Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning by Nikolaos A. Fountas, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

“…The results indicated that the feed rate was the dominant parameter affecting both objectives, namely the main cutting force and surface roughness, while the NSGA-II algorithm was capable of delivering advantageous solutions for enhancing machinability with less than 10% error predictions when comparing simulated and actual machining results.…”
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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

“…A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM) with particle swarm optimization (PSO) algorithm for plunger pump in truck crane is proposed. …”
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Machine-Learning-Powered Information Systems: A Systematic Literature Review for Developing Multi-Objective Healthcare Management by Maryam Bagheri, Mohsen Bagheritabar, Sohila Alizadeh, Mohammad (Sam) Salemizadeh Parizi, Parisa Matoufinia, Yang Luo

“…The incorporation of machine learning (ML) into healthcare information systems (IS) has transformed multi-objective healthcare management by improving patient monitoring, diagnostic accuracy, and treatment optimization. …”
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An Explainable Machine Learning-Based Prediction of Backbone Curves for Reduced Beam Section Connections Under Cyclic Loading by Emrah Tasdemir, Mustafa Yavuz Cetinkaya, Furkan Uysal, Samer El-Zahab

“…In particular, flange thickness (<i>t_f</i>), flange width (<i>b_f</i>), and the parameter “<i>c</i>” are critical factors, as the flanges contribute the most substantially to resisting bending moments.…”
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Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds by Gaganpreet Kaur, M. Braveen, Singamaneni Krishnapriya, Surindar Gopalrao Wawale, Jorge Castillo-Picon, Dheeraj Malhotra, Jonathan Osei-Owusu

“…The goal of this study is to provide a machine learning (ML)-based aquaculture solution that boosts prawn growth and production in ponds. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. …”
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Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor by Shiliang Zhou, Menghan Tang, Jun Wu, Chunru Ke

“…For intelligent monitoring of existing terminals, this research chooses Chongqing Xintian Port as the study object and proposes a support vector machine (SVM) damage-inducing factor (DIF) inversion model based on particle swarm optimization (PSO). …”
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Enhancing Structural Health Monitoring of Super-Tall Buildings Using Support Vector Machines, MEMD, and Wavelet Transform by Rouzbeh Doroudi, Seyed Hossein Hosseini Lavasani, Mohsen Shahrouzi, Aref Afshar

“…SVMs efficiently identify damage patterns. However, require parameter tuning, addressed using Observer-Teacher-Learner-Based Optimization. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Low-Carbon Steel Using a Machine Learning Approach by Michael Gyaabeng, Ramadan Ahmed, Nayem Ahmed, Catalin Teodoriu, Deepak Devegowda

“…A machine learning (ML) model was constructed by analyzing data from relevant literature to evaluate the fracture toughness of steels exposed to hydrogen environments. …”
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Advancing in creep index of soil prediction: A groundbreaking machine learning approach with Multivariate Adaptive Regression Splines by Mohammed E. Seno, Husein Ali Zeini, Hamza Imran, Mohammed Noori, Sadiq N. Henedy, Nouby M. Ghazaly

“…Finally, the model's performance was compared to previously developed machine learning models and empirical equations across the entire dataset. …”
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Intelligent Prediction of the Horizontal Deformation During the Excavation Process Based on Particle Swarm Optimisation and Support Vector Machine by Yu Zhang, Chen Zhang, Zhiduo Zhu, Liu Yang, Hao Tang

“…The aim of this paper is to obtain suitable soil layer parameters for finite element simulation of highway tunnel based on the particle swarm optimisation (PSO) and support vector machine (SVM). …”
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Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography by Mohamed Jaber, Robert D. Breininger, Farag Hamad, Nezamoddin N. Kachouie

“…An important parameter in the monitoring and surveillance systems is the probability of detection. …”
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An Efficient Network-Based QoE Assessment Framework for Multimedia Networks Using a Machine Learning Approach by Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, Abolfazl Diyanat

“…The framework leverages Machine Learning (ML) to model the relationship between network parameters and QoE, providing a scalable and efficient solution for real-time QoE evaluation in multimedia networks.By focusing exclusively on network metrics (e.g., delay, jitter, and packet loss), it eliminates the need for video-specific parameters to calculate MOS. …”
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A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing by Chin-Yi Lin, Tzu-Liang (Bill) Tseng, Tsung-Han Tsai

“…The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. …”
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Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…PurposeTo develop effective machine learning models that analyze pattern visual evoked potentials (PVEPs) to predict the stabilized visual acuity (VA) of patients with treated ocular trauma.MethodsThis experiment included 260 patients (220 males, average age 42.54 years) with unilateral ocular trauma. …”
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Real-Time Detection, Evaluation, and Mapping of Crowd Panic Emergencies Based on Geo-Biometrical Data and Machine Learning by Ilias Lazarou, Anastasios L. Kesidis, Andreas Tsatsaris

“…In this paper, we propose a real-time system for detecting and mapping crowd panic emergencies based on machine learning and georeferenced biometric data from wearable devices and smartphones. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The clinical community has a lot of diabetes diagnostic data. Machine learning algorithms may simplify finding hidden patterns, retrieving data from databases, and predicting outcomes. …”
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Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests by Min Fang, Chengjie Pan, Xiaoyi Yu, Wenjuan Li, Ben Wang, Huajian Zhou, Zhenying Xu, Genyuan Yang

“…Subsequently, a risk prediction model is constructed based on the parameter optimization of five machine learning models using the PSO algorithm. …”
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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

“…This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. …”
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