Temporally-consistent koopman autoencoders for forecasting dynamical systems by Indranil Nayak, Ananda Chakrabarti, Mrinal Kumar, Fernando L. Teixeira, Debdipta Goswami

Get full text

Towards a Sustainable Disruptive Growth Model: Integrating Foresight, Wild Cards and Weak Signals Analysis by Rafael Popper, Yuli Villarroel, Raimund Popper

Get full text

AgriFusionNet: A Lightweight Deep Learning Model for Multisource Plant Disease Diagnosis by Saleh Albahli

“…This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. …”
Get full text

Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study by Qi Huang, Xiantong Zou, Zhouhui Lian, Xianghai Zhou, Xueyao Han, Yingying Luo, Shuohua Chen, Yanxiu Wang, Shouling Wu, Linong Ji

“…Both the trajectory and machine learning algorithm contributed significantly to the enhancement of model performance. …”
Get full text

Deep learning time-series modeling for assessing land subsidence under reduced groundwater use by Chih-Yu Liu, Cheng-Yu Ku, Chuen‑Fa Ni

“…Abstract Intensive groundwater extraction and a severe 2021 drought have worsened land subsidence in Taiwan’s Choshui Delta, highlighting the need for effective predictive modeling to guide mitigation. In this study, we develop a machine learning framework for subsidence analysis using electricity consumption data from pumping wells as a proxy for groundwater extraction. …”
Get full text

Mapping Spatiotemporal Dynamic Changes in Urban CO₂ Emissions in China by Using the Machine Learning Method and Geospatial Big Data by Wei Guo, Yongxing Li, Ximin Cui, Xuesheng Zhao, Yongjia Teng, Andreas Rienow

Get full text

Review of machine learning-assisted multi-property design of high-entropy alloys: phase structure, mechanical, tribological, corrosion, and hydrogen storage properties by Yunlong Li, Jialiang Tan, Cheng Qian, Xiaochao Liu, Rui Nie

“…In recent years, the rapid development of artificial intelligence has led to the widespread adoption of machine learning (ML) as a powerful tool in HEAs research. …”
Get full text

Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process‐Based Interpretable Machine Learning Method by Huimin Li, Yang Yang, Hang Su, Hailong Wang, Pinya Wang, Hong Liao

Get full text

Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

“…This study aimed to systematically evaluate and compare the performance of traditional statistical methods and machine learning approaches within the hdPS framework, focusing on key metrics such as bias, standard error (SE), and coverage, under various exposure and outcome prevalence scenarios.…”
Get full text

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. by Shang-Ming Zhou, Fabiola Fernandez-Gutierrez, Jonathan Kennedy, Roxanne Cooksey, Mark Atkinson, Spiros Denaxas, Stefan Siebert, William G Dixon, Terence W O'Neill, Ernest Choy, Cathie Sudlow, UK Biobank Follow-up and Outcomes Group, Sinead Brophy

“…<h4>Methods</h4>This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. …”
Get full text

Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids by Ahmad N. Alkuwari, Abdullatif Albaseer, Saif Al-Kuwari, Marwa Qaraqe

“…In response, anomaly detection models have been tested and evaluated against machine-generated adversarial attacks, such as the fast gradient sign method (FGSM) and Carlini and Wagner (C&amp;W). …”
Get full text

Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes by Sheng Du, Li Jin, Zixin Huang, Xiongbo Wan

“…The integration of advanced technologies such as machine learning, artificial intelligence, and data analytics play a pivotal role in achieving energy efficiency, reducing environmental impacts and ensuring the sustainability of industrial operations. …”
Get full text

Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking by Harry Hoa Huynh, Cheol-Hong Min

“…Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. …”
Get full text

Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…This study presents a novel and optimized breast cancer classification system using machine learning models enhanced through advanced hyperparameter tuning techniques and statistical validation methods. …”
Get full text

Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum by Shanpeng Xu, Jingshan Lu, Yin Wu, Huahao Liu, Fadi Chen, Fei Zhang, Sumei Chen, Weimin Fang, Zhiyong Guan

“…To address these limitations, we developed a lightweight deep learning and machine learning pipeline for automated trait extraction in over 30 chrysanthemum cultivars. …”
Get full text

Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. …”
Get full text

Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites by Ghazaleh Ramezani, Ixchel Ocampo Silva, Ion Stiharu, Theo G. M. van de Ven, Vahe Nerguizian

“…The findings, supported by machine learning optimization, have significant implications for flexible electronics, smart packaging, and biomedical applications, paving the way for future research on scalability, long-term stability, and advanced modeling techniques for these sustainable, multifunctional materials.…”
Get full text

The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syn... by Taylor Miners, Jeremy Witchalls, Jaquelin A. Bousie, Ceridwen R. Radcliffe, Phillip Newman

“…This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. …”
Get full text

Predicting Early Outcomes of Prostatic Artery Embolization Using <i>n</i>-Butyl Cyanoacrylate Liquid Embolic Agent: A Machine Learning Study by Burak Berksu Ozkara, David Bamshad, Ramita Gowda, Mert Karabacak, Vivian Bishay, Kirema Garcia-Reyes, Ardeshir R. Rastinehad, Dan Shilo, Aaron Fischman

“…Nevertheless, a proportion of patients undergoing PAE fail to demonstrate clinical improvement. Machine learning models have the potential to provide valuable prognostic insights for patients undergoing PAE. …”
Get full text

Connecting climate science and society: reflections from early and mid-career researchers at the World Climate Research Programme Open Science Conference 2023 by Leandro B. Díaz, Leandro B. Díaz, Leandro B. Díaz, Carla Gulizia, Carla Gulizia, Carla Gulizia, Shipra Jain, Shipra Jain, Gaby S. Langendijk, Hindumathi Palanisamy, Valentina Rabanal, Valentina Rabanal, Candice S. Charlton, Alima Dajuma, Felix K. Donkor, Temitope S. Egbebiyi, Temitope S. Egbebiyi, Ghafi Kondi Akara, Ghafi Kondi Akara, Amna Jrrar, Md Rezwan Siddiqui, Yassine Ait Brahim, Yassine Ait Brahim, Bapentire D. Angnuureng, Anna-Adriana Anschütz, Founi M. Awo, Founi M. Awo, Alejandro Builes-Jaramillo, Alison Cobb, Mohamadou A. Diallo, Mohamadou A. Diallo, Houda Er-Retby, Houda Er-Retby, Houda Er-Retby, Thierry C. Fotso-Nguemo, Benjamin Gwinneth, Masilin Gudoshava, Franck E. Kemgang Ghomsi, Franck E. Kemgang Ghomsi, Franck E. Kemgang Ghomsi, Inés M. Leyba, Inés M. Leyba, Redouane Lguensat, Redouane Lguensat, Roché Mahon, Shailendra K. Mandal, Lucia Mumo, Meshel Muzuva, Meshel Muzuva, Rodric M. Nonki, Juan A. Rivera, Juan A. Rivera, Alcade C. Segnon, Alcade C. Segnon, Laouali I. Tanimoune, Erlich-Honest E. I. Zauisomue, Gilbert Siame

Get full text