Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients by Yong Si, Armin Abdollahi, Negin Ashrafi, Greg Placencia, Elham Pishgar, Kamiar Alaei, Maryam Pishgar

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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. …”
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Optimizing base fluid composition for PEMFC cooling: A machine learning approach to balance thermal and rheological performance by Praveen Kumar Kanti, Prashantha Kumar H. G, Nejla Mahjoub Said, V. Vicki Wanatasanappan, Prabhu Paramasivam, Leliso Hobicho Dabelo

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A Bi-Level Programming-Based Method for Service Composition Optimization of Collaborative Manufacturing of Sewing Machine Cases by Gan Shi, Shanhui Liu, Keqiang Shi, Langze Zhu, Zhenjie Gao, Jiayue Zhang

“…We also introduce the idea of bi-level programming, construct the optimization model of manufacturing service composition of sewing machine cases based on bi-level planning, analyze the characteristics of NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm II) algorithm and the improvement strategy, and complete the solution of the optimization model of manufacturing service composition of sewing machine cases. …”
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Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques by Xuemei Wang, Ronghua Liu, Chaoxing Sun, Xiaoyan Zhai, Liuqian Ding, Xiao Liu, Xiaolei Zhang

“…Study region: China Study focus: We developed machine learning (ML) models for design flood estimation in mountainous catchments (≤ 500 km²) across China. …”
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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

“…Also, Random Forest model yielded a higher test MSE of 0.0045 and MRE of 17.83%. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. …”
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COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA by Fadilah Amirul Adhel, Sri Astuti Thamrin, Siswanto Siswanto

“…This shows that the SVM classification method with firefly optimization provides quite good classification performance compared to the SVM model without optimization.…”
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Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis by Süleyman Burçin Şüyun, Mustafa Yurdakul, Şakir Taşdemir, Serkan Biliş

“…In the second stage, the deep features obtained from these three models were combined and classified using machine learning (ML) algorithms including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm by Wenliao Du, Ansheng Li, Pengfei Ye, Chengliang Liu

“…The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN), ant colony optimization artificial neural network (ANT-ANN), RVM, and support vectors, machines with particle swarm optimization (PSO-SVM), respectively. …”
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Optimizing guest experience in smart hospitality: Integrated fuzzy-AHP and machine learning for centralized hotel operations with IoT by Sinan Andan Diwan

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Multi-target property prediction and optimization using latent spaces of generative model by Anirudh Jain, Markus Heinonen, Heikki Käsnänen, Julius Sipilä, Samuel Kaski

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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…The hyperparameters of the model are optimized using the Bayesian optimization algorithm to obtain the best performance. …”
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Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface by Nobuaki Kobayashi, Musashi Ino

“…Therefore, by optimizing the MI measurement conditions and various parameters of the deep-learning model, we attempted to reduce the power consumption and improve the response latency of the system by minimizing the computational cost while maintaining high classification accuracy. …”
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Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration by Runcheng Fang, Scott Collingwood, Yue Zhang, Joseph B. Stanford, Christina Porucznik, Darrah Sleeth

“…This study compares linear regression (LR) and machine learning (ML) techniques, particularly random forest (RF), to determine optimal calibration strategies. …”
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Optimization of Hybrid Machining of Nomex Honeycomb Structures: Effect of the CZ10 Tool and Ultrasonic Vibrations on the Cutting Process by Oussama Beldi, Tarik Zarrouk, Ahmed Abbadi, Mohammed Nouari, Jamal-Eddine Salhi, Mohammed Abbadi, Mohamed Barboucha

“…These improvements thus contribute to a substantial optimization of the overall efficiency of the machining process.…”
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Process parameter optimization of laser beam machining for AISI -P20 mold steel using ANFIS method by Abdullah Eaysin, Sarower Kabir, Ebru Gunister, Nur Jahan, Amir Hamza, Muhammad Ali Zinnah, Adib Bin Rashid

“…This study employed a fiber laser beam for precise machining of AISI P20 mold steel. The experimental design, based on the Taguchi 27 model, was carried out using Minitab software to optimize machining parameters, including cutting speed, gas pressure, and laser power. …”
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Optimizing WEDM Parameters Using Swarm Intelligence: A Multi‐Objective Approach to Improve Machinability and Cost‐Efficiency by Abhijit Bhowmik, Raman Kumar, Nikunj Rachchh, T. Ramachandran, A. Karthikeyan, Rahul Singh, Deepak Gupta, Dhirendra Nath Thatoi, Bhavik Jain, A. Johnson Santhosh

“…The research leverages Particle Swarm Optimization (PSO) to improve machining outcomes, including material removal rate, surface finish, and cost‐efficiency. …”
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An Impact of Technological Factors on the Kinematic Accuracy of Cylindrical Gear Wheels during Machining by Tetiana Halchuk, Oleksandr Povstyanoy, Michal Bembenek, Bohdan Valetskyi, Roman Polinkevych, Bohdan Smetjukh

“…During the research, metrological means and methods of gear wheel control were implemented. A model for the rational selection of technological modes for machining was developed to ensure the required kinematic accuracy of the gear after its milling with a worm cutter due to the error of the accumulated step and to reduce the number of experiments. …”
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