Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

“…Traditional flood prediction models often fail to capture spatial correlations across districts and the temporal patterns within different types of features. To address this problem, this study proposes a hybrid deep learning framework combining Graph Convolution Network (GCN) and the Temporal Fusion Transformer (TFT) for predicting flood hazard levels in 50 Bangkok districts. …”
Get full text

A Multi-Level SAR-Guided Contextual Attention Network for Satellite Images Cloud Removal by Ganchao Liu, Jiawei Qiu, Yuan Yuan

“…In this paper, we introduce a novel cloud removal method named the Multi-Level SAR-Guided Contextual Attention Network (MSGCA-Net). MSGCA-Net is designed with a multi-level architecture that integrates a SAR-Guided Contextual Attention (SGCA) module to fuse the dependable global contextual information from SAR images with the local features of optical images effectively. …”
Get full text

ACNet: An Attention–Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving by Qiliang Zhang, Kaiwen Hua, Zi Zhang, Yiwei Zhao, Pengpeng Chen

“…In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. …”
Get full text

The use of artificial intelligence-based Siamese neural network in personalized guidance for sports dance teaching by Yi Xie, Yao Yan, Yuwei Li

“…The SNN employs a twin network structure, where two identical and parameter-sharing feature extraction networks process two input samples and calculate their distance or similarity in a high-dimensional feature space. …”
Get full text

Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…The resuts also show that the Transformer-CNN-LSTM hybrid model outperforms other hybrid models on five evaluation metrics, indicating that the proposed model provides more accurate predictions and more stable fitting results.…”
Get full text

Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. …”
Get full text

A hybrid local-global neural network for visual classification using raw EEG signals by Shuning Xue, Bu Jin, Jie Jiang, Longteng Guo, Jing Liu

“…To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. …”
Get full text

SSTMNet: Spectral-Spatio-Temporal and Multiscale Deep Network for EEG-Based Motor Imagery Classification by Albandari Alotaibi, Muhammad Hussain, Hatim Aboalsamh

“…Finally, these features are deeply analyzed by a sequential block to extract high-level features, which are used to detect an MI task. …”
Get full text

Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…Today is possible to extract features specific to various fields of application with the application of modern machine learning techniques, such as Convolutional Neural Networks (CNN) on MultiSpectral Images (MSI). …”
Get full text

T_SRNET: A multimodal model based on convolutional neural network for emotional speech enhancement by Shaoqiang Wang, Lei Feng, Li Zhang

“…Secondly, the features can be extracted by using the speech feature extraction network SRNET based on the improved transform structure. …”
Get full text

A Novel Two-Stream Network for Few-Shot Remote Sensing Image Scene Classification by Yaolin Lei, Yangyang Li, Heting Mao

“…Furthermore, DEADN4 uses deep global–local descriptors that extract both the overall features and detailed features, adjusts the loss function to distinguish between different classes better, and adds a term to make features within the same class closer together. …”
Get full text

SpeakerNet for Cross-lingual Text-Independent Speaker Verification by Hafsa HABIB, Huma TAUSEEF, Muhammad Abuzar FAHIEM, Saima FARHAN, Ghousia USMAN

“…Siamese networks are twin networks with shared weights. Feature space can be learnt easily by training these networks even if similar observations are placed in proximity. …”
Get full text

Alzheimer’s Disease Prediction via Brain Structural-Functional Deep Fusing Network by Qiankun Zuo, Yanyan Shen, Ning Zhong, C. L. Philip Chen, Baiying Lei, Shuqiang Wang

“…Moreover, the swapping bi-attention mechanism is designed to gradually align common features and effectively enhance the complementary features between modalities. …”
Get full text

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks by Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav

“…However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. …”
Get full text

Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…However, baseline Convolutional Neural Network (CNN) commonly faced obstacles to fully capture the temporal dependencies present in sequential medical imaging data, limiting their diagnostic performance. …”
Get full text

An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

“…The paper describes a method for improving urban traffic studies using Real-time Dense Analysis and Management using Visual Graph Networks (RDAMVGN) that utilizes deep learning techniques along with Visual Graph Networks based on visualizations. …”
Get full text

FTRNet: triplet fusion temporal relationship network for change detection in bitemporal remote sensing images by Wei Wu, Tong Li, Qi Xuan, QiMing Wan, Zuohui Chen

“…We design a change attention module to enhance bitemporal features, making the backbone network retain temporal information and fuse cross-scale features to extract the high-level location information. …”
Get full text

Integrated deep network model with multi-head twofold attention for drug–target interaction prediction by Angelin Jeba P, Tamilpavai G

“…This model leverages convolutional and recurrent neural networks to extract both local and sequential features from drug molecular structures and target protein sequences. …”
Get full text

An image‐based facial acupoint detection approach using high‐resolution network and attention fusion by Tingting Zhang, Hongyu Yang, Wenyi Ge, Yi Lin

“…In the proposed approach, high‐resolution networks are selected as the backbone to learn informative facial features with different resolution paths. …”
Get full text

Energy-Efficient Islanding Detection Using CEEMDAN and Neural Network Integration in Photovoltaic Distribution System by Sulayman Kujabi, Emmanuel Asuming Frimpong, Francis Boafo Effah

“…The extracted features were used to train the PANN. The model was evaluated using cross-validation and several performance metrics, including accuracy, precision, recall, and F1 score. …”
Get full text