Exploring the typhoon intensity forecasting through integrating AI weather forecasting with regional numerical weather model by Hongxiong Xu, Yang Zhao, Zhao Dajun, Yihong Duan, Xiangde Xu

“…This is largely due to constraints inherent in regression algorithm properties including deep neural networks and inability of coarse resolution to capture the finer-scale weather processes. …”
Get full text

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers by Dawei Li, Xiaojian Hu, Cheng-jie Jin, Jun Zhou

“…The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN) based and multilayer feed forward (MLF) neural networks based algorithms with the same AYE data. …”
Get full text

Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. …”
Get full text

Unfixed Bias Iterator: A New Iterative Format by Zeqing Zhang, Xue Wang, Jiamin Shen, Man Zhang, Sen Yang, Fanchang Yang, Wei Zhao, Jia Wang

“…With the renaissance of neural networks, many scholars have considered using deep learning to speed up solving PDEs, however, these methods leave poor theoretical guarantees or sub-convergence. …”
Get full text

Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach by Saima Latif, Faheem Aslam, Paulo Ferreira, Sohail Iqbal

“…We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and predictive accuracy. …”
Get full text

Automated quantification of Enchytraeus crypticus juveniles in different soil types using RootPainter by Bart G. van Hall, Cornelis A.M. van Gestel

“…The manual counting of juveniles in enchytraeid soil toxicity tests is time-consuming, labour-intensive, repetitive, prone to subjectivity, but can potentially be automated through deep learning methods using convolutional neural networks. This study investigated if RootPainter can be used as a tool to automatically quantify Enchytraeus crypticus juveniles in toxicity tests using different soil types. …”
Get full text

Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility by Heung‐Kook Choi, Torsten Jarkrans, Ewert Bengtsson, Janos Vasko, Kenneth Wester, Per-Uno Malmström, Christer Busch

“…The large numbers of extracted features were evaluated in relation to subjective grading and to factors related to prognosis using multivariate statistical methods and multilayer backpropagation neural networks. All the methods were originally developed and tested on material from one patient and then tested for reproducibility on entirely different patient material. …”
Get full text

Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches by Pablo Antúnez, Christian Wehenkel, Erickson Basave-Villalobos, Celi Gloria Calixto-Valencia, César Valenzuela-Encinas, Faustino Ruiz-Aquino, David Sarmiento-Bustos

“…The novelty of this study lies in applying five machine learning algorithms—Random Forest, Neural Networks, Gradient Boosting Machines, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN)—to predict these metrics, using data from the destructive analysis of 98 individual trees aged from eight months to five years. …”
Get full text

A computational fluid dynamics analysis of the aerodynamic influence of angles of attack on the Skylon spaceplane by Vivekamanickam Koothan Venkateswaran, Unai Fernandez Gamiz, Ana Boyano, Jesus Maria Blanco

“…The total time consumed by the simulation and the possibility of using its data for other less time-consuming methods, such as convolutional neural networks, are considered. This research establishes a foundation for understanding the aerodynamic effects of specific angles of attack by comparing theoretical and simulation values. …”
Get full text

Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning by Tongyue Li, Dianxi Shi, Songchang Jin, Zhen Wang, Huanhuan Yang, Yang Chen

“…Specifically, graph neural networks encode agent observations as single feature-embedding vectors, maintaining a constant dimensionality irrespective of the number of agents, which improves model scalability. …”
Get full text

Experience in applying large language models to analyse quantitative sociological data by E. G. Ashikhmin, V. V. Levchenko, G. I. Seletkova

“…Also, attention is paid to the actor-network theory, according to which neural networks act as active participants of social interaction. …”
Get full text

Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks by Chaoan Lai, Yina Wang, Jianhua Zhu, Xuequan Zhou

“…To overcome the limitations of existing energy consumption forecasting methods, which inadequately consider the specific energy usage characteristics and user behaviors in parks and often perform poorly at predicting extreme values, this study proposes a hybrid energy consumption forecasting model combines Singular Spectrum Analysis (SSA) and Long Short-Term Memory (LSTM) neural networks. Initially, SSA is used to extract the autocorrelation of the electricity consumption series and eliminate the mutual interference caused by component mixing. …”
Get full text

Cross-Modality 2D-3D Face Recognition via Multiview Smooth Discriminant Analysis Based on ELM by Yi Jin, Jiuwen Cao, Qiuqi Ruan, Xueqiao Wang

“…To speed up the learning phase of the classifier, the recent popular algorithm named Extreme Learning Machine (ELM) is adopted to train the single hidden layer feedforward neural networks (SLFNs). To evaluate the effectiveness of our proposed FR framework, experimental results on a benchmark face recognition dataset are presented. …”
Get full text

Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation by Thantip Sittiruk, Kiattisak Sengchuai, Apidet Booranawong, Pornchai Phukpattaranont

“…., Gaussian process regression (GPR), neural networks (NN), linear regression (LR), and support vector machines (SVM)) are applied to estimate the forearm muscle forces exerted by different elbow placement patterns for rehabilitation applications. …”
Get full text

Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…This study proposes a novel Content-Based Medical Image Retrieval (CBMIR) framework using Convolutional Neural Networks (CNN) and Transfer Learning (TL) for MS diagnosis using MRI data. …”
Get full text

Inception networks, data augmentation and transfer learning in EEG-based photosensitivity diagnosis by Fernando Moncada Martins, Víctor M González, José R Villar, Beatriz García López, Ana Isabel Gómez-Menéndez

“…This research tackles this problem and proposes using Inception-based deep learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. …”
Get full text

Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

“…The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. …”
Get full text

A New GLLD Operator for Mass Detection in Digital Mammograms by N. Gargouri, A. Dammak Masmoudi, D. Sellami Masmoudi, R. Abid

“…We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. …”
Get full text

Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning by Xiaoqing Liu, Miaoran Wang, Rui Wen, Haoyue Zhu, Ying Xiao, Qian He, Yangdi Shi, Zhe Hong, Bing Xu

“…An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. …”
Get full text

Prospective de novo drug design with deep interactome learning by Kenneth Atz, Leandro Cotos, Clemens Isert, Maria Håkansson, Dorota Focht, Mattis Hilleke, David F. Nippa, Michael Iff, Jann Ledergerber, Carl C. G. Schiebroek, Valentina Romeo, Jan A. Hiss, Daniel Merk, Petra Schneider, Bernd Kuhn, Uwe Grether, Gisbert Schneider

“…This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. …”
Get full text