Dynamic deep learning based super-resolution for the shallow water equations by Maximilian Witte, Fabrício R Lapolli, Philip Freese, Sebastian Götschel, Daniel Ruprecht, Peter Korn, Christopher Kadow

Get full text

CNN-based remote dental diagnosis model for caries detection with grad-CAM by Donghyeok Kim, Jangkyum Kim, Seong Gon Choi

Get full text

Potato plant disease detection: leveraging hybrid deep learning models by Jackson Herbert Sinamenye, Ayan Chatterjee, Raju Shrestha

“…This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). …”
Get full text

DP-FWCA: A Prompt-Enhanced Model for Named Entity Recognition in Educational Domains by Zhenkai Qin, Dongze Wu, Jiajing He, Jingming Xie, Aimin Wei

“…However, the inherent complexity and contextual variability of educational texts, compounded by a limited supply of domain-specific annotated data, impose formidable challenges on conventional NER methods. …”
Get full text

Deep multi-task learning framework for gastrointestinal lesion-aided diagnosis and severity estimation by Zenebe Markos Lonseko, Dingcan Hu, Kaixuan Zhang, Helen Haile Hayeso, Tao Gan, Jinlin Yang, Nini Rao

“…Traditional methods for diagnosing lesions face challenges in accurately estimating severity due to requiring interpretable biomarkers, inter-observer variability, and overlapping lesions. Moreover, existing deep-learning models treat lesion classification and severity estimation as separate tasks, complicating diagnosis. …”
Get full text

Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

“…Uncertainty quantification (UQ) is important as it helps decision-makers gauge their confidence in predictions and consider variability in the model inputs. Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. …”
Get full text

SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics by Yong Bai, Xiangyu Guo, Keyin Liu, Bingjie Zheng, Yilin Wei, Yingyue Wang, Wenxi Zhang, Qiuhong Luo, Jianhua Yin, Liang Wu, Yuxiang Li, Yong Zhang, Ao Chen, Xiangdong Wang, Xun Xu, Chuanyu Liu, Xin Jin

Get full text

MFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos by Wenqin Yan, Jialiang Zhuang, Yuheng Chen, Yun Zhang, Xiujuan Zheng

“…In addition, in order to help the network extract the characteristics of different modal signals effectively, we designed a temporal multiscale convolution module (TMSC-module) and spectrum self-attention module (SSA-module). …”
Get full text

Fault diagnosis method for rolling bearings based on CWT-IDenseNet by Guangfei JIA, Hanwen LIANG, Jinqiu YANG, Zhe WU, Yuxin HAN

“…Aiming at the problems of incomplete information contained in one-dimensional signals and overfitting of the DenseNet under variable working conditions, a rolling bearing fault diagnosis method based on continuous wavelet transform (CWT) time-frequency images and an improved densely connected convolutional network (IDenseNet) was proposed. …”
Get full text

Deep Learning Method for Bearing Fault Diagnosis by LIU Xiu, MA Shan-tao, XIE Yi-ning, HE Yong-jun

“…In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. …”
Get full text

PDCG-Enhanced CNN for Pattern Recognition in Time Series Data by Feng Xie, Ming Xie, Cheng Wang, Dongwei Li, Xuan Zhang

“…This study compares the effectiveness of three methods—Fréchet Distance, Dynamic Time Warping (DTW), and Convolutional Neural Networks (CNNs)—in detecting similarities and pattern recognition in time series. …”
Get full text

Military Training Aircraft Structural Health Monitoring Leveraging an Innovative Biologically Inspired Feedback Mechanism for Neural Networks by Tarek Berghout

“…Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue. …”
Get full text

A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics by Hunter Dlugas, Seongho Kim

“…The datasets varied widely in size, mass spectrometry method, and response variable. <b>Results</b>: With respect to AUC on test data, BNN, CNN, FNN, KAN, and SNN were the top-performing models in 4, 1, 5, 3, and 4 of the 17 datasets, respectively. …”
Get full text

IoUT-Oriented an Efficient CNN Model for Modulation Schemes Recognition in Optical Wireless Communication Systems by M. Mokhtar Zayed, Saeed Mohsen, Abdullah Alghuried, Hassan Hijry, Mona Shokair

“…However, accurate modulation recognition in these systems remains a significant challenge due to the variable nature of underwater channels. This paper explores the application of Convolutional Neural Networks (CNNs) for modulation recognition in the OWC systems, focusing specifically on 64-QAM (Quadrature Amplitude Modulation) and 32-PSK (Phase Shift Keying). …”
Get full text

Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mechanism is utilized for regression which extracts temporal and spatial complex features. …”
Get full text

Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI by Reem Ibrahim Alkanhel, Hager Saleh, Ahmed Elaraby, Saleh Alharbi, Hela Elmannai, Saad Alaklabi, Saeed Hamood Alsamhi, Sherif Mostafa

“…Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. …”
Get full text

CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems by Yafei Li, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, Zelin Wang

“…In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). …”
Get full text

A Review of Developments and Metrology in Machine Learning and Deep Learning for Wearable IoT Devices by Minh Long Hoang

“…AI-powered wearables incorporate metrology and advanced computational techniques, with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) driving applications in activity recognition, health monitoring, and personalized recommendations. …”
Get full text

CMHFE-DAN: A Transformer-Based Feature Extractor with Domain Adaptation for EEG-Based Emotion Recognition by Manal Hilali, Abdellah Ezzati, Said Ben Alla

“…The architecture tackles key challenges in EEG emotion recognition, including generalisability, inter-subject variability, and temporal dynamics modelling. The results highlight the effectiveness of combining convolutional feature learning with adversarial domain adaptation for robust EEG-ER.…”
Get full text

From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Current methods for OCT image classification encounter specific challenges, such as the inherent complexity of retinal structures and considerable variability across different OCT datasets. Methods: This paper introduces a novel hybrid model that integrates the strengths of convolutional neural networks (CNNs) and vision transformer (ViT) to overcome these obstacles. …”
Get full text