Optimizing software crowdsourcing requirements design through machine learning by Turki Alelyani

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Machine learning-driven optimization of arsenic phytoextraction using amendments by Huading Shi, Yunxian Yan, Zhaoyang Han, Liang Wang, Guanghui Guo, Jun Yang

“…In this study, we analyzed 2299 data points from 121 published datasets and used machine learning to predict and optimize the performance of amendments to enhance the phytoextraction efficiency. …”
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Machine Learning Optimization and Challenges in Used Car Price Prediction by Zheng Yufan

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Noise-augmented chaotic Ising machines for combinatorial optimization and sampling by Kyle Lee, Shuvro Chowdhury, Kerem Y. Camsari

“…Abstract Ising machines are hardware accelerators for combinatorial optimization and probabilistic sampling, using stochasticity to explore spin configurations and avoid local minima. …”
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Prediction and optimization of struvite recovery from wastewater by machine learning by TONG Ying, JIANG Shaojian, KANG Bingyan, LENG Lijian*, LI Hailong

“…Furthermore, experimental validation was conducted with initial phosphorus concentrations of 10 mg/L and 1000 mg/L to determine the optimized process conditions for struvite recovery using the multi-objective model. …”
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Aerodynamics-guided machine learning for design optimization of electric vehicles by Jonathan Tran, Kai Fukami, Kenta Inada, Daisuke Umehara, Yoshimichi Ono, Kenta Ogawa, Kunihiko Taira

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Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

“…The outcomes establish that machine learning models, particularly XGBoost, can accurately predict welding characteristics, marking a significant advancement in the optimization of TIG welding parameters. …”
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A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification... by Q.Y. Huang, N.D. Dizon, N. Jeyakumar, V. Jeyakumar

“…Based on Wasserstein distributionally robust optimization, we develop computationally feasible robust SVM models along with efficient second-order cone programming methods using an integrated application of tools from convex non-smooth analysis and difference-of-convex optimization. …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…In addition, samples are needed to solve such system optimization problems. Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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The Evolution of Portfolio Theory: Integrating Machine Learning with Markowitz Optimization by Xu Junhao

“…This paper investigates incorporating Machine Learning (ML) techniques into the traditional Markowitz optimization framework to enhance portfolio construction and risk management processes. …”
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Optimal Placement of Wind Power System Using Machine Learning by Abdul Karim, Muhammad Amir Raza, Darakhshan Ara, Muhammad Shahid, Shakir Ali Soomro

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A review on multi-fidelity hyperparameter optimization in machine learning by Jonghyeon Won, Hyun-Suk Lee, Jang-Won Lee

“…Tuning hyperparameters effectively is crucial for improving the performance of machine learning models. However, hyperparameter optimization (HPO) often demands significant computational budget, which is typically limited. …”
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Optimization of machine learning methods for de-anonymization in social networks by Nurzhigit Smailov, Fatima Uralova, Rashida Kadyrova, Raiymbek Magazov, Akezhan Sabibolda

“…In experiments conducted on real and synthetic data, the optimized models consistently outperform baseline methods on average. …”
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Efficiency Optimization of PMSM-Type Elevator Machine: An Industrial Comparison by Mucahit Soyaslan, Osman Eldogan, Ahmet Fenercioglu, Yusuf Avsar, Nurdogan Ceylan, Muhammed Salih Sarıkaya

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Optimization of Geometric Characteristics of Cycloidal Profiles of Gerotor Hydraulic Machines by S. O. Kireev, A. R. Lebedev, M. V. Korchagina

“…Materials included known methods of profile parameters calculation, based on application of classical  formulas  of  hypocycloidal  equidistant  used  for  outlining  profiles  of  teeth  of  working  elements  of gerotor machines. The basic research method was modeling the gerotor machine profile using Mathcad computer mathematics system. …”
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A Review of IoT and Machine Learning for Environmental Optimization in Aeroponics by Muhammad Amjad, Elanchezhian Arulmozhi, Yeong-Hyeon Shin, Moon-Kyung Kang, Woo-Jae Cho

“…IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. …”
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Optimizing Internet of Things Honeypots with Machine Learning: A Review by Stefanie Lanz, Sarah Lily-Rose Pignol, Patrick Schmitt, Haochen Wang, Maria Papaioannou, Gaurav Choudhary, Nicola Dragoni

“…Various classifiers for machine learning are analyzed to optimize honeypot architectures. …”
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Simultaneous OPEX and carbon footprint reduction with hydrogen enhancement in autothermal reforming: a machine learning–based surrogate modeling and optimization framework by Sahar Shahriari, Davood Iranshahi

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Machine learning model optimization for compressional sonic log prediction using well logs in Shahd SE field, Western Desert, Egypt by Khaled Saleh, Walid M. Mabrouk, Ahmed Metwally

“…Model performance is optimized through hyperparameter tuning and evaluated using correlation coefficients and root mean square error (RMSE) metrics. …”
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Bare ground classification using a spectral index ensemble and machine learning models optimized across 12 international study sites by Sarah J. Becker, Megan C. Maloney, Andrew W. H. Griffin, Kristofer Lasko, Heather S. Sussman

“…This research investigates a global approach to map bare ground across diverse geographies with an ensemble of spectral indices using optimal thresholds identified in testing to train and evaluate machine learning models to extract bare ground pixels from Sentinel-2 imagery. …”
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