Research on the Parametric Design and Application of Ceramic Modeling Based on Python by Zhenjie Wang, Longyao Xu, Jing Dai, Chengqian Zeng, Yu Zhong, Liyan Liu, Shuanghua Wang

“…The designs are optimized using fuzzy comprehensive evaluation for aesthetic appeal. …”
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Artificial intelligence model in the cognitive and learning activities of university subjects by N. Abishev, R. Ramazanov, M. Abaideldanova, K. Chesnokova, A. Baizhumayeva

“…The novelty of the study suggests in the personalization of the educational process for the learner, the application of adaptive cognitive learning strategies, and the incorporation of intelligent feedback mechanisms. The proposed model stands out from others due to the inclusion of components such as machine learning, intelligent tutoring systems, automated assessment systems, and large-scale data analysis. …”
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Lumped Parameter Model for Structural Analysis of Over-Constrained Multi-Legged Parallel Mechanism Supporting System Applied to Cryogenic Devices by Luca Piacentini, Luca Dassa, Diego Perini, Andris Ratkus, Toms Torims, Stefano Uberti

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Chemical reaction mechanisms and models of energetic materials: A perspective by Li Meng, Qing-guan Song, Chuang Yao, Lei Zhang, Si-ping Pang

“…Then, quantitatively characterized expressions of the physical models derived from the revealed mechanisms, including mathematical expressions like elementary and phenomenological reaction kinetic models, and emerging data-driven machine learning models, are reviewed. …”
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A high-efficiency modeling method for analog integrated circuits by Dongdong Chen, Yunqi Yang, Xianglong Wang, Di Li, Guoqing Xin, Yintang Yang

“…Two typical analog ICs were selected to verify the effectiveness of the CNN-IC model. The results show that the accuracy of the CNN-IC model could reach over 99% and that its convergence rate was the fastest compared with the machine learning models in the state of the art.…”
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Scale-equivariant deep model-based optoacoustic image reconstruction by Christoph Dehner, Ledia Lilaj, Vasilis Ntziachristos, Guillaume Zahnd, Dominik Jüstel

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The NFDA-Nonsmooth Feasible Directions Algorithm applied to construction of Pareto Fronts of Ridge and Lasso Regressions by W. P. Freire

“… Ridge and Lasso regressions are types of linear regression, a machine learning tool for dealing with data. Based on multiobjective optimization theory, we transform Ridge and Lasso regression into bi-objective optimization problems. …”
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Estimating Import Lead Times Using Business Intelligence and Machine Learning Within the CRISP-DM Framework: A Case Study in Oil and Gas Services Industry by Mohamed Annis Souames, Larbi Abderrahmane Mohammedi, Iskander Zouaghi, Angappa Gunasekaran, Samia Beldjoudi, Abderrazak Laghouag

“…BI tools facilitate the visualization of key insights, allowing for enhanced data comprehension and performance tracking. Machine learning models are then applied to predict lead times under varying import conditions, optimizing decision-making in terms of transport mode, entry port, and customs handling. …”
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Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer by J.-S. Chou, H.-M. Nguyen, H.-P. Phan, K.-L. Wang

“…This research contributes to developing predictive early warning systems for deep-seated landslide displacement by employing advanced computational models for environmental risk management. The novel framework evaluates machine learning, time series deep learning, and convolutional neural networks (CNNs), identifying the most effective models to be enhanced by the Age of Exploration-Inspired Optimizer (AEIO) algorithm. …”
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Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis by Eduardo Aguilar-Bejarano, Ender Özcan, Raja K. Rit, Hongyi Li, Hon Wai Lam, Jonathan C. Moore, Simon Woodward, Grazziela Figueredo

“…We propose the Homogeneous Catalyst Graph Neural Network (HCat-GNet), a machine learning model capable of aiding ligand optimization. …”
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A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process by Mahesh Gopal, Endalkachew Mosisa Gutema, Hirpa G. Lemu, Jaleta Sori

“…Spindle speed, rate of feed, radial and axial depth of cut, and radial rake angle of the tool are the parameters utilized to machine aluminium 6063 using the HSS tool on CNC milling to estimate spindle and worktable vibration using a prediction model. …”
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A variable metric proximal stochastic gradient method: An application to classification problems by Pasquale Cascarano, Giorgia Franchini, Erich Kobler, Federica Porta, Andrea Sebastiani

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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network by Xuesen Xu, Shijia Luo, Xuchen Zhang, Weiming Xu, Rong Shu, Jianyu Wang, Xiangfeng Liu, Ping Li, Changheng Li, Luning Li

“…The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). …”
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A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN by S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda, S. Rohith

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A data-centric approach to terminal unit’s fault categorization and optimal positioning in building HVAC systems using ensemble learning by Maitreyee Dey, Preeti Patel, Soumya Prakash Rana

“…Abstract This paper focuses on the fault detection and diagnosis of terminal units (TUs) in a building located in London, utilizing real operational historical data to assess their performance and optimal placement across multiple floors. While precise locations of the TUs are unavailable, our method analyzes their operational behaviour for one month, applying popular machine learning models to detect and analyze faults effectively. …”
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A Statistical Optimization Method for Sound Speed Profiles Inversion in the South China Sea Based on Acoustic Stability Pre-Clustering by Zixuan Zhang, Ke Qu, Zhanglong Li

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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…One of the primary goals of this study is to construct an optimized deep-learning model capable of accurately predicting lattice-physics parameters for future applications of machine learning in nuclear reactor analysis. …”
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Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine lear... by Jianing Cui, Ping Wang, Xiaodong Zhang, Ping Zhang, Yuming Yin, Rongjie Bai

“…Seven machine learning algorithms were evaluated to determine the optimal model for predicting the failure of conservative treatment. …”
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A multiobjective continuation method to compute the regularization path of deep neural networks by Augustina Chidinma Amakor, Konstantin Sonntag, Sebastian Peitz

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Enhanced early detection of dysarthric speech disabilities using stacking ensemble deep learning model by Jagat Chaitanya Prabhala, Ravi Ragoju, Venkatanareshbabu Kuppili, Christophe Chesneau

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