ANALYSIS AND PARAMETRIC OPTIMIZATION OF ENERGY-AND-TECHNOLOGY UNITS ON THE BASIS OF THE POWER EQUIPMENT OF COMPRESSOR PLANTS OF MAIN GAS PIPELINES by V. A. Sednin, A. A. Abrazovskii

“…On the basis of the gas compressor units of compressor plants of a main gas pipeline mathematical models of the macro-level were generated for analysis and parametric optimization of combined energy-and-technology units. …”
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Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning by YANG Yinghe, WEI Handi, FAN Dixia, LI Ang

“…The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. …”
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Data-driven model discovery with Kolmogorov-Arnold networks by Shirin Panahi, Mohammadamin Moradi, Erik M. Bollt, Ying-Cheng Lai

“…Data-driven model discovery of complex dynamical systems can be done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical functions. …”
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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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Credit Rating Model Based on Improved TabNet by Shijie Wang, Xueyong Zhang

“…The experimental results demonstrate that the proposed TabNet-based credit rating model outperforms benchmark models across multiple metrics, including accuracy, precision, recall, F1-score, AUC (Area Under the Curve), and KS (Kolmogorov–Smirnov statistic).…”
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Design and Optimization of Collimator for Preclinical Single-photon Emission Computed Tomography Scanner: A Monte Carlo Study by Mohammad Mirdoraghi, Behnoosh Teimourian Fard, Olga Kochebina, Hojjat Mahani, Mohammad Reza Ay

“…The purpose of this investigation was to implement and validate a MC model for a preclinical single-photon emission computed tomography (SPECT) known as high-resolution SPECT II (HiReSPECT II) machine developed in our laboratory and also optimize different collimator materials and geometries for improving the sensitivity and spatial resolution. …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

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Neural Network Optimization of Mechanical Properties of ABS-like Photopolymer Utilizing Stereolithography (SLA) 3D Printing by Abdulkader Ali Abdulkader Kadauw

“…Also, multi-objective optimization was conducted using these models to find the SLA printer’s optimum parameter combination to achieve optimal mechanical properties. …”
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Classification of biomedical lung cancer images using optimized binary bat technique by constructing oblique decision trees by Shobha Aswal, Neelu Jyothi Ahuja, Ritika Mehra

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Review of machine learning-assisted multi-property design of high-entropy alloys: phase structure, mechanical, tribological, corrosion, and hydrogen storage properties by Yunlong Li, Jialiang Tan, Cheng Qian, Xiaochao Liu, Rui Nie

“…It outlines the basic workflow, including data collection, data preprocessing, ML algorithm selection, hyperparameter optimization, model evaluation and model interpretability. …”
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Basrah Score: a novel machine learning-based score for differentiating iron deficiency anemia and beta thalassemia trait using RBC indices by Salma A. Mahmood, Asaad A. Khalaf, Saad S. Hamadi

“…These results underscore the importance of incorporating advanced pre-processing techniques, class balancing, hyperparameter optimization, and rigorous cross-validation to ensure the robustness of diagnostic models. …”
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A Lightweight YOLOv8s Algorithm for Ceiling Fan Blade Defect Detection With Optimized Pruning and Knowledge Distillation by Qinyuan Huang, Chen Fan, Yuqi Sun, Jiaxiong Huang, Wengziyang Jiang

“…We propose a lightweight algorithm for detecting surface defects on ceiling fan blades using YOLOv8s, incorporating optimizations and enhancements in model pruning and knowledge distillation. …”
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Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization by Riyadh M. Alzahrani, Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, Eddie Y. K. Ng

“…An Enhanced Particle Swarm Optimization (EPSO) algorithm is integrated to automatically fine-tune CNN hyperparameters, thereby minimizing manual effort and enhancing computational efficiency. …”
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A high-gain THz microstrip patch antenna designed for IoT and 6G communications with predicted efficiency using machine learning approaches by Md Sharif Ahammed, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Narinderjit Singh Sawaran Singh, Md. Ashraful Haque

“…To validate the regression machine learning model for THz MIMO antenna design, a comprehensive dataset was generated using full-wave electromagnetic simulations. …”
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Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

“…Noise profoundly affects the quality of electromagnetic data, and selecting the appropriate hyperparameters for machine learning models poses a significant challenge. …”
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Optimization of thermal efficiency and energy consumption in a hybrid infrared heating technique: Artificial intelligence and computational simulations by Fangfang Liu, Hany S. El-Mesery, Ahmed H. ElMesiry, Mansuur Husein, Amer Ali Mahdi

“…This situation has sparked a quest for innovative dryer designs, emphasizing the need for ongoing experimental research and new decision-support models to optimize drying processes. The application of Artificial Neural Networks (ANNs) to enhance food drying has gained popularity in the industry for optimizing conditions and reducing energy consumption. …”
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Life cycle assessment and multicriteria decision making analysis of additive manufacturing processes towards optimal performance and sustainability by Jayant M. Raut, Prashant B. Pande, Kamlesh V. Madurwar, Rajesh M. Bhagat, Satyajit S. Uparkar, Nilesh Shelke, Haytham F. Isleem, Arpita, Vikrant S. Vairagade

“…This framework overcomes the present limitations of the LCA model by introducing dynamic predictive modeling using Gaussian Process Regression, real-time adaptive decision-making through Stochastic Forest, and multi-objective optimization through Particle Swarm Optimization. …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. …”
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Power System Stabilizer based on Model Predictive Control by Manuel A Duarte-Mermoud, Freddy Milla

“…A model predictive power system stabilizer is proposed in this paper to damp power oscillations in an electric power system (EPS). …”
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