ENHANCING NETWORK INTRUSION DETECTION USING MACHINE LEARNING AND META-MODELLING FOR IMPROVED CYBER SECURITY PERFORMANCE by Sunita, Pankaj Verma, Nitika, Jaspreet Kaur, Vijay Rana

“…This study is based on the analysis of network intrusion detection and the improvement of various machine learning methods that produce high accuracy and guarantee secure network traffic from malicious activities. …”
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Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model by Yu Zhao, Zhenrui Peng

“…A frequency response function- (FRF-) based surrogate model for finite element model updating (FEMU) is presented in this paper. Extreme learning machine (ELM) is introduced as the surrogate model of the finite element model (FEM) to construct the relationship between updating parameters and structural responses. …”
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Calculation Methodology and Results of Optimization of Design Parameters of the Tractor Recuperative Mounted System When Aggregated with Forest Tillage Implements by Valeryi I. Posmetyev, Maksim A. Savinkov, Viktor V. Posmetyev, Andrey Yu. Manukovskii, Vadim O. Nikonov

“…A methodology for calculating the efficiency indicators and two-factor optimization of the design parameters of the tractor recuperative mounted system is presented. …”
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Effects of Transducer Placement on Load–Velocity Relationships in Smith Machine and Free Weight Squats in Trained Women by Athanasios Tsoukos, Gregory C. Bogdanis

“…<b>Background</b>: We examined the effects of linear position transducer placement during Smith machine (SM) and free weight (FW) full squats on the mean velocity and the load–velocity relationship in trained women. …”
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CONDITIONS FOR STABLE CHIP BREAKING AND PROVISION OF MACHINED SURFACE QUALITY WHILE TURNING WITH ASYMMETRIC TOOL VIBRATIONS by V. K. Sheleh, V. I. Molochko, S. S. Danilchyk

“…Therefore, the choice of the coefficient of the vibration cycle asymmetry is based on the parameters of surface roughness which must be obtained after machining and intensity of tool wear rate.…”
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ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…We have developed a machine learning algorithm based on Region‐based Convolutional Neural Networks (Mask‐RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes—ML‐UrineQuant. …”
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Predicting postharvest weight loss and texture changes in table grapes using fruit color and machine learning by Xiaoyan Cheng, Yao Zhou, Zhengyang Huo, Ruiying Li, Shiqian Xu, Hao Qi, Jianyuan Zhu, Fei Wang, Yang Bi

“…This study explores the potential of fruit color parameters as non-invasive indicators of postharvest weight loss and textural changes. …”
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COMPREHENSIVE ANALYSIS OF THE ABSORPTION LITHIUM BROMIDE REFRIGERATING MACHINE AS PART OF THE CCGT-110 AND ALBRM POWER SYSTEM by L. V. Galimova, D. Z. Bayramov

“…It is essential to  maintain the design values for which constant monitoring is carried out and  the operation parameters are adjusted. The objective of this work is to  conduct a comprehensive analysis of the absorption lithium bromide  refrigerating machine as part of an energy-saving system. …”
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Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers by Hamidreza Amirhassankhani, Abbas Toloie Eshlaghy, Reza Radfar, Alireza pourebrahimi

“…To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. …”
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Comparative RSM analysis of CO2 laser and abrasive waterjet machining on Armox 500 T armored steel by M. M. Salem, Tamer Ismail, Y. M. Elattar, A. M. El-Wardany, Ahmed Salama

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A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset by Rulyansyah Permata Putra, Amarudin Amarudin

“…The primary objective of this study is to design and implement a machine learning model for detecting network intrusions efficiently while minimizing latency, through a comparative analysis of several algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and Boosting. …”
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The Optimization of Micro-Edm Machining Process when Using Carbon Coated Micro Electrode as a Tool by Bich Thi Mac, Shailesh Shirguppikar, Vaibhav Ganachari

“…The amount of electrode wear in micro-EDM has a direct effect on the dimensional accuracy of the machined hole. Therefore, improving the corrosion resistance of electrodes in micro-EDM is still of great interest. …”
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A Study on the Design of a Fault-Tolerant Consequent-Pole Hybrid Excited Machine for Electric Vehicles by Guangyu Qu, Jinyi Yu, Zhenghan Li, Wei Liu

“…Second, the operation principle, the magnetic circuit, and the design procedure of the FTCPHE machine are analyzed and illustrated. Third, several key structural parameters of the proposed FTCPHE machine are discussed and designed to improve electromagnetic performances. …”
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Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data by Arianna Beatrice Malaguti, Claudia Corradino, Alessandro La Spina, Stefano Branca, Ciro Del Negro

“…Hazard assessment can be supported by Artificial Intelligence (AI) techniques, such as machine learning, which are used for data exploration to identify features of interest in the data. …”
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A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA by Bhagya M. Patil, Vishwanath Burkpalli

“…Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). GMSMFO enhances population diversity and avoids local optima through Gaussian mutation (GM), while the shrink mechanism (SM) improves exploration–exploitation balance. …”
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SIMULATION OF THE CONTACT PROCESS INTERACTIONS OF LAND-MOVING AND EARTH-TRANSPORT MACHINES’ WORKING BODIES WITH FROZEN SOIL by V. N. Kuznetsova, I. S. Kuznetsov

“…The carried out researches are the basis for the justifying and determining methodology for the working equipment’s optimal parameters of earth-moving machines with increased efficiency by reducing the energy intensity of the developing frozen soils’ process.…”
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Comparison of Machine Learning Methods and Ordinary Kriging for Gravimetric Mapping: Application to Yagoua Area (Northern Cameroon) by Mfenjou Martin Luther, Boroh Andre William, Kasi Njeudjang, Kabe Moukete Eric Bruno, Amaya Adama

“…This work focuses on the comparison of a number of machine learning methods (random forest, support vector machine (SVM), and artificial neural networks (ANN)) and ordinary kriging (OK). …”
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Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

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Leveraging Automated Machine Learning for Environmental Data‐Driven Genetic Analysis and Genomic Prediction in Maize Hybrids by Kunhui He, Tingxi Yu, Shang Gao, Shoukun Chen, Liang Li, Xuecai Zhang, Changling Huang, Yunbi Xu, Jiankang Wang, Boddupalli M. Prasanna, Sarah Hearne, Xinhai Li, Huihui Li

“…Dimensionality‐reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype. …”
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