Angular Stabilization of a Multirotor Aircraft in Venus’ Atmosphere by Vladislav V. Ryzhkov

“…Numerical simulations confirm the effectiveness of the proposed stabilization algorithm. The suggested approach to automated PID parameter tuning minimizes the integral orientation error and improves the dynamic performance of the multirotor flight control system. …”
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Innovative Approaches to Sustainable Urban Planning: Analysing Current Trends by Meruyert Baidrakhmanova, Gani Karabayev, Seimur Mamedov

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Explainable machine learning for predicting distant metastases in renal cell carcinoma patients: a population-based retrospective study by Zhao Hou, Zhao Hou, Peipei Wang, Peipei Wang, Dingyang Lv, Dingyang Lv, Huiyu Zhou, Huiyu Zhou, Zhiwei Guo, Zhiwei Guo, Jinshuai Li, Jinshuai Li, Mohan Jia, Mohan Jia, Hongyang Du, Hongyang Du, Weibing Shuang, Weibing Shuang

“…Early prediction of metastasis is crucial for developing personalized treatment plans and improving patient outcomes. This study aimed to establish and validate a clinical prediction model for distant metastasis in RCC patients.MethodsTen machine learning algorithms were employed to develop a predictive model for distant metastasis in RCC. …”
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Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation by Amirreza Kandiri, Ramin Ghiasi, Maria Nogal, Rui Teixeira

“…The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. …”
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Forecasting Tunnel-Induced Ground Settlement: A Hybrid Deep Learning Approach and Traditional Statistical Techniques With Sensor Data by Syed Mujtaba Hussaine, Linlong Mu, Yimin Lu, Syed Sajid Hussain

“…Additionally, the statistical Autoregressive Integrated Moving Average (ARIMA)/Seasonal ARIMA (SARIMA) models were enhanced through seasonality removal, automated model selection using the auto_arima algorithm, and parameter fine-tuning via grid search to improve their predictive accuracy. …”
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Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms by Jorge Enrique Chaparro, José Edinson Aedo, Felipe Lumbreras Ruiz

“…In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. …”
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SIMSPIRE: A Simulator of the Respiratory System by Andrea Bombarda, Silvia Bonfanti, Angelo Gargantini, Elvinia Riccobene

“…It provides a highly accurate and customizable platform for improving the design of ventilation systems and optimizing patient outcomes. …”
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Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy by Lorenzo Villani, Martina Casciola, Davide Astiaso Garcia

“…Results demonstrate the effectiveness of smart energy management, showcasing significant potential for scalability in similar building typologies. Future improvements include integrating a temporal evolution model, refining feature selection using advanced optimization techniques, and expanding validation across multiple case studies. …”
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Agile DQN: adaptive deep recurrent attention reinforcement learning for autonomous UAV obstacle avoidance by Fadi AlMahamid, Katarina Grolinger

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Dynamic Monitoring and Precision Fertilization Decision System for Agricultural Soil Nutrients Using UAV Remote Sensing and GIS by Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

“…Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). …”
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Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik, Hossein Bonakdari

“…Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. …”
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Trade-Space Exploration With Data Preprocessing and Machine Learning for Satellite Anomalies Reliability Classification by Abdul Mutholib, Nadirah Abdul Rahim, Teddy Surya Gunawan, Mira Kartiwi

“…Leveraging a Seradata dataset spanning 66 years and 4,455 satellite records, the framework systematically evaluates four data cleaning methods, four data transformation techniques, five normalization strategies, and seven machine learning algorithms across 480 configurations. The optimal configuration, comprising Iterative Imputation, FastText, Robust Scaling, and Decision Tree, achieved the highest testing accuracy of 95.74% with competitive computational efficiency. …”
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Multi-exit Kolmogorov–Arnold networks: enhancing accuracy and parsimony by James Bagrow, Josh Bongard

“…This architecture provides deep supervision that improves training while discovering the right level of model complexity for each task. …”
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AI-Enhanced Design and Application of High School Geography Field Studies in China: A Case Study of the Yellow (Bohai) Sea Migratory Bird Habitat Curriculum by Binglin Liu, Weijia Zeng, Weijiang Liu, Yi Peng, Nini Yao

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THEORETICAL AND METHODOLOGICAL ASPECTS OF USING INFORMATION ANDCOGNITIVE TECHNOLOGIES IN THE TRAINING OF TRANSPORT SPECIALISTS by Lavrentieva Olena, Krupskyi Oleksandr

“…It has been established that information-cognitive technologies combine methods and algorithms based on insights into the cognition processes, learning, communication, and information processing, grounded in the achievements of neuroscience, digital and information technologies, and the mathematical modelling of consciousness. …”
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Lung Cancer Prediction Using an Enhanced Neutrosophic Set Combined with a Machine Learning Approach by Vakeel A. Khan, Asheesh Kumar Yadav, Mohammad Arshad, Nadeem Akhtar

“…To address this issue, we propose an Enhanced Neutrosophic Set (ENS) framework integrated with machine learning algorithms to improve the prediction accuracy of lung cancer. …”
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Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle by Keshu Zhang, Jiandong Jia, Xiaodan Shangguan, Jing Dong

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A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions by Xiong Yang, Hao Wang, Qi Zhou, Lei Lu, Lijuan Zhang, Changming Sun, Guilu Wu

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A Semi-Supervised-Learning-Aided Explainable Belief Rule-Based Approach to Predict the Energy Consumption of Buildings by Sami Kabir, Mohammad Shahadat Hossain, Karl Andersson

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Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy by Chenyang Dong, Shiyu Xi, Yinchao Che, Shufeng Xiong, Xinming Ma, Lei Xi, Shuping Xiong

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