Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering. by Guanqun Wang

“…Furthermore, four widely recognized machine learning methods are employed to classify the clustering results, achieving over 95% classification accuracy on the test set. …”
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Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High‐Strength Materials via Deep Reinforcement Learning by Yunhao Yang, Runnan Bai, Wenli Gao, Leitao Cao, Jing Ren, Zhengzhong Shao, Shengjie Ling

“…This study addresses the challenge by investigating the structure‐property relationships and stability of biomimetic 3D‐DFNS using large datasets generated through procedural modeling, coarse‐grained molecular dynamics simulations, and machine learning. …”
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A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

“…However, when these machine learning methods are applied without careful consideration of data quality, model biases, and other critical factors, their potential is often compromised. …”
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Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning by Alyaman H. Massarani, Mahmoud M. Badr, Mohamed Baza, Hani Alshahrani, Ali Alshehri

“…The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. …”
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Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles by Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, Yahia Said, Ali M. Al-Sharafi

“…After developing driver assistance and AV methods, adversarial weather conditions have become an essential problem. Nowadays, deep learning (DL) and machine learning (ML) models are critical to enhancing object detection in AVs, particularly in adversarial weather conditions. …”
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Early diagnosis of autism across developmental stages through scalable and interpretable ensemble model by Nasirul Mumenin, Maisha Mumtaz Rahman, Mohammad Abu Yousuf, Farzan M. Noori, Md Zia Uddin

“…Comparative analysis with standard machine learning models underscores the superior performance of the proposed framework. …”
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The Gust Factor Models Involving Wind Speed and Temperature Profiles for Wind Gust Estimation by Haichuan Hu, Chuanhai Qian, Shibo Gao

“…A unified upper-level gust impact model was developed through multiple regression (GF-L) and machine learning (GF-M) methods based on data from these stations to improve gust estimation accuracy. …”
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Analysis of the 10-day ultra-marathon using a predictive XG boost model by Beat Knechtle, Elias Villiger, David Valero, Lorin Braschler, Katja Weiss, Rodrigo Luiz Vancini, Marilia S. Andrade, Volker Scheer, Pantelis T. Nikolaidis, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

“…The aim of the present study was to investigate the origin and performance of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, country where the race takes place, the type of race and the kind of running surface. …”
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Neutrosophic OWA-TOPSIS Model for Decision-Making in AI Systems with Large Volumes of Data by Juan Roberto Pereira Salcedo, Karla Melissa Ruiz Quezada, Edison Luis Cruz Navarrete, Pedro Manuel García Arias

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SMART-FL: Single-Shot Merged Adaptive Resource-Aware Tensor-Fusion for Efficient Federated Learning in Heterogeneous Cross-Silo Environments by Vineetha Pais, Santhosha Rao, Balachandra Muniyal

“…Federated Learning (FL) is an evolutionary approach for privacy-preserving distributed machine learning and is particularly significant in cross-silo settings where direct data sharing is restricted. …”
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A Novel Data-Driven Method of Real-Time Transient Stability Assessment for AC/DC Hybrid Power Systems by Haifeng Li, Zhiwei Wang, Tao Jin, Xian Xu, Lin Shi

“…Next, transient stability assessment models are trained based on attention mechanisms, improved convolutional deep belief networks (CDBN), and multiple convolution-constrained Boltzmann machines to learn effective features from the input data adaptively. …”
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A convolutional neural network model and algorithm driven prototype for sustainable tilling and fertilizer optimization by Sajeev Magesh

“…The machine learning model utilizes a camera-captured field image to determine existing tilling intensity on a 7-point scale. …”
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Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. by Xiaohua Zeng, Changzhou Liang, Qian Yang, Fei Wang, Jieping Cai

“…Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. …”
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Random forest models highlight early Homo sapiens habitats and their relationship to lithic assemblage composition by Lucy Timbrell, James Blinkhorn, Matt Grove

“…We apply random forests, a powerful and highly flexible machine-learning tool for niche modelling, in combination with palaeoclimatic simulations at high temporal resolution. …”
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Research on Fault Diagnosis of UAV Rotor Motor Bearings Based on WPT-CEEMD-CNN-LSTM by Xianyi Shang, Wei Li, Fang Yuan, Haifeng Zhi, Zhilong Gao, Min Guo, Bo Xin

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Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model by Seunghee Lee, Geonchul Shin, Jeongseok Hwang, Yunjeong Hwang, Hyunwoo Jang, Ju Han Park, Sunmi Han, Kyeongmin Ryu, Jong-Yeup Kim

“…However, a state-of-the-art model has not yet been developed. In this study, we developed a predictive model for the early detection of sepsis by leveraging advanced machine learning techniques, specifically the Gated Recurrent Unit (GRU-D) and Multitask Gaussian Process-Temporal Convolutional Network (MGP-TCN) models. …”
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Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review by Shaina Mackin, Vincent J. Major, Rumi Chunara, Remle Newton-Dame

“…This umbrella review sought to identify post-processing bias mitigation methods and tools applicable to binary healthcare classification models in healthcare and summarize bias reduction effectiveness and accuracy loss. …”
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Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models by Shubham Baisthakur, Breiffni Fitzgerald

“…The developed model offers significant computational cost reductions compared to full‐dynamic simulations and also allows virtual sensing. …”
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A Novel Approach to Pine Nut Classification: Combining Near-Infrared Spectroscopy and Image Shape Features with Soft Voting-Based Ensemble Learning by Yueyun Yu, Xin Huang, Danjv Lv, Benjamin K. Ng, Chan-Tong Lam

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