ECG‐based epileptic seizure prediction: Challenges of current data‐driven models by Sotirios Kalousios, Jens Müller, Hongliu Yang, Matthias Eberlein, Ortrud Uckermann, Gabriele Schackert, Witold H. Polanski, Georg Leonhardt

Get full text

An attention-enhanced few-shot model for event detection in online social networks by Sielvie Sharma, Tanvir Ahmad, Niyaz Ahmad Wani, Naveed Ahmad, Sadique Ahmad

“…This operation emphasizes pivotal dimensions within the feature space while addressing data sparsity. AttendFew is evaluated on real-world datasets and exhibits significantly better performance than state-of-the-art (SOTA) and other baseline methods in terms of accuracy, F1-score. …”
Get full text

Advancing e-waste classification with customizable YOLO based deep learning models by P. Akhil Rajeev, Vivek Dharewa, D. Lakshmi, G. Vishnuvarthanan, Jayant Giri, T. Sathish, Mubarak Alrashoud

“…The ‘You Only Look Once’ (YOLO) methodology underpins our research, highlighting the distinctive architectural features of each model, including the CSPDarknet53 backbone, PANet, and advanced anchor-free detection. …”
Get full text

Simulation and Modelling of Electricity Usage Control and Monitoring System using ThingSpeak by Nurhazwani Anang, Mohammad Safwan AB Hamid, Wan Mariam Wan Muda

“…Since the smart grid and its components are usually modeled using MATLAB/Simulink, the communication between MATLAB/Simulink, IoT platform such as ThingSpeak and mobile application is crucial to be explored to gain a better understanding of the features of the smart grid. …”
Get full text

EMSAM: enhanced multi-scale segment anything model for leaf disease segmentation by Junlong Li, Quan Feng, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…However, existing studies fall short in addressing issues such as blurred disease spot boundaries and complex feature distributions in disease images. Although the vision foundation model, Segment Anything Model (SAM), performs well in general segmentation tasks within natural scenes, it does not exhibit good performance in plant disease segmentation. …”
Get full text

Attention mechanism augmented random forest model for multiple air pollutants estimation by Xinyu Yu, Man Sing Wong, Kwon-Ho Lee

“…Satellite observations from Advanced Himawari Imager (AHI) in three major urban agglomerations in China were extracted to demonstrate the model performance using sample- and site-based cross-validation schemes. …”
Get full text

Advanced air quality prediction using multimodal data and dynamic modeling techniques by Umesh Kumar Lilhore, Sarita Simaiya, Rajesh Kumar Singh, Abdullah M. Baqasah, Roobaea Alroobaea, Majed Alsafyani, Afnan Alhazmi, M. D. Monish Khan

“…The attention mechanism directs the model’s focus to the most informative features, improving predictive accuracy. …”
Get full text

Research on a PTSD Risk Assessment Model Using Multi-Modal Data Fusion by Youxi Luo, Yucui Shang, Dongfeng Zhu, Tian Zhang, Chaozhu Hu

“…For multi-modal data fusion, two sets of solutions are proposed: the first is to extract EEG features using B-spline basis functions, combined with questionnaire data, to construct a multi-modal Zero-Inflated Poisson regression model; the second is to build a multi-modal deep neural network fusion prediction model to automatically extract and fuse multi-modal data features. …”
Get full text

Development and validation of a carotid plaque risk prediction model for coal miners by Yi-Chun Li, Yi-Chun Li, Yi-Chun Li, Tie-Ru Zhang, Tie-Ru Zhang, Tie-Ru Zhang, Fan Zhang, Fan Zhang, Fan Zhang, Chao-Qun Cui, Chao-Qun Cui, Chao-Qun Cui, Yu-Tong Yang, Yu-Tong Yang, Yu-Tong Yang, Jian-Guang Hao, Jian-Ru Wang, Jiao Wu, Hai-Wang Gao, Ying-Bo Liu, Ming-Zhong Luo, Li-Jian Lei, Li-Jian Lei, Li-Jian Lei

“…The features were initially screened using extreme gradient boosting (XGBoost), random forest, and LASSO regression, and the model was subsequently constructed using logistic regression. …”
Get full text

Construction of a machine learning-based prediction model for mitral annular calcification by LI Runqian, TAN Yanyi, GE Tiantian, QI Lei, BAI Song, TONG Jiayi

“…The Shapley additive explanations (SHAP) method was used to assess feature importance, and feature selection was performed to construct the final model. …”
Get full text

Probabilistic distribution model of grain boundary α phase length in titanium alloys by Huizhi Peng, Wan Ye, Jianwen Liu, Shun Wu, Yichi Zhang, Yichi Zhang, Yuman Zhu, Alberto Boretti, Aijun Huang

“…The grain boundary α phase (GB-α) is a crucial microstructure feature in many titanium alloys, significantly impacting their mechanical properties and performance in various applications. …”
Get full text

Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…The primary objective of this study is to develop accurate, data-driven approaches for predicting the axial load-carrying capacity (P cc​) of these columns and to benchmark their performance against existing analytical solutions. Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models – LightGBM, XGBoost, and CatBoost – and three deep learning (DL) models – Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). …”
Get full text

A PSO-CNN-LSTM Model for Seismic Facies Analysis: Methodology and Applications by Luyao Liao, Huailai Zhou, Junping Liu, Jie Zhou, Donghang Zhang, Jian Wang

“…The model systematically extracts spatial features of seismic reflections through CNN architecture while capturing temporal waveform dependencies via LSTM networks, with PSO automatically optimizing critical parameters including initial learning rate and LSTM neuron count. …”
Get full text

Label-Efficient Fine-Tuning for Remote Sensing Imagery Segmentation with Diffusion Models by Yiyun Luo, Jinnian Wang, Jean Sequeira, Xiankun Yang, Dakang Wang, Jiabin Liu, Grekou Yao, Sébastien Mavromatis

“…It performs channel-wise optimization to suppress feature redundancy and refine representations. …”
Get full text

Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. …”
Get full text

Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…The GMU_D model constructed by discriminative analysis based on machine learning screening features had an excellent discriminative performance (AUC = 0.866, 95% CI: 0.858–0.874), and higher accuracy than the PKUPH model (AUC = 0.559, 95% CI: 0.552–0.567) and the Block model (AUC = 0.823, 95% CI: 0.814–0.833). …”
Get full text

VFQB: A Novel Deep Learning Model for Rolling Bearing Fault Diagnosis by Zhiru Xiao, Yanfang Xu, Junjie Cui

“…Experimental results demonstrate that this model significantly enhances the ability to capture weak fault features in complex environments. …”
Get full text

Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…After feature selection, the ensemble learning model constructed by integrating k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) yielded the ROC AUC of 0.892 (0.841–0.944) and accuracy of 85.0% on the test data, and the ROC AUC of 0.782 (0.727–0.838) and accuracy of 68.1% on the validation data, which performed best. …”
Get full text

BERT-Residual Quantum Language Model Inspired by ODE Multi-Step Method by Shaohui Liang, Yingkui Wang, Shuxin Chen

“…Therefore, in this paper, we propose the BERT-Residual quantum language model inspired by the multi-step method of ordinary differential equations (ODE), using the density matrix to capture the semantic high-order interaction features missing in the BERT modeling process, and obtain the sentence representation, and perform the first step Residuals. …”
Get full text

Lightweight construction safety behavior detection model based on improved YOLOv8 by Kan Huang, Mideth B. Abisado

“…Through experimental results, the improved YOLOv8 model performed excellently in detecting five common unsafe behaviors of construction workers, with an mAP of 0.86, a precision of 0.84, a recall rate of 0.87, an F1 value of 0.85, and an IoU of 0.8, which are significantly better than traditional methods. …”
Get full text