Hybrid evolutionary algorithm for maximizing medical equipment supply during pandemic✰ by C. D James, Sandeep Mondal

“…In this paper, we make use of a simulation-based model to demonstrate solution to this problem because experimental setups involve high cost and delivery risks.Firstly, we identified thirty-one factors that affect hi-tech machine efficiency. …”
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Optimizing Decision Making on Business Processes Using a Combination of Process Mining, Job Shop, and Multivariate Resource Clustering by Hanung Nindito Prasetyo, Riyanarto Sarno, Dedy Rahman Wijaya, Raden Budiraharjo, Indra Waspada, Kelly Rossa Sungkono, Abdullah Faqih Septiyanto

“…In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. …”
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Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study by C. Banerjee, K. Nguyen, C. Fookes, G. Hancock, T. Coulthard

“…Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. …”
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Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach by Jun Zhang, Jinpeng Cheng, Cuiping Liu, Qiang Wu, Shuping Xiong, Hao Yang, Shenglong Chang, Yuanyuan Fu, Mohan Yang, Shiyu Zhang, Guijun Yang, Xinming Ma

“…A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. …”
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Optimizing electric vehicle driving range prediction using deep learning: A deep neural network (DNN) approach by Shahid A. Hasib, Muhammad Majid Gulzar, Adnan Shakoor, Salman Habib, Ali Faisal Murtaza

“…This study addresses the challenges of EV range prediction by presenting a novel deep learning technique that uses a Deep Neural Network (DNN) model optimized with the RMSProp optimizer. This approach leverages a unique real-world dataset that reflects varied driving environments, leading to superior performance. …”
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Intrusion Detection System for Network Security Using Novel Adaptive Recurrent Neural Network-Based Fox Optimizer Concept by R. Manivannan, S. Senthilkumar

“…The gray level co-occurrence matrix (GLCM) method is proposed for selecting the optimal subset of features for the ARNN-FOX method. …”
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Design and Development of Gorilla Optimized Deep Resilient Architecture for Prediction of Agro-Climatic Changes to Increase the Crop–Yield Production by Deepa Devarashetti, S. S. Aravinth

“…However, the existing models for climatic prediction require improvements in computational complexity and performance. …”
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Accelerated Bayesian optimization for CNN+LSTM learning rate tuning via precomputed Gaussian process subspaces in soil analysis by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong, Zhengchun Song

“…PurposeWe propose an accelerated Bayesian optimization framework for tuning the learning rate of CNN+LSTM models in soil analysis, addressing the computational inefficiency of traditional Gaussian Process (GP)-based methods. …”
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber–Physical Security by Nalini Manogaran, Yamini Bhavani Shankar, Malarvizhi Nandagopal, Hui-Kai Su, Wen-Kai Kuo, Sanmugasundaram Ravichandran, Koteeswaran Seerangan

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Optimal Fuzzy Deep Neural Networks-Based Plant Disease Detection and Classification on UAV-Based Remote Sensed Data by M. Pajany, S. Venkatraman, U. Sakthi, M. Sujatha, Mohamad Khairi Ishak

“…Moreover, this model’s scalability and efficiency improve its value for precision agriculture, optimizing resource usage and promoting sustainable farming practices. …”
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Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images by Khaled Tarmissi, Jamal Alsamri, Mashael Maashi, Mashael M. Asiri, Abdulsamad Ebrahim Yahya, Abdulwhab Alkharashi, Monir Abdullah, Marwa Obayya

“…Finally, the snake optimization algorithm (SOA) is implemented to tune the parameter of the HKELM model. …”
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Machine learning assisted design of Fe-Ni-Cr-Al based multi-principal elements alloys with ultra-high microhardness and unexpected wear resistance by Ling Qiao, Jingchuan Zhu, Junya Inoue

“…Generalized Regression Neural Network (GRNN) showed high accuracy to construct the composition-microhardness model and was used for microhardness prediction and composition optimization. …”
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Integrating Advanced Techniques: RFE-SVM Feature Engineering and Nelder-Mead Optimized XGBoost for Accurate Lung Cancer Prediction by Sarah Ayad, Hamdi A. Al-Jamimi, Ammar El Kheir

“…Evaluating the model’s generalizability on two distinct lung cancer datasets, results show that our approach outperforms traditional machine learning models, achieving 100% accuracy. …”
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Optimization of Sensor Targeting Configuration for Intelligent Tire Force Estimation Based on Global Sensitivity Analysis and RBF Neural Networks by Yu Zhang, Guolin Wang, Haichao Zhou, Jintao Zhang, Xiangliang Li, Xin Wang

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Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm by Nikolaos Fountas, Angelos Koutsomichalis, John Kechagias, Nikolaos Vaxevanidis

“…Full quadratic regression models were de­veloped to correlate the machining conditions with the imparted machinability characteristics. …”
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Multi-stage adaptive speed control with torque ripple optimization for a switched reluctance motor in electric vehicle applications by Youness Boumaalif, Hamid Ouadi, Fouad Giri

“…The speed controller is designed with the backstepping approach based on a SRM nonlinear model taking into account the magnetic saturation phenomenon of this machine. …”
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Prediction of clinical pregnancy after frozen embryo transfer based on ultrasound radiomics: an analysis based on the optimal periendometrial zone by Fangfang Xu, Ying Zhang, Qianqing Ma, Lili Hu, Yu Li, Chuanfen Gao, Peipei Guo, Xianyue Yang, Yi Zhou, Jie Zhang, Heng Wang, Chaoxue Zhang

“…We determined the radiomics characteristics based on the ROIs of the endometrium and PEZ, then compared the different sizes of PEZ to determine the optimal PEZ. We constructed models of the EN and optimal PEZ using six machine learning algorithms. …”
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Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks by K. Lakshmi Prabha, Hanan Abdullah Mengash, Hamed Alqahtani, Randa Allafi

“…Existing methodologies, including regression-based models, heuristic approaches, and optimization-driven methods, struggle to generalize across dynamic environments due to their reliance on static parameter configurations. …”
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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…Here, we present how our EGF Model—trained on procedural outcomes from 199 fully anonymized retrospective patient datasets—identifies clinically significant sources of AF and how this machine learning–driven hyperparameter optimization underlies its clinical effectiveness. …”
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A New Framework for Dynamic Educational Marketing Segmentation in Student Recruitment: Optimizing Fuzzy C-Means with Metaheuristic Techniques by Rizal Bakri, Bobur Sobirov, Niken Probondani Astuti, Ansari Saleh Ahmar, Pawan Kumar Singh

“…However, the performance of FCM highly depends on determining parameters such as the number of clusters (k) and the level of fuzziness (m), which are not always optimal when determined manually. This study develops a new framework for dynamic educational marketing segmentation in student recruitment by optimizing FCM using three metaheuristic techniques: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). …”
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