Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9870</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.44</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>NSE</mi><mo>=</mo><mn>0.9651</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>KGE</mi><mo>=</mo><mn>0.8950</mn></mrow></semantics></math></inline-formula>), followed closely by CatBoost (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9826</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.83</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>). …”
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…The generalized soft sensor can then be fine-tuned and validated for a particular machine. In this paper, we test the effects of data aggregation for high performing Feedforward Neural Network (FNN) models that are constructed in two ways: first as a classifier to estimate product PASS/FAIL outcomes and second as a regressor to quantitatively estimate oxide thickness. …”
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Cutting-Edge Phishing Detection Using Novel Features and Hybrid Machine Learning Techniques by Jin Zhang

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Optimizing corn yield prediction: Integrating multi-temporal UAS data and machine learning by Huihui Zhang, Yuting Zhou, Shengfang Ma, Kevin Yemoto

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Tensor tree learns hidden relational structures in data to construct generative models by Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima

Subjects: “…generative modeling…”
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Swin-PSO-SVM: A Novel Hybrid Model for Monkeypox Early Detection by Hager Saleh, Sherif Mostafa, Alshimaa H. Ismail, Sumit Srivastava, Saeed Hamood Alsamhi

“…We introduce a novel hybrid Swin-PSO-SVM to improve the precision and dependability of monkeypox detection. The Swin-PSO-SVM model incorporates a Swin Transformer for complex feature extraction, Particle Swarm Optimization (PSO) for extracting the best features from complex feature extraction, and a Support Vector Machine (SVM) for accurate classification. …”
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Optimization of Multi-Source Remote Sensing Soil Salinity Estimation Based on Different Salinization Degrees by Huifang Chen, Jingwei Wu, Chi Xu

“…Specifically, this study integrated in situ hyperspectral data and satellite multispectral data using spectral response functions. Subsequently, machine learning methods such as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR) were employed, in combination with sensitive spectral indices, to develop a multi-source remote sensing soil salinity estimation model optimized for different salinization degrees (mild or lower salinization vs. moderate or higher salinization). …”
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Optimization design of cam linkage mechanism for paper folding section of carton folding machine by XIE Dingyue, DENG Yuanchao, CAI Yufei, SONG Zhicheng

“…Using the NSGA-Ⅱ optimization algorithm, perform multi-objective optimization design on the cam linkage mechanism and select the optimal solution from the generated Pareto solution set. …”
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Servo System Control of Turntable Lipstick-Filling Machine Based on Particle Swarm Optimization by Xiangqiang Zhong, Wenbo Tao, Lingling Zou, Huaian Yang, Shunyu Yao

“…In order to improve the production quality of the lipstick-filling machines and the performance of the equipment, the structure of a turntable lipstick-filling machine was designed and the servo control was optimized, considering the known production defects. …”
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MIMO antenna optimization for millimeter-wave 5G applications using machine learning by Masuod Ghasemi

“…Abstract This paper presents the design and machine learning-based optimization of a compact single-port MIMO antenna operating at 37 GHz for millimeter-wave 5G applications. …”
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Pre-design optimization of the manipulation system with hydraulic drive for mobile transport-technological machine by Lagerev I.A.

“…In the article has developed an optimization mathematical model and methodology of selection of optimal parameters of manipulation of systems of transport and technological machines, which allows to increase their service performance and competitiveness. …”
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Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine by Ajeet Singh, Anurag Jain, Seblewongel Esseynew Biable

“…In the first level, the firefly algorithm (FFA) and the CfsSubsetEval feature section method have been applied to optimize the subset of features, while in the second level, the support vector machine classifier has been used to build the training model for the detection of credit card fraud cases. …”
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Optimal design of frame structures equipped with viscous dampers using machine learning techniques by Yi Wen, Jianze Wang, Jun Xu, Kaoshan Dai, Yuanfeng Shi, Reza Sharbati

“…To generate the dataset for the XGBoost model development, an automated modeling and simulation tool for damping structures was developed. …”
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Optimizing Xylanase Production: Bridging Statistical Design and Machine Learning for Improved Protein Production by Merve Aslı Ergün, Başak Esin Köktürk-Güzel, Tuğba Keskin-Gündoğdu

“…Machine learning methods have emerged as effective alternatives to traditional statistical approaches in optimization. …”
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Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization by Dedy Rahman Wijaya, Nailatul Fadhilah Syarwan, Muhammad Agus Nugraha, Dahliar Ananda, Tora Fahrudin, Rini Handayani

“…This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. Machine learning algorithms are used for classification and regression tasks. …”
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The Optimization of Culture Conditions for Injectable Recombinant Collagen Hydrogel Preparation Using Machine Learning by Mengyu Li, Long Zhao, Yanan Ren, Linfei Zuo, Ziyi Shen, Jiawei Wu

“…In this study, a machine learning approach was introduced to explore the correlation between hydrogel characteristics and culture conditions and determine the optimal culture conditions. …”
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Optimizing EDM process parameters with the use of GRA-based RSM for machining titanium alloy by Velusamy Mohankumar, Marudhamuthu Shanmugam Senthil Kumar, Aruchamy Karthik, Amanuel Kumsa Bojer, Enas Ali, Sherif S.M. Ghoneim, Ahmed B. Abou Sharaf

“…This study optimizes the electrical discharge machining (EDM) process for Ti-6Al-4 V alloy using Response Surface Methodology (RSM) and Grey Relational Analysis (GRA). …”
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Network Intrusion Detection Using Knapsack Optimization, Mutual Information Gain, and Machine Learning by Akindele S. Afolabi, Olubunmi A. Akinola

“…Protection against such attacks may be achieved through the use of an intrusion detection system (IDS), which can be designed by training machine learning models to detect cyberattacks. In this paper, the KOMIG (knapsack optimization and mutual information gain) IDS was developed to detect network intrusions. …”
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Variational Autoencoder-Based Multiobjective Topology Optimization of Electrical Machines Using Vector Graphics by Michael Heroth, Helmut C. Schmid, Magda Gregorova, Rainer Herrler, Wilfried Hofmann

“…This paper presents an innovative approach to simultaneously model and optimize different geometries of electrical machines. …”
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Machine Learning-Driven Optimization for Solution Space Reduction in the Quadratic Multiple Knapsack Problem by Diego Yanez-Oyarce, Carlos Contreras-Bolton, Fredy Troncoso-Espinosa, Carlos Rey

“…One way to improve the performance of these exact approaches is by reducing the solution space in different instances, considering the properties of the items in the context of QMKP. In this paper, machine learning (ML) models are employed to support an exact optimization solver by predicting the inclusion of items with a certain level of confidence and classifying them. …”
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