Low-emission methane fueled dual-bypass turbofan engine optimization based on machine learning: Energy-economic-environmental (3E) analysis by Mohammadreza Sabzehali, Mahdi Alibeigi, Saeed Karimian Aliabadi

“…The optimum design point of the proposed engine has been drawn based on optimization. The proposed methodology and the mathematical model presented here, could be assumed as a basis for comprehensive analysis of the dual bypass engine. …”
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Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…In the second set, rake angle, cutting angle and nose radius of the tool insert are varied and roughness of the machined components is measured. The machining performance indicators of the first set are optimized using graphical method of contour plots. …”
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Evaluation and Optimization of Urban Street Spatial Quality Based on Street View Images and Machine Learning: A Case Study of the Jinan Old City by Peipei Li, Yabing Xu, Zichuan Liu, Haitao Jiang, Anzhen Liu

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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

“…It is assumed that the improvement of ML-based models and their introduction into clinical practice will help support medical decision-making, increase the effectiveness of treatment and optimize health care costs.…”
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Optimized YOLO Model for Accurate and Real-Time Detection of Machinery Around Shovels in Copper Mining by Mohaddeseh Ghiasi, MasoudReza Aghabozorgi

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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

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Optimizing the early-stage of composting process emissions – artificial intelligence primary tests by Joanna Rosik, Maciej Karczewski, Sylwia Stegenta-Dąbrowska

“…One promising approach to enhancing composting conditions is using novel analytical methods based on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during the early-stage of composting process machine learning (ML) models were utilized. …”
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optRF: Optimising random forest stability by determining the optimal number of trees by Thomas M. Lange, Mehmet Gültas, Armin O. Schmitt, Felix Heinrich

“…Based on these findings, we have developed the R package optRF which models the relationship between the number of trees and the stability of random forest, providing recommendations for the optimal number of trees for any given data set.…”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …”
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Optimizing translation for low-resource languages: Efficient fine-tuning with custom prompt engineering in large language models by Pitso Walter Khoboko, Vukosi Marivate, Joseph Sefara

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Fractional Intuitionistic Fuzzy Support Vector Machine: Diabetes Tweet Classification by Hassan Badi, Alina-Mihaela Patriciu, Karim El Moutaouakil

“…Support vector machine (SVM) models apply the Karush–Kuhn–Tucker (KKT-OC) optimality conditions in the ordinary derivative to the primal optimisation problem, which has a major influence on the weights associated with the dissimilarity between the selected support vectors and subsequently on the quality of the model’s predictions. …”
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Optimizing Solar Radiation Prediction Based on The Internet of Things Platform in Photovoltaic Power Plant by Neda Ashrafi Khozani, Maryam Mahmoudi, Shabnam Nasr Esfahani

“…Employing meta-heuristic methods as the main innovation in this research not only optimizes machine learning model settings but also mitigates the impact of noise, outliers, and ineffective inputs, thereby enhancing the final output quality. …”
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TO THE QUESTION OF OPTIMISING THE DYNAMIC CHARACTERISTICS OF A VIBRATIONAL TREE UPROOTING MACHINE by A. R. Mikhitarov, V. L. Savich, V. K. Khegai

“…To solve this problem, a mathematical model of the “machine-tree-soil-root system” system was developed, which takes into account the mutual influence of the dynamic characteristics of the machine’s technological equipment and tree and soil-root system, which allows a rational (optimal) frequency range of vibration equipment to be selected by analysing the amplitude-frequency characteristics of a given system. …”
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Study on the temperature prediction model of residual coal in goaf based on ACO-KELM by ZHAI Xiaowei, WANG Chen, HAO Le, LI Xintian, HOU Qinyuan, MA Teng

“…To address this gap, a prediction model based on ant colony optimization (ACO) and kernel extreme learning machine (KELM) (ACO-KELM) was proposed. …”
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Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms by Xiaohua Wan, Ruihuan Zhang, Yanan Wang, Wei Wei, Biao Song, Lin Zhang, Yanwei Hu

“…Using 39 optimal variables, a prediction model was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm and compared with four other algorithms: support vector machine (SVM), gradient boosting decision tree (GBDT), neural network (NN), and logistic regression (LR). …”
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HirePool: Optimizing Resource Reuse Based on a Hybrid Resource Pool in the Cloud by Runqun Xiong, Xiuyang Li, Jiyuan Shi, Zhiang Wu, Jiahui Jin

“…In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. …”
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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…This highlights the significance of selecting an optimal machine learning algorithm for a particular predictive task.…”
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Development of a Prognostic Nomogram for Nonneutropenic Invasive Pulmonary Aspergillosis Based on Machine Learning by Du W, Ji W, Luo T, Zhang Y, Guo W, Liang J, Lv Y

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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

“…The XGBoost model predicted bandgaps, yielding 99 lead-free double perovskites with ideal bandgaps (1.3~1.4 eV). …”
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Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn by Josselin Bontemps, Isa Ebtehaj, Gabriel Deslauriers, Alain N. Rousseau, Hossein Bonakdari, Jacynthe Dessureault-Rompré

“…In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). …”
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