Lightweight human activity recognition method based on the MobileHARC model by Xingyu Gong, Xinyang Zhang, Na Li

“…However, due to the fact that these models have sequential network structures and are unable to simultaneously focus on local and global features, thus, resulting in a reduction in recognition performance. …”
Get full text

Destripe Any Scale: Effective Stripe Removal Via Multiscale Decomposition Using the Luojia3-02 Stripe-Noise Dataset by Ru Chen, Mi Wang, Yingdong Pi, Ru Wang, Tao Peng, Fan Yang, Rongfan Dai

“…Building upon this dataset, we propose a multilevel perceptual stripe-noise-removal network. This network employs an independent-weight learning strategy to separately capture and optimize large-scale and small-scale noise features, facilitating the precise extraction of multiscale noise features. …”
Get full text

NeuroTutor: Neural Decoding of Student Engagement During Virtual and Robotic Tutoring by Shahd Elgammal, Shaymaa Alsereidi, Mahra Almansoori, Shamma Alblooshi, Abdelhadi Hireche, Abdelkader Nasreddine Belkacem

Get full text

Analyzing the performance of biomedical time-series segmentation with electrophysiology data by Richard Redina, Jakub Hejc, Marina Filipenska, Zdenek Starek

“…We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). …”
Get full text

Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management by Ming Chen, Dingyu Zhang

“…Logistic regression, random forest, XGBoost, and neural network models were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA). …”
Get full text

Energy-based open set domain adaptation with dynamic weighted synergistic mechanism by Zihao Fu, Dong Liu, Shengsheng Wang, Hao Chai

“…To further refine separation, we apply a coarse-to-fine method that iteratively improves the separation outcomes, which are integrated as weighted inputs in the alignment process to enhance feature distribution alignment. In the alignment stage, we employ a dynamic weighted synergistic mechanism, where the separation network and alignment network co-evolve through continuous alternating training. …”
Get full text

Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine by Shiyu Li, Hong Wan, Qun Yu, Xinyuan Wang

“…Finally, the downscaling accuracy of the network was evaluated through simulated data experiments and real data experiments and compared with the Random Forest (RF) method. …”
Get full text

Bayesian variable selection with graphical structure learning: Applications in integrative genomics. by Suprateek Kundu, Yichen Cheng, Minsuk Shin, Ganiraju Manyam, Bani K Mallick, Veerabhadran Baladandayuthapani

“…We evaluate our methods through rigorous simulations to establish superiority over existing methods that do not take the network and/or prior information into account. …”
Get full text

Statistical and machine learning models for predicting university dropout and scholarship impact. by Stephan Romero, Xiyue Liao

“…The predictive classifiers evaluated are Lasso regression, generalized additive model, random forest, XGBoost and single-layer neural network. …”
Get full text

A Novel Hybrid Model for Loan Default Prediction in Maritime Finance Based on Topological Data Analysis and Machine Learning by Mohammad Amin Kheneifar, Babak Amiri

“…By constructing correlation-based networks of shipping firms and extracting topological persistence features, such as cyclical trends and structural interdependencies, via Vietoris-Rips complexes, the model captures nonlinear risk patterns overlooked by conventional metrics. …”
Get full text

TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM by Elsaid Md. Abdelrahim, Hasan Hashim, El-Sayed Atlam, Radwa Ahmed Osman, Ibrahim Gad

“…The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. …”
Get full text

AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification—Decoding Lateral Epicondylitis in Forearm Muscles by Mehdi Shirzadi, Mónica Rojas Martínez, Joan Francesc Alonso, Leidy Yanet Serna, Joaquim Chaler, Miguel Angel Mañanas, Hamid Reza Marateb

“…We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. …”
Get full text

A PET-CT radiomics model for immunotherapy response and prognosis prediction in patients with metastatic colorectal cancer by Wenbiao Chen, Peng Zhu, Yeda Chen, Guoping Sun

“…High-dimensional radiomic features were extracted from PET/CT images using a deep neural network (DNN), and RNA-Seq was used to screen for features associated with TME phenotypes to construct a radiomic score (Rad-Score). …”
Get full text

A Hybrid GARCH and Deep Learning Method for Volatility Prediction by Hailabe T. Araya, Jane Aduda, Tesfahun Berhane

“…Thus, the study integrated four powerful methods: seasonal autoregressive (AR) integrated moving average (MA), generalized AR conditional heteroskedasticity (ARCH) family models, convolutional neural network (CNN), and bidirectional long short-term memory (LSTM) network. …”
Get full text

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response by Lamia Alhoraibi, Daniyal Alghazzawi, Reemah Alhebshi, Liqaa F. Nawaf, Fiona Carroll

“…However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. …”
Get full text

Interactive Maintenance of Space Station Devices Using Scene Semantic Segmentation by Haoting Liu, Chuanxin Liao, Xikang Li, Zhen Tian, Mengmeng Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The convolutional block attention module (CBAM) is introduced to improve the network’s feature perception ability. The atrous spatial pyramid pooling (ASPP) module is also considered to enable an accurate calculation of encoding multi-scale information. …”
Get full text

Insulin-Related Skin Lipohypertrophy in Type Two Diabetes: A Clinical Study of a Case Series, with Ultrasonographic and Histopathologic Implications by Sandro Gentile, Felice Strollo, Giuseppina Guarino, Andrea Ronchi, Ersilia Satta, Teresa Della-Corte, Elisabetta Fulgione, Graziella Babino, Edi Mattera, Emilia Martedì, Roberta Di Martino, Raffaella Fiorentino, Roberta Porcini, Carmine Romano, Maria Chiarello, Giuseppe Caccavale, Renato Franco, Giuseppe Argenziano

Get full text

MobilitApp: A Deep Learning-Based Tool for Transport Mode Detection to Support Sustainable Urban Mobility by Gerard Caravaca Ibanez, Luis J. de la Cruz Llopis, Adrian Catalin Diaconeasa, Alberto Bazan Guillen, Monica Aguilar Igartua

“…Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. …”
Get full text

A Novel Hybrid Deep Learning-Based Framework for Intelligent Anomaly Detection in Smart Meters by Simarjit Kaur, Priyansh Chowhan, Aashima Sharma

“…This approach combines Bidirectional Long Short-Term Memory (BiLSTM) networks for feature extraction and a random forest model to detect anomalies. …”
Get full text

Physics-enhanced machine learning for predicting strength of high-carbon chromium steel during thermomechanical processing and spheroidizing annealing by Changqing Shu, Shasha Zhang, Peiheng Ding, Yaxin Sun, Xuewei Tao, Xiaolin Zhu, Qiuhao Gu, Liukai Hua, Song Xue, Zhengjun Yao

“…To mitigate dimensionality issues, pearson correlation and random forest rankings are applied for feature selection. A physical loss function is integrated into the neural network, ensuring alignment with metallurgical principles. …”
Get full text