Assessing the feasibility of using Machine learning algorithms to determine reservoir water quality based on a reduced set of predictors by Natalia Walczak, Zbigniew Walczak

“…The construction and training of ML models for reduced sets and types of predictors will enable early water quality estimation based on only a few selected parameters. The implementation of ML algorithms will enable more effective water quality management and significantly improve the precision of predictions for critical water parameters.…”
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Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation by Milad Shoryabi, Ahmad Hajipour, Afshin Shoeibi, Ali Foroutannia

“…Key kinematic features such as step length, step width, cadence, and eight additional gait parameters were extracted from the recorded data. Various machine learning techniques were applied to classify and estimate anxiety and depression levels, including Linear Discriminant Analysis (LDA), Naive Bayes, Multi-class Support Vector Machines (SVM) with a polynomial kernel, and a Deep Neural Network (DNN) for classification. …”
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A Machine Learning Method for the Fast Simulation of the Scattering Characteristics of a Target Under a Planar Layered Medium by Zhaoyu Wang, Qinghe Zhang, Zhaoyang Shen, Lei Zhang, Han Liu

“…The numerical results demonstrate that, compared with traditional machine learning methods, the cyclic nested machine learning network architecture offers higher prediction accuracy and learning efficiency, validating the effectiveness of the proposed method.…”
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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm by Yang Yang, Huiwen Hou, Gang Yao, Bo Wu

“…To predict the VIV performance of a double-deck steel truss (DDST) girder with additional aerodynamic measures, the VIV response of a DDST bridge was investigated using wind tunnel tests and numerical simulation, a learning sample database was established with numerical simulation results, and a prediction model for the amplitude of the DDST girder and VIV parameters was established based on three machine learning algorithms. …”
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FEATURES OF THE OPERATION OF A WIDE-CUT FRONTAL-MOVING IRRIGATION MACHINE AND JUSTIFICATION OF THE FORCE ANALYSIS OF THE CENTRAL ARTICULATED SUPPORT by Alexey V. Kravchuk, Boris N. Beltikov, Tatyana A. Pankova

“…When substantiating the technical and operational parameters of the design, a scientific method for calculating the traction load on the structure of the central support of wide-cut frontal irrigation machines from the water supply movable pipe, was used. …”
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Bidirectionally Coupled FE-CFD Simulation Study on MQL Machining Process of Ti-6Al-4V Alloy by Xiaorong Zhou, Lin He, Sen Yuan, Hongwan Jiang, Jing Deng, Feilong Du, Jingdou Yang, Zebin Su

“…Since fluid flow characteristics critically influence tribological and thermal management at the tool–workpiece interface during machining, CFD simulations were initially performed to evaluate how MQL parameters govern fluid flow behavior. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…However, no automated system exists that can effectively recommend LS parameters to reduce material waste, which is often neglected in traditional methods. …”
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Machine Learning Models for Predicting Seismic Response of a Novel Two-Stage Friction Pendulum Isolated Bridge Structure by Hanzlah Akhlaq, Tianbo Peng, Kawsu Jitteh, Muhammad Salman Khan

“…This study proposes a Machine Learning (ML) approach as a faster and simplified alternative to NLTHA for predicting the structural response of TSFPB-isolated bridge structures. …”
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Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification by Xiaorou Zheng, Jianxin Jia, Shoubin Dong, Yawei Wang, Runuo Lu, Yuwei Chen, Yueming Wang

“…To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. Through mathematical reasoning, TEAF models the training and inference time as functions (ψ) of complex data scales and core model parameters. …”
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Process compatibility analysis for hybrid electrochemical removal and accretion towards a multifunctional machine-tool: An application-oriented approach by Muhammad Hazak Arshad, Anton Peeters, Xiaolei Chen, Dominiek Reynaerts, Krishna Kumar Saxena

“…The experiments examined the influence of process parameters and investigated process compatibility to demonstrate the potential of this hybrid technique. …”
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Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making by Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

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Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization by Longjian Li, Jinchuan Jie, Xiaoyu Guo, Gaojie Liu, Huijun Kang, Zongning Chen, Enyu Guo, Tongmin Wang

“…Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. …”
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Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery by E. Z. Golukhova, M. A. Keren, T. V. Zavalikhina, N. I. Bulaeva, D. S. Akatov, I. Yu. Sigaev, K. B. Yakhyaeva, D. A. Kolesnikov

“…To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data.Material and methods. …”
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Biofilm Formation in Dairy: A Food Safety Concern—Biofilms in the milking machine, from laboratory scale to on-farm results by Chloé Desmousseaux, Morgan Guilbaud, Gwenaëlle Jard, Hélène Tormo, Nadia Oulahal, Aurélie Hanin, Erwan Bourdonnais, Piyush Kumar Jha, Cécile Laithier

“…This work aims to describe biofilms in milking machines at both the laboratory and farm scales. Encouraging studies on the microbiota of milking machine biofilms, the parameters influencing changes in biofilm composition, and the methods used to characterize them are essential for managing the formation and composition of these biofilms. …”
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Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach [version 2; peer review: 2 approved] by Erica Agudelo-Ceballos, Ezequiel Martínez Rojas, Sebastián Cardona-Acevedo, Carlos Flores Goycochea, Juana De La Cruz Ramírez Dávila, Diana Arango-Botero, Jesus Alberto Jimenez Garcia, Alejandro Valencia-Arias

“…However, there are still several research gaps, so the objective is to examine the research trends in the use of machine learning in marketing. A bibliometric analysis is proposed to assess the current scientific activity, following the parameters established by PRISMA-2020. …”
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Aquatic system assessment of potentially toxic elements in El Manzala Lake, Egypt: A statistical and machine learning approach by Asmaa Nour Aly Al-Falal, Salah Elsayed, Ezzat A. El Fadaly, Aissam Gaagai, Hani Amir Aouissi, Mohamed S. Abd El-baki, Mohamed Hamdy Eid, Abdallah Elshawadfy Elwakeel, Zaher Mundher Yaseen, Osama Elsherbiny, E.I. Eltahir, Mohamed Gad

“…These indices were refined using multivariate techniques, such as Principal Component Analysis (PCA) and Cluster Analysis (CA). Additionally, six machine learning models, including Multiple Linear Regression (MLR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting Regression (AdaBoost), and Multilayer Perceptron (MLP), were developed to predict water quality parameters. …”
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Machine learning and parametrisation of multi-cell structures of secondary circulation in a tight open channel bend using LES by H. Katie Schreiner, Abdolmajid Mohammadian, Colin D. Rennie

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Prediction of asthma outpatients using cumulative particulate matter and machine learning algorithms: a case study in Adiyaman, Turkey by Abdurrahman Özbeyaz, Mustafa Yıldırım, Fatih Tufaner

“…Using PM10 data from the Adıyaman urban region, we built machine learning models to estimate asthma prevalence. …”
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An Active-Set Algorithm for Convex Quadratic Programming Subject to Box Constraints with Applications in Non-Linear Optimization and Machine Learning by Konstantinos Vogklis, Isaac E. Lagaris

“…The algorithm, at each iteration, modifies both the minimization parameters in the primal space and the Lagrange multipliers in the dual space. …”
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Public Sentiment Analysis of Nadiem Makarim as Minister of Education, Culture, Research, and Technology using Support Vector Machine (SVM) by Shasha Ramadhani Putri, Muhammad Arifin, Supriyono Supriyono

“…The methodology employs a Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) through three scenarios: tuning TF-IDF parameters, selecting the best SVM kernel, and applying the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. …”
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