A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing by Peng Zhang, Mengyu Jin, Ming Wang, Jie Zhang, Junjie He, Peng Zheng

“…To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. …”
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Optimal ensemble-based framework for ground-fault protection in radial MV distribution networks with resonant grounding by Boštjan Polajžer, Younes Mohammadi, Thomas Olofsson, Gorazd Štumberger

“…In order to enhance the performance of GFRs, particularly during high-impedance faults, the scope of this paper is to propose a straightforward, machine-learning-based protection framework. The enhanced GFR is modeled as a classification task. …”
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Automated Design and Parametric Modeling of Excavator Buckets by Nikolay Stankov, Aleksandar Ivanov

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GRADIENT DESCENT METHOD AND PARAMETER SELECTION FOR IMAGE RESTORATION PROBLEM by Nguyen Dinh Dung

“…Image restoration is often considered one of the data processing steps before the training process for machine learning models is performed. Image restoration problems are often solved by iterative algorithms, where the choice of iteration parameters plays an important role in improving the algorithm's convergence rate. …”
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Vertically Recurrent Neural Networks for Sub‐Grid Parameterization by P. Ukkonen, M. Chantry

“…Abstract Machine learning has the potential to improve the physical realism and/or computational efficiency of parameterizations. …”
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Architectural Exploration and Performance Enhancement of the CVA6 RISC-V Core Using the Gem5 Simulation Framework by Umer Shahid, Muhammad Tahir, Bilal Zafar, Ayesha Ahmad, Shanzay Wasim, Bisal Saeed

“…This approach resulted in two distinct optimized models: a resource-efficient variant that reduced resource consumption by 12% with only a 0.2% decrease in RMS micro-benchmark IPC, and a performance-optimized model that improved performance by 29.5% while increasing resource usage by just 4.20%. …”
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A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence by Emrah Aslan, Yildirim Ozupak, Feyyaz Alpsalaz, Zakaria M. S. Elbarbary

“…This study aims to improve the reliability of power systems by predicting transformer failures through the integration of IoT technologies and advanced machine learning techniques. The proposed hybrid model combines the LightGBM algorithm with GridSearch optimization to achieve both high predictive accuracy and computational efficiency. …”
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Enhancing Efficiency and Reducing the Carbon Footprint of Cloud-Based Healthcare Applications through Optimal Data Preprocessing by El Aziz Btissam, Eddabbah Mohammed, Laaziz Yassin

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A novel measure for characterizing ultrasound device use and wear by Hamid Shokoohi, Andrew Goldsmith, Kay Negishi, Jeffrey R. Herrala, Eden Diamond, Sigmund Kharasch, Michael Blaivas, Andrew S. Liteplo

“…We propose the ULTrA model as a method to identify underperforming devices which could be upgraded or eliminated, and to compare relative performance amongst a group of departmental ultrasound machines. …”
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SRL-ACO: A text augmentation framework based on semantic role labeling and ant colony optimization by Aytuğ Onan

“…The process of creating high-quality labeled data is crucial for training machine-learning models, but it can be a time-consuming and labor-intensive process. …”
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Towards the Digital Model of Tool Lifecycle Management in Sheet Metal Forming by Vidosav D. Majstorovic, Vojin Vukadinovic, Jovan Zivkovic

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A Lightweight Terrain‐Constraint Model for Wind Spatial Downscaling by Anboyu Guo, Qiyang Liu, Jiuke Wang, Yuanyong Gao, Lixin Wei, Xiaojiang Song, Chi Zhang, Kai Liu, Shuping Ma, Jiu Jiang

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Reinforcement Learning-Based Augmentation of Data Collection for Bayesian Optimization Towards Radiation Survey and Source Localization by Jeremy Marquardt, Leonard Lucas, Stylianos Chatzidakis

“…Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum within noisy contexts while also minimizing the number of trials. …”
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Multi-objective: hybrid particle swarm optimization with firefly algorithm for feature selection with Leaky ReLU by Ashish Kumar Singh, Anoj Kumar

“…Abstract High-dimensional datasets often pose challenges due to the presence of numerous irrelevant and redundant features, which can compromise the performance of machine learning models. This study proposes a novel optimization algorithm, LR-GPSOFA, designed to improve feature selection by enhancing computational efficiency and classification accuracy. …”
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Outdoor Environment Design Optimization of an Office Building Based on Indoor Thermal Conditions and Building Energy Performance by Yaolin Lin, Tao Huang, Wei Yang, Melissa Chan, Chun-Qing Li, Mingqi Dai, Pengju Chen

“…Fourthly, the artificial neural network models were used as fitness functions for seven optimization algorithms and Pareto front solutions were obtained during the optimization process. …”
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Load Forecasting in Electrical Grids: Analysis of Methods and their Trends by Kyryk V.V., Shatalov Y.O.

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Hybrid machine learning techniques for knowledge extraction in communication networks and advanced decision based complex q-Rung orthopair fuzzy frameworks by Hushuang Zeng, Yonghua Zhang

“…Abstract Hybrid machine learning techniques for knowledge extraction in communication networks combine the strengths of multiple learning algorithms to analyze complex, high-dimensional, and dynamic network data effectively. …”
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Effects of EDM parameters on residual stress and microhardness in Al-10 %SiCmicro-SiCnano hybrid composites: A Simulation and Experimental study by Umair Arif, Imtiaz ali Khan, Faisal Hasan, Afsar Husain, Sanan H. Khan

“…In the first part, a thermo-mechanical model was developed using Ansys software to predict the residual stresses generated in Al-SiC-based hybrid composites after Electrical Discharge Machining (EDM). …”
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Research into performance optimization control strategy of a hydrostatic drive bulldozer based on the constant speed cruise by Hongbin Qiang, He Li, Shaopeng Kang, Kailei Liu, Jing Yang, Xingfei Luo

“…This strategy achieves optimal overall machine efficiency under cruise control by adjusting the engine throttle and the displacement of the hydraulic pump and motor. …”
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Leveraging Zero-Shot Detection Mechanisms to Accelerate Image Annotation for Machine Learning in Wild Blueberry (<i>Vaccinium angustifolium</i> Ait.) by Connor C. Mullins, Travis J. Esau, Qamar U. Zaman, Chloe L. Toombs, Patrick J. Hennessy

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