Forecasting outbound student mobility: A machine learning approach. by Stephanie Yang, Hsueh-Chih Chen, Wen-Ching Chen, Cheng-Hong Yang

“…<h4>Methods</h4>To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy.…”
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A hybrid model combining environmental analysis and machine learning for predicting AI education quality by Xinyu Ren

“…Abstract Due to the evolution of the management system of educational institutions, the need to use artificial intelligence-based tools in the university is felt more and more. …”
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Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia by Yi Yang, Yao Dong, Yanhua Chen, Caihong Li

“…However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. …”
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Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study by C. Banerjee, K. Nguyen, C. Fookes, G. Hancock, T. Coulthard

“…Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. …”
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Research on Stacking Distribution of Steel Plates Input Based on Improved Multi-objective Particle Swarm Optimization by ZHONG Chuanjie, CHENG Wenming, DU Run, GAO Xuchao, ZHANG Lin

“…The distribution of Pareto solution sets from different algorithms further illustrated these conclusions. …”
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A numerical framework for simulating fluid-structure interaction phenomena by A. De Rosis, S. de Miranda, C. Burrafato, F. Ubertini

“…The Immersed Boundary method is used to account for the presence of an immersed solid in the lattice fluid background and to handle fluid-structure interface conditions, while a Volume-of-Fluid-based method is adopted to take trace of the evolution of the free surface. These ingredients are combined through a partitioned staggered explicit strategy, according to an efficient and accurate algorithm recently developed by the authors. …”
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DMSO-free cryopreservation of hiPSC-derived cardiomyocytes: low temperature characterization and protocol development by Akshat S. Mallya, Tessa Burrows, Jeanne Hsieh, Troy Louwagie, James R. Dutton, Brenda M. Ogle, Allison Hubel

“…Subsequently, biophysical characterization of the cells was performed. A differential evolution (DE) algorithm was utilized to determine the optimal composition of a mixture of a sugar, sugar alcohol and amino acid to replace DMSO as the CPA. …”
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Self-Optimization of Coverage and Capacity in LTE Networks Based on Central Control and Decentralized Fuzzy Q-Learning by Jingyu Li, Jie Zeng, Xin Su, Wei Luo, Jing Wang

“…This paper proposes a central control mechanism that utilizes the fuzzy Q-learning algorithm in a decentralized fashion for this task. …”
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A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma by Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang

“…The single-individual Levenberg–Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters. …”
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Compensation for vibration signal power variance when changing equipment speed mode by D. A. Kechik

“…Vibration power dependencies on the main shaft speed have been estimated in different frequency ranges. The paper proposes an algorithm compensating variations of instantaneous power of vibration signal where equipment operation speed varies. …”
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Balancing mitigation strategies for viral outbreaks by Hamed Karami, Pejman Sanaei, Alexandra Smirnova

“…Our innovative approach to modeling the cost of control gave rise to a robust computational algorithm for solving optimal control problems associated with different public health regulations. …”
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Smart Odour Sensing for Automated Monitoring of Bread Products by Carmen Bax, Bianca di Diodoro, Alessandro Ticozzi, Nicolò Dellarosa, Flavio Corazza, Giacomo Langfelder, Laura Capelli

“…This work proposes an electronic nose (e-nose) system based on resistive gas sensors to predict the cooking evolution of different types of bread. The e-nose includes six metal-oxide semiconductor (MOS) gas sensors, a low-noise electronic system for signal conditioning and data acquisition, and a classification algorithm for real-time detection of the cooking stage. …”
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General Three-Body Problem in Conformal-Euclidean Space: New Properties of a Low-Dimensional Dynamical System by Ashot S. Gevorkyan, Aleksander V. Bogdanov, Vladimir V. Mareev

“…An effective mathematical algorithm is developed for the numerical simulation of the general three-body problem, which is traditionally a difficult-to-solve system of stiff ordinary differential equations.…”
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Research on incremental deployment mechanism of network modality for polymorphic network environment by Jiong LI, Yuxiang HU, Pengshuai CUI, Le TIAN, Yongji DONG

“…Polymorphic networks support diverse network requirements and can coexist, evolve, and change on a unified physical or logical infrastructure in the form of network modality.They can maximize the innovation vitality of network technology driven by new applications.The evolution and development of modality require the incremental deployment of modality to meet the needs of convenience and privacy protection in the multi-user deployment process.However, the deployment of current network functions still uses the one-by-one deployment method, resulting in high complexity of the deployment of new modality, and mutual exposure of processing logic between different users.To address these issues, an incremental deployment mechanism for polymorphic network environments was proposed.Firstly, an incremental deployment mechanism for network modality was designed.Then, the incremental deployment of modality was abstracted as a graph merging problem based on this mechanism, and an adjacency list-based merge parsing graph algorithm was designed.The simulation results indicate that the proposed mechanism can achieve fast and correct merging of network modality, support incremental modality deployment, and greatly reduce the application deployment and service provision threshold of new network systems.…”
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Research on incremental deployment mechanism of network modality for polymorphic network environment by Jiong LI, Yuxiang HU, Pengshuai CUI, Le TIAN, Yongji DONG

“…Polymorphic networks support diverse network requirements and can coexist, evolve, and change on a unified physical or logical infrastructure in the form of network modality.They can maximize the innovation vitality of network technology driven by new applications.The evolution and development of modality require the incremental deployment of modality to meet the needs of convenience and privacy protection in the multi-user deployment process.However, the deployment of current network functions still uses the one-by-one deployment method, resulting in high complexity of the deployment of new modality, and mutual exposure of processing logic between different users.To address these issues, an incremental deployment mechanism for polymorphic network environments was proposed.Firstly, an incremental deployment mechanism for network modality was designed.Then, the incremental deployment of modality was abstracted as a graph merging problem based on this mechanism, and an adjacency list-based merge parsing graph algorithm was designed.The simulation results indicate that the proposed mechanism can achieve fast and correct merging of network modality, support incremental modality deployment, and greatly reduce the application deployment and service provision threshold of new network systems.…”
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Classification of Fungal Pigments by Simulating Their Optical Properties Using Evolutionary Optimization by Roman Y. Pishchalnikov, Denis D. Chesalin, Vasiliy A. Kurkov, Andrei P. Razjivin, Sergey V. Gudkov, Andrey A. Grishin, Alexey S. Dorokhov, Andrey Yu. Izmailov

“…Based on the differential evolution algorithm and semiclassical quantum simulations, we have recently proposed a method for classifying and analyzing the optical properties of organic pigments. …”
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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

“…Using three evolutionary algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) we optimize two ensemble models: XGBoost and LightGBM. …”
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Secure Data Transmission Using GS3 in an Armed Surveillance System by Francisco Alcaraz-Velasco, José M. Palomares, Fernando León-García, Joaquín Olivares

“…Nowadays, the evolution and growth of machine learning (ML) algorithms and the Internet of Things (IoT) are enabling new applications. …”
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Energy Storage Configuration Optimization of a Wind–Solar–Thermal Complementary Energy System, Considering Source-Load Uncertainty by Guangxiu Yu, Ping Zhou, Zhenzhong Zhao, Yiheng Liang, Weijun Wang

“…Then, wind and solar output, as well as loads, are treated as fuzzy variables based on fuzzy chance constraints, and uncertainty constraints are transformed using clear equivalence class processing to establish a bi-level optimization model that considers uncertainties. A differential evolution algorithm and CPLEX are used for solving the upper and lower levels, respectively. …”
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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…In this study, the integration of various machine learning algorithms with Bayesian optimisation, firefly algorithm, Levenberg-Marquardt, and differential evolution techniques were investigated for hydrogen production via thermocatalytic methane decomposition. …”
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