Optimizing Contrastive Learning with Semi-Online Triplet Mining by Przemysław Buczkowski, Marek Kozłowski, Piotr Brzeziński

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Understanding EMS response times: a machine learning-based analysis by Peter Hill, Jakob Lederman, Daniel Jonsson, Peter Bolin, Veronica Vicente

“…Abstract Background Emergency Medical Services (EMS) response times are critical for optimizing patient outcomes, particularly in time-sensitive emergencies. …”
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A Novel Inertia Delay Optimization Control Strategy for New Power Systems Based on Crisscross Optimization by Xue WANG, Lin LIU, Wendi LIU, Yanpeng ZHAI, Ling YANG, Fangyuan XU, Yan GAO, Jixin ZHANG

“…Therefore, this study first builds a model of a three-machine, nine-node system where VSGs are the primary controllers and conducts transient simulations using differential equations. …”
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Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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Chamber shape optimization for ultra-high-pressure water-jet nozzle based on computational fluid dynamics method and a data-driven surrogate model by Wen-Tao Zhao, Zheng-Shou Chen, Yuan-Jie Chen, Jiang-Long Li

Subjects: “…Nozzle chamber optimization…”
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Pharmacokinetic and pharmacodynamic analyses of nafamostat in ECMO patients: comparing central vein and ECMO machine samples by Dong Hwan Lee, Jae Ha Lee, Ji Hoon Jang, Yong Kyun Kim, Gaeun Kang, So Young Jung, Minyoung Her, Hang Jea Jang

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Application of machine learning in predicting adolescent Internet behavioral addiction by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…ObjectiveTo explore the risk factors affecting adolescents’ Internet addiction behavior and build a prediction model for adolescents’ Internet addiction behavior based on machine learning algorithms.MethodsA total of 4461 high school students in Chongqing were selected using stratified cluster sampling, and questionnaires were administered. …”
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Optimization of compressor unit operation using artificial intelligence technologies by Alexander E. Vlasov, Lyubov V. Lazareva

“…The work examined the possibility of using neural networks and machine learning algorithms to optimize fuel gas consumption by a compressor unit. …”
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Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

“…A comparative study with traditional diagnostic methods demonstrated that the proposed model excelled in both fault classification accuracy and adaptability across operating conditions. …”
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Credit card fraud Detection using Feature select method and improved machine learning algorithm by Mohammed AL-Hammadi

“…The SVM classification model then performs the final classification, with its hyperparameters optimized through the particle swarm optimization (PSO) technique. …”
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SABO-ELM model for remaining life prediction of lithium-ion batteries under multiple health factors by Jiabo LI, Zhonglin SUN, Di TIAN, Zhixuan WANG

“…However, RUL prediction accuracy is affected by factors such as capacity regeneration and model performance. This paper introduces a novel approach that combines advanced signal processing, health feature extraction, and machine learning optimization to improve RUL predictive precision for lithium-ion batteries. …”
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Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications by Seyeon Park, Hojun Lee, Daeun Kim, Hyeun Jun Moon, Seong-Je Cho, Youngsup Hwang, Hyoil Han, Kyoungwon Suh

“…Future research will explore non-machine-learning-based release year prediction methods to further optimize training efficiency and improve adaptability to the rapidly evolving malware detection landscape.…”
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A Novel Optimization Approach for Revolutionizing Architectural Design in Chinese Cultural Heritage by Xueyong Li, Xiaoqing Yang

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Application of Machine Learning Algorithms for Predicting the Dynamic Stiffness of Rail Pads Based on Static Stiffness and Operating Conditions by Isaac Rivas, Jose A. Sainz-Aja, Diego Ferreño, Víctor Calzada, Isidro Carrascal, Jose Casado, Soraya Diego

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BRIDGING TRADITION AND INNOVATION: A LITERATURE REVIEW ON PORTFOLIO OPTIMIZATION by Ștefan RUSU, Marcel BOLOȘ

“…While, from the artificial intelligence realm, techniques such as machine learning efficient frontiers, performance-based regularization, neural network predictors and deep learning models for direct optimization of portfolio Sharpe ratio are highlighted. …”
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Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle by Rachappa Jopate, Piyush Kumar Pareek, DivyaJyothi M. G, Ariam Saleh Zuwayid Juma Al Hasani

“…This research presents the Adaptive Elephant Herd Optimisation Algorithm (AEHOA) model for selecting optimal attributes in order to circumvent these limitations. …”
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Prediction of pulmonary embolism by an explainable machine learning approach in the real world by Qiao Zhou, Ruichen Huang, Xingyu Xiong, Zongan Liang, Wei Zhang

“…To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. …”
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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

“…This study introduces an intelligent concrete mix design method that optimizes performance and integrates machine learning and multi-criteria decision-making techniques. …”
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Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM by Cheng Liu, Weiming Yang

“…Following this, the bald eagle search (BES) intelligent optimization algorithm is utilized to optimize the hyperparameters of the light gradient boosting machine (LGBM) model, further improving its predictive capability. …”
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Optimal Operation of a Tablet Pressing Machine Using Deep-Neural-Network-Embedded Mixed-Integer Linear Programming by Jialong Li, Lan Wu, Yuang Qin, Haojun Zhi

“…This paper presents a deep neural network (DNN)-embedded mixed-integer linear programming (MILP) model for fault prediction and production optimization in tablet pressing machines. …”
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