MSVMD-Informer: A Multi-Variate Multi-Scale Method to Wind Power Prediction by Zhijian Liu, Jikai Chen, Hang Dong, Zizhuo Wang

“…Existing prediction methods demonstrate insufficient integration of multi-variate features, such as wind speed, temperature, and humidity, along with inadequate extraction of correlations between variables. This paper proposes a novel multi-variate multi-scale wind power prediction method named multi-scale variational mode decomposition informer (MSVMD-Informer). …”
Get full text

Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks by Kun Mei, Zikang Feng, Hui Liu, Min Wang, Chao Ce, Shi Yin, Xiaoying Zhang, Bin Wang

“…Abstract Objective The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. …”
Get full text

Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

“…The present study aims to forecast the concentrations of various air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered. …”
Get full text

Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR by Shihab Ahmad Shahriar, Yunsoo Choi, Rashik Islam

“…This study analyzed FWI trends across the Continental United States (CONUS) from 2014 to 2023, using meteorological data from the gridMET dataset. Key variables, including temperature, relative humidity, wind speed, and precipitation, were utilized to calculate the FWI at a fine spatial resolution of 4 km, ensuring the precise identification of wildfire-prone areas. …”
Get full text

Classification of pulmonary diseases from chest radiographs using deep transfer learning. by Muneeba Shamas, Huma Tauseef, Ashfaq Ahmad, Ali Raza, Yazeed Yasin Ghadi, Orken Mamyrbayev, Kymbat Momynzhanova, Tahani Jaser Alahmadi

“…In clinical practice, diagnosing pulmonary diseases using chest radiographs is challenging due to Overlapping and complex anatomical Structures, variability in radiographs, and their quality. The availability of a medical specialist with extensive professional experience is profoundly required. …”
Get full text

A Lightweight CNN for Multi-Class Classification of Handwritten Digits and Mathematical Symbols by Nicholas Abisha, Tita Putri Redytadevi, Sri Nurdiati, Elis Khatizah, Mohamad Khoirun Najib

“…Recognizing handwritten digits and mathematical symbols remains a nontrivial challenge due to handwriting variability and visual similarity among classes. While deep learning, particularly Convolutional Neural Networks (CNNs), has significantly advanced handwriting recognition, many existing solutions rely on deep, resource-intensive architectures. …”
Get full text

Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China by Yunjin WANG, Fujian ZHOU, Hang SU, Leyi ZHENG, Minghui LI, Fuwei YU, Yuan LI, Tianbo LIANG

“…With this database, 22 geological and engineering variables are selected for correlation analysis. A separated fracturing effect prediction model is proposed, with the fracturing learning curve decomposed into two parts: (1) overall trend, which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance; and (2) local fluctuation, which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight. …”
Get full text

Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura by Senlin Zhu, Ryuichiro Shinohara, Shin–Ichiro S. Matsuzaki, Ayato Kohzu, Mirai Watanabe, Megumi Nakagawa, Fabio Di Nunno, Jiang Sun, Quan Zhou, Francesco Granata

“…Incorporating additional input variables does not necessarily improve model performance; however, using surface water temperatures and air temperatures as inputs produces acceptable results for modeling vertical temperature profiles. …”
Get full text

Intelligent Design Method for Thermal Conductivity Topology Based on a Deep Generative Network by Qiyin Lin, Feiyu Gu, Chen Wang, Hao Guan, Tao Wang, Kaiyi Zhou, Lian Liu, Desheng Yao

“…Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables, researchers have exploited deep learning to expedite the optimization of material properties, such as the heat dissipation of solid isotropic materials with penalization (SIMP). …”
Get full text

Advanced phenotyping in tomato fruit classification through artificial intelligence by Sandra Eulália Santos Faria, Alcinei Místico Azevedo, Nayany Gomes Rabelo, Varlen Zeferino Anastácio, Valentina de Melo Maciel, Deltimara Viana Matos, Elias Barbosa Rodrigues, Phelipe Souza Amorim, Janete Ramos da Silva, Fernanda de Souza Santos

“…The InceptionResNetV2 architecture was the most efficient, achieving metrics such as precision and recall exceeding 93 % for most analyzed variables, and shorter classification times. This study advances the understanding of CNN applications in agriculture and research and provides valuable guidelines for optimizing classification tasks in distinct types of fruits.…”
Get full text

Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG by Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang

“…Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. …”
Get full text

Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters by Jiuxiao Zhao, Jianping Tao, Shirui Zhang, Jingjing Li, Teng Li, Feifei Shan, Wengang Zheng

“…In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (<i>R</i>²) of 0.953. …”
Get full text

A New Modeling Method for Meteorological Information of Regional Distributed Photovoltaic Power Generation Based on Multi‐Source Information Fusion by Yuhang Wang, Dengxuan Li, Wenwen Ma, Xi Zhang, Honglu Zhu

“…The Current challenges in DPV meteorological information fusion computation include feature engineering, the reasonable selection of input variables and preliminary establishment of mapping relationships. …”
Get full text

Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model by Shunsuke Kubota, Taiichi Wakiya, Hajime Morohashi, Takuya Miura, Taishu Kanda, Masashi Matsuzaka, Yoshihiro Sasaki, Yoshiyuki Sakamoto, Kenichi Hakamada

“…Binary logistic regression analysis of prechemotherapy clinical factors showed that none of the independent variables were significantly associated with the non-responders. …”
Get full text

Gully Erosion Susceptibility Prediction Using High-Resolution Data: Evaluation, Comparison, and Improvement of Multiple Machine Learning Models by Heyang Li, Jizhong Jin, Feiyang Dong, Jingyao Zhang, Lei Li, Yucheng Zhang

“…The primary objective is to evaluate and optimize the top-performing model under high-resolution UAV data conditions, utilize the optimized best model to identify key factors influencing the occurrence of gully erosion from 11 variables, and generate a local gully erosion susceptibility map. …”
Get full text

Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…This approach holds promise for future applications in downscaling other atmospheric variables.…”
Get full text

Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…Future work should refine algorithms and incorporate additional environmental variables to improve model performance across various reef types.…”
Get full text

Forecasting Indoor Air Quality in Mexico City Using Deep Learning Architectures by Jorge Altamirano-Astorga, J. Octavio Gutierrez-Garcia, Edgar Roman-Rangel

“…Our empirical results show that deep learning models can forecast an indoor air quality index based on outdoor concentration levels of pollutants in conjunction with indoor and outdoor meteorological variables. In addition, our findings show that the proposed method performs with a mean squared error of 0.0179 and a mean absolute error of 0.1038. …”
Get full text

Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model by Xuming Shao, Wenhao Liu, Gang Bai, Yan Chen, Yu Liu, Jiahe Guang

“…Pearson correlation analysis identified six key gas indicators—O₂, CO, C₂H₄, CO/ΔO₂, C₂H₄/C₂H₆, and C₂H₆—highly correlated with spontaneous combustion temperature. Based on these variables, a deep learning framework combining an Improved Tornado Optimization with Coriolis force (ITOC) strategy and a CNN-BiGRU-CBAM model is proposed. …”
Get full text

Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…Over the past couple of decades, the academic literature has transitioned from conventional statistical time series models to embracing EVT and machine learning algorithms for the modelling of environmental variables. This study adds value to the literature and knowledge of modelling wind speed using both EVT and machine learning. …”
Get full text