Single-cell RNA-seq data augmentation using generative Fourier transformer by Nima Nouri

“…However, its full potential to achieve statistically reliable conclusions is often constrained by the limited number of cells profiled, particularly in studies of rare diseases, specialized tissues, and uncommon cell types. Deep learning-based generative models (GMs) designed to address data scarcity often face similar limitations due to their reliance on pre-training or fine-tuning, inadvertently perpetuating a cycle of data inadequacy. …”
Get full text

Bearing Fault Diagnosis Based on Multilayer Domain Adaptation by Bingru Yang, Qi Li, Liang Chen, Changqing Shen

“…Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working conditions. …”
Get full text

Unsupervised monocular depth estimation with omnidirectional camera for 3D reconstruction of grape berries in the wild. by Yasuto Tamura, Yuzuko Utsumi, Yuka Miwa, Masakazu Iwamura, Koichi Kise

“…To satisfy the practical constraints of this task, we extend a deep learning-based unsupervised monocular depth estimation method to an omnidirectional camera and propose using it. …”
Get full text

Classification of User Expressions on Social Media Using LSTM and GRU Models by I Gede Putra Mas Yusadara, I Gusti Ayu Desi Saryanti

“…This research is expected to contribute to emotion analysis systems based on deep learning.…”
Get full text

Recognition of Transportation State by Smartphone Sensors Using Deep Bi-LSTM Neural Network by Hong Zhao, Chunning Hou, Hala Alrobassy, Xiangyan Zeng

“…The deep Bi-LSTM (bidirectional long short-term memory) neural network structure, the crowd-sourcing model, and the TensorFlow deep learning system are used to classify the transportation states. …”
Get full text

Ensemble-Based Alzheimer's Disease Classification Using Features Extracted from Hog Descriptor and Pre-trained Models by Enver Akbacak, Nedim Muzoglu

“…Then, an ensemble learning-based hybrid deep learning model was developed to reduce the misclassification rate for all classes. …”
Get full text

Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data by Zhu Zhimeng

“…Deep learning models have become essential for automated medical image analysis in brain tumor detection. …”
Get full text

Generating Deeply-Engineered Technical Features for Basketball Video Understanding by Shaohua Fang, Guifeng Wang, Yongbin Li, Yue Yu, Jun Li

“…Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. …”
Get full text

UAV target tracking method based on global feature interaction and anchor-frame-free perceptual feature modulation. by Yuanhong Dan, Jinyan Li, Yu Jin, Yong Ji, Zhihao Wang, Dong Cheng

“…Target tracking techniques in the UAV perspective utilize UAV cameras to capture video streams and identify and track specific targets in real-time. Deep learning UAV target tracking methods based on the Siamese family have achieved significant results but still face challenges regarding accuracy and speed compatibility. …”
Get full text

Formation and creative manifestation of functional ensembles of intellectual agents based on live information in various spheres of life activity by Evgeny Bryndin

“…Neural networks with deep learning and reinforcement are able to compose poetry and music, draw paintings, and write short stories, as well as come up with scripts for films. …”
Get full text

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning by Jing An, Ping Ai, Dakun Liu

“…Deep learning techniques have been widely used to achieve promising results for fault diagnosis. …”
Get full text

The impact of artificial intelligence on business performance: a bibliometric analysis of publication trends by Rachid ZIKY, Hicham BAHIDA, Ahmed ABRIANE

“…Results underscore the importance of deep learning and performance optimization. Limitations include the reliance on the Scopus database and the restricted analysis period. …”
Get full text

STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…In order to detect abnormal wind turbine generator and reduce the occurrence of outages, a deep learning framework combining K-CNN and N-GRU is proposed based on multi-dimensional sensor parameters recorded in real wind farm SCADA system, and a wind turbine generator state prediction model is established. …”
Get full text

Examine the Prediction Error of Ride-Hailing Travel Demands with Various Ignored Sparse Demand Effects by Zhiju Chen, Kai Liu, Tao Feng

“…To obtain the spatiotemporal characteristics of the travel demand, three hexagon-based deep learning models (H-CNN-LSTM, H-CNN-GRU, and H-ConvLSTM) are compared by setting various threshold values. …”
Get full text

A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model by Dongmei Shi, Hongyu Tang

“…Deep learning theory is widely used in face recognition. …”
Get full text

Retinal revelations: Seeing beyond the eye with artificial intelligence by John Davis Akkara

“…This field, known as oculomics, leverages AI and deep learning algorithms to process vast amounts of data from imaging techniques such as fundus photography, optical coherence tomography (OCT), OCT angiography, infrared iris imaging, slit-lamp photography, and external eye photography. …”
Get full text

Attention Based Energy Demand Forecasting in Smart Grid Environments by Yunus Emre Işıkdemir, Fuat Akal

“…Experimental evaluations showed up to an 8% better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 1/3 of the training time other models took. …”
Get full text

Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods by Meisheng Xiao, Qianhui Zheng, Paul Popa, Xinlei Mi, Jianhua Hu, Fei Zou, Baiming Zou

“…Our findings reveal that the inclusion of molecular representations from either PubChem fingerprints or SMILES can significantly enhance the performance of DRPs when used in conjunction with deep learning models. However, the optimal choice of drug molecular representation can vary depending on the predictive model and the specific DRP task. …”
Get full text

Method on Efficient Operation of Multiple Models for Vision-Based In-Flight Risky Behavior Recognition in UAM Safety and Security by Byeonghun Kim, Byeongjoon Noh, Kyowon Song

“…In addition, conventional vision-based deep learning models require substantial computational power, potentially reducing the operational sustainability of UAMs with limited electrical resources. …”
Get full text

The application of series multi-pooling convolutional neural networks for medical image segmentation by Feng Wang, Siwei Huang, Lei Shi, Weiguo Fan

“…To solve the said problems, the model of convolutional neural network in the deep learning approach was used in this article to cope with classification and labeling tasks of brain tumor images. …”
Get full text