A predictive healthcare model using machine learning and psychological factors for medication adherence by Junwu Dong, Minyi Chu, Yirou Xu

“…Five machine learning algorithms – multiple logistic regression, decision tree, adaptive boosting, random forest and support vector machine (SVM) – were utilized to identify MAB levels and assess feature importance. …”
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Predictive estimations of health systems resilience using machine learning by Alessandro Jatobá, Paula de Castro-Nunes, Paloma Palmieri, Omara Machado Araujo de Oliveira, Patricia Passos Simões, Valéria da Silva Fonseca, Paulo Victor Rodrigues de Carvalho

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Enhancing Geomagnetic Navigation with PPO-LSTM: Robust Navigation Utilizing Observed Geomagnetic Field Data by Xiaohui Zhang, Wenqi Bai, Jun Liu, Songnan Yang, Ting Shang, Haolin Liu

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Functional pelvic tilt frequently differs from the anterior pelvic plane in non-arthritic hip disorders: implications for 3D motion analysis by Mark R. J. Jenkinson, Calvin Cheung, Alastair G Dick, Johan Witt, Jonathan Hutt

“…Conclusion: There is a wide variation in patients’ functional pelvic positioning in both supine and standing radiographs, in all different subgroups, which rarely correlates with the APP. Commercial 3D motion analysis may therefore give misleading results for both the extent and location of hip impingement as well as femoral head coverage, which may affect surgical decision-making. …”
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A novel Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) algorithm for hybrid feature extraction and multi-class brain tumor classification using ResNet-50 and... by Mahananda Malkauthekar, Avinash Gulve, Ratnadeep Deshmukh

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GrapeSLAM: UAV-based monocular visual dataset for SLAM, SfM and 3D reconstruction with trajectories under challenging illumination conditionszenodo by Kaiwen Wang, Sergio Vélez, Lammert Kooistra, Wensheng Wang, João Valente

“…SLAM (Simultaneous Localization and Mapping) is an efficient method for robot to percept surrendings and make decisions, especially for robots in agricultural scenarios. …”
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Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges by Vasileios Nittas, Kelly E. Ormond, Effy Vayena, Alessandro Blasimme

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DQN Simulation Model of Green Cooperative Market Behavior of Multi-Load Users for Optimal Evolution of New-Type Power System by ZHANG Shuo, YUAN Chunhui, LI Yingzi, XIAO Yangming, WEI Ming, HE Yunzheng

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Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques by Raghavendra C. Kamath, G. S. Vijay, Ganesha Prasad, P. Krishnananda Rao, Uday Kumar Shetty, Gautham Parameshwaran, Aniket Shenoy, Prithvi Shetty

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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

“…Among the tested models, the extra trees (ET) algorithm demonstrated superior performance, achieving an average absolute percent relative error (AAPRE) of approximately 3%, indicating its high reliability for Rs prediction. …”
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Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand by Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti

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A real-time mapping method for knowledge graph-driven large language models: a focus on indoor fire evacuations by Tianxin Wang, Ping Du, Pei Dang, Tao Liu, Pengpeng Li

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Short-Term Power Load Forecasting Based on DPSO-LSSVM Model by Shujun Ji, Linhao Zhang, Jinteng Wang, Tao Wei, Jiadong Li, Bu Ling, Jinglong Xu, Zuoping Wu

“…The dynamic particle swarm optimization algorithm is utilized to dynamically adjust the parameters to achieve higher accuracy in load forecasting. …”
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Dynamic appliance scheduling and energy management in smart homes using adaptive reinforcement learning techniques by Poonam Saroha, Gopal Singh, Umesh Kumar Lilhore, Sarita Simaiya, Monish Khan, Roobaea Alroobaea, Majed Alsafyani, Hamed Alsufyani

“…The experimental results show that the outperforming multiobjective reinforcement learning puma optimizer algorithm (MORL–POA), SAPOA, and POA methods, the suggested solution dramatically lowers the peak-to-average ratio (PAR) value from 3.4286 to 1.9765 without RES and 1.0339 with RES. …”
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Container Liner Shipping System Design Considering Methanol-Powered Vessels by Zhaokun Li, Xinke Yu, Jianning Shang, Kang Chen, Xu Xin, Wei Zhang, Shaoqiang Yu

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Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles by Yiling Ye, Xiaowen Zhuang, Cai Yi, Dinggao Liu, Zhenpeng Tang

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Quantum-enhanced beetle swarm optimized ELM for high-dimensional smart grid intrusion detection by Na Cheng, Shuqing Wang, Lihong Zhao, Yan Hu

“…The experimental results demonstrate that QBOA-ELM achieves significantly better accuracy (97.5%), recall (96.8%), precision (97.2%), F1-Score (0.972), sensitivity (96.8%), and specificity (98.1%) in intrusion detection tasks when compared to traditional Beetle Swarm Optimization Extreme Learning Machine (BOA-ELM) and other classical algorithms such as Support Vector Machine (SVM) and Decision Tree (DT). …”
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Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China by Yanan Zuo, Min Ji, Jiutao Yang, Zhenjin Li, Jing Wang

“…In terms of clustering performance, the weighted K-means clustering algorithm achieves higher Silhouette coefficient by 0.0138 and 0.1885 compared with the weighted agglomerative hierarchical clustering algorithm (weighted AHC) and weighted DBSCAN, respectively, the Calinski-Harabasz index is higher by 3.8017 and 22.4039, and the Davies-Bouldin index is lower by 0.1006 and 0.4889, demonstrating superior clustering results. …”
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Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children by Gizachew Mulu Setegn, Belayneh Endalamaw Dejene

“…Results The ensemble ML models exhibited robust predictive performance, and light gradient boosting outperformed the other ensemble ML algorithms by 95.3%. The explainability of the Light Gradient Boosting Ensemble Model was determined using Eli5 and LIME. …”
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Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications by Asghar Khan, Saeed Islam, Muhammad Ismail, Abdulaziz Alotaibi

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