Multiscale Feature Modeling and Interpretability Analysis of the SHAP Method for Predicting the Lifespan of Landslide Dams by Zhengze Huang, Yuqi Bai, Hengyu Liu, Yun Lin

“…This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. …”
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ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. by Brett A McKinney, Bill C White, Diane E Grill, Peter W Li, Richard B Kennedy, Gregory A Poland, Ann L Oberg

“…We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Revisiting the Control Systems of Autonomous Vehicles in the Agricultural Sector: A Systematic Literature Review by Vinayambika S. Bhat, Yong Wang

“…This approach enhances clarity in understanding algorithm suitability, adaptability, and scalability across different agricultural settings. …”
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Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…Relevant indices of TACE refractoriness were selected. ML algorithms, including a support vector machine, random forest, logistic regression and adaptive boosting, were used to construct the radiomics, clinical prediction, and combined models. …”
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Mathematical Models for Management Information Systems on Digital Platforms: from Resource Management to Demand Forecasting by Viktor Godliuk

“…The use of optimization methods, graph algorithms, forecasting, and machine learning to improve the efficiency of digital systems is investigated. …”
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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…As a result, grapevine growers require reliable spatial and temporal information on vine water status to adapt practices. This research evaluates the use of Landsat 8 satellite imagery in conjunction with weather data, and a machine learning algorithm (Gradient Boosting Machine) to predict vine water status in large vineyard blocks. …”
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A comprehensive review on the integration of artificial intelligence in friction stir welding for monitoring, modelling, and process optimization by Mostafa Akbari, Ezatollah Hassanzadeh, Yaghuob Dadgar Asl, Amirhossein Moghanian

“…Lastly, the third section pertains to the optimization of FSW parameters, illustrating how AI-driven algorithms analyze complex interactions among multiple variables to determine the most effective process settings. …”
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The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning by David M. Hannon, Jaffar David Abbas Syed, Bairbre McNicholas, Michael Madden, John G. Laffey

“…The decision to perform prone positioning was based on the criteria in the PROSEVA study. A C5.0 classifier algorithm with adaptive boosting was trained on data gathered before, during, and after initial proning. …”
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The ZTF Source Classification Project. III. A Catalog of Variable Sources by Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, Theophile Jegou du Laz, Andrew Drake, Matthew J. Graham, Lynne A. Hillenbrand, Jan van Roestel, Paula Szkody, LeighAnna Zielske, Mohammed Guiga, Muhammad Yusuf Hassan, Jill L. Hughes, Guy Nir, Saagar Parikh, Sungmin Park, Palak Purohit, Umaa Rebbapragada, Draco Reed, Daniel Warshofsky, Avery Wold, Joshua S. Bloom, Frank J. Masci, Reed Riddle, Roger Smith

“…Building on previous work, this paper reports the results of the ZTF Source Classification Project ( SCoPe ), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. …”
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Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering by Liulong Yao, Jinrong Cui, Yazi Xie, Chengli Sun

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Application research and effectiveness evaluation mechanism of hybrid intelligent algorithm integrating cognitive computing and deep learning for dynamically adjusting employee per... by Zhenlin Luo, Kebin Lu

“…Additional experiments involving noise interference indicate that the hybrid algorithm exhibits strong adaptability across varying data volumes. …”
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Hyperspectral Detection of Pesticide Residues in Black Vegetable Based on Multi-Classifier Entropy Weight Method by Rongchang Jiang, Guoqiang Zhuang, Shijie Xie, Yang Wang, Guoqi Zhang, Dandan Qu, Wanzhi Wen

“…Three dimensionality reduction techniques, competitive adaptive reweighted sampling, random frog leaping, and successive projections algorithm, were compared. …”
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Refining source-specific lung cancer risk assessment from PM2.5-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China by Lord Famiyeh, Ke Chen, Fiseha Berhanu Tesema, Celeb Kelly, Dongsheng Ji, Hang Xiao, Lei Tong, Zongshuang Wang, Jun He

“…We identified a moderate PAH exposure risk level (>1.0 ×10⁻⁶) in Ningbo and used advanced machine learning (ML) algorithms, random forest (RF), extremely randomized trees (ERT), and extreme gradient boosting (XGBoost), to improve the accuracy of source-specific LECR assessments. …”
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Demand Forecasting in Data-Scarce and Resource-Restricted Environments by David Gogelein, Marianne von Schwerin, Thomas Walter

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Enhancing Cloud Job Failure Prediction With a Novel Multilayer Voting-Based Framework by Ahmed Elkaradawy, Ayman Elshenawy, Hany Harb

“…To address this challenge, researchers have progressively developed machine learning and deep learning techniques that examine cloud logs to identify patterns linked to such failures. …”
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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

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Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

“…This study proposes a novel hybrid model integrating Multi-Layer Perceptron (MLP) neural networks with Self-Adaptive Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) frameworks. …”
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Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations by Bogdan Felician Abaza, Vlad Gheorghita

“…The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. …”
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