Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation paramet... by Xin-Yue Song, Xin-Peng Xie, Wen-Jing Xu, Yu-Jia Cao, Bin-Miao Liang

“…We applied several supervised ML algorithms and feature selection strategies to distinguish between DN and DA, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Naive Bayes (BAYES), K-Nearest Neighbors (KNN), SelectKBest, Recursive Feature Elimination with Cross-Validation (RFECV), and SelectFromModel. …”
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Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data by Lixiran Yu, Hongfei Tao, Qiao Li, Hong Xie, Yan Xu, Aihemaiti Mahemujiang, Youwei Jiang

“…Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. …”
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Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics by Sándor Szénási, Gábor Légrádi, Gábor Kovács

“…Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. …”
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Integrating IoT sensors and machine learning for sustainable precision agroecology: enhancing crop resilience and resource efficiency through data-driven strategies, challenges, an... by Val Hyginus Udoka Eze, Esther Chidinma Eze, George Uwadiegwu Alaneme, Pius Erheyovwe BUBU, Ezekiel Oluwaseun Ejiofor Nnadi, Michael Ben Okon

“…Abstract The integration of Internet of Things (IoT) sensors and Machine Learning (ML) technologies has transformed precision agriculture by enabling data-driven, adaptive, and efficient farming practices. …”
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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). …”
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Combining Physical and Network Data for Attack Detection in Water Distribution Networks by Côme Frappé - - Vialatoux, Pierre Parrend

“…This paper addresses this problem by providing a multi-layer approach to applying machine learning to cyber-physical systems, by combining physical and network traffic data and assessing their effects on the attack detection performance of machine learning algorithms, as well as the cross-impact with data enriched with graph metrics.…”
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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…The positive correlation between judge evaluations and sensor data indicates judges’ ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). …”
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Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…A combined model was further constructed by integrating both feature sets, and model performance was compared to identify the optimal predictive model.Results‍　‍This study screened the features from non-contrast CT images and ultimately selected 7 radiomic features for constructing radiomic model. Among 6 machine learning algorithms, the adaptive boosting (Adaboost) model demonstrated the best overall predictive performance, with an area under the curve (AUC) of 0.866 (95% CI: 0.808~0.923; accuracy: 0.832, specificity: 0.884) in the training set and of 0.806 (95% CI: 0.630~0.983; accuracy: 0.795, specificity: 0.971) in the validation set. …”
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Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold... by Zhixin Xu, Xiaoming Li, Bo Sun, Yueming Wen, Peipei Tang

“…In conclusion, this study aimed to integrate performance assessment and machine learning algorithms into the decision-making process for optimizing traditional village environments, offering new approaches for sustainable rural development.…”
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Robust Tracking Method for Small and Weak Multiple Targets Under Dynamic Interference Based on Q-IMM-MHT by Ziqian Yang, Hongbin Nie, Yuxuan Liu, Chunjiang Bian

“…Furthermore, the algorithm utilizes Support Vector Machines (SVMs) for anomaly detection and trajectory recovery, thereby enhancing the accuracy of data association and the overall robustness of the system. …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…To address this gap, we propose a framework that co-optimizes the discrete hyperparameter spaces of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN) models under strict memory constraints. An adaptive Simulated Annealing algorithm with memory-based rejection (aSAR) navigates the discrete design space, employing nine objective functions that balance Mean Absolute Percentage Error (MAPE) against model compactness. …”
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Intelligent Damage Prediction During Vehicle Collisions Based on Simulation Datasets by Sheng Liu, Conghao Liu, Xunan An, Xin Liu, Liang Hao

“…In this study, we proposed an intelligent damage prediction model that significantly reduces the computational time required for collision simulations by leveraging collision simulation datasets in conjunction with the random forest (RF) algorithm. A finite element model for vehicle collision simulation was first established. …”
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Neural-Driven heuristic for strip packing trained with Black-Box optimization by Mariusz Kaleta, Tomasz Śliwiński

“…Our study highlights the potential of machine learning-driven heuristics in combinatorial optimization and opens avenues for further research into adaptive decision-making strategies in packing and scheduling problems…”
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Artificial intelligence model in the cognitive and learning activities of university subjects by N. Abishev, R. Ramazanov, M. Abaideldanova, K. Chesnokova, A. Baizhumayeva

“…The authors design this model using algorithms–sets of rules that enable programs to make decisions, recognize patterns, and generate predictions based on input data relevant to the learning and cognitive processes of university subjects. …”
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Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches by Franciszek Seredynski, Miroslaw Szaban, Jaroslaw Skaruz, Piotr Switalski, Michal Seredynski

“…In this paper, we focus on developing self-organizing algorithms aimed at solving, in a distributed way, the coverage problem in Wireless Sensor Networks (WSNs). …”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction.…”
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Research on Bearing Fault Diagnosis Method Based on MESO-TCN by Ruibin Gao, Jing Zhu, Yifan Wu, Kaiwen Xiao, Yang Shen

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Methods of controlling the course for an self-driving grader by R. Yu. Sukharev

“…The construction machine closest in terms of control algorithm to an unmanned vehicle is an auto grader. …”
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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. …”
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The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses by Daniel J. Lee, Mohammad Hamghalam, Lily Wang, Hui-Ming Lin, Errol Colak, Muhammad Mamdani, Amber L. Simpson, John M. Lee

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