Modules of Organizational and Technical Systems for Solving Problems of Adaptation in a Rapidly Changing Environment by A. Mikryukov, V. M. Trembach, A. V. Danilov

“…The main stages of solving problems of adaptation of cyberphysical systems are presented. An adaptation algorithm using the planning mechanism is presented. …”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…This study proposes novel hybrid models through a comprehensive investigation of resampling techniques and machine learning algorithms. Four ensemble methods—Random Forest (RF), Extremely Randomized Trees (ET), Adaptive Boosting (ADA), and Extreme Gradient Boosting (XGB)—along with traditional methods such as Support Vector Regression (SVR) and K-Nearest Neighbors (KNN), were employed and compared for daily streamflow forecasting in the Thale Sap Songkhla Basin, southern Thailand. …”
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FedACT: An adaptive chained training approach for federated learning in computing power networks by Min Wei, Qianying Zhao, Bo Lei, Yizhuo Cai, Yushun Zhang, Xing Zhang, Wenbo Wang

“…Federated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. …”
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Learning Random Access Schemes for Massive Machine-Type Communication With MARL by Muhammad Awais Jadoon, Adriano Pastore, Monica Navarro, Alvaro Valcarce

“…We also present a correlated traffic model, which is more descriptive of mMTC scenarios, and show that the proposed algorithm can easily adapt to traffic non-stationarities. …”
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Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey by Harshit Maheshwari, Li Yang, Richard W. Pazzi

“…This survey systematically reviews the state-of-the-art Machine Learning techniques applied to urban traffic simulation. …”
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Adaptation of k-means to automated forecasting of poorly structured time series of economic dynamics by Dunskaia Lada, Popova Elena

“…Data mining methods used in machine learning or deep learning allow to take into account complex patterns and nonlinear dependencies in the data. …”
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Integration of AI in Self-Powered IoT Sensor Systems by Cosmina-Mihaela Rosca, Adrian Stancu

“…The conclusions drawn from these results underscore the need for an interdisciplinary approach and detailed exploration of ML algorithms to be adapted to the hardware infrastructures of autonomous sensors. …”
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THE INVERSE GAUSSIAN PLUME METHOD FOR ESTIMATING THE LEVEL OF AIR POLLUTION by Volodymyr Hura, Liubomyr Monastyrskyi

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A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis by Guanghui Li, Salif Komi, Jakob Fleng Sorensen, Rune W. Berg

“…We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. …”
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Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm by Fen Yang

“…The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. …”
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Adaptive differential current relay based on form/ripple factors for busbar current signals by R. A. Mahmoud

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Adaptive continuous-discrete variables optimization for active learning with extremely sparse data in optical material design by Serang Jung, Eungkyu Lee

“…In the adaptive scheme, we examine the performance of three machine learning (ML) models—Gaussian process regression, factorization machines (FM), and field-aware FM—for a surrogate function, and ML model-specific optimization algorithms such as discrete particle swarm optimization, artificial bee colony optimization, and simulated annealing. …”
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A novel Smishing defense approach based on meta-heuristic optimization algorithms by Mohammad Alshinwan, Osama A. Khashan, Zakwan Alarnaout, Salam Salameh Shreem, Ahmed Younes Shdefat, Nader Abdel Karim

“…This paper introduces an approach for detecting SMS phishing based on machine learning algorithms. The suggested system incorporates feature extraction, oversampling, and selection and classification optimization algorithms. …”
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AMC With a BP-ANN Scheme for 5G Enhanced Mobile Broadband by Li-Sheng Chen, Wei-Ho Chung, Ing-Yi Chen, Sy-Yen Kuo

“…Because of equipment limitations at the receiving end, channel estimation algorithms cannot be used to acquire ideal solutions, and thus estimation errors are inevitable. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…This paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. Focusing on Dublin, we integrate diverse geospatial datasets with building-specific and neighbourhood-scale features to classify BER. …”
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A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization by Zeinab Hassani, vahid Hajihashemi, Keivan Borna, Iman Sahraei Dehmajnoonie

“…This approach is considered a hybrid of optimization algorithms and classifiers in machine learning. Binary Whale Optimization (BWO) and Binary Grey Wolf Optimization (BGWO) algorithms are used for feature selection and K-Nearest Neighbor (KNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms are applied as the classifiers in this research. …”
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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…These algorithms were chosen for their strong global search capabilities and adaptability to complex datasets, ensuring optimal parameter selection for improved model performance. …”
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Enhancing Power Quality in Grid-Integrated Hybrid Renewable Energy System using ANFIS-FBSO by Singh Manpreet, Singh Lakhwinder

“…An intelligent, adaptive and predictive control mechanism that combines machine learning (ANFIS) and optimisation (FBSO) is used in the adaptive neuro-fuzzy inference system-based firebug swarm optimisation (ANFIS-FBSO) framework to implement power balancing and frequency stabilisation. …”
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A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment by Mohammad Hossein Zadeh, Marina Barbiroli, Franco Fuschini

“…By employing Machine Learning techniques, the objective is to achieve flexibility and adaptability in modeling, enabling the system to effectively generalize across diverse industrial scenarios. …”
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