataya, Rashid Khan, Mohamed Athmane Yallese, Aissa Laouissi

“…This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. …”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…Finally, the model’s prediction accuracy is further enhanced by optimizing the hyperparameters of XGBoost through Bayesian optimization (BO) algorithms, resulting in the development of MM–BO–XGBoost models. …”
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Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization by Muhammad Hilal, Haider TH. Salim ALRikabi, Ibtisam A. Aljazaery

“… The Generalized Recurrent Neural Network (GRNN) and Invasive Weed Optimization (IWO) algorithms are two powerful techniques that can be used to optimize motor drive speed. …”
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A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals by Priyaranjan Kumar, Prabhat Kumar Upadhyay

“…Abstract In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms have been used in the model’s optimization to improve efficiency and accelerate convergence while extracting features from time and frequency domain EEG data. …”
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Optimization of heat and mass transfer in chemically radiative nanofluids using Cattaneo-Christov fluxes and advanced machine learning techniques by Shazia Habib, Saleem Nasir, Zeeshan Khan, Abdallah S. Berrouk, Waseem Khan, Saeed Islam

“…This functionality empowers specialists to oversee the progression of optimization, identify convergence patterns, and adjust algorithms to achieve superior results, thereby making a remarkable contribution to heat transfer and fluid dynamics.…”
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Energy‐based PINNs for solving coupled field problems: Concepts and application to the multi‐objective optimal design of an induction heater by Marco Baldan, Paolo Di Barba

“…Abstract Physics‐informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy‐based approaches that take a different path by minimizing the variational energy of the model. …”
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Mathematical Modeling of Optimal Drone Flight Trajectories for Enhanced Object Detection in Video Streams Using Kolmogorov–Arnold Networks by Aida Issembayeva, Oleksandr Kuznetsov, Anargul Shaushenova, Ardak Nurpeisova, Gabit Shuitenov, Maral Ongarbayeva

“…While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. …”
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Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

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Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization by Zainab Nadhim Jawad, Balázs Villányi

“…The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. …”
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Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction by Shakeel Ahmad, Sheikh Muhammad Saqib, Asif Hassan Syed, Nashwan Alromema, Ali Kararay

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Enhanced supervised classification of seasonal pastures on the Qinghai-Tibet Plateau (1990–2020) using Landsat optimal time window by Panfei Fang, Jia Wang, Haifeng Xu, Ruonan Li, Long Yu, Shaodong Huang, Yuying Liang, Pengfei Zheng

“…The framework leverages Landsat imagery within the optimal time window (Days of Year, DOYs 190–280), incorporates algorithms for automated sample generation and refinement, and employs the Random Forest (RF) classifier to enable fully automated seasonal pasture mapping. …”
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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

“…Purpose: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused on ROR/Prosigna. …”
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