Comparative analysis of machine learning platforms to optimize DevOps: application of the Neutrosophic OWA-TOPSIS model by Miguel Angel Quiroz Martinez, Keyko Garces Salazar, Joshua Montesdeoca Soriano, Mónica Gomez-Rios

“…As software systems and their associated information become increasingly complex within DevOps environments, Machine Learning (ML) platforms are growing in importance for optimizing development and deployment processes. …”
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Comparison and Optimization of Generalized Stamping Machine Fault Diagnosis Models Using Various Transfer Learning Methodologies by Po-Wen Hwang, Yuan-Jen Chang, Hsieh-Chih Tsai, Yu-Ta Tu, Hung-Pin Yang

“…Specifically, the model is designed to monitor four distinct machine models: OCP-110, G2-110, G2-160, and ST1-110. …”
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Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology by Qiong Wang, Zuohu Chen, Yongbo Zhou, Zhiyuan Liu, Zhenguo Peng

“…In response to the problems of insufficient model accuracy and poor real-time performance in real-time monitoring and optimization of data models in traditional power systems, this paper explored them based on machine learning. …”
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Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

Subjects: “…Multi-objective optimization…”
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Artificial liver classifier: a new alternative to conventional machine learning models by Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid

“…IntroductionSupervised machine learning classifiers sometimes face challenges related to the performance, accuracy, or overfitting.MethodsThis paper introduces the Artificial Liver Classifier (ALC), a novel supervised learning model inspired by the human liver's detoxification function. …”
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Comparative Analysis of Inner and Outer Rotor Surface PM Machines Based on Optimization Dataset by Dong Myeong Choi, Seun Guy Min

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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

Subjects: “…machine learning…”
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Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods by Wenchao Li, Houmin Li, Cai Liu, Kai Min

“…Therefore, in this study, three machine learning (ML) models, a Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting Machine (XGBoost), are constructed, and the Hybrid Snake Optimization Algorithm (HSOA) is proposed, which can reduce the risk of the ML model falling into the local optimum while improving its prediction performance. …”
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Optimization strategy of virtual machine online migration with awareness of application characteristics and network bandwidth migration by Xiang LI, Ning-jiang CHEN, Shang-lin YANG, Hua LI

“…Firstly the experiments to verify the relationship between the number of dirty memory pages and application characteristics which exist in virtual machine migration was conducted.Then,different virtual machine application characteristics were perceived,with which the number of dirty memory pages produced during the migrations was predicted by the use of GM(1,N) grey prediction model.At the same time,using residual correction to adjust error makes results more reliable.According to the prediction of memory dirty pages,network bandwidth was adjusted and reserved.Compared with the traditional pre-copy strategy,the given experiments show that the optimized strategy proposed can improve the performance of network and reduce migratory cost for the memory-intensive and network-intensive applications.…”
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Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market by Nikolaos Koutantos, Maria Fotopoulou, Dimitrios Rakopoulos

Subjects: “…ensemble-based model…”
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Qualitative enhancement in machining efficiency of sncm8 alloy through hybrid ann-taguchi optimization approach by Sharad Chaudhari, Neeraj Sunheriya, Jayant Giri, Mohammad Kanan, Rajkumar Chadge, T. Sathish

“…The present study optimizes hard-to-machine materials using hybrid modeling involving artificial neural networks (ANN) and the Taguchi method. …”
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Integrated neural network and AspenPlus model for entrained flow gasification kinetics investigation by Balaban Dario, Lubura Jelena, Kojić Predrag

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Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach by Qiulin Li, Jinchao He, Dewei Mu, Hao Liu, Shicheng Li

“…To this end, the present study contributes a Bayesian-optimized explainable machine learning (ML) model to reveal DO dynamics and predict DO concentrations. …”
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Fast and effective assessment for individuals with special needs form optimization and prediction models by Bilal Baris Alkan, Muhammet Kumartas, Serafettin Kuzucuk, Nesrin Alkan

“…In addition, we wanted to develop new predictive models using machine learning methods based on these items. …”
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Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization by Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li

“…This paper proposes a novel gas pipeline failure risk assessment model based on Bayesian network optimized by machine learning. …”
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Developing a Machine Learning-Driven Model that Leverages Meta-Heuristic Algorithms to Forecast the Load-Bearing Capacity of Piles by Tianhua Zhou

“…This study used the Gaussian Process Regression (GPR) model as a problem-solving method for building a machine-learning model. …”
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Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm by Xuyan Tan, Weizhong Chen, Luyu Wang, Wei Ye

“…Therefore, with the aim of optimizing the monitoring scheme, this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm. …”
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Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models by Wenxuan Yu, Xizhe Li, Wei Guo, Hongming Zhan, Xuefeng Yang, Yongyang Liu, Xiangyang Pei, Weikang He, Longyi Wang, Yaoqiang Lin

“…Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). …”
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Prediction of retention time in larger antisense oligonucleotide datasets using machine learning by Manal Rahal, Bestoun S. Ahmed, Christoph A. Bauer, Johan Ulander, Jörgen Samuelsson

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