Machine Learning based OTN Network Performance Degradation Prediction by CHEN Liping, LIAO Liang, ZHANG Peng, ZHU Dehan, PENG Zhichong, ZHOU Hao

“…【Conclusion】The proposed solution meets the requirements for engineering applications, providing a new and effective method for predicting performance degradation in OTN networks. …”
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Performance Test of Coconut Shell Grinding Machine For Pyrolysis Process by Sri Aulia Novita, Santosa Santosa, Nofialdi Nofialdi, Andasuryani Andasuryani, Ahmad Fudholi

“…The aim of this research was to test the performance of the modified coconut shell grinding machine, determine the effect of water content on the milling process, achieve coconut shell sizes of 3, 5, and 10 mm to enhance the pyrolysis process, and analyze the economics of grinding machine engineering. …”
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OTSENA AMPHIBIAN OF CONTROL MACHINES USING AFLOAT ON PERFORMANCE INDICATORS by O. A. Seryakov, S. S. Zinoviev, M. Ya. Manzin

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Two General Architectures for Intelligent Machine Performance Degradation Assessment by Yanwei Xu, Aijun Xu, Tancheng Xie

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Pretreatment Methods for Enhancing Machine Learning Performance on Metabolomics Data by Rustam

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Machine Learning-Based Highway Pavement Performance Prediction in Xinjiang by Qi Yang, Wei Tian, Xiaomin Dai

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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

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Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning by Bruno Matos Porto, Flavio Sanson Fogliatto

“…Feature engineering (FE) improved the performance of the ML algorithms. …”
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Improving Vehicle Dynamics: A Fractional-Order PI<i>^λ</i>D<i>^μ</i> Control Approach to Active Suspension Systems by Zongjun Yin, Chenyang Cui, Ru Wang, Rong Su, Xuegang Ma

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Predicting drug-target interactions using machine learning with improved data balancing and feature engineering by Md. Alamin Talukder, Mohsin Kazi, Ammar Alazab

“…This study makes several contributions to address these issues, introducing a novel hybrid framework that combines advanced machine learning (ML) and deep learning (DL) techniques. …”
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Classification of NOx Emission in Marine Engines Utilizing kNN-Based Machine Learning Algorithms by Samet Memiş, Ramazan Şener

“…Leveraging machine learning techniques, particularly k-nearest neighbors (kNN)-based algorithms, the research classifies NOx emissions in marine engines operating under the Reactivity-Controlled Compression Ignition (RCCI) strategy. …”
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Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation by A. Srinivaas, N. R. Sakthivel, Binoy B. Nair

“…Additionally, this study incorporates advanced deep learning techniques, including a deep neural network (DNN), a one-dimensional convolutional neural network (1D-CNN), Transformer and a hybrid Transformer and DNN model which demonstrate superior performance in fault detection compared to traditional machine learning methods.…”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…An empirical equation was further extracted from the optimized model, offering a user-friendly solution for practical engineering applications without requiring machine learning proficiency.…”
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Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine by Jiusheng Chen, Xingkai Xu, Xiaoyu Zhang

“…Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. …”
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Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers by Bernardo Luis Tuleski, Cristina Keiko Yamaguchi, Stefano Frizzo Stefenon, Leandro dos Santos Coelho, Viviana Cocco Mariani

“…Engine fault diagnosis is a critical task in automotive aftermarket management. …”
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Structural Topology Design for Electromagnetic Performance Enhancement of Permanent-Magnet Machines by Pengjie Xiang, Liang Yan, Xiaoshuai Liu, Xinghua He, Nannan Du, Han Wang

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Parameter Optimization and Machining Performance of Inconel 625 with Nanoparticles Dispersed in Biolubricant by T. Mohanraj, N. Radhika, S. Aswin Nanda, V. Vignesh, B. Jayaraman, K. R. Ratana Selvan, Yesgat Admassu

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Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images by Lubna Farhi, Razia Zia, Zain Anwar Ali

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MACHINE RELIABILITY CONTROL BASED ON ECONOMIC CRITERIA by Igor A. Khozyayev, Victor V. Radin

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Machine learning predictions on the output parameters of common rail direct injection engines fueled with ternary blend by Subramanian Karthikeyan, Paramasivam Sathiyagnanam Amudhavalli, Dillikannan Damodharan

“…This study aims to employ a machine learning algorithm (MLA) to predict Common Rail Direct Injection (CRDI) engine emissions and performance using alternative feedstock. …”
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